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   "source": [
    "<h1 align=center><font size = 5>Analyzing NYC High School Data</font></h1>\n",
    "\n",
    "This guided project project has the purpose of analyzing and seeing the aspects that may influence the SAT scores in New York city high schools. \n",
    "The project consists in cleaning, combining and analyzing several datasets related to the [SAT scores](https://en.wikipedia.org/wiki/SAT) of high schoolers from different boroughs. Calculating the average of each school and checking the attributes per school. As explained in the [article](https://www.dataquest.io/blog/data-science-portfolio-project/):\n",
    "> The SAT, or Scholastic Aptitude Test, is a test that high schoolers take in the US before applying to college. Colleges take the test scores into account when making admissions decisions, so it's fairly important to do well on. The test is divided into 3 sections, each of which is scored out of 800 points. The total score is out of 2400 (although this has changed back and forth a few times, the scores in this dataset are out of 2400). High schools are often ranked by their average SAT scores, and high SAT scores are considered a sign of how good a school district is.\n",
    "\n",
    "The analysis reaches to look into the different characteristics of the schools to see how gender, localization and race differences influence the average SAT scores of the schools, to check out if there are evidences of the SAT tests being unfair with racial groups in the US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Collecting our data\n",
    "\n",
    "To make an analysis like it, we need to take a great variety of data. The data we are going to use is described bellow:\n",
    "* [High School Directory](https://data.cityofnewyork.us/Education/2014-2015-DOE-High-School-Directory/n3p6-zve2) - directory containing information about each high school\n",
    "* [SAT scores by school](https://data.cityofnewyork.us/Education/SAT-Results/f9bf-2cp4) — SAT scores for each high school in New York City.\n",
    "* [School attendance](https://data.cityofnewyork.us/Education/School-Attendance-and-Enrollment-Statistics-by-Dis/7z8d-msnt) — attendance information on every school in NYC.\n",
    "* [Math test results](https://data.cityofnewyork.us/Education/NYS-Math-Test-Results-By-Grade-2006-2011-School-Le/jufi-gzgp) — math test results for every school in NYC.\n",
    "* [Class size](https://data.cityofnewyork.us/Education/2010-2011-Class-Size-School-level-detail/urz7-pzb3) — class size information for each school in NYC.\n",
    "* [AP test results](https://data.cityofnewyork.us/Education/AP-College-Board-2010-School-Level-Results/itfs-ms3e) — Advanced Placement exam results for each high school. Passing AP exams can get you college credit in the US.\n",
    "* [Graduation outcomes](https://data.cityofnewyork.us/Education/Graduation-Outcomes-Classes-Of-2005-2010-School-Le/vh2h-md7a) — percentage of students who graduated, and other outcome information.\n",
    "* [Demographics](https://data.cityofnewyork.us/Education/School-Demographics-and-Accountability-Snapshot-20/ihfw-zy9j) — demographic information for each school.\n",
    "* [School survey](https://data.cityofnewyork.us/Education/NYC-School-Survey-2011/mnz3-dyi8) — surveys of parents, teachers, and students at each school.\n",
    "* [School district maps](https://data.cityofnewyork.us/Education/School-Districts/r8nu-ymqj) — contains information on the layout of the school districts, so that we can map them out.\n",
    "\n",
    "To make it easier to use, all the datasets were uploaded in my Github repository that can be found in [this link](https://github.com/nathpignaton/guided_projects/tree/main/nyc-sat-analysis). \n",
    "\n",
    "And to complement our understanding about New York city, here are some background information to contextualize it better:  \n",
    "- New York city is divided into 5 boroughs, which are essentially distinct regions;\n",
    "- Schools in New York city are divided into several school districts, each of which can contain dozens of schools;\n",
    "- Not all the schools in all of the dataset are high schools;\n",
    "- Each school in New York city has a unique code called a `DNB` or District Borough Number;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Understanding our data\n",
    "To start to learn about our datasets, we need to prepare the jupyter notebook with the libraries we are going to use. We are also going to change some default options so the information can be more clearly presented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### 2.1 Setting up the environment\n",
    "Lets import all libraries we need to clean, analyze and display the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# showing the graphs inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To help the visualization of the information, we are going to change the default options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "<style>\n",
       ".output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
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       "}\n",
       ".prompt \n",
       "    display:none;\n",
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       "<IPython.core.display.HTML object>"
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    "# telling pandas to show all columns when asked\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# centering the image outputs\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
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    "\"\"\")"
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   "cell_type": "markdown",
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   "source": [
    "### 2.2 Opening and reading the csv files\n",
    "Now we are going to open our data from the Github repo and read it into pandas dataframes so we can start exploring it and cleaning it. First we are going to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
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     "text": [
      "\n",
      " ap_2010 \n",
      " Index(['DBN', 'SchoolName', 'AP Test Takers ', 'Total Exams Taken',\n",
      "       'Number of Exams with scores 3 4 or 5'],\n",
      "      dtype='object')\n",
      "\n",
      " class_size \n",
      " Index(['CSD', 'BOROUGH', 'SCHOOL CODE', 'SCHOOL NAME', 'GRADE ',\n",
      "       'PROGRAM TYPE', 'CORE SUBJECT (MS CORE and 9-12 ONLY)',\n",
      "       'CORE COURSE (MS CORE and 9-12 ONLY)', 'SERVICE CATEGORY(K-9* ONLY)',\n",
      "       'NUMBER OF STUDENTS / SEATS FILLED', 'NUMBER OF SECTIONS',\n",
      "       'AVERAGE CLASS SIZE', 'SIZE OF SMALLEST CLASS', 'SIZE OF LARGEST CLASS',\n",
      "       'DATA SOURCE', 'SCHOOLWIDE PUPIL-TEACHER RATIO'],\n",
      "      dtype='object')\n",
      "\n",
      " demographics \n",
      " Index(['DBN', 'Name', 'schoolyear', 'fl_percent', 'frl_percent',\n",
      "       'total_enrollment', 'prek', 'k', 'grade1', 'grade2', 'grade3', 'grade4',\n",
      "       'grade5', 'grade6', 'grade7', 'grade8', 'grade9', 'grade10', 'grade11',\n",
      "       'grade12', 'ell_num', 'ell_percent', 'sped_num', 'sped_percent',\n",
      "       'ctt_num', 'selfcontained_num', 'asian_num', 'asian_per', 'black_num',\n",
      "       'black_per', 'hispanic_num', 'hispanic_per', 'white_num', 'white_per',\n",
      "       'male_num', 'male_per', 'female_num', 'female_per'],\n",
      "      dtype='object')\n",
      "\n",
      " graduation \n",
      " Index(['Demographic', 'DBN', 'School Name', 'Cohort', 'Total Cohort',\n",
      "       'Total Grads - n', 'Total Grads - % of cohort', 'Total Regents - n',\n",
      "       'Total Regents - % of cohort', 'Total Regents - % of grads',\n",
      "       'Advanced Regents - n', 'Advanced Regents - % of cohort',\n",
      "       'Advanced Regents - % of grads', 'Regents w/o Advanced - n',\n",
      "       'Regents w/o Advanced - % of cohort',\n",
      "       'Regents w/o Advanced - % of grads', 'Local - n', 'Local - % of cohort',\n",
      "       'Local - % of grads', 'Still Enrolled - n',\n",
      "       'Still Enrolled - % of cohort', 'Dropped Out - n',\n",
      "       'Dropped Out - % of cohort'],\n",
      "      dtype='object')\n",
      "\n",
      " hs_directory \n",
      " Index(['dbn', 'school_name', 'borough', 'building_code', 'phone_number',\n",
      "       'fax_number', 'grade_span_min', 'grade_span_max', 'expgrade_span_min',\n",
      "       'expgrade_span_max', 'bus', 'subway', 'primary_address_line_1', 'city',\n",
      "       'state_code', 'postcode', 'website', 'total_students', 'campus_name',\n",
      "       'school_type', 'overview_paragraph', 'program_highlights',\n",
      "       'language_classes', 'advancedplacement_courses', 'online_ap_courses',\n",
      "       'online_language_courses', 'extracurricular_activities',\n",
      "       'psal_sports_boys', 'psal_sports_girls', 'psal_sports_coed',\n",
      "       'school_sports', 'partner_cbo', 'partner_hospital', 'partner_highered',\n",
      "       'partner_cultural', 'partner_nonprofit', 'partner_corporate',\n",
      "       'partner_financial', 'partner_other', 'addtl_info1', 'addtl_info2',\n",
      "       'start_time', 'end_time', 'se_services', 'ell_programs',\n",
      "       'school_accessibility_description', 'number_programs', 'priority01',\n",
      "       'priority02', 'priority03', 'priority04', 'priority05', 'priority06',\n",
      "       'priority07', 'priority08', 'priority09', 'priority10', 'Location 1',\n",
      "       'Community Board', 'Council District', 'Census Tract', 'BIN', 'BBL',\n",
      "       'NTA'],\n",
      "      dtype='object')\n",
      "\n",
      " math_test_results \n",
      " Index(['DBN', 'Grade', 'Year', 'Category', 'Number Tested', 'Mean Scale Score',\n",
      "       'Level 1 #', 'Level 1 %', 'Level 2 #', 'Level 2 %', 'Level 3 #',\n",
      "       'Level 3 %', 'Level 4 #', 'Level 4 %', 'Level 3+4 #', 'Level 3+4 %'],\n",
      "      dtype='object')\n",
      "\n",
      " sat_results \n",
      " Index(['DBN', 'SCHOOL NAME', 'Num of SAT Test Takers',\n",
      "       'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',\n",
      "       'SAT Writing Avg. Score'],\n",
      "      dtype='object')\n",
      "\n",
      " school_attendence \n",
      " Index(['District', 'YTD % Attendance (Avg)', 'YTD Enrollment(Avg)'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# creating a list with the name of the csv files\n",
    "csv_files = ['ap_2010.csv', \n",
    "             'class_size.csv', \n",
    "             'demographics.csv', \n",
    "             'graduation.csv', \n",
    "             'hs_directory.csv', \n",
    "             'math_test_results.csv', \n",
    "             'sat_results.csv', \n",
    "             'school_attendence.csv']\n",
    "\n",
    "# populating the dataframes with the csv files from the repository\n",
    "data = {}\n",
    "for f in csv_files:\n",
    "    d = pd.read_csv('https://raw.githubusercontent.com/nathpignaton/guided_projects/main/nyc-sat-analysis/{}'.format(f))\n",
    "    # creating the key without the .csv in the name and storing the data\n",
    "    data[f.replace('.csv', '')] = d\n",
    "    \n",
    "# looking at the columns of each dataframe\n",
    "for k in data.keys():\n",
    "    print('\\n {} \\n {}'.format(k, data[k].columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Looking at our data we can see some things:\n",
    "- Most of the datasets contain the `DBN` column;\n",
    "- Some fields look interesting for mapping;\n",
    "- Some of the datasets appear to contain multiple rows for each school, showing that we'll have to do some preprocessing.  \n",
    "\n",
    "Before coming back to our dataframes, let's read the information in the survey txt files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 2.3 Opening and reading the txt files\n",
    "Now we are going to open our survey data and read it into pandas dataframes, almost in the same way we did before.  \n",
    "We're going to store each survey in a different dataframe, they'll be called `all_survey` and `d75_survey`, after it we'll concatenate both into one `survey` dataframe. This files need a specific encoding to be read: `windows-1252`."
   ]
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       "      <th>p_q4d_3</th>\n",
       "      <th>p_q4d_4</th>\n",
       "      <th>p_q4d_5</th>\n",
       "      <th>p_q4e_1</th>\n",
       "      <th>p_q4e_2</th>\n",
       "      <th>p_q4e_3</th>\n",
       "      <th>p_q4e_4</th>\n",
       "      <th>p_q4e_5</th>\n",
       "      <th>p_q5a</th>\n",
       "      <th>p_q5b</th>\n",
       "      <th>p_q5c</th>\n",
       "      <th>p_q5d</th>\n",
       "      <th>p_q5e</th>\n",
       "      <th>p_q5f</th>\n",
       "      <th>p_q5g</th>\n",
       "      <th>p_q5h</th>\n",
       "      <th>p_q5i</th>\n",
       "      <th>p_q5j</th>\n",
       "      <th>p_q6a_1</th>\n",
       "      <th>p_q6a_2</th>\n",
       "      <th>p_q6a_3</th>\n",
       "      <th>p_q6a_4</th>\n",
       "      <th>p_q6a_5</th>\n",
       "      <th>p_q6b_1</th>\n",
       "      <th>p_q6b_2</th>\n",
       "      <th>p_q6b_3</th>\n",
       "      <th>p_q6b_4</th>\n",
       "      <th>p_q6b_5</th>\n",
       "      <th>p_q7a_1</th>\n",
       "      <th>p_q7a_2</th>\n",
       "      <th>p_q7a_3</th>\n",
       "      <th>p_q7a_4</th>\n",
       "      <th>p_q7a_5</th>\n",
       "      <th>p_q7b_1</th>\n",
       "      <th>p_q7b_2</th>\n",
       "      <th>p_q7b_3</th>\n",
       "      <th>p_q7b_4</th>\n",
       "      <th>p_q7b_5</th>\n",
       "      <th>p_q7c_1</th>\n",
       "      <th>p_q7c_2</th>\n",
       "      <th>p_q7c_3</th>\n",
       "      <th>p_q7c_4</th>\n",
       "      <th>p_q7c_5</th>\n",
       "      <th>p_q7d_1</th>\n",
       "      <th>p_q7d_2</th>\n",
       "      <th>p_q7d_3</th>\n",
       "      <th>p_q7d_4</th>\n",
       "      <th>p_q7d_5</th>\n",
       "      <th>p_q8a_1</th>\n",
       "      <th>p_q8a_2</th>\n",
       "      <th>p_q8a_3</th>\n",
       "      <th>p_q8a_4</th>\n",
       "      <th>p_q8a_5</th>\n",
       "      <th>p_q8b_1</th>\n",
       "      <th>p_q8b_2</th>\n",
       "      <th>p_q8b_3</th>\n",
       "      <th>p_q8b_4</th>\n",
       "      <th>p_q8b_5</th>\n",
       "      <th>p_q8c_1</th>\n",
       "      <th>p_q8c_2</th>\n",
       "      <th>p_q8c_3</th>\n",
       "      <th>p_q8c_4</th>\n",
       "      <th>p_q8c_5</th>\n",
       "      <th>p_q8d_1</th>\n",
       "      <th>p_q8d_2</th>\n",
       "      <th>p_q8d_3</th>\n",
       "      <th>p_q8d_4</th>\n",
       "      <th>p_q8d_5</th>\n",
       "      <th>p_q8e_1</th>\n",
       "      <th>p_q8e_2</th>\n",
       "      <th>p_q8e_3</th>\n",
       "      <th>p_q8e_4</th>\n",
       "      <th>p_q8e_5</th>\n",
       "      <th>p_q8f_1</th>\n",
       "      <th>p_q8f_2</th>\n",
       "      <th>p_q8f_3</th>\n",
       "      <th>p_q8f_4</th>\n",
       "      <th>p_q8f_5</th>\n",
       "      <th>p_q9_1</th>\n",
       "      <th>p_q9_2</th>\n",
       "      <th>p_q9_3</th>\n",
       "      <th>p_q9_4</th>\n",
       "      <th>p_q9_5</th>\n",
       "      <th>p_q9_6</th>\n",
       "      <th>p_q9_7</th>\n",
       "      <th>p_q9_8</th>\n",
       "      <th>p_q9_9</th>\n",
       "      <th>p_q9_10</th>\n",
       "      <th>p_q10a</th>\n",
       "      <th>p_q10b</th>\n",
       "      <th>p_q10c</th>\n",
       "      <th>p_q10d</th>\n",
       "      <th>p_q10e</th>\n",
       "      <th>p_q10f</th>\n",
       "      <th>p_q10g</th>\n",
       "      <th>p_q10h</th>\n",
       "      <th>p_q10i</th>\n",
       "      <th>p_q10j</th>\n",
       "      <th>p_q10k</th>\n",
       "      <th>p_q10l</th>\n",
       "      <th>p_q11a_1</th>\n",
       "      <th>p_q11a_2</th>\n",
       "      <th>p_q11a_3</th>\n",
       "      <th>p_q11a_4</th>\n",
       "      <th>p_q11b_1</th>\n",
       "      <th>p_q11b_2</th>\n",
       "      <th>p_q11b_3</th>\n",
       "      <th>p_q11b_4</th>\n",
       "      <th>p_q11c_1</th>\n",
       "      <th>p_q11c_2</th>\n",
       "      <th>p_q11c_3</th>\n",
       "      <th>p_q11c_4</th>\n",
       "      <th>p_q11d_1</th>\n",
       "      <th>p_q11d_2</th>\n",
       "      <th>p_q11d_3</th>\n",
       "      <th>p_q11d_4</th>\n",
       "      <th>p_q11e_1</th>\n",
       "      <th>p_q11e_2</th>\n",
       "      <th>p_q11e_3</th>\n",
       "      <th>p_q11e_4</th>\n",
       "      <th>p_q11f_1</th>\n",
       "      <th>p_q11f_2</th>\n",
       "      <th>p_q11f_3</th>\n",
       "      <th>p_q11f_4</th>\n",
       "      <th>p_q12aa_1</th>\n",
       "      <th>p_q12aa_2</th>\n",
       "      <th>p_q12aa_3</th>\n",
       "      <th>p_q12aa_4</th>\n",
       "      <th>p_q12aa_5</th>\n",
       "      <th>p_q12ab_1</th>\n",
       "      <th>p_q12ab_2</th>\n",
       "      <th>p_q12ab_3</th>\n",
       "      <th>p_q12ab_4</th>\n",
       "      <th>p_q12ab_5</th>\n",
       "      <th>p_q12ac_1</th>\n",
       "      <th>p_q12ac_2</th>\n",
       "      <th>p_q12ac_3</th>\n",
       "      <th>p_q12ac_4</th>\n",
       "      <th>p_q12ac_5</th>\n",
       "      <th>p_q12ad_1</th>\n",
       "      <th>p_q12ad_2</th>\n",
       "      <th>p_q12ad_3</th>\n",
       "      <th>p_q12ad_4</th>\n",
       "      <th>p_q12ad_5</th>\n",
       "      <th>p_q12ba_1</th>\n",
       "      <th>p_q12ba_2</th>\n",
       "      <th>p_q12ba_3</th>\n",
       "      <th>p_q12ba_4</th>\n",
       "      <th>p_q12ba_5</th>\n",
       "      <th>p_q12bb_1</th>\n",
       "      <th>p_q12bb_2</th>\n",
       "      <th>p_q12bb_3</th>\n",
       "      <th>p_q12bb_4</th>\n",
       "      <th>p_q12bb_5</th>\n",
       "      <th>p_q12bc_1</th>\n",
       "      <th>p_q12bc_2</th>\n",
       "      <th>p_q12bc_3</th>\n",
       "      <th>p_q12bc_4</th>\n",
       "      <th>p_q12bc_5</th>\n",
       "      <th>p_q12bd_1</th>\n",
       "      <th>p_q12bd_2</th>\n",
       "      <th>p_q12bd_3</th>\n",
       "      <th>p_q12bd_4</th>\n",
       "      <th>p_q12bd_5</th>\n",
       "      <th>p_N_q1_1</th>\n",
       "      <th>p_N_q1_2</th>\n",
       "      <th>p_N_q1_3</th>\n",
       "      <th>p_N_q1_4</th>\n",
       "      <th>p_N_q1_5</th>\n",
       "      <th>p_N_q1_6</th>\n",
       "      <th>p_N_q1_7</th>\n",
       "      <th>p_N_q1_8</th>\n",
       "      <th>p_N_q1_9</th>\n",
       "      <th>p_N_q1_10</th>\n",
       "      <th>p_N_q1_11</th>\n",
       "      <th>p_N_q1_12</th>\n",
       "      <th>p_N_q1_13</th>\n",
       "      <th>p_N_q1_14</th>\n",
       "      <th>p_N_q2a_1</th>\n",
       "      <th>p_N_q2a_2</th>\n",
       "      <th>p_N_q2a_3</th>\n",
       "      <th>p_N_q2a_4</th>\n",
       "      <th>p_N_q2a_5</th>\n",
       "      <th>p_N_q2b_1</th>\n",
       "      <th>p_N_q2b_2</th>\n",
       "      <th>p_N_q2b_3</th>\n",
       "      <th>p_N_q2b_4</th>\n",
       "      <th>p_N_q2b_5</th>\n",
       "      <th>p_N_q2c_1</th>\n",
       "      <th>p_N_q2c_2</th>\n",
       "      <th>p_N_q2c_3</th>\n",
       "      <th>p_N_q2c_4</th>\n",
       "      <th>p_N_q2c_5</th>\n",
       "      <th>p_N_q2d_1</th>\n",
       "      <th>p_N_q2d_2</th>\n",
       "      <th>p_N_q2d_3</th>\n",
       "      <th>p_N_q2d_4</th>\n",
       "      <th>p_N_q2d_5</th>\n",
       "      <th>p_N_q2e_1</th>\n",
       "      <th>p_N_q2e_2</th>\n",
       "      <th>p_N_q2e_3</th>\n",
       "      <th>p_N_q2e_4</th>\n",
       "      <th>p_N_q2e_5</th>\n",
       "      <th>p_N_q2f_1</th>\n",
       "      <th>p_N_q2f_2</th>\n",
       "      <th>p_N_q2f_3</th>\n",
       "      <th>p_N_q2f_4</th>\n",
       "      <th>p_N_q2f_5</th>\n",
       "      <th>p_N_q2g_1</th>\n",
       "      <th>p_N_q2g_2</th>\n",
       "      <th>p_N_q2g_3</th>\n",
       "      <th>p_N_q2g_4</th>\n",
       "      <th>p_N_q2g_5</th>\n",
       "      <th>p_N_q2h_1</th>\n",
       "      <th>p_N_q2h_2</th>\n",
       "      <th>p_N_q2h_3</th>\n",
       "      <th>p_N_q2h_4</th>\n",
       "      <th>p_N_q2h_5</th>\n",
       "      <th>p_N_q3a_1</th>\n",
       "      <th>p_N_q3a_2</th>\n",
       "      <th>p_N_q3a_3</th>\n",
       "      <th>p_N_q3a_4</th>\n",
       "      <th>p_N_q3a_5</th>\n",
       "      <th>p_N_q3b_1</th>\n",
       "      <th>p_N_q3b_2</th>\n",
       "      <th>p_N_q3b_3</th>\n",
       "      <th>p_N_q3b_4</th>\n",
       "      <th>p_N_q3b_5</th>\n",
       "      <th>p_N_q3c_1</th>\n",
       "      <th>p_N_q3c_2</th>\n",
       "      <th>p_N_q3c_3</th>\n",
       "      <th>p_N_q3c_4</th>\n",
       "      <th>p_N_q3c_5</th>\n",
       "      <th>p_N_q3d_1</th>\n",
       "      <th>p_N_q3d_2</th>\n",
       "      <th>p_N_q3d_3</th>\n",
       "      <th>p_N_q3d_4</th>\n",
       "      <th>p_N_q3d_5</th>\n",
       "      <th>p_N_q4a_1</th>\n",
       "      <th>p_N_q4a_2</th>\n",
       "      <th>p_N_q4a_3</th>\n",
       "      <th>p_N_q4a_4</th>\n",
       "      <th>p_N_q4a_5</th>\n",
       "      <th>p_N_q4b_1</th>\n",
       "      <th>p_N_q4b_2</th>\n",
       "      <th>p_N_q4b_3</th>\n",
       "      <th>p_N_q4b_4</th>\n",
       "      <th>p_N_q4b_5</th>\n",
       "      <th>p_N_q4c_1</th>\n",
       "      <th>p_N_q4c_2</th>\n",
       "      <th>p_N_q4c_3</th>\n",
       "      <th>p_N_q4c_4</th>\n",
       "      <th>p_N_q4c_5</th>\n",
       "      <th>p_N_q4d_1</th>\n",
       "      <th>p_N_q4d_2</th>\n",
       "      <th>p_N_q4d_3</th>\n",
       "      <th>p_N_q4d_4</th>\n",
       "      <th>p_N_q4d_5</th>\n",
       "      <th>p_N_q4e_1</th>\n",
       "      <th>p_N_q4e_2</th>\n",
       "      <th>p_N_q4e_3</th>\n",
       "      <th>p_N_q4e_4</th>\n",
       "      <th>p_N_q4e_5</th>\n",
       "      <th>p_N_q5a</th>\n",
       "      <th>p_N_q5b</th>\n",
       "      <th>p_N_q5c</th>\n",
       "      <th>p_N_q5d</th>\n",
       "      <th>p_N_q5e</th>\n",
       "      <th>p_N_q5f</th>\n",
       "      <th>p_N_q5g</th>\n",
       "      <th>p_N_q5h</th>\n",
       "      <th>p_N_q5i</th>\n",
       "      <th>p_N_q5j</th>\n",
       "      <th>p_N_q6a_1</th>\n",
       "      <th>p_N_q6a_2</th>\n",
       "      <th>p_N_q6a_3</th>\n",
       "      <th>p_N_q6a_4</th>\n",
       "      <th>p_N_q6a_5</th>\n",
       "      <th>p_N_q6b_1</th>\n",
       "      <th>p_N_q6b_2</th>\n",
       "      <th>p_N_q6b_3</th>\n",
       "      <th>p_N_q6b_4</th>\n",
       "      <th>p_N_q6b_5</th>\n",
       "      <th>p_N_q7a_1</th>\n",
       "      <th>p_N_q7a_2</th>\n",
       "      <th>p_N_q7a_3</th>\n",
       "      <th>p_N_q7a_4</th>\n",
       "      <th>p_N_q7a_5</th>\n",
       "      <th>p_N_q7b_1</th>\n",
       "      <th>p_N_q7b_2</th>\n",
       "      <th>p_N_q7b_3</th>\n",
       "      <th>p_N_q7b_4</th>\n",
       "      <th>p_N_q7b_5</th>\n",
       "      <th>p_N_q7c_1</th>\n",
       "      <th>p_N_q7c_2</th>\n",
       "      <th>p_N_q7c_3</th>\n",
       "      <th>p_N_q7c_4</th>\n",
       "      <th>p_N_q7c_5</th>\n",
       "      <th>p_N_q7d_1</th>\n",
       "      <th>p_N_q7d_2</th>\n",
       "      <th>p_N_q7d_3</th>\n",
       "      <th>p_N_q7d_4</th>\n",
       "      <th>p_N_q7d_5</th>\n",
       "      <th>p_N_q8a_1</th>\n",
       "      <th>p_N_q8a_2</th>\n",
       "      <th>p_N_q8a_3</th>\n",
       "      <th>p_N_q8a_4</th>\n",
       "      <th>p_N_q8a_5</th>\n",
       "      <th>p_N_q8b_1</th>\n",
       "      <th>p_N_q8b_2</th>\n",
       "      <th>p_N_q8b_3</th>\n",
       "      <th>p_N_q8b_4</th>\n",
       "      <th>p_N_q8b_5</th>\n",
       "      <th>p_N_q8c_1</th>\n",
       "      <th>p_N_q8c_2</th>\n",
       "      <th>p_N_q8c_3</th>\n",
       "      <th>p_N_q8c_4</th>\n",
       "      <th>p_N_q8c_5</th>\n",
       "      <th>p_N_q8d_1</th>\n",
       "      <th>p_N_q8d_2</th>\n",
       "      <th>p_N_q8d_3</th>\n",
       "      <th>p_N_q8d_4</th>\n",
       "      <th>p_N_q8d_5</th>\n",
       "      <th>p_N_q8e_1</th>\n",
       "      <th>p_N_q8e_2</th>\n",
       "      <th>p_N_q8e_3</th>\n",
       "      <th>p_N_q8e_4</th>\n",
       "      <th>p_N_q8e_5</th>\n",
       "      <th>p_N_q8f_1</th>\n",
       "      <th>p_N_q8f_2</th>\n",
       "      <th>p_N_q8f_3</th>\n",
       "      <th>p_N_q8f_4</th>\n",
       "      <th>p_N_q8f_5</th>\n",
       "      <th>p_N_q9_1</th>\n",
       "      <th>p_N_q9_2</th>\n",
       "      <th>p_N_q9_3</th>\n",
       "      <th>p_N_q9_4</th>\n",
       "      <th>p_N_q9_5</th>\n",
       "      <th>p_N_q9_6</th>\n",
       "      <th>p_N_q9_7</th>\n",
       "      <th>p_N_q9_8</th>\n",
       "      <th>p_N_q9_9</th>\n",
       "      <th>p_N_q9_10</th>\n",
       "      <th>p_N_q10a</th>\n",
       "      <th>p_N_q10b</th>\n",
       "      <th>p_N_q10c</th>\n",
       "      <th>p_N_q10d</th>\n",
       "      <th>p_N_q10e</th>\n",
       "      <th>p_N_q10f</th>\n",
       "      <th>p_N_q10g</th>\n",
       "      <th>p_N_q10h</th>\n",
       "      <th>p_N_q10i</th>\n",
       "      <th>p_N_q10j</th>\n",
       "      <th>p_N_q10k</th>\n",
       "      <th>p_N_q10l</th>\n",
       "      <th>p_N_q11a_1</th>\n",
       "      <th>p_N_q11a_2</th>\n",
       "      <th>p_N_q11a_3</th>\n",
       "      <th>p_N_q11a_4</th>\n",
       "      <th>p_N_q11b_1</th>\n",
       "      <th>p_N_q11b_2</th>\n",
       "      <th>p_N_q11b_3</th>\n",
       "      <th>p_N_q11b_4</th>\n",
       "      <th>p_N_q11c_1</th>\n",
       "      <th>p_N_q11c_2</th>\n",
       "      <th>p_N_q11c_3</th>\n",
       "      <th>p_N_q11c_4</th>\n",
       "      <th>p_N_q11d_1</th>\n",
       "      <th>p_N_q11d_2</th>\n",
       "      <th>p_N_q11d_3</th>\n",
       "      <th>p_N_q11d_4</th>\n",
       "      <th>p_N_q11e_1</th>\n",
       "      <th>p_N_q11e_2</th>\n",
       "      <th>p_N_q11e_3</th>\n",
       "      <th>p_N_q11e_4</th>\n",
       "      <th>p_N_q11f_1</th>\n",
       "      <th>p_N_q11f_2</th>\n",
       "      <th>p_N_q11f_3</th>\n",
       "      <th>p_N_q11f_4</th>\n",
       "      <th>p_N_q12aa_1</th>\n",
       "      <th>p_N_q12aa_2</th>\n",
       "      <th>p_N_q12aa_3</th>\n",
       "      <th>p_N_q12aa_4</th>\n",
       "      <th>p_N_q12aa_5</th>\n",
       "      <th>p_N_q12ab_1</th>\n",
       "      <th>p_N_q12ab_2</th>\n",
       "      <th>p_N_q12ab_3</th>\n",
       "      <th>p_N_q12ab_4</th>\n",
       "      <th>p_N_q12ab_5</th>\n",
       "      <th>p_N_q12ac_1</th>\n",
       "      <th>p_N_q12ac_2</th>\n",
       "      <th>p_N_q12ac_3</th>\n",
       "      <th>p_N_q12ac_4</th>\n",
       "      <th>p_N_q12ac_5</th>\n",
       "      <th>p_N_q12ad_1</th>\n",
       "      <th>p_N_q12ad_2</th>\n",
       "      <th>p_N_q12ad_3</th>\n",
       "      <th>p_N_q12ad_4</th>\n",
       "      <th>p_N_q12ad_5</th>\n",
       "      <th>p_N_q12ba_1</th>\n",
       "      <th>p_N_q12ba_2</th>\n",
       "      <th>p_N_q12ba_3</th>\n",
       "      <th>p_N_q12ba_4</th>\n",
       "      <th>p_N_q12ba_5</th>\n",
       "      <th>p_N_q12bb_1</th>\n",
       "      <th>p_N_q12bb_2</th>\n",
       "      <th>p_N_q12bb_3</th>\n",
       "      <th>p_N_q12bb_4</th>\n",
       "      <th>p_N_q12bb_5</th>\n",
       "      <th>p_N_q12bc_1</th>\n",
       "      <th>p_N_q12bc_2</th>\n",
       "      <th>p_N_q12bc_3</th>\n",
       "      <th>p_N_q12bc_4</th>\n",
       "      <th>p_N_q12bc_5</th>\n",
       "      <th>p_N_q12bd_1</th>\n",
       "      <th>p_N_q12bd_2</th>\n",
       "      <th>p_N_q12bd_3</th>\n",
       "      <th>p_N_q12bd_4</th>\n",
       "      <th>p_N_q12bd_5</th>\n",
       "      <th>t_q6d</th>\n",
       "      <th>t_q6e</th>\n",
       "      <th>t_q6f</th>\n",
       "      <th>t_q11a</th>\n",
       "      <th>t_q11b</th>\n",
       "      <th>t_q11c</th>\n",
       "      <th>t_q11d</th>\n",
       "      <th>t_q11e</th>\n",
       "      <th>t_q11f</th>\n",
       "      <th>t_q11g</th>\n",
       "      <th>t_q11h</th>\n",
       "      <th>t_q11j</th>\n",
       "      <th>t_q11k</th>\n",
       "      <th>t_q11l</th>\n",
       "      <th>t_q11m</th>\n",
       "      <th>t_q11n</th>\n",
       "      <th>t_q11o</th>\n",
       "      <th>t_q1a</th>\n",
       "      <th>t_q1b</th>\n",
       "      <th>t_q1c</th>\n",
       "      <th>t_q1f</th>\n",
       "      <th>t_q1g</th>\n",
       "      <th>t_q6h</th>\n",
       "      <th>t_q8c</th>\n",
       "      <th>t_q10b</th>\n",
       "      <th>t_q10d</th>\n",
       "      <th>t_q10e</th>\n",
       "      <th>t_q4</th>\n",
       "      <th>t_q5a</th>\n",
       "      <th>t_q5b</th>\n",
       "      <th>t_q5c</th>\n",
       "      <th>t_q6b</th>\n",
       "      <th>t_q6c</th>\n",
       "      <th>t_q7a</th>\n",
       "      <th>t_q7d</th>\n",
       "      <th>t_q7e</th>\n",
       "      <th>t_q8a</th>\n",
       "      <th>t_q8b</th>\n",
       "      <th>t_q11i</th>\n",
       "      <th>t_q1d</th>\n",
       "      <th>t_q1e</th>\n",
       "      <th>t_q2a</th>\n",
       "      <th>t_q2b</th>\n",
       "      <th>t_q2c</th>\n",
       "      <th>t_q2d</th>\n",
       "      <th>t_q2e</th>\n",
       "      <th>t_q2f</th>\n",
       "      <th>t_q3_1</th>\n",
       "      <th>t_q3_2</th>\n",
       "      <th>t_q6a</th>\n",
       "      <th>t_q6g</th>\n",
       "      <th>t_q6i</th>\n",
       "      <th>t_q6j</th>\n",
       "      <th>t_q6k</th>\n",
       "      <th>t_q6l</th>\n",
       "      <th>t_q7b</th>\n",
       "      <th>t_q7c</th>\n",
       "      <th>t_q6m</th>\n",
       "      <th>t_q9</th>\n",
       "      <th>t_q10a</th>\n",
       "      <th>t_q10c</th>\n",
       "      <th>t_q12a</th>\n",
       "      <th>t_q12b</th>\n",
       "      <th>t_q12c</th>\n",
       "      <th>t_q12d</th>\n",
       "      <th>t_q13a</th>\n",
       "      <th>t_q13b</th>\n",
       "      <th>t_q13c</th>\n",
       "      <th>t_q13d</th>\n",
       "      <th>t_q14</th>\n",
       "      <th>t_q15a</th>\n",
       "      <th>t_q15b</th>\n",
       "      <th>t_q15c</th>\n",
       "      <th>t_q15d</th>\n",
       "      <th>t_q15e</th>\n",
       "      <th>t_q15f</th>\n",
       "      <th>t_q1a_1</th>\n",
       "      <th>t_q1a_2</th>\n",
       "      <th>t_q1a_3</th>\n",
       "      <th>t_q1a_4</th>\n",
       "      <th>t_q1b_1</th>\n",
       "      <th>t_q1b_2</th>\n",
       "      <th>t_q1b_3</th>\n",
       "      <th>t_q1b_4</th>\n",
       "      <th>t_q1c_1</th>\n",
       "      <th>t_q1c_2</th>\n",
       "      <th>t_q1c_3</th>\n",
       "      <th>t_q1c_4</th>\n",
       "      <th>t_q1d_1</th>\n",
       "      <th>t_q1d_2</th>\n",
       "      <th>t_q1d_3</th>\n",
       "      <th>t_q1d_4</th>\n",
       "      <th>t_q1e_1</th>\n",
       "      <th>t_q1e_2</th>\n",
       "      <th>t_q1e_3</th>\n",
       "      <th>t_q1e_4</th>\n",
       "      <th>t_q1f_1</th>\n",
       "      <th>t_q1f_2</th>\n",
       "      <th>t_q1f_3</th>\n",
       "      <th>t_q1f_4</th>\n",
       "      <th>t_q1g_1</th>\n",
       "      <th>t_q1g_2</th>\n",
       "      <th>t_q1g_3</th>\n",
       "      <th>t_q1g_4</th>\n",
       "      <th>t_q2a_1</th>\n",
       "      <th>t_q2a_2</th>\n",
       "      <th>t_q2a_3</th>\n",
       "      <th>t_q2a_4</th>\n",
       "      <th>t_q2b_1</th>\n",
       "      <th>t_q2b_2</th>\n",
       "      <th>t_q2b_3</th>\n",
       "      <th>t_q2b_4</th>\n",
       "      <th>t_q2c_1</th>\n",
       "      <th>t_q2c_2</th>\n",
       "      <th>t_q2c_3</th>\n",
       "      <th>t_q2c_4</th>\n",
       "      <th>t_q2d_1</th>\n",
       "      <th>t_q2d_2</th>\n",
       "      <th>t_q2d_3</th>\n",
       "      <th>t_q2d_4</th>\n",
       "      <th>t_q2e_1</th>\n",
       "      <th>t_q2e_2</th>\n",
       "      <th>t_q2e_3</th>\n",
       "      <th>t_q2e_4</th>\n",
       "      <th>t_q2f_1</th>\n",
       "      <th>t_q2f_2</th>\n",
       "      <th>t_q2f_3</th>\n",
       "      <th>t_q2f_4</th>\n",
       "      <th>t_q3a_1</th>\n",
       "      <th>t_q3a_2</th>\n",
       "      <th>t_q3a_3</th>\n",
       "      <th>t_q3b_1</th>\n",
       "      <th>t_q3b_2</th>\n",
       "      <th>t_q3b_3</th>\n",
       "      <th>t_q3c_1</th>\n",
       "      <th>t_q3c_2</th>\n",
       "      <th>t_q3c_3</th>\n",
       "      <th>t_q3d_1</th>\n",
       "      <th>t_q3d_2</th>\n",
       "      <th>t_q3d_3</th>\n",
       "      <th>t_q3e_1</th>\n",
       "      <th>t_q3e_2</th>\n",
       "      <th>t_q3e_3</th>\n",
       "      <th>t_q3f_1</th>\n",
       "      <th>t_q3f_2</th>\n",
       "      <th>t_q3f_3</th>\n",
       "      <th>t_q3g_1</th>\n",
       "      <th>t_q3g_2</th>\n",
       "      <th>t_q3g_3</th>\n",
       "      <th>t_q3h_1</th>\n",
       "      <th>t_q3h_2</th>\n",
       "      <th>t_q3h_3</th>\n",
       "      <th>t_q3i_1</th>\n",
       "      <th>t_q3i_2</th>\n",
       "      <th>t_q3i_3</th>\n",
       "      <th>t_q3j_1</th>\n",
       "      <th>t_q3j_2</th>\n",
       "      <th>t_q3j_3</th>\n",
       "      <th>t_q4_1</th>\n",
       "      <th>t_q4_2</th>\n",
       "      <th>t_q4_3</th>\n",
       "      <th>t_q4_4</th>\n",
       "      <th>t_q5a_1</th>\n",
       "      <th>t_q5a_2</th>\n",
       "      <th>t_q5a_3</th>\n",
       "      <th>t_q5a_4</th>\n",
       "      <th>t_q5a_5</th>\n",
       "      <th>t_q5b_1</th>\n",
       "      <th>t_q5b_2</th>\n",
       "      <th>t_q5b_3</th>\n",
       "      <th>t_q5b_4</th>\n",
       "      <th>t_q5b_5</th>\n",
       "      <th>t_q5c_1</th>\n",
       "      <th>t_q5c_2</th>\n",
       "      <th>t_q5c_3</th>\n",
       "      <th>t_q5c_4</th>\n",
       "      <th>t_q5c_5</th>\n",
       "      <th>t_q6a_1</th>\n",
       "      <th>t_q6a_2</th>\n",
       "      <th>t_q6a_3</th>\n",
       "      <th>t_q6a_4</th>\n",
       "      <th>t_q6b_1</th>\n",
       "      <th>t_q6b_2</th>\n",
       "      <th>t_q6b_3</th>\n",
       "      <th>t_q6b_4</th>\n",
       "      <th>t_q6c_1</th>\n",
       "      <th>t_q6c_2</th>\n",
       "      <th>t_q6c_3</th>\n",
       "      <th>t_q6c_4</th>\n",
       "      <th>t_q6d_1</th>\n",
       "      <th>t_q6d_2</th>\n",
       "      <th>t_q6d_3</th>\n",
       "      <th>t_q6d_4</th>\n",
       "      <th>t_q6e_1</th>\n",
       "      <th>t_q6e_2</th>\n",
       "      <th>t_q6e_3</th>\n",
       "      <th>t_q6e_4</th>\n",
       "      <th>t_q6f_1</th>\n",
       "      <th>t_q6f_2</th>\n",
       "      <th>t_q6f_3</th>\n",
       "      <th>t_q6f_4</th>\n",
       "      <th>t_q6g_1</th>\n",
       "      <th>t_q6g_2</th>\n",
       "      <th>t_q6g_3</th>\n",
       "      <th>t_q6g_4</th>\n",
       "      <th>t_q6h_1</th>\n",
       "      <th>t_q6h_2</th>\n",
       "      <th>t_q6h_3</th>\n",
       "      <th>t_q6h_4</th>\n",
       "      <th>t_q6i_1</th>\n",
       "      <th>t_q6i_2</th>\n",
       "      <th>t_q6i_3</th>\n",
       "      <th>t_q6i_4</th>\n",
       "      <th>t_q6j_1</th>\n",
       "      <th>t_q6j_2</th>\n",
       "      <th>t_q6j_3</th>\n",
       "      <th>t_q6j_4</th>\n",
       "      <th>t_q6k_1</th>\n",
       "      <th>t_q6k_2</th>\n",
       "      <th>t_q6k_3</th>\n",
       "      <th>t_q6k_4</th>\n",
       "      <th>t_q6l_1</th>\n",
       "      <th>t_q6l_2</th>\n",
       "      <th>t_q6l_3</th>\n",
       "      <th>t_q6l_4</th>\n",
       "      <th>t_q6m_1</th>\n",
       "      <th>t_q6m_2</th>\n",
       "      <th>t_q6m_3</th>\n",
       "      <th>t_q6m_4</th>\n",
       "      <th>t_q7a_1</th>\n",
       "      <th>t_q7a_2</th>\n",
       "      <th>t_q7a_3</th>\n",
       "      <th>t_q7a_4</th>\n",
       "      <th>t_q7a_5</th>\n",
       "      <th>t_q7b_1</th>\n",
       "      <th>t_q7b_2</th>\n",
       "      <th>t_q7b_3</th>\n",
       "      <th>t_q7b_4</th>\n",
       "      <th>t_q7b_5</th>\n",
       "      <th>t_q7c_1</th>\n",
       "      <th>t_q7c_2</th>\n",
       "      <th>t_q7c_3</th>\n",
       "      <th>t_q7c_4</th>\n",
       "      <th>t_q7c_5</th>\n",
       "      <th>t_q7d_1</th>\n",
       "      <th>t_q7d_2</th>\n",
       "      <th>t_q7d_3</th>\n",
       "      <th>t_q7d_4</th>\n",
       "      <th>t_q7d_5</th>\n",
       "      <th>t_q7e_1</th>\n",
       "      <th>t_q7e_2</th>\n",
       "      <th>t_q7e_3</th>\n",
       "      <th>t_q7e_4</th>\n",
       "      <th>t_q7e_5</th>\n",
       "      <th>t_q8a_1</th>\n",
       "      <th>t_q8a_2</th>\n",
       "      <th>t_q8a_3</th>\n",
       "      <th>t_q8a_4</th>\n",
       "      <th>t_q8b_1</th>\n",
       "      <th>t_q8b_2</th>\n",
       "      <th>t_q8b_3</th>\n",
       "      <th>t_q8b_4</th>\n",
       "      <th>t_q8c_1</th>\n",
       "      <th>t_q8c_2</th>\n",
       "      <th>t_q8c_3</th>\n",
       "      <th>t_q8c_4</th>\n",
       "      <th>t_q9_1</th>\n",
       "      <th>t_q9_2</th>\n",
       "      <th>t_q9_3</th>\n",
       "      <th>t_q9_4</th>\n",
       "      <th>t_q9_5</th>\n",
       "      <th>t_q10a_1</th>\n",
       "      <th>t_q10a_2</th>\n",
       "      <th>t_q10a_3</th>\n",
       "      <th>t_q10a_4</th>\n",
       "      <th>t_q10a_5</th>\n",
       "      <th>t_q10a_6</th>\n",
       "      <th>t_q10b_1</th>\n",
       "      <th>t_q10b_2</th>\n",
       "      <th>t_q10b_3</th>\n",
       "      <th>t_q10b_4</th>\n",
       "      <th>t_q10b_5</th>\n",
       "      <th>t_q10b_6</th>\n",
       "      <th>t_q10c_1</th>\n",
       "      <th>t_q10c_2</th>\n",
       "      <th>t_q10c_3</th>\n",
       "      <th>t_q10c_4</th>\n",
       "      <th>t_q10c_5</th>\n",
       "      <th>t_q10c_6</th>\n",
       "      <th>t_q10d_1</th>\n",
       "      <th>t_q10d_2</th>\n",
       "      <th>t_q10d_3</th>\n",
       "      <th>t_q10d_4</th>\n",
       "      <th>t_q10d_5</th>\n",
       "      <th>t_q10d_6</th>\n",
       "      <th>t_q10e_1</th>\n",
       "      <th>t_q10e_2</th>\n",
       "      <th>t_q10e_3</th>\n",
       "      <th>t_q10e_4</th>\n",
       "      <th>t_q10e_5</th>\n",
       "      <th>t_q10e_6</th>\n",
       "      <th>t_q11a_1</th>\n",
       "      <th>t_q11a_2</th>\n",
       "      <th>t_q11a_3</th>\n",
       "      <th>t_q11a_4</th>\n",
       "      <th>t_q11b_1</th>\n",
       "      <th>t_q11b_2</th>\n",
       "      <th>t_q11b_3</th>\n",
       "      <th>t_q11b_4</th>\n",
       "      <th>t_q11c_1</th>\n",
       "      <th>t_q11c_2</th>\n",
       "      <th>t_q11c_3</th>\n",
       "      <th>t_q11c_4</th>\n",
       "      <th>t_q11d_1</th>\n",
       "      <th>t_q11d_2</th>\n",
       "      <th>t_q11d_3</th>\n",
       "      <th>t_q11d_4</th>\n",
       "      <th>t_q11e_1</th>\n",
       "      <th>t_q11e_2</th>\n",
       "      <th>t_q11e_3</th>\n",
       "      <th>t_q11e_4</th>\n",
       "      <th>t_q11f_1</th>\n",
       "      <th>t_q11f_2</th>\n",
       "      <th>t_q11f_3</th>\n",
       "      <th>t_q11f_4</th>\n",
       "      <th>t_q11g_1</th>\n",
       "      <th>t_q11g_2</th>\n",
       "      <th>t_q11g_3</th>\n",
       "      <th>t_q11g_4</th>\n",
       "      <th>t_q11h_1</th>\n",
       "      <th>t_q11h_2</th>\n",
       "      <th>t_q11h_3</th>\n",
       "      <th>t_q11h_4</th>\n",
       "      <th>t_q11i_1</th>\n",
       "      <th>t_q11i_2</th>\n",
       "      <th>t_q11i_3</th>\n",
       "      <th>t_q11i_4</th>\n",
       "      <th>t_q11j_1</th>\n",
       "      <th>t_q11j_2</th>\n",
       "      <th>t_q11j_3</th>\n",
       "      <th>t_q11j_4</th>\n",
       "      <th>t_q11k_1</th>\n",
       "      <th>t_q11k_2</th>\n",
       "      <th>t_q11k_3</th>\n",
       "      <th>t_q11k_4</th>\n",
       "      <th>t_q11l_1</th>\n",
       "      <th>t_q11l_2</th>\n",
       "      <th>t_q11l_3</th>\n",
       "      <th>t_q11l_4</th>\n",
       "      <th>t_q11m_1</th>\n",
       "      <th>t_q11m_2</th>\n",
       "      <th>t_q11m_3</th>\n",
       "      <th>t_q11m_4</th>\n",
       "      <th>t_q11n_1</th>\n",
       "      <th>t_q11n_2</th>\n",
       "      <th>t_q11n_3</th>\n",
       "      <th>t_q11n_4</th>\n",
       "      <th>t_q11o_1</th>\n",
       "      <th>t_q11o_2</th>\n",
       "      <th>t_q11o_3</th>\n",
       "      <th>t_q11o_4</th>\n",
       "      <th>t_q12a_1</th>\n",
       "      <th>t_q12a_2</th>\n",
       "      <th>t_q12a_3</th>\n",
       "      <th>t_q12a_4</th>\n",
       "      <th>t_q12a_5</th>\n",
       "      <th>t_q12b_1</th>\n",
       "      <th>t_q12b_2</th>\n",
       "      <th>t_q12b_3</th>\n",
       "      <th>t_q12b_4</th>\n",
       "      <th>t_q12b_5</th>\n",
       "      <th>t_q12c_1</th>\n",
       "      <th>t_q12c_2</th>\n",
       "      <th>t_q12c_3</th>\n",
       "      <th>t_q12c_4</th>\n",
       "      <th>t_q12c_5</th>\n",
       "      <th>t_q12d_1</th>\n",
       "      <th>t_q12d_2</th>\n",
       "      <th>t_q12d_3</th>\n",
       "      <th>t_q12d_4</th>\n",
       "      <th>t_q12d_5</th>\n",
       "      <th>t_q13a_1</th>\n",
       "      <th>t_q13a_2</th>\n",
       "      <th>t_q13a_3</th>\n",
       "      <th>t_q13a_4</th>\n",
       "      <th>t_q13a_5</th>\n",
       "      <th>t_q13b_1</th>\n",
       "      <th>t_q13b_2</th>\n",
       "      <th>t_q13b_3</th>\n",
       "      <th>t_q13b_4</th>\n",
       "      <th>t_q13b_5</th>\n",
       "      <th>t_q13c_1</th>\n",
       "      <th>t_q13c_2</th>\n",
       "      <th>t_q13c_3</th>\n",
       "      <th>t_q13c_4</th>\n",
       "      <th>t_q13c_5</th>\n",
       "      <th>t_q13d_1</th>\n",
       "      <th>t_q13d_2</th>\n",
       "      <th>t_q13d_3</th>\n",
       "      <th>t_q13d_4</th>\n",
       "      <th>t_q13d_5</th>\n",
       "      <th>t_q14_1</th>\n",
       "      <th>t_q14_2</th>\n",
       "      <th>t_q14_3</th>\n",
       "      <th>t_q14_4</th>\n",
       "      <th>t_q14_5</th>\n",
       "      <th>t_q15a_1</th>\n",
       "      <th>t_q15a_2</th>\n",
       "      <th>t_q15a_3</th>\n",
       "      <th>t_q15a_4</th>\n",
       "      <th>t_q15b_1</th>\n",
       "      <th>t_q15b_2</th>\n",
       "      <th>t_q15b_3</th>\n",
       "      <th>t_q15b_4</th>\n",
       "      <th>t_q15c_1</th>\n",
       "      <th>t_q15c_2</th>\n",
       "      <th>t_q15c_3</th>\n",
       "      <th>t_q15c_4</th>\n",
       "      <th>t_q15d_1</th>\n",
       "      <th>t_q15d_2</th>\n",
       "      <th>t_q15d_3</th>\n",
       "      <th>t_q15d_4</th>\n",
       "      <th>t_q15e_1</th>\n",
       "      <th>t_q15e_2</th>\n",
       "      <th>t_q15e_3</th>\n",
       "      <th>t_q15e_4</th>\n",
       "      <th>t_q15f_1</th>\n",
       "      <th>t_q15f_2</th>\n",
       "      <th>t_q15f_3</th>\n",
       "      <th>t_q15f_4</th>\n",
       "      <th>t_N_q1a_1</th>\n",
       "      <th>t_N_q1a_2</th>\n",
       "      <th>t_N_q1a_3</th>\n",
       "      <th>t_N_q1a_4</th>\n",
       "      <th>t_N_q1b_1</th>\n",
       "      <th>t_N_q1b_2</th>\n",
       "      <th>t_N_q1b_3</th>\n",
       "      <th>t_N_q1b_4</th>\n",
       "      <th>t_N_q1c_1</th>\n",
       "      <th>t_N_q1c_2</th>\n",
       "      <th>t_N_q1c_3</th>\n",
       "      <th>t_N_q1c_4</th>\n",
       "      <th>t_N_q1d_1</th>\n",
       "      <th>t_N_q1d_2</th>\n",
       "      <th>t_N_q1d_3</th>\n",
       "      <th>t_N_q1d_4</th>\n",
       "      <th>t_N_q1e_1</th>\n",
       "      <th>t_N_q1e_2</th>\n",
       "      <th>t_N_q1e_3</th>\n",
       "      <th>t_N_q1e_4</th>\n",
       "      <th>t_N_q1f_1</th>\n",
       "      <th>t_N_q1f_2</th>\n",
       "      <th>t_N_q1f_3</th>\n",
       "      <th>t_N_q1f_4</th>\n",
       "      <th>t_N_q1g_1</th>\n",
       "      <th>t_N_q1g_2</th>\n",
       "      <th>t_N_q1g_3</th>\n",
       "      <th>t_N_q1g_4</th>\n",
       "      <th>t_N_q2a_1</th>\n",
       "      <th>t_N_q2a_2</th>\n",
       "      <th>t_N_q2a_3</th>\n",
       "      <th>t_N_q2a_4</th>\n",
       "      <th>t_N_q2b_1</th>\n",
       "      <th>t_N_q2b_2</th>\n",
       "      <th>t_N_q2b_3</th>\n",
       "      <th>t_N_q2b_4</th>\n",
       "      <th>t_N_q2c_1</th>\n",
       "      <th>t_N_q2c_2</th>\n",
       "      <th>t_N_q2c_3</th>\n",
       "      <th>t_N_q2c_4</th>\n",
       "      <th>t_N_q2d_1</th>\n",
       "      <th>t_N_q2d_2</th>\n",
       "      <th>t_N_q2d_3</th>\n",
       "      <th>t_N_q2d_4</th>\n",
       "      <th>t_N_q2e_1</th>\n",
       "      <th>t_N_q2e_2</th>\n",
       "      <th>t_N_q2e_3</th>\n",
       "      <th>t_N_q2e_4</th>\n",
       "      <th>t_N_q2f_1</th>\n",
       "      <th>t_N_q2f_2</th>\n",
       "      <th>t_N_q2f_3</th>\n",
       "      <th>t_N_q2f_4</th>\n",
       "      <th>t_N_q3a_1</th>\n",
       "      <th>t_N_q3a_2</th>\n",
       "      <th>t_N_q3a_3</th>\n",
       "      <th>t_N_q3b_1</th>\n",
       "      <th>t_N_q3b_2</th>\n",
       "      <th>t_N_q3b_3</th>\n",
       "      <th>t_N_q3c_1</th>\n",
       "      <th>t_N_q3c_2</th>\n",
       "      <th>t_N_q3c_3</th>\n",
       "      <th>t_N_q3d_1</th>\n",
       "      <th>t_N_q3d_2</th>\n",
       "      <th>t_N_q3d_3</th>\n",
       "      <th>t_N_q3e_1</th>\n",
       "      <th>t_N_q3e_2</th>\n",
       "      <th>t_N_q3e_3</th>\n",
       "      <th>t_N_q3f_1</th>\n",
       "      <th>t_N_q3f_2</th>\n",
       "      <th>t_N_q3f_3</th>\n",
       "      <th>t_N_q3g_1</th>\n",
       "      <th>t_N_q3g_2</th>\n",
       "      <th>t_N_q3g_3</th>\n",
       "      <th>t_N_q3h_1</th>\n",
       "      <th>t_N_q3h_2</th>\n",
       "      <th>t_N_q3h_3</th>\n",
       "      <th>t_N_q3i_1</th>\n",
       "      <th>t_N_q3i_2</th>\n",
       "      <th>t_N_q3i_3</th>\n",
       "      <th>t_N_q3j_1</th>\n",
       "      <th>t_N_q3j_2</th>\n",
       "      <th>t_N_q3j_3</th>\n",
       "      <th>t_N_q4_1</th>\n",
       "      <th>t_N_q4_2</th>\n",
       "      <th>t_N_q4_3</th>\n",
       "      <th>t_N_q4_4</th>\n",
       "      <th>t_N_q5a_1</th>\n",
       "      <th>t_N_q5a_2</th>\n",
       "      <th>t_N_q5a_3</th>\n",
       "      <th>t_N_q5a_4</th>\n",
       "      <th>t_N_q5a_5</th>\n",
       "      <th>t_N_q5b_1</th>\n",
       "      <th>t_N_q5b_2</th>\n",
       "      <th>t_N_q5b_3</th>\n",
       "      <th>t_N_q5b_4</th>\n",
       "      <th>t_N_q5b_5</th>\n",
       "      <th>t_N_q5c_1</th>\n",
       "      <th>t_N_q5c_2</th>\n",
       "      <th>t_N_q5c_3</th>\n",
       "      <th>t_N_q5c_4</th>\n",
       "      <th>t_N_q5c_5</th>\n",
       "      <th>t_N_q6a_1</th>\n",
       "      <th>t_N_q6a_2</th>\n",
       "      <th>t_N_q6a_3</th>\n",
       "      <th>t_N_q6a_4</th>\n",
       "      <th>t_N_q6b_1</th>\n",
       "      <th>t_N_q6b_2</th>\n",
       "      <th>t_N_q6b_3</th>\n",
       "      <th>t_N_q6b_4</th>\n",
       "      <th>t_N_q6c_1</th>\n",
       "      <th>t_N_q6c_2</th>\n",
       "      <th>t_N_q6c_3</th>\n",
       "      <th>t_N_q6c_4</th>\n",
       "      <th>t_N_q6d_1</th>\n",
       "      <th>t_N_q6d_2</th>\n",
       "      <th>t_N_q6d_3</th>\n",
       "      <th>t_N_q6d_4</th>\n",
       "      <th>t_N_q6e_1</th>\n",
       "      <th>t_N_q6e_2</th>\n",
       "      <th>t_N_q6e_3</th>\n",
       "      <th>t_N_q6e_4</th>\n",
       "      <th>t_N_q6f_1</th>\n",
       "      <th>t_N_q6f_2</th>\n",
       "      <th>t_N_q6f_3</th>\n",
       "      <th>t_N_q6f_4</th>\n",
       "      <th>t_N_q6g_1</th>\n",
       "      <th>t_N_q6g_2</th>\n",
       "      <th>t_N_q6g_3</th>\n",
       "      <th>t_N_q6g_4</th>\n",
       "      <th>t_N_q6h_1</th>\n",
       "      <th>t_N_q6h_2</th>\n",
       "      <th>t_N_q6h_3</th>\n",
       "      <th>t_N_q6h_4</th>\n",
       "      <th>t_N_q6i_1</th>\n",
       "      <th>t_N_q6i_2</th>\n",
       "      <th>t_N_q6i_3</th>\n",
       "      <th>t_N_q6i_4</th>\n",
       "      <th>t_N_q6j_1</th>\n",
       "      <th>t_N_q6j_2</th>\n",
       "      <th>t_N_q6j_3</th>\n",
       "      <th>t_N_q6j_4</th>\n",
       "      <th>t_N_q6k_1</th>\n",
       "      <th>t_N_q6k_2</th>\n",
       "      <th>t_N_q6k_3</th>\n",
       "      <th>t_N_q6k_4</th>\n",
       "      <th>t_N_q6l_1</th>\n",
       "      <th>t_N_q6l_2</th>\n",
       "      <th>t_N_q6l_3</th>\n",
       "      <th>t_N_q6l_4</th>\n",
       "      <th>t_N_q6m_1</th>\n",
       "      <th>t_N_q6m_2</th>\n",
       "      <th>t_N_q6m_3</th>\n",
       "      <th>t_N_q6m_4</th>\n",
       "      <th>t_N_q7a_1</th>\n",
       "      <th>t_N_q7a_2</th>\n",
       "      <th>t_N_q7a_3</th>\n",
       "      <th>t_N_q7a_4</th>\n",
       "      <th>t_N_q7a_5</th>\n",
       "      <th>t_N_q7b_1</th>\n",
       "      <th>t_N_q7b_2</th>\n",
       "      <th>t_N_q7b_3</th>\n",
       "      <th>t_N_q7b_4</th>\n",
       "      <th>t_N_q7b_5</th>\n",
       "      <th>t_N_q7c_1</th>\n",
       "      <th>t_N_q7c_2</th>\n",
       "      <th>t_N_q7c_3</th>\n",
       "      <th>t_N_q7c_4</th>\n",
       "      <th>t_N_q7c_5</th>\n",
       "      <th>t_N_q7d_1</th>\n",
       "      <th>t_N_q7d_2</th>\n",
       "      <th>t_N_q7d_3</th>\n",
       "      <th>t_N_q7d_4</th>\n",
       "      <th>t_N_q7d_5</th>\n",
       "      <th>t_N_q7e_1</th>\n",
       "      <th>t_N_q7e_2</th>\n",
       "      <th>t_N_q7e_3</th>\n",
       "      <th>t_N_q7e_4</th>\n",
       "      <th>t_N_q7e_5</th>\n",
       "      <th>t_N_q8a_1</th>\n",
       "      <th>t_N_q8a_2</th>\n",
       "      <th>t_N_q8a_3</th>\n",
       "      <th>t_N_q8a_4</th>\n",
       "      <th>t_N_q8b_1</th>\n",
       "      <th>t_N_q8b_2</th>\n",
       "      <th>t_N_q8b_3</th>\n",
       "      <th>t_N_q8b_4</th>\n",
       "      <th>t_N_q8c_1</th>\n",
       "      <th>t_N_q8c_2</th>\n",
       "      <th>t_N_q8c_3</th>\n",
       "      <th>t_N_q8c_4</th>\n",
       "      <th>t_N_q9_1</th>\n",
       "      <th>t_N_q9_2</th>\n",
       "      <th>t_N_q9_3</th>\n",
       "      <th>t_N_q9_4</th>\n",
       "      <th>t_N_q9_5</th>\n",
       "      <th>t_N_q10a_1</th>\n",
       "      <th>t_N_q10a_2</th>\n",
       "      <th>t_N_q10a_3</th>\n",
       "      <th>t_N_q10a_4</th>\n",
       "      <th>t_N_q10a_5</th>\n",
       "      <th>t_N_q10a_6</th>\n",
       "      <th>t_N_q10b_1</th>\n",
       "      <th>t_N_q10b_2</th>\n",
       "      <th>t_N_q10b_3</th>\n",
       "      <th>t_N_q10b_4</th>\n",
       "      <th>t_N_q10b_5</th>\n",
       "      <th>t_N_q10b_6</th>\n",
       "      <th>t_N_q10c_1</th>\n",
       "      <th>t_N_q10c_2</th>\n",
       "      <th>t_N_q10c_3</th>\n",
       "      <th>t_N_q10c_4</th>\n",
       "      <th>t_N_q10c_5</th>\n",
       "      <th>t_N_q10c_6</th>\n",
       "      <th>t_N_q10d_1</th>\n",
       "      <th>t_N_q10d_2</th>\n",
       "      <th>t_N_q10d_3</th>\n",
       "      <th>t_N_q10d_4</th>\n",
       "      <th>t_N_q10d_5</th>\n",
       "      <th>t_N_q10d_6</th>\n",
       "      <th>t_N_q10e_1</th>\n",
       "      <th>t_N_q10e_2</th>\n",
       "      <th>t_N_q10e_3</th>\n",
       "      <th>t_N_q10e_4</th>\n",
       "      <th>t_N_q10e_5</th>\n",
       "      <th>t_N_q10e_6</th>\n",
       "      <th>t_N_q11a_1</th>\n",
       "      <th>t_N_q11a_2</th>\n",
       "      <th>t_N_q11a_3</th>\n",
       "      <th>t_N_q11a_4</th>\n",
       "      <th>t_N_q11b_1</th>\n",
       "      <th>t_N_q11b_2</th>\n",
       "      <th>t_N_q11b_3</th>\n",
       "      <th>t_N_q11b_4</th>\n",
       "      <th>t_N_q11c_1</th>\n",
       "      <th>t_N_q11c_2</th>\n",
       "      <th>t_N_q11c_3</th>\n",
       "      <th>t_N_q11c_4</th>\n",
       "      <th>t_N_q11d_1</th>\n",
       "      <th>t_N_q11d_2</th>\n",
       "      <th>t_N_q11d_3</th>\n",
       "      <th>t_N_q11d_4</th>\n",
       "      <th>t_N_q11e_1</th>\n",
       "      <th>t_N_q11e_2</th>\n",
       "      <th>t_N_q11e_3</th>\n",
       "      <th>t_N_q11e_4</th>\n",
       "      <th>t_N_q11f_1</th>\n",
       "      <th>t_N_q11f_2</th>\n",
       "      <th>t_N_q11f_3</th>\n",
       "      <th>t_N_q11f_4</th>\n",
       "      <th>t_N_q11g_1</th>\n",
       "      <th>t_N_q11g_2</th>\n",
       "      <th>t_N_q11g_3</th>\n",
       "      <th>t_N_q11g_4</th>\n",
       "      <th>t_N_q11h_1</th>\n",
       "      <th>t_N_q11h_2</th>\n",
       "      <th>t_N_q11h_3</th>\n",
       "      <th>t_N_q11h_4</th>\n",
       "      <th>t_N_q11i_1</th>\n",
       "      <th>t_N_q11i_2</th>\n",
       "      <th>t_N_q11i_3</th>\n",
       "      <th>t_N_q11i_4</th>\n",
       "      <th>t_N_q11j_1</th>\n",
       "      <th>t_N_q11j_2</th>\n",
       "      <th>t_N_q11j_3</th>\n",
       "      <th>t_N_q11j_4</th>\n",
       "      <th>t_N_q11k_1</th>\n",
       "      <th>t_N_q11k_2</th>\n",
       "      <th>t_N_q11k_3</th>\n",
       "      <th>t_N_q11k_4</th>\n",
       "      <th>t_N_q11l_1</th>\n",
       "      <th>t_N_q11l_2</th>\n",
       "      <th>t_N_q11l_3</th>\n",
       "      <th>t_N_q11l_4</th>\n",
       "      <th>t_N_q11m_1</th>\n",
       "      <th>t_N_q11m_2</th>\n",
       "      <th>t_N_q11m_3</th>\n",
       "      <th>t_N_q11m_4</th>\n",
       "      <th>t_N_q11n_1</th>\n",
       "      <th>t_N_q11n_2</th>\n",
       "      <th>t_N_q11n_3</th>\n",
       "      <th>t_N_q11n_4</th>\n",
       "      <th>t_N_q11o_1</th>\n",
       "      <th>t_N_q11o_2</th>\n",
       "      <th>t_N_q11o_3</th>\n",
       "      <th>t_N_q11o_4</th>\n",
       "      <th>t_N_q12a_1</th>\n",
       "      <th>t_N_q12a_2</th>\n",
       "      <th>t_N_q12a_3</th>\n",
       "      <th>t_N_q12a_4</th>\n",
       "      <th>t_N_q12a_5</th>\n",
       "      <th>t_N_q12b_1</th>\n",
       "      <th>t_N_q12b_2</th>\n",
       "      <th>t_N_q12b_3</th>\n",
       "      <th>t_N_q12b_4</th>\n",
       "      <th>t_N_q12b_5</th>\n",
       "      <th>t_N_q12c_1</th>\n",
       "      <th>t_N_q12c_2</th>\n",
       "      <th>t_N_q12c_3</th>\n",
       "      <th>t_N_q12c_4</th>\n",
       "      <th>t_N_q12c_5</th>\n",
       "      <th>t_N_q12d_1</th>\n",
       "      <th>t_N_q12d_2</th>\n",
       "      <th>t_N_q12d_3</th>\n",
       "      <th>t_N_q12d_4</th>\n",
       "      <th>t_N_q12d_5</th>\n",
       "      <th>t_N_q13a_1</th>\n",
       "      <th>t_N_q13a_2</th>\n",
       "      <th>t_N_q13a_3</th>\n",
       "      <th>t_N_q13a_4</th>\n",
       "      <th>t_N_q13a_5</th>\n",
       "      <th>t_N_q13b_1</th>\n",
       "      <th>t_N_q13b_2</th>\n",
       "      <th>t_N_q13b_3</th>\n",
       "      <th>t_N_q13b_4</th>\n",
       "      <th>t_N_q13b_5</th>\n",
       "      <th>t_N_q13c_1</th>\n",
       "      <th>t_N_q13c_2</th>\n",
       "      <th>t_N_q13c_3</th>\n",
       "      <th>t_N_q13c_4</th>\n",
       "      <th>t_N_q13c_5</th>\n",
       "      <th>t_N_q13d_1</th>\n",
       "      <th>t_N_q13d_2</th>\n",
       "      <th>t_N_q13d_3</th>\n",
       "      <th>t_N_q13d_4</th>\n",
       "      <th>t_N_q13d_5</th>\n",
       "      <th>t_N_q14_1</th>\n",
       "      <th>t_N_q14_2</th>\n",
       "      <th>t_N_q14_3</th>\n",
       "      <th>t_N_q14_4</th>\n",
       "      <th>t_N_q14_5</th>\n",
       "      <th>t_N_q15a_1</th>\n",
       "      <th>t_N_q15a_2</th>\n",
       "      <th>t_N_q15a_3</th>\n",
       "      <th>t_N_q15a_4</th>\n",
       "      <th>t_N_q15b_1</th>\n",
       "      <th>t_N_q15b_2</th>\n",
       "      <th>t_N_q15b_3</th>\n",
       "      <th>t_N_q15b_4</th>\n",
       "      <th>t_N_q15c_1</th>\n",
       "      <th>t_N_q15c_2</th>\n",
       "      <th>t_N_q15c_3</th>\n",
       "      <th>t_N_q15c_4</th>\n",
       "      <th>t_N_q15d_1</th>\n",
       "      <th>t_N_q15d_2</th>\n",
       "      <th>t_N_q15d_3</th>\n",
       "      <th>t_N_q15d_4</th>\n",
       "      <th>t_N_q15e_1</th>\n",
       "      <th>t_N_q15e_2</th>\n",
       "      <th>t_N_q15e_3</th>\n",
       "      <th>t_N_q15e_4</th>\n",
       "      <th>t_N_q15f_1</th>\n",
       "      <th>t_N_q15f_2</th>\n",
       "      <th>t_N_q15f_3</th>\n",
       "      <th>t_N_q15f_4</th>\n",
       "      <th>s_q6a</th>\n",
       "      <th>s_q6b</th>\n",
       "      <th>s_q6c</th>\n",
       "      <th>s_q12a</th>\n",
       "      <th>s_q12b</th>\n",
       "      <th>s_q12c</th>\n",
       "      <th>s_q13a</th>\n",
       "      <th>s_q13b</th>\n",
       "      <th>s_q13c</th>\n",
       "      <th>s_q13d</th>\n",
       "      <th>s_q13e</th>\n",
       "      <th>s_q13f</th>\n",
       "      <th>s_q13g</th>\n",
       "      <th>s_q14a</th>\n",
       "      <th>s_q14b</th>\n",
       "      <th>s_q14c</th>\n",
       "      <th>s_q14d</th>\n",
       "      <th>s_q14e</th>\n",
       "      <th>s_q14f</th>\n",
       "      <th>s_q14g</th>\n",
       "      <th>s_q2b</th>\n",
       "      <th>s_q4a</th>\n",
       "      <th>s_q4b</th>\n",
       "      <th>s_q8b</th>\n",
       "      <th>s_q8c</th>\n",
       "      <th>s_q8d</th>\n",
       "      <th>s_q2a</th>\n",
       "      <th>s_q2c</th>\n",
       "      <th>s_q5a</th>\n",
       "      <th>s_q5b</th>\n",
       "      <th>s_q6d</th>\n",
       "      <th>s_q6e</th>\n",
       "      <th>s_q6f</th>\n",
       "      <th>s_q6g</th>\n",
       "      <th>s_q6h</th>\n",
       "      <th>s_q9</th>\n",
       "      <th>s_q10</th>\n",
       "      <th>s_q11</th>\n",
       "      <th>s_q3a</th>\n",
       "      <th>s_q3b</th>\n",
       "      <th>s_q3c</th>\n",
       "      <th>s_q3d</th>\n",
       "      <th>s_q3e</th>\n",
       "      <th>s_q3f</th>\n",
       "      <th>s_q3g</th>\n",
       "      <th>s_q3h</th>\n",
       "      <th>s_q3i</th>\n",
       "      <th>s_q7a</th>\n",
       "      <th>s_q7b</th>\n",
       "      <th>s_q1</th>\n",
       "      <th>s_q8a</th>\n",
       "      <th>s_q1_1</th>\n",
       "      <th>s_q1_2</th>\n",
       "      <th>s_q1_3</th>\n",
       "      <th>s_q1_4</th>\n",
       "      <th>s_q1_5</th>\n",
       "      <th>s_q1_6</th>\n",
       "      <th>s_q1_7</th>\n",
       "      <th>s_q2a_1</th>\n",
       "      <th>s_q2a_2</th>\n",
       "      <th>s_q2a_3</th>\n",
       "      <th>s_q2a_4</th>\n",
       "      <th>s_q2b_1</th>\n",
       "      <th>s_q2b_2</th>\n",
       "      <th>s_q2b_3</th>\n",
       "      <th>s_q2b_4</th>\n",
       "      <th>s_q2c_1</th>\n",
       "      <th>s_q2c_2</th>\n",
       "      <th>s_q2c_3</th>\n",
       "      <th>s_q2c_4</th>\n",
       "      <th>s_q3a_1</th>\n",
       "      <th>s_q3a_2</th>\n",
       "      <th>s_q3a_3</th>\n",
       "      <th>s_q3a_4</th>\n",
       "      <th>s_q3b_1</th>\n",
       "      <th>s_q3b_2</th>\n",
       "      <th>s_q3b_3</th>\n",
       "      <th>s_q3b_4</th>\n",
       "      <th>s_q3c_1</th>\n",
       "      <th>s_q3c_2</th>\n",
       "      <th>s_q3c_3</th>\n",
       "      <th>s_q3c_4</th>\n",
       "      <th>s_q3d_1</th>\n",
       "      <th>s_q3d_2</th>\n",
       "      <th>s_q3d_3</th>\n",
       "      <th>s_q3d_4</th>\n",
       "      <th>s_q3e_1</th>\n",
       "      <th>s_q3e_2</th>\n",
       "      <th>s_q3e_3</th>\n",
       "      <th>s_q3e_4</th>\n",
       "      <th>s_q3f_1</th>\n",
       "      <th>s_q3f_2</th>\n",
       "      <th>s_q3f_3</th>\n",
       "      <th>s_q3f_4</th>\n",
       "      <th>s_q3g_1</th>\n",
       "      <th>s_q3g_2</th>\n",
       "      <th>s_q3g_3</th>\n",
       "      <th>s_q3g_4</th>\n",
       "      <th>s_q3h_1</th>\n",
       "      <th>s_q3h_2</th>\n",
       "      <th>s_q3h_3</th>\n",
       "      <th>s_q3h_4</th>\n",
       "      <th>s_q3i_1</th>\n",
       "      <th>s_q3i_2</th>\n",
       "      <th>s_q3i_3</th>\n",
       "      <th>s_q3i_4</th>\n",
       "      <th>s_q4a_1</th>\n",
       "      <th>s_q4a_2</th>\n",
       "      <th>s_q4a_3</th>\n",
       "      <th>s_q4a_4</th>\n",
       "      <th>s_q4b_1</th>\n",
       "      <th>s_q4b_2</th>\n",
       "      <th>s_q4b_3</th>\n",
       "      <th>s_q4b_4</th>\n",
       "      <th>s_q5a_1</th>\n",
       "      <th>s_q5a_2</th>\n",
       "      <th>s_q5a_3</th>\n",
       "      <th>s_q5a_4</th>\n",
       "      <th>s_q5b_1</th>\n",
       "      <th>s_q5b_2</th>\n",
       "      <th>s_q5b_3</th>\n",
       "      <th>s_q5b_4</th>\n",
       "      <th>s_q6a_1</th>\n",
       "      <th>s_q6a_2</th>\n",
       "      <th>s_q6a_3</th>\n",
       "      <th>s_q6a_4</th>\n",
       "      <th>s_q6a_5</th>\n",
       "      <th>s_q6b_1</th>\n",
       "      <th>s_q6b_2</th>\n",
       "      <th>s_q6b_3</th>\n",
       "      <th>s_q6b_4</th>\n",
       "      <th>s_q6b_5</th>\n",
       "      <th>s_q6c_1</th>\n",
       "      <th>s_q6c_2</th>\n",
       "      <th>s_q6c_3</th>\n",
       "      <th>s_q6c_4</th>\n",
       "      <th>s_q6c_5</th>\n",
       "      <th>s_q6d_1</th>\n",
       "      <th>s_q6d_2</th>\n",
       "      <th>s_q6d_3</th>\n",
       "      <th>s_q6d_4</th>\n",
       "      <th>s_q6d_5</th>\n",
       "      <th>s_q6e_1</th>\n",
       "      <th>s_q6e_2</th>\n",
       "      <th>s_q6e_3</th>\n",
       "      <th>s_q6e_4</th>\n",
       "      <th>s_q6e_5</th>\n",
       "      <th>s_q6f_1</th>\n",
       "      <th>s_q6f_2</th>\n",
       "      <th>s_q6f_3</th>\n",
       "      <th>s_q6f_4</th>\n",
       "      <th>s_q6f_5</th>\n",
       "      <th>s_q6g_1</th>\n",
       "      <th>s_q6g_2</th>\n",
       "      <th>s_q6g_3</th>\n",
       "      <th>s_q6g_4</th>\n",
       "      <th>s_q6g_5</th>\n",
       "      <th>s_q6h_1</th>\n",
       "      <th>s_q6h_2</th>\n",
       "      <th>s_q6h_3</th>\n",
       "      <th>s_q6h_4</th>\n",
       "      <th>s_q6h_5</th>\n",
       "      <th>s_q7a_1</th>\n",
       "      <th>s_q7a_2</th>\n",
       "      <th>s_q7a_3</th>\n",
       "      <th>s_q7a_4</th>\n",
       "      <th>s_q7b_1</th>\n",
       "      <th>s_q7b_2</th>\n",
       "      <th>s_q7b_3</th>\n",
       "      <th>s_q7b_4</th>\n",
       "      <th>s_q8a_1</th>\n",
       "      <th>s_q8a_2</th>\n",
       "      <th>s_q8a_3</th>\n",
       "      <th>s_q8a_4</th>\n",
       "      <th>s_q8a_5</th>\n",
       "      <th>s_q8b_1</th>\n",
       "      <th>s_q8b_2</th>\n",
       "      <th>s_q8b_3</th>\n",
       "      <th>s_q8b_4</th>\n",
       "      <th>s_q8b_5</th>\n",
       "      <th>s_q8c_1</th>\n",
       "      <th>s_q8c_2</th>\n",
       "      <th>s_q8c_3</th>\n",
       "      <th>s_q8c_4</th>\n",
       "      <th>s_q8c_5</th>\n",
       "      <th>s_q8d_1</th>\n",
       "      <th>s_q8d_2</th>\n",
       "      <th>s_q8d_3</th>\n",
       "      <th>s_q8d_4</th>\n",
       "      <th>s_q8d_5</th>\n",
       "      <th>s_q9a_1</th>\n",
       "      <th>s_q9a_2</th>\n",
       "      <th>s_q9a_3</th>\n",
       "      <th>s_q9b_1</th>\n",
       "      <th>s_q9b_2</th>\n",
       "      <th>s_q9b_3</th>\n",
       "      <th>s_q9c_1</th>\n",
       "      <th>s_q9c_2</th>\n",
       "      <th>s_q9c_3</th>\n",
       "      <th>s_q9d_1</th>\n",
       "      <th>s_q9d_2</th>\n",
       "      <th>s_q9d_3</th>\n",
       "      <th>s_q9e_1</th>\n",
       "      <th>s_q9e_2</th>\n",
       "      <th>s_q9e_3</th>\n",
       "      <th>s_q9f_1</th>\n",
       "      <th>s_q9f_2</th>\n",
       "      <th>s_q9f_3</th>\n",
       "      <th>s_q9g_1</th>\n",
       "      <th>s_q9g_2</th>\n",
       "      <th>s_q9g_3</th>\n",
       "      <th>s_q9h_1</th>\n",
       "      <th>s_q9h_2</th>\n",
       "      <th>s_q9h_3</th>\n",
       "      <th>s_q10a_1</th>\n",
       "      <th>s_q10a_2</th>\n",
       "      <th>s_q10a_3</th>\n",
       "      <th>s_q10b_1</th>\n",
       "      <th>s_q10b_2</th>\n",
       "      <th>s_q10b_3</th>\n",
       "      <th>s_q10c_1</th>\n",
       "      <th>s_q10c_2</th>\n",
       "      <th>s_q10c_3</th>\n",
       "      <th>s_q10d_1</th>\n",
       "      <th>s_q10d_2</th>\n",
       "      <th>s_q10d_3</th>\n",
       "      <th>s_q10e_1</th>\n",
       "      <th>s_q10e_2</th>\n",
       "      <th>s_q10e_3</th>\n",
       "      <th>s_q10f_1</th>\n",
       "      <th>s_q10f_2</th>\n",
       "      <th>s_q10f_3</th>\n",
       "      <th>s_q10g_1</th>\n",
       "      <th>s_q10g_2</th>\n",
       "      <th>s_q10g_3</th>\n",
       "      <th>s_q10h_1</th>\n",
       "      <th>s_q10h_2</th>\n",
       "      <th>s_q10h_3</th>\n",
       "      <th>s_q11_1</th>\n",
       "      <th>s_q11_2</th>\n",
       "      <th>s_q11_3</th>\n",
       "      <th>s_q11_4</th>\n",
       "      <th>s_q12a_1</th>\n",
       "      <th>s_q12a_2</th>\n",
       "      <th>s_q12a_3</th>\n",
       "      <th>s_q12a_4</th>\n",
       "      <th>s_q12b_1</th>\n",
       "      <th>s_q12b_2</th>\n",
       "      <th>s_q12b_3</th>\n",
       "      <th>s_q12b_4</th>\n",
       "      <th>s_q12c_1</th>\n",
       "      <th>s_q12c_2</th>\n",
       "      <th>s_q12c_3</th>\n",
       "      <th>s_q12c_4</th>\n",
       "      <th>s_q13a_1</th>\n",
       "      <th>s_q13a_2</th>\n",
       "      <th>s_q13a_3</th>\n",
       "      <th>s_q13a_4</th>\n",
       "      <th>s_q13b_1</th>\n",
       "      <th>s_q13b_2</th>\n",
       "      <th>s_q13b_3</th>\n",
       "      <th>s_q13b_4</th>\n",
       "      <th>s_q13c_1</th>\n",
       "      <th>s_q13c_2</th>\n",
       "      <th>s_q13c_3</th>\n",
       "      <th>s_q13c_4</th>\n",
       "      <th>s_q13d_1</th>\n",
       "      <th>s_q13d_2</th>\n",
       "      <th>s_q13d_3</th>\n",
       "      <th>s_q13d_4</th>\n",
       "      <th>s_q13e_1</th>\n",
       "      <th>s_q13e_2</th>\n",
       "      <th>s_q13e_3</th>\n",
       "      <th>s_q13e_4</th>\n",
       "      <th>s_q13f_1</th>\n",
       "      <th>s_q13f_2</th>\n",
       "      <th>s_q13f_3</th>\n",
       "      <th>s_q13f_4</th>\n",
       "      <th>s_q13g_1</th>\n",
       "      <th>s_q13g_2</th>\n",
       "      <th>s_q13g_3</th>\n",
       "      <th>s_q13g_4</th>\n",
       "      <th>s_q14a_1</th>\n",
       "      <th>s_q14a_2</th>\n",
       "      <th>s_q14a_3</th>\n",
       "      <th>s_q14a_4</th>\n",
       "      <th>s_q14b_1</th>\n",
       "      <th>s_q14b_2</th>\n",
       "      <th>s_q14b_3</th>\n",
       "      <th>s_q14b_4</th>\n",
       "      <th>s_q14c_1</th>\n",
       "      <th>s_q14c_2</th>\n",
       "      <th>s_q14c_3</th>\n",
       "      <th>s_q14c_4</th>\n",
       "      <th>s_q14d_1</th>\n",
       "      <th>s_q14d_2</th>\n",
       "      <th>s_q14d_3</th>\n",
       "      <th>s_q14d_4</th>\n",
       "      <th>s_q14e_1</th>\n",
       "      <th>s_q14e_2</th>\n",
       "      <th>s_q14e_3</th>\n",
       "      <th>s_q14e_4</th>\n",
       "      <th>s_q14f_1</th>\n",
       "      <th>s_q14f_2</th>\n",
       "      <th>s_q14f_3</th>\n",
       "      <th>s_q14f_4</th>\n",
       "      <th>s_q14g_1</th>\n",
       "      <th>s_q14g_2</th>\n",
       "      <th>s_q14g_3</th>\n",
       "      <th>s_q14g_4</th>\n",
       "      <th>s_N_q1_1</th>\n",
       "      <th>s_N_q1_2</th>\n",
       "      <th>s_N_q1_3</th>\n",
       "      <th>s_N_q1_4</th>\n",
       "      <th>s_N_q1_5</th>\n",
       "      <th>s_N_q1_6</th>\n",
       "      <th>s_N_q1_7</th>\n",
       "      <th>s_N_q2a_1</th>\n",
       "      <th>s_N_q2a_2</th>\n",
       "      <th>s_N_q2a_3</th>\n",
       "      <th>s_N_q2a_4</th>\n",
       "      <th>s_N_q2b_1</th>\n",
       "      <th>s_N_q2b_2</th>\n",
       "      <th>s_N_q2b_3</th>\n",
       "      <th>s_N_q2b_4</th>\n",
       "      <th>s_N_q2c_1</th>\n",
       "      <th>s_N_q2c_2</th>\n",
       "      <th>s_N_q2c_3</th>\n",
       "      <th>s_N_q2c_4</th>\n",
       "      <th>s_N_q3a_1</th>\n",
       "      <th>s_N_q3a_2</th>\n",
       "      <th>s_N_q3a_3</th>\n",
       "      <th>s_N_q3a_4</th>\n",
       "      <th>s_N_q3b_1</th>\n",
       "      <th>s_N_q3b_2</th>\n",
       "      <th>s_N_q3b_3</th>\n",
       "      <th>s_N_q3b_4</th>\n",
       "      <th>s_N_q3c_1</th>\n",
       "      <th>s_N_q3c_2</th>\n",
       "      <th>s_N_q3c_3</th>\n",
       "      <th>s_N_q3c_4</th>\n",
       "      <th>s_N_q3d_1</th>\n",
       "      <th>s_N_q3d_2</th>\n",
       "      <th>s_N_q3d_3</th>\n",
       "      <th>s_N_q3d_4</th>\n",
       "      <th>s_N_q3e_1</th>\n",
       "      <th>s_N_q3e_2</th>\n",
       "      <th>s_N_q3e_3</th>\n",
       "      <th>s_N_q3e_4</th>\n",
       "      <th>s_N_q3f_1</th>\n",
       "      <th>s_N_q3f_2</th>\n",
       "      <th>s_N_q3f_3</th>\n",
       "      <th>s_N_q3f_4</th>\n",
       "      <th>s_N_q3g_1</th>\n",
       "      <th>s_N_q3g_2</th>\n",
       "      <th>s_N_q3g_3</th>\n",
       "      <th>s_N_q3g_4</th>\n",
       "      <th>s_N_q3h_1</th>\n",
       "      <th>s_N_q3h_2</th>\n",
       "      <th>s_N_q3h_3</th>\n",
       "      <th>s_N_q3h_4</th>\n",
       "      <th>s_N_q3i_1</th>\n",
       "      <th>s_N_q3i_2</th>\n",
       "      <th>s_N_q3i_3</th>\n",
       "      <th>s_N_q3i_4</th>\n",
       "      <th>s_N_q4a_1</th>\n",
       "      <th>s_N_q4a_2</th>\n",
       "      <th>s_N_q4a_3</th>\n",
       "      <th>s_N_q4a_4</th>\n",
       "      <th>s_N_q4b_1</th>\n",
       "      <th>s_N_q4b_2</th>\n",
       "      <th>s_N_q4b_3</th>\n",
       "      <th>s_N_q4b_4</th>\n",
       "      <th>s_N_q5a_1</th>\n",
       "      <th>s_N_q5a_2</th>\n",
       "      <th>s_N_q5a_3</th>\n",
       "      <th>s_N_q5a_4</th>\n",
       "      <th>s_N_q5b_1</th>\n",
       "      <th>s_N_q5b_2</th>\n",
       "      <th>s_N_q5b_3</th>\n",
       "      <th>s_N_q5b_4</th>\n",
       "      <th>s_N_q6a_1</th>\n",
       "      <th>s_N_q6a_2</th>\n",
       "      <th>s_N_q6a_3</th>\n",
       "      <th>s_N_q6a_4</th>\n",
       "      <th>s_N_q6a_5</th>\n",
       "      <th>s_N_q6b_1</th>\n",
       "      <th>s_N_q6b_2</th>\n",
       "      <th>s_N_q6b_3</th>\n",
       "      <th>s_N_q6b_4</th>\n",
       "      <th>s_N_q6b_5</th>\n",
       "      <th>s_N_q6c_1</th>\n",
       "      <th>s_N_q6c_2</th>\n",
       "      <th>s_N_q6c_3</th>\n",
       "      <th>s_N_q6c_4</th>\n",
       "      <th>s_N_q6c_5</th>\n",
       "      <th>s_N_q6d_1</th>\n",
       "      <th>s_N_q6d_2</th>\n",
       "      <th>s_N_q6d_3</th>\n",
       "      <th>s_N_q6d_4</th>\n",
       "      <th>s_N_q6d_5</th>\n",
       "      <th>s_N_q6e_1</th>\n",
       "      <th>s_N_q6e_2</th>\n",
       "      <th>s_N_q6e_3</th>\n",
       "      <th>s_N_q6e_4</th>\n",
       "      <th>s_N_q6e_5</th>\n",
       "      <th>s_N_q6f_1</th>\n",
       "      <th>s_N_q6f_2</th>\n",
       "      <th>s_N_q6f_3</th>\n",
       "      <th>s_N_q6f_4</th>\n",
       "      <th>s_N_q6f_5</th>\n",
       "      <th>s_N_q6g_1</th>\n",
       "      <th>s_N_q6g_2</th>\n",
       "      <th>s_N_q6g_3</th>\n",
       "      <th>s_N_q6g_4</th>\n",
       "      <th>s_N_q6g_5</th>\n",
       "      <th>s_N_q6h_1</th>\n",
       "      <th>s_N_q6h_2</th>\n",
       "      <th>s_N_q6h_3</th>\n",
       "      <th>s_N_q6h_4</th>\n",
       "      <th>s_N_q6h_5</th>\n",
       "      <th>s_N_q7a_1</th>\n",
       "      <th>s_N_q7a_2</th>\n",
       "      <th>s_N_q7a_3</th>\n",
       "      <th>s_N_q7a_4</th>\n",
       "      <th>s_N_q7b_1</th>\n",
       "      <th>s_N_q7b_2</th>\n",
       "      <th>s_N_q7b_3</th>\n",
       "      <th>s_N_q7b_4</th>\n",
       "      <th>s_N_q8a_1</th>\n",
       "      <th>s_N_q8a_2</th>\n",
       "      <th>s_N_q8a_3</th>\n",
       "      <th>s_N_q8a_4</th>\n",
       "      <th>s_N_q8a_5</th>\n",
       "      <th>s_N_q8b_1</th>\n",
       "      <th>s_N_q8b_2</th>\n",
       "      <th>s_N_q8b_3</th>\n",
       "      <th>s_N_q8b_4</th>\n",
       "      <th>s_N_q8b_5</th>\n",
       "      <th>s_N_q8c_1</th>\n",
       "      <th>s_N_q8c_2</th>\n",
       "      <th>s_N_q8c_3</th>\n",
       "      <th>s_N_q8c_4</th>\n",
       "      <th>s_N_q8c_5</th>\n",
       "      <th>s_N_q8d_1</th>\n",
       "      <th>s_N_q8d_2</th>\n",
       "      <th>s_N_q8d_3</th>\n",
       "      <th>s_N_q8d_4</th>\n",
       "      <th>s_N_q8d_5</th>\n",
       "      <th>s_N_q9a_1</th>\n",
       "      <th>s_N_q9a_2</th>\n",
       "      <th>s_N_q9a_3</th>\n",
       "      <th>s_N_q9b_1</th>\n",
       "      <th>s_N_q9b_2</th>\n",
       "      <th>s_N_q9b_3</th>\n",
       "      <th>s_N_q9c_1</th>\n",
       "      <th>s_N_q9c_2</th>\n",
       "      <th>s_N_q9c_3</th>\n",
       "      <th>s_N_q9d_1</th>\n",
       "      <th>s_N_q9d_2</th>\n",
       "      <th>s_N_q9d_3</th>\n",
       "      <th>s_N_q9e_1</th>\n",
       "      <th>s_N_q9e_2</th>\n",
       "      <th>s_N_q9e_3</th>\n",
       "      <th>s_N_q9f_1</th>\n",
       "      <th>s_N_q9f_2</th>\n",
       "      <th>s_N_q9f_3</th>\n",
       "      <th>s_N_q9g_1</th>\n",
       "      <th>s_N_q9g_2</th>\n",
       "      <th>s_N_q9g_3</th>\n",
       "      <th>s_N_q9h_1</th>\n",
       "      <th>s_N_q9h_2</th>\n",
       "      <th>s_N_q9h_3</th>\n",
       "      <th>s_N_q10a_1</th>\n",
       "      <th>s_N_q10a_2</th>\n",
       "      <th>s_N_q10a_3</th>\n",
       "      <th>s_N_q10b_1</th>\n",
       "      <th>s_N_q10b_2</th>\n",
       "      <th>s_N_q10b_3</th>\n",
       "      <th>s_N_q10c_1</th>\n",
       "      <th>s_N_q10c_2</th>\n",
       "      <th>s_N_q10c_3</th>\n",
       "      <th>s_N_q10d_1</th>\n",
       "      <th>s_N_q10d_2</th>\n",
       "      <th>s_N_q10d_3</th>\n",
       "      <th>s_N_q10e_1</th>\n",
       "      <th>s_N_q10e_2</th>\n",
       "      <th>s_N_q10e_3</th>\n",
       "      <th>s_N_q10f_1</th>\n",
       "      <th>s_N_q10f_2</th>\n",
       "      <th>s_N_q10f_3</th>\n",
       "      <th>s_N_q10g_1</th>\n",
       "      <th>s_N_q10g_2</th>\n",
       "      <th>s_N_q10g_3</th>\n",
       "      <th>s_N_q10h_1</th>\n",
       "      <th>s_N_q10h_2</th>\n",
       "      <th>s_N_q10h_3</th>\n",
       "      <th>s_N_q11_1</th>\n",
       "      <th>s_N_q11_2</th>\n",
       "      <th>s_N_q11_3</th>\n",
       "      <th>s_N_q11_4</th>\n",
       "      <th>s_N_q12a_1</th>\n",
       "      <th>s_N_q12a_2</th>\n",
       "      <th>s_N_q12a_3</th>\n",
       "      <th>s_N_q12a_4</th>\n",
       "      <th>s_N_q12b_1</th>\n",
       "      <th>s_N_q12b_2</th>\n",
       "      <th>s_N_q12b_3</th>\n",
       "      <th>s_N_q12b_4</th>\n",
       "      <th>s_N_q12c_1</th>\n",
       "      <th>s_N_q12c_2</th>\n",
       "      <th>s_N_q12c_3</th>\n",
       "      <th>s_N_q12c_4</th>\n",
       "      <th>s_N_q13a_1</th>\n",
       "      <th>s_N_q13a_2</th>\n",
       "      <th>s_N_q13a_3</th>\n",
       "      <th>s_N_q13a_4</th>\n",
       "      <th>s_N_q13b_1</th>\n",
       "      <th>s_N_q13b_2</th>\n",
       "      <th>s_N_q13b_3</th>\n",
       "      <th>s_N_q13b_4</th>\n",
       "      <th>s_N_q13c_1</th>\n",
       "      <th>s_N_q13c_2</th>\n",
       "      <th>s_N_q13c_3</th>\n",
       "      <th>s_N_q13c_4</th>\n",
       "      <th>s_N_q13d_1</th>\n",
       "      <th>s_N_q13d_2</th>\n",
       "      <th>s_N_q13d_3</th>\n",
       "      <th>s_N_q13d_4</th>\n",
       "      <th>s_N_q13e_1</th>\n",
       "      <th>s_N_q13e_2</th>\n",
       "      <th>s_N_q13e_3</th>\n",
       "      <th>s_N_q13e_4</th>\n",
       "      <th>s_N_q13f_1</th>\n",
       "      <th>s_N_q13f_2</th>\n",
       "      <th>s_N_q13f_3</th>\n",
       "      <th>s_N_q13f_4</th>\n",
       "      <th>s_N_q13g_1</th>\n",
       "      <th>s_N_q13g_2</th>\n",
       "      <th>s_N_q13g_3</th>\n",
       "      <th>s_N_q13g_4</th>\n",
       "      <th>s_N_q14a_1</th>\n",
       "      <th>s_N_q14a_2</th>\n",
       "      <th>s_N_q14a_3</th>\n",
       "      <th>s_N_q14a_4</th>\n",
       "      <th>s_N_q14b_1</th>\n",
       "      <th>s_N_q14b_2</th>\n",
       "      <th>s_N_q14b_3</th>\n",
       "      <th>s_N_q14b_4</th>\n",
       "      <th>s_N_q14c_1</th>\n",
       "      <th>s_N_q14c_2</th>\n",
       "      <th>s_N_q14c_3</th>\n",
       "      <th>s_N_q14c_4</th>\n",
       "      <th>s_N_q14d_1</th>\n",
       "      <th>s_N_q14d_2</th>\n",
       "      <th>s_N_q14d_3</th>\n",
       "      <th>s_N_q14d_4</th>\n",
       "      <th>s_N_q14e_1</th>\n",
       "      <th>s_N_q14e_2</th>\n",
       "      <th>s_N_q14e_3</th>\n",
       "      <th>s_N_q14e_4</th>\n",
       "      <th>s_N_q14f_1</th>\n",
       "      <th>s_N_q14f_2</th>\n",
       "      <th>s_N_q14f_3</th>\n",
       "      <th>s_N_q14f_4</th>\n",
       "      <th>s_N_q14g_1</th>\n",
       "      <th>s_N_q14g_2</th>\n",
       "      <th>s_N_q14g_3</th>\n",
       "      <th>s_N_q14g_4</th>\n",
       "      <th>p_q1c</th>\n",
       "      <th>p_q1b</th>\n",
       "      <th>p_q1e</th>\n",
       "      <th>p_q1f</th>\n",
       "      <th>p_q6c</th>\n",
       "      <th>p_q12c</th>\n",
       "      <th>p_q1a</th>\n",
       "      <th>p_q1d</th>\n",
       "      <th>p_q3e</th>\n",
       "      <th>p_q6e</th>\n",
       "      <th>p_q7</th>\n",
       "      <th>p_q12d</th>\n",
       "      <th>p_q1g</th>\n",
       "      <th>p_q6d</th>\n",
       "      <th>p_q6f</th>\n",
       "      <th>p_q6g</th>\n",
       "      <th>p_q6h</th>\n",
       "      <th>p_q12a</th>\n",
       "      <th>p_q12b</th>\n",
       "      <th>p_q12e</th>\n",
       "      <th>p_q12f</th>\n",
       "      <th>p_q13a</th>\n",
       "      <th>p_q13b</th>\n",
       "      <th>p_q13c</th>\n",
       "      <th>p_q13d</th>\n",
       "      <th>p_q14a</th>\n",
       "      <th>p_q14b</th>\n",
       "      <th>p_q14c</th>\n",
       "      <th>p_q14d</th>\n",
       "      <th>p_q1a_1</th>\n",
       "      <th>p_q1a_2</th>\n",
       "      <th>p_q1a_3</th>\n",
       "      <th>p_q1a_4</th>\n",
       "      <th>p_q1a_5</th>\n",
       "      <th>p_q1b_1</th>\n",
       "      <th>p_q1b_2</th>\n",
       "      <th>p_q1b_3</th>\n",
       "      <th>p_q1b_4</th>\n",
       "      <th>p_q1b_5</th>\n",
       "      <th>p_q1c_1</th>\n",
       "      <th>p_q1c_2</th>\n",
       "      <th>p_q1c_3</th>\n",
       "      <th>p_q1c_4</th>\n",
       "      <th>p_q1c_5</th>\n",
       "      <th>p_q1d_1</th>\n",
       "      <th>p_q1d_2</th>\n",
       "      <th>p_q1d_3</th>\n",
       "      <th>p_q1d_4</th>\n",
       "      <th>p_q1d_5</th>\n",
       "      <th>p_q1e_1</th>\n",
       "      <th>p_q1e_2</th>\n",
       "      <th>p_q1e_3</th>\n",
       "      <th>p_q1e_4</th>\n",
       "      <th>p_q1e_5</th>\n",
       "      <th>p_q1f_1</th>\n",
       "      <th>p_q1f_2</th>\n",
       "      <th>p_q1f_3</th>\n",
       "      <th>p_q1f_4</th>\n",
       "      <th>p_q1f_5</th>\n",
       "      <th>p_q1g_1</th>\n",
       "      <th>p_q1g_2</th>\n",
       "      <th>p_q1g_3</th>\n",
       "      <th>p_q1g_4</th>\n",
       "      <th>p_q1g_5</th>\n",
       "      <th>p_q3e_1</th>\n",
       "      <th>p_q3e_2</th>\n",
       "      <th>p_q3e_3</th>\n",
       "      <th>p_q3e_4</th>\n",
       "      <th>p_q3e_5</th>\n",
       "      <th>p_q5k</th>\n",
       "      <th>p_q5l</th>\n",
       "      <th>p_q5m</th>\n",
       "      <th>p_q5n</th>\n",
       "      <th>p_q6c_1</th>\n",
       "      <th>p_q6c_2</th>\n",
       "      <th>p_q6c_3</th>\n",
       "      <th>p_q6c_4</th>\n",
       "      <th>p_q6c_5</th>\n",
       "      <th>p_q6d_1</th>\n",
       "      <th>p_q6d_2</th>\n",
       "      <th>p_q6d_3</th>\n",
       "      <th>p_q6d_4</th>\n",
       "      <th>p_q6d_5</th>\n",
       "      <th>p_q6e_1</th>\n",
       "      <th>p_q6e_2</th>\n",
       "      <th>p_q6e_3</th>\n",
       "      <th>p_q6e_4</th>\n",
       "      <th>p_q6e_5</th>\n",
       "      <th>p_q6f_1</th>\n",
       "      <th>p_q6f_2</th>\n",
       "      <th>p_q6f_3</th>\n",
       "      <th>p_q6f_4</th>\n",
       "      <th>p_q6f_5</th>\n",
       "      <th>p_q6g_1</th>\n",
       "      <th>p_q6g_2</th>\n",
       "      <th>p_q6g_3</th>\n",
       "      <th>p_q6g_4</th>\n",
       "      <th>p_q6g_5</th>\n",
       "      <th>p_q6h_1</th>\n",
       "      <th>p_q6h_2</th>\n",
       "      <th>p_q6h_3</th>\n",
       "      <th>p_q6h_4</th>\n",
       "      <th>p_q6h_5</th>\n",
       "      <th>p_q7e</th>\n",
       "      <th>p_q7f</th>\n",
       "      <th>p_q7g</th>\n",
       "      <th>p_q7h</th>\n",
       "      <th>p_q7i</th>\n",
       "      <th>p_q7j</th>\n",
       "      <th>p_q7k</th>\n",
       "      <th>p_q7l</th>\n",
       "      <th>p_q7m</th>\n",
       "      <th>p_q7n</th>\n",
       "      <th>p_q9_11</th>\n",
       "      <th>p_q9_12</th>\n",
       "      <th>p_q9_13</th>\n",
       "      <th>p_q9_14</th>\n",
       "      <th>p_q9_15</th>\n",
       "      <th>p_q10a_1</th>\n",
       "      <th>p_q10a_2</th>\n",
       "      <th>p_q10a_3</th>\n",
       "      <th>p_q10a_4</th>\n",
       "      <th>p_q10a_5</th>\n",
       "      <th>p_q10b_1</th>\n",
       "      <th>p_q10b_2</th>\n",
       "      <th>p_q10b_3</th>\n",
       "      <th>p_q10b_4</th>\n",
       "      <th>p_q10b_5</th>\n",
       "      <th>p_q10c_1</th>\n",
       "      <th>p_q10c_2</th>\n",
       "      <th>p_q10c_3</th>\n",
       "      <th>p_q10c_4</th>\n",
       "      <th>p_q10c_5</th>\n",
       "      <th>p_q10d_1</th>\n",
       "      <th>p_q10d_2</th>\n",
       "      <th>p_q10d_3</th>\n",
       "      <th>p_q10d_4</th>\n",
       "      <th>p_q10d_5</th>\n",
       "      <th>p_q10e_1</th>\n",
       "      <th>p_q10e_2</th>\n",
       "      <th>p_q10e_3</th>\n",
       "      <th>p_q10e_4</th>\n",
       "      <th>p_q10e_5</th>\n",
       "      <th>p_q10f_1</th>\n",
       "      <th>p_q10f_2</th>\n",
       "      <th>p_q10f_3</th>\n",
       "      <th>p_q10f_4</th>\n",
       "      <th>p_q10f_5</th>\n",
       "      <th>p_q11a_5</th>\n",
       "      <th>p_q11b_5</th>\n",
       "      <th>p_q11c_5</th>\n",
       "      <th>p_q11d_5</th>\n",
       "      <th>p_q11e_5</th>\n",
       "      <th>p_q12a_1</th>\n",
       "      <th>p_q12a_2</th>\n",
       "      <th>p_q12a_3</th>\n",
       "      <th>p_q12a_4</th>\n",
       "      <th>p_q12b_1</th>\n",
       "      <th>p_q12b_2</th>\n",
       "      <th>p_q12b_3</th>\n",
       "      <th>p_q12b_4</th>\n",
       "      <th>p_q12c_1</th>\n",
       "      <th>p_q12c_2</th>\n",
       "      <th>p_q12c_3</th>\n",
       "      <th>p_q12c_4</th>\n",
       "      <th>p_q12d_1</th>\n",
       "      <th>p_q12d_2</th>\n",
       "      <th>p_q12d_3</th>\n",
       "      <th>p_q12d_4</th>\n",
       "      <th>p_q12e_1</th>\n",
       "      <th>p_q12e_2</th>\n",
       "      <th>p_q12e_3</th>\n",
       "      <th>p_q12e_4</th>\n",
       "      <th>p_q12f_1</th>\n",
       "      <th>p_q12f_2</th>\n",
       "      <th>p_q12f_3</th>\n",
       "      <th>p_q12f_4</th>\n",
       "      <th>p_q13a_1</th>\n",
       "      <th>p_q13a_2</th>\n",
       "      <th>p_q13a_3</th>\n",
       "      <th>p_q13a_4</th>\n",
       "      <th>p_q13a_5</th>\n",
       "      <th>p_q13b_1</th>\n",
       "      <th>p_q13b_2</th>\n",
       "      <th>p_q13b_3</th>\n",
       "      <th>p_q13b_4</th>\n",
       "      <th>p_q13b_5</th>\n",
       "      <th>p_q13c_1</th>\n",
       "      <th>p_q13c_2</th>\n",
       "      <th>p_q13c_3</th>\n",
       "      <th>p_q13c_4</th>\n",
       "      <th>p_q13c_5</th>\n",
       "      <th>p_q13d_1</th>\n",
       "      <th>p_q13d_2</th>\n",
       "      <th>p_q13d_3</th>\n",
       "      <th>p_q13d_4</th>\n",
       "      <th>p_q13d_5</th>\n",
       "      <th>p_q14a_1</th>\n",
       "      <th>p_q14a_2</th>\n",
       "      <th>p_q14a_3</th>\n",
       "      <th>p_q14a_4</th>\n",
       "      <th>p_q14a_5</th>\n",
       "      <th>p_q14b_1</th>\n",
       "      <th>p_q14b_2</th>\n",
       "      <th>p_q14b_3</th>\n",
       "      <th>p_q14b_4</th>\n",
       "      <th>p_q14b_5</th>\n",
       "      <th>p_q14c_1</th>\n",
       "      <th>p_q14c_2</th>\n",
       "      <th>p_q14c_3</th>\n",
       "      <th>p_q14c_4</th>\n",
       "      <th>p_q14c_5</th>\n",
       "      <th>p_q14d_1</th>\n",
       "      <th>p_q14d_2</th>\n",
       "      <th>p_q14d_3</th>\n",
       "      <th>p_q14d_4</th>\n",
       "      <th>p_q14d_5</th>\n",
       "      <th>p_N_q1a_1</th>\n",
       "      <th>p_N_q1a_2</th>\n",
       "      <th>p_N_q1a_3</th>\n",
       "      <th>p_N_q1a_4</th>\n",
       "      <th>p_N_q1a_5</th>\n",
       "      <th>p_N_q1b_1</th>\n",
       "      <th>p_N_q1b_2</th>\n",
       "      <th>p_N_q1b_3</th>\n",
       "      <th>p_N_q1b_4</th>\n",
       "      <th>p_N_q1b_5</th>\n",
       "      <th>p_N_q1c_1</th>\n",
       "      <th>p_N_q1c_2</th>\n",
       "      <th>p_N_q1c_3</th>\n",
       "      <th>p_N_q1c_4</th>\n",
       "      <th>p_N_q1c_5</th>\n",
       "      <th>p_N_q1d_1</th>\n",
       "      <th>p_N_q1d_2</th>\n",
       "      <th>p_N_q1d_3</th>\n",
       "      <th>p_N_q1d_4</th>\n",
       "      <th>p_N_q1d_5</th>\n",
       "      <th>p_N_q1e_1</th>\n",
       "      <th>p_N_q1e_2</th>\n",
       "      <th>p_N_q1e_3</th>\n",
       "      <th>p_N_q1e_4</th>\n",
       "      <th>p_N_q1e_5</th>\n",
       "      <th>p_N_q1f_1</th>\n",
       "      <th>p_N_q1f_2</th>\n",
       "      <th>p_N_q1f_3</th>\n",
       "      <th>p_N_q1f_4</th>\n",
       "      <th>p_N_q1f_5</th>\n",
       "      <th>p_N_q1g_1</th>\n",
       "      <th>p_N_q1g_2</th>\n",
       "      <th>p_N_q1g_3</th>\n",
       "      <th>p_N_q1g_4</th>\n",
       "      <th>p_N_q1g_5</th>\n",
       "      <th>p_N_q3e_1</th>\n",
       "      <th>p_N_q3e_2</th>\n",
       "      <th>p_N_q3e_3</th>\n",
       "      <th>p_N_q3e_4</th>\n",
       "      <th>p_N_q3e_5</th>\n",
       "      <th>p_N_q5k</th>\n",
       "      <th>p_N_q5l</th>\n",
       "      <th>p_N_q5m</th>\n",
       "      <th>p_N_q5n</th>\n",
       "      <th>p_N_q6c_1</th>\n",
       "      <th>p_N_q6c_2</th>\n",
       "      <th>p_N_q6c_3</th>\n",
       "      <th>p_N_q6c_4</th>\n",
       "      <th>p_N_q6c_5</th>\n",
       "      <th>p_N_q6d_1</th>\n",
       "      <th>p_N_q6d_2</th>\n",
       "      <th>p_N_q6d_3</th>\n",
       "      <th>p_N_q6d_4</th>\n",
       "      <th>p_N_q6d_5</th>\n",
       "      <th>p_N_q6e_1</th>\n",
       "      <th>p_N_q6e_2</th>\n",
       "      <th>p_N_q6e_3</th>\n",
       "      <th>p_N_q6e_4</th>\n",
       "      <th>p_N_q6e_5</th>\n",
       "      <th>p_N_q6f_1</th>\n",
       "      <th>p_N_q6f_2</th>\n",
       "      <th>p_N_q6f_3</th>\n",
       "      <th>p_N_q6f_4</th>\n",
       "      <th>p_N_q6f_5</th>\n",
       "      <th>p_N_q6g_1</th>\n",
       "      <th>p_N_q6g_2</th>\n",
       "      <th>p_N_q6g_3</th>\n",
       "      <th>p_N_q6g_4</th>\n",
       "      <th>p_N_q6g_5</th>\n",
       "      <th>p_N_q6h_1</th>\n",
       "      <th>p_N_q6h_2</th>\n",
       "      <th>p_N_q6h_3</th>\n",
       "      <th>p_N_q6h_4</th>\n",
       "      <th>p_N_q6h_5</th>\n",
       "      <th>p_N_q7a</th>\n",
       "      <th>p_N_q7b</th>\n",
       "      <th>p_N_q7c</th>\n",
       "      <th>p_N_q7d</th>\n",
       "      <th>p_N_q7e</th>\n",
       "      <th>p_N_q7f</th>\n",
       "      <th>p_N_q7g</th>\n",
       "      <th>p_N_q7h</th>\n",
       "      <th>p_N_q7i</th>\n",
       "      <th>p_N_q7j</th>\n",
       "      <th>p_N_q7k</th>\n",
       "      <th>p_N_q7l</th>\n",
       "      <th>p_N_q7m</th>\n",
       "      <th>p_N_q7n</th>\n",
       "      <th>p_N_q9_11</th>\n",
       "      <th>p_N_q9_12</th>\n",
       "      <th>p_N_q9_13</th>\n",
       "      <th>p_N_q9_14</th>\n",
       "      <th>p_N_q9_15</th>\n",
       "      <th>p_N_q10a_1</th>\n",
       "      <th>p_N_q10a_2</th>\n",
       "      <th>p_N_q10a_3</th>\n",
       "      <th>p_N_q10a_4</th>\n",
       "      <th>p_N_q10a_5</th>\n",
       "      <th>p_N_q10b_1</th>\n",
       "      <th>p_N_q10b_2</th>\n",
       "      <th>p_N_q10b_3</th>\n",
       "      <th>p_N_q10b_4</th>\n",
       "      <th>p_N_q10b_5</th>\n",
       "      <th>p_N_q10c_1</th>\n",
       "      <th>p_N_q10c_2</th>\n",
       "      <th>p_N_q10c_3</th>\n",
       "      <th>p_N_q10c_4</th>\n",
       "      <th>p_N_q10c_5</th>\n",
       "      <th>p_N_q10d_1</th>\n",
       "      <th>p_N_q10d_2</th>\n",
       "      <th>p_N_q10d_3</th>\n",
       "      <th>p_N_q10d_4</th>\n",
       "      <th>p_N_q10d_5</th>\n",
       "      <th>p_N_q10e_1</th>\n",
       "      <th>p_N_q10e_2</th>\n",
       "      <th>p_N_q10e_3</th>\n",
       "      <th>p_N_q10e_4</th>\n",
       "      <th>p_N_q10e_5</th>\n",
       "      <th>p_N_q10f_1</th>\n",
       "      <th>p_N_q10f_2</th>\n",
       "      <th>p_N_q10f_3</th>\n",
       "      <th>p_N_q10f_4</th>\n",
       "      <th>p_N_q10f_5</th>\n",
       "      <th>p_N_q11a_5</th>\n",
       "      <th>p_N_q11b_5</th>\n",
       "      <th>p_N_q11c_5</th>\n",
       "      <th>p_N_q11d_5</th>\n",
       "      <th>p_N_q11e_5</th>\n",
       "      <th>p_N_q12a_1</th>\n",
       "      <th>p_N_q12a_2</th>\n",
       "      <th>p_N_q12a_3</th>\n",
       "      <th>p_N_q12a_4</th>\n",
       "      <th>p_N_q12b_1</th>\n",
       "      <th>p_N_q12b_2</th>\n",
       "      <th>p_N_q12b_3</th>\n",
       "      <th>p_N_q12b_4</th>\n",
       "      <th>p_N_q12c_1</th>\n",
       "      <th>p_N_q12c_2</th>\n",
       "      <th>p_N_q12c_3</th>\n",
       "      <th>p_N_q12c_4</th>\n",
       "      <th>p_N_q12d_1</th>\n",
       "      <th>p_N_q12d_2</th>\n",
       "      <th>p_N_q12d_3</th>\n",
       "      <th>p_N_q12d_4</th>\n",
       "      <th>p_N_q12e_1</th>\n",
       "      <th>p_N_q12e_2</th>\n",
       "      <th>p_N_q12e_3</th>\n",
       "      <th>p_N_q12e_4</th>\n",
       "      <th>p_N_q12f_1</th>\n",
       "      <th>p_N_q12f_2</th>\n",
       "      <th>p_N_q12f_3</th>\n",
       "      <th>p_N_q12f_4</th>\n",
       "      <th>p_N_q13a_1</th>\n",
       "      <th>p_N_q13a_2</th>\n",
       "      <th>p_N_q13a_3</th>\n",
       "      <th>p_N_q13a_4</th>\n",
       "      <th>p_N_q13a_5</th>\n",
       "      <th>p_N_q13b_1</th>\n",
       "      <th>p_N_q13b_2</th>\n",
       "      <th>p_N_q13b_3</th>\n",
       "      <th>p_N_q13b_4</th>\n",
       "      <th>p_N_q13b_5</th>\n",
       "      <th>p_N_q13c_1</th>\n",
       "      <th>p_N_q13c_2</th>\n",
       "      <th>p_N_q13c_3</th>\n",
       "      <th>p_N_q13c_4</th>\n",
       "      <th>p_N_q13c_5</th>\n",
       "      <th>p_N_q13d_1</th>\n",
       "      <th>p_N_q13d_2</th>\n",
       "      <th>p_N_q13d_3</th>\n",
       "      <th>p_N_q13d_4</th>\n",
       "      <th>p_N_q13d_5</th>\n",
       "      <th>p_N_q14a_1</th>\n",
       "      <th>p_N_q14a_2</th>\n",
       "      <th>p_N_q14a_3</th>\n",
       "      <th>p_N_q14a_4</th>\n",
       "      <th>p_N_q14a_5</th>\n",
       "      <th>p_N_q14b_1</th>\n",
       "      <th>p_N_q14b_2</th>\n",
       "      <th>p_N_q14b_3</th>\n",
       "      <th>p_N_q14b_4</th>\n",
       "      <th>p_N_q14b_5</th>\n",
       "      <th>p_N_q14c_1</th>\n",
       "      <th>p_N_q14c_2</th>\n",
       "      <th>p_N_q14c_3</th>\n",
       "      <th>p_N_q14c_4</th>\n",
       "      <th>p_N_q14c_5</th>\n",
       "      <th>p_N_q14d_1</th>\n",
       "      <th>p_N_q14d_2</th>\n",
       "      <th>p_N_q14d_3</th>\n",
       "      <th>p_N_q14d_4</th>\n",
       "      <th>p_N_q14d_5</th>\n",
       "      <th>t_q13e</th>\n",
       "      <th>t_q13f</th>\n",
       "      <th>t_q13g</th>\n",
       "      <th>t_q13h</th>\n",
       "      <th>t_q13i</th>\n",
       "      <th>t_q13j</th>\n",
       "      <th>t_q13k</th>\n",
       "      <th>t_q13l</th>\n",
       "      <th>t_q13m</th>\n",
       "      <th>t_q13n</th>\n",
       "      <th>t_q12</th>\n",
       "      <th>t_q10</th>\n",
       "      <th>t_q7f</th>\n",
       "      <th>t_q16a</th>\n",
       "      <th>t_q16b</th>\n",
       "      <th>t_q16c</th>\n",
       "      <th>t_q16d</th>\n",
       "      <th>t_q17a</th>\n",
       "      <th>t_q17b</th>\n",
       "      <th>t_q17c</th>\n",
       "      <th>t_q17d</th>\n",
       "      <th>t_q17e</th>\n",
       "      <th>t_q17f</th>\n",
       "      <th>t_q3k_1</th>\n",
       "      <th>t_q3k_2</th>\n",
       "      <th>t_q3k_3</th>\n",
       "      <th>t_q3l_1</th>\n",
       "      <th>t_q3l_2</th>\n",
       "      <th>t_q3l_3</th>\n",
       "      <th>t_q7f_1</th>\n",
       "      <th>t_q7f_2</th>\n",
       "      <th>t_q7f_3</th>\n",
       "      <th>t_q7f_4</th>\n",
       "      <th>t_q7f_5</th>\n",
       "      <th>t_q10_1</th>\n",
       "      <th>t_q10_2</th>\n",
       "      <th>t_q10_3</th>\n",
       "      <th>t_q10_4</th>\n",
       "      <th>t_q10_5</th>\n",
       "      <th>t_q11a_5</th>\n",
       "      <th>t_q11b_5</th>\n",
       "      <th>t_q11c_5</th>\n",
       "      <th>t_q11d_5</th>\n",
       "      <th>t_q12_1</th>\n",
       "      <th>t_q12_2</th>\n",
       "      <th>t_q12_3</th>\n",
       "      <th>t_q12_4</th>\n",
       "      <th>t_q12_5</th>\n",
       "      <th>t_q12_6</th>\n",
       "      <th>t_q13e_1</th>\n",
       "      <th>t_q13e_2</th>\n",
       "      <th>t_q13e_3</th>\n",
       "      <th>t_q13e_4</th>\n",
       "      <th>t_q13f_1</th>\n",
       "      <th>t_q13f_2</th>\n",
       "      <th>t_q13f_3</th>\n",
       "      <th>t_q13f_4</th>\n",
       "      <th>t_q13g_1</th>\n",
       "      <th>t_q13g_2</th>\n",
       "      <th>t_q13g_3</th>\n",
       "      <th>t_q13g_4</th>\n",
       "      <th>t_q13h_1</th>\n",
       "      <th>t_q13h_2</th>\n",
       "      <th>t_q13h_3</th>\n",
       "      <th>t_q13h_4</th>\n",
       "      <th>t_q13i_1</th>\n",
       "      <th>t_q13i_2</th>\n",
       "      <th>t_q13i_3</th>\n",
       "      <th>t_q13i_4</th>\n",
       "      <th>t_q13j_1</th>\n",
       "      <th>t_q13j_2</th>\n",
       "      <th>t_q13j_3</th>\n",
       "      <th>t_q13j_4</th>\n",
       "      <th>t_q13k_1</th>\n",
       "      <th>t_q13k_2</th>\n",
       "      <th>t_q13k_3</th>\n",
       "      <th>t_q13k_4</th>\n",
       "      <th>t_q13l_1</th>\n",
       "      <th>t_q13l_2</th>\n",
       "      <th>t_q13l_3</th>\n",
       "      <th>t_q13l_4</th>\n",
       "      <th>t_q13m_1</th>\n",
       "      <th>t_q13m_2</th>\n",
       "      <th>t_q13m_3</th>\n",
       "      <th>t_q13m_4</th>\n",
       "      <th>t_q13n_1</th>\n",
       "      <th>t_q13n_2</th>\n",
       "      <th>t_q13n_3</th>\n",
       "      <th>t_q13n_4</th>\n",
       "      <th>t_q14_6</th>\n",
       "      <th>t_q15a_5</th>\n",
       "      <th>t_q15b_5</th>\n",
       "      <th>t_q15c_5</th>\n",
       "      <th>t_q15d_5</th>\n",
       "      <th>t_q16a_1</th>\n",
       "      <th>t_q16a_2</th>\n",
       "      <th>t_q16a_3</th>\n",
       "      <th>t_q16a_4</th>\n",
       "      <th>t_q16a_5</th>\n",
       "      <th>t_q16b_1</th>\n",
       "      <th>t_q16b_2</th>\n",
       "      <th>t_q16b_3</th>\n",
       "      <th>t_q16b_4</th>\n",
       "      <th>t_q16b_5</th>\n",
       "      <th>t_q16c_1</th>\n",
       "      <th>t_q16c_2</th>\n",
       "      <th>t_q16c_3</th>\n",
       "      <th>t_q16c_4</th>\n",
       "      <th>t_q16c_5</th>\n",
       "      <th>t_q16d_1</th>\n",
       "      <th>t_q16d_2</th>\n",
       "      <th>t_q16d_3</th>\n",
       "      <th>t_q16d_4</th>\n",
       "      <th>t_q16d_5</th>\n",
       "      <th>t_q17a_1</th>\n",
       "      <th>t_q17a_2</th>\n",
       "      <th>t_q17a_3</th>\n",
       "      <th>t_q17a_4</th>\n",
       "      <th>t_q17b_1</th>\n",
       "      <th>t_q17b_2</th>\n",
       "      <th>t_q17b_3</th>\n",
       "      <th>t_q17b_4</th>\n",
       "      <th>t_q17c_1</th>\n",
       "      <th>t_q17c_2</th>\n",
       "      <th>t_q17c_3</th>\n",
       "      <th>t_q17c_4</th>\n",
       "      <th>t_q17d_1</th>\n",
       "      <th>t_q17d_2</th>\n",
       "      <th>t_q17d_3</th>\n",
       "      <th>t_q17d_4</th>\n",
       "      <th>t_q17e_1</th>\n",
       "      <th>t_q17e_2</th>\n",
       "      <th>t_q17e_3</th>\n",
       "      <th>t_q17e_4</th>\n",
       "      <th>t_q17f_1</th>\n",
       "      <th>t_q17f_2</th>\n",
       "      <th>t_q17f_3</th>\n",
       "      <th>t_q17f_4</th>\n",
       "      <th>t_N_q3k_1</th>\n",
       "      <th>t_N_q3k_2</th>\n",
       "      <th>t_N_q3k_3</th>\n",
       "      <th>t_N_q3l_1</th>\n",
       "      <th>t_N_q3l_2</th>\n",
       "      <th>t_N_q3l_3</th>\n",
       "      <th>t_N_q7f_1</th>\n",
       "      <th>t_N_q7f_2</th>\n",
       "      <th>t_N_q7f_3</th>\n",
       "      <th>t_N_q7f_4</th>\n",
       "      <th>t_N_q7f_5</th>\n",
       "      <th>t_N_q10_1</th>\n",
       "      <th>t_N_q10_2</th>\n",
       "      <th>t_N_q10_3</th>\n",
       "      <th>t_N_q10_4</th>\n",
       "      <th>t_N_q10_5</th>\n",
       "      <th>t_N_q11a_5</th>\n",
       "      <th>t_N_q11b_5</th>\n",
       "      <th>t_N_q11c_5</th>\n",
       "      <th>t_N_q11d_5</th>\n",
       "      <th>t_N_q12_1</th>\n",
       "      <th>t_N_q12_2</th>\n",
       "      <th>t_N_q12_3</th>\n",
       "      <th>t_N_q12_4</th>\n",
       "      <th>t_N_q12_5</th>\n",
       "      <th>t_N_q12_6</th>\n",
       "      <th>t_N_q13e_1</th>\n",
       "      <th>t_N_q13e_2</th>\n",
       "      <th>t_N_q13e_3</th>\n",
       "      <th>t_N_q13e_4</th>\n",
       "      <th>t_N_q13f_1</th>\n",
       "      <th>t_N_q13f_2</th>\n",
       "      <th>t_N_q13f_3</th>\n",
       "      <th>t_N_q13f_4</th>\n",
       "      <th>t_N_q13g_1</th>\n",
       "      <th>t_N_q13g_2</th>\n",
       "      <th>t_N_q13g_3</th>\n",
       "      <th>t_N_q13g_4</th>\n",
       "      <th>t_N_q13h_1</th>\n",
       "      <th>t_N_q13h_2</th>\n",
       "      <th>t_N_q13h_3</th>\n",
       "      <th>t_N_q13h_4</th>\n",
       "      <th>t_N_q13i_1</th>\n",
       "      <th>t_N_q13i_2</th>\n",
       "      <th>t_N_q13i_3</th>\n",
       "      <th>t_N_q13i_4</th>\n",
       "      <th>t_N_q13j_1</th>\n",
       "      <th>t_N_q13j_2</th>\n",
       "      <th>t_N_q13j_3</th>\n",
       "      <th>t_N_q13j_4</th>\n",
       "      <th>t_N_q13k_1</th>\n",
       "      <th>t_N_q13k_2</th>\n",
       "      <th>t_N_q13k_3</th>\n",
       "      <th>t_N_q13k_4</th>\n",
       "      <th>t_N_q13l_1</th>\n",
       "      <th>t_N_q13l_2</th>\n",
       "      <th>t_N_q13l_3</th>\n",
       "      <th>t_N_q13l_4</th>\n",
       "      <th>t_N_q13m_1</th>\n",
       "      <th>t_N_q13m_2</th>\n",
       "      <th>t_N_q13m_3</th>\n",
       "      <th>t_N_q13m_4</th>\n",
       "      <th>t_N_q13n_1</th>\n",
       "      <th>t_N_q13n_2</th>\n",
       "      <th>t_N_q13n_3</th>\n",
       "      <th>t_N_q13n_4</th>\n",
       "      <th>t_N_q14_6</th>\n",
       "      <th>t_N_q15a_5</th>\n",
       "      <th>t_N_q15b_5</th>\n",
       "      <th>t_N_q15c_5</th>\n",
       "      <th>t_N_q15d_5</th>\n",
       "      <th>t_N_q16a_1</th>\n",
       "      <th>t_N_q16a_2</th>\n",
       "      <th>t_N_q16a_3</th>\n",
       "      <th>t_N_q16a_4</th>\n",
       "      <th>t_N_q16a_5</th>\n",
       "      <th>t_N_q16b_1</th>\n",
       "      <th>t_N_q16b_2</th>\n",
       "      <th>t_N_q16b_3</th>\n",
       "      <th>t_N_q16b_4</th>\n",
       "      <th>t_N_q16b_5</th>\n",
       "      <th>t_N_q16c_1</th>\n",
       "      <th>t_N_q16c_2</th>\n",
       "      <th>t_N_q16c_3</th>\n",
       "      <th>t_N_q16c_4</th>\n",
       "      <th>t_N_q16c_5</th>\n",
       "      <th>t_N_q16d_1</th>\n",
       "      <th>t_N_q16d_2</th>\n",
       "      <th>t_N_q16d_3</th>\n",
       "      <th>t_N_q16d_4</th>\n",
       "      <th>t_N_q16d_5</th>\n",
       "      <th>t_N_q17a_1</th>\n",
       "      <th>t_N_q17a_2</th>\n",
       "      <th>t_N_q17a_3</th>\n",
       "      <th>t_N_q17a_4</th>\n",
       "      <th>t_N_q17b_1</th>\n",
       "      <th>t_N_q17b_2</th>\n",
       "      <th>t_N_q17b_3</th>\n",
       "      <th>t_N_q17b_4</th>\n",
       "      <th>t_N_q17c_1</th>\n",
       "      <th>t_N_q17c_2</th>\n",
       "      <th>t_N_q17c_3</th>\n",
       "      <th>t_N_q17c_4</th>\n",
       "      <th>t_N_q17d_1</th>\n",
       "      <th>t_N_q17d_2</th>\n",
       "      <th>t_N_q17d_3</th>\n",
       "      <th>t_N_q17d_4</th>\n",
       "      <th>t_N_q17e_1</th>\n",
       "      <th>t_N_q17e_2</th>\n",
       "      <th>t_N_q17e_3</th>\n",
       "      <th>t_N_q17e_4</th>\n",
       "      <th>t_N_q17f_1</th>\n",
       "      <th>t_N_q17f_2</th>\n",
       "      <th>t_N_q17f_3</th>\n",
       "      <th>t_N_q17f_4</th>\n",
       "      <th>s_q5c</th>\n",
       "      <th>s_q11a</th>\n",
       "      <th>s_q11b</th>\n",
       "      <th>s_q11c</th>\n",
       "      <th>s_q12d</th>\n",
       "      <th>s_q12e</th>\n",
       "      <th>s_q12f</th>\n",
       "      <th>s_q12g</th>\n",
       "      <th>s_q1b</th>\n",
       "      <th>s_q7c</th>\n",
       "      <th>s_q7d</th>\n",
       "      <th>s_q1a</th>\n",
       "      <th>s_q1c</th>\n",
       "      <th>s_q5d</th>\n",
       "      <th>s_q5e</th>\n",
       "      <th>s_q5g</th>\n",
       "      <th>s_q8</th>\n",
       "      <th>s_q2d</th>\n",
       "      <th>s_q2e</th>\n",
       "      <th>s_q2f</th>\n",
       "      <th>s_q2g</th>\n",
       "      <th>s_q5f</th>\n",
       "      <th>s_q14</th>\n",
       "      <th>s_q1a_1</th>\n",
       "      <th>s_q1a_2</th>\n",
       "      <th>s_q1a_3</th>\n",
       "      <th>s_q1a_4</th>\n",
       "      <th>s_q1b_1</th>\n",
       "      <th>s_q1b_2</th>\n",
       "      <th>s_q1b_3</th>\n",
       "      <th>s_q1b_4</th>\n",
       "      <th>s_q1c_1</th>\n",
       "      <th>s_q1c_2</th>\n",
       "      <th>s_q1c_3</th>\n",
       "      <th>s_q1c_4</th>\n",
       "      <th>s_q2d_1</th>\n",
       "      <th>s_q2d_2</th>\n",
       "      <th>s_q2d_3</th>\n",
       "      <th>s_q2d_4</th>\n",
       "      <th>s_q2e_1</th>\n",
       "      <th>s_q2e_2</th>\n",
       "      <th>s_q2e_3</th>\n",
       "      <th>s_q2e_4</th>\n",
       "      <th>s_q2f_1</th>\n",
       "      <th>s_q2f_2</th>\n",
       "      <th>s_q2f_3</th>\n",
       "      <th>s_q2f_4</th>\n",
       "      <th>s_q2g_1</th>\n",
       "      <th>s_q2g_2</th>\n",
       "      <th>s_q2g_3</th>\n",
       "      <th>s_q2g_4</th>\n",
       "      <th>s_q5a_5</th>\n",
       "      <th>s_q5b_5</th>\n",
       "      <th>s_q5c_1</th>\n",
       "      <th>s_q5c_2</th>\n",
       "      <th>s_q5c_3</th>\n",
       "      <th>s_q5c_4</th>\n",
       "      <th>s_q5c_5</th>\n",
       "      <th>s_q5d_1</th>\n",
       "      <th>s_q5d_2</th>\n",
       "      <th>s_q5d_3</th>\n",
       "      <th>s_q5d_4</th>\n",
       "      <th>s_q5d_5</th>\n",
       "      <th>s_q5e_1</th>\n",
       "      <th>s_q5e_2</th>\n",
       "      <th>s_q5e_3</th>\n",
       "      <th>s_q5e_4</th>\n",
       "      <th>s_q5e_5</th>\n",
       "      <th>s_q5f_1</th>\n",
       "      <th>s_q5f_2</th>\n",
       "      <th>s_q5f_3</th>\n",
       "      <th>s_q5f_4</th>\n",
       "      <th>s_q5f_5</th>\n",
       "      <th>s_q5g_1</th>\n",
       "      <th>s_q5g_2</th>\n",
       "      <th>s_q5g_3</th>\n",
       "      <th>s_q5g_4</th>\n",
       "      <th>s_q5g_5</th>\n",
       "      <th>s_q7a_5</th>\n",
       "      <th>s_q7b_5</th>\n",
       "      <th>s_q7c_1</th>\n",
       "      <th>s_q7c_2</th>\n",
       "      <th>s_q7c_3</th>\n",
       "      <th>s_q7c_4</th>\n",
       "      <th>s_q7c_5</th>\n",
       "      <th>s_q7d_1</th>\n",
       "      <th>s_q7d_2</th>\n",
       "      <th>s_q7d_3</th>\n",
       "      <th>s_q7d_4</th>\n",
       "      <th>s_q7d_5</th>\n",
       "      <th>s_q8e_1</th>\n",
       "      <th>s_q8e_2</th>\n",
       "      <th>s_q8e_3</th>\n",
       "      <th>s_q8f_1</th>\n",
       "      <th>s_q8f_2</th>\n",
       "      <th>s_q8f_3</th>\n",
       "      <th>s_q8g_1</th>\n",
       "      <th>s_q8g_2</th>\n",
       "      <th>s_q8g_3</th>\n",
       "      <th>s_q8h_1</th>\n",
       "      <th>s_q8h_2</th>\n",
       "      <th>s_q8h_3</th>\n",
       "      <th>s_q8i_1</th>\n",
       "      <th>s_q8i_2</th>\n",
       "      <th>s_q8i_3</th>\n",
       "      <th>s_q8j_1</th>\n",
       "      <th>s_q8j_2</th>\n",
       "      <th>s_q8j_3</th>\n",
       "      <th>s_q8k_1</th>\n",
       "      <th>s_q8k_2</th>\n",
       "      <th>s_q8k_3</th>\n",
       "      <th>s_q8l_1</th>\n",
       "      <th>s_q8l_2</th>\n",
       "      <th>s_q8l_3</th>\n",
       "      <th>s_q9i_1</th>\n",
       "      <th>s_q9i_2</th>\n",
       "      <th>s_q9i_3</th>\n",
       "      <th>s_q9j_1</th>\n",
       "      <th>s_q9j_2</th>\n",
       "      <th>s_q9j_3</th>\n",
       "      <th>s_q9k_1</th>\n",
       "      <th>s_q9k_2</th>\n",
       "      <th>s_q9k_3</th>\n",
       "      <th>s_q9l_1</th>\n",
       "      <th>s_q9l_2</th>\n",
       "      <th>s_q9l_3</th>\n",
       "      <th>s_q10_1</th>\n",
       "      <th>s_q10_2</th>\n",
       "      <th>s_q10_3</th>\n",
       "      <th>s_q10_4</th>\n",
       "      <th>s_q11a_1</th>\n",
       "      <th>s_q11a_2</th>\n",
       "      <th>s_q11a_3</th>\n",
       "      <th>s_q11a_4</th>\n",
       "      <th>s_q11b_1</th>\n",
       "      <th>s_q11b_2</th>\n",
       "      <th>s_q11b_3</th>\n",
       "      <th>s_q11b_4</th>\n",
       "      <th>s_q11c_1</th>\n",
       "      <th>s_q11c_2</th>\n",
       "      <th>s_q11c_3</th>\n",
       "      <th>s_q11c_4</th>\n",
       "      <th>s_q12d_1</th>\n",
       "      <th>s_q12d_2</th>\n",
       "      <th>s_q12d_3</th>\n",
       "      <th>s_q12d_4</th>\n",
       "      <th>s_q12e_1</th>\n",
       "      <th>s_q12e_2</th>\n",
       "      <th>s_q12e_3</th>\n",
       "      <th>s_q12e_4</th>\n",
       "      <th>s_q12f_1</th>\n",
       "      <th>s_q12f_2</th>\n",
       "      <th>s_q12f_3</th>\n",
       "      <th>s_q12f_4</th>\n",
       "      <th>s_q12g_1</th>\n",
       "      <th>s_q12g_2</th>\n",
       "      <th>s_q12g_3</th>\n",
       "      <th>s_q12g_4</th>\n",
       "      <th>s_q14_1</th>\n",
       "      <th>s_q14_2</th>\n",
       "      <th>s_q14_3</th>\n",
       "      <th>s_q14_4</th>\n",
       "      <th>s_q14_5</th>\n",
       "      <th>s_q14_6</th>\n",
       "      <th>s_q14_7</th>\n",
       "      <th>s_q14_8</th>\n",
       "      <th>s_q14_9</th>\n",
       "      <th>s_q14_10</th>\n",
       "      <th>s_q14_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M015</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>150</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M019</td>\n",
       "      <td>M019</td>\n",
       "      <td>P.S. 019 Asher Levy</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>269</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M020</td>\n",
       "      <td>M020</td>\n",
       "      <td>P.S. 020 Anna Silver</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>505</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M034</td>\n",
       "      <td>M034</td>\n",
       "      <td>P.S. 034 Franklin D. Roosevelt</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Elementary / Middle School</td>\n",
       "      <td>89.0</td>\n",
       "      <td>73</td>\n",
       "      <td>50</td>\n",
       "      <td>145.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>163</td>\n",
       "      <td>40</td>\n",
       "      <td>301</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M063</td>\n",
       "      <td>M063</td>\n",
       "      <td>P.S. 063 William McKinley</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>151</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn    bn                      schoolname  d75 studentssurveyed  \\\n",
       "0  01M015  M015       P.S. 015 Roberto Clemente    0               No   \n",
       "1  01M019  M019             P.S. 019 Asher Levy    0               No   \n",
       "2  01M020  M020            P.S. 020 Anna Silver    0               No   \n",
       "3  01M034  M034  P.S. 034 Franklin D. Roosevelt    0              Yes   \n",
       "4  01M063  M063       P.S. 063 William McKinley    0               No   \n",
       "\n",
       "   highschool                  schooltype  rr_s  rr_t  rr_p    N_s   N_t  \\\n",
       "0         0.0           Elementary School   NaN    88    60    NaN  22.0   \n",
       "1         0.0           Elementary School   NaN   100    60    NaN  34.0   \n",
       "2         0.0           Elementary School   NaN    88    73    NaN  42.0   \n",
       "3         0.0  Elementary / Middle School  89.0    73    50  145.0  29.0   \n",
       "4         0.0           Elementary School   NaN   100    60    NaN  23.0   \n",
       "\n",
       "     N_p  nr_s  nr_t  nr_p  saf_p_11  com_p_11  eng_p_11  aca_p_11  saf_t_11  \\\n",
       "0   90.0     0    25   150       8.5       7.6       7.5       7.8       7.5   \n",
       "1  161.0     0    33   269       8.4       7.6       7.6       7.8       8.6   \n",
       "2  367.0     0    48   505       8.9       8.3       8.3       8.6       7.6   \n",
       "3  151.0   163    40   301       8.8       8.2       8.0       8.5       7.0   \n",
       "4   90.0     0    23   151       8.7       7.9       8.1       7.9       8.4   \n",
       "\n",
       "   com_t_11  eng_t_11  aca_t_11  saf_s_11  com_s_11  eng_s_11  aca_s_11  \\\n",
       "0       7.8       7.6       7.9       NaN       NaN       NaN       NaN   \n",
       "1       8.5       8.9       9.1       NaN       NaN       NaN       NaN   \n",
       "2       6.3       6.8       7.5       NaN       NaN       NaN       NaN   \n",
       "3       6.2       6.8       7.8       6.2       5.9       6.5       7.4   \n",
       "4       7.3       7.8       8.1       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   saf_tot_11  com_tot_11  eng_tot_11  aca_tot_11  p_q2h  p_q7a  p_q7b  p_q7c  \\\n",
       "0         8.0         7.7         7.5         7.9    8.0    8.2    8.3    7.5   \n",
       "1         8.5         8.1         8.2         8.4    7.7    7.9    8.0    7.3   \n",
       "2         8.2         7.3         7.5         8.0    8.1    8.8    8.9    8.5   \n",
       "3         7.3         6.7         7.1         7.9    8.1    8.5    8.8    8.2   \n",
       "4         8.5         7.6         7.9         8.0    8.0    8.4    8.6    8.0   \n",
       "\n",
       "   p_q7d  p_q8a  p_q8b  p_q8c  p_q8d  p_q8e  p_q8f  p_q2b  p_q2d  p_q2e  \\\n",
       "0    7.9    6.8    8.7    9.7    8.7    9.9    9.9    7.7    8.3    7.9   \n",
       "1    7.7    6.5    8.8    9.4    8.7   10.0    9.9    7.5    8.2    7.9   \n",
       "2    8.4    7.6    9.2    9.4    9.2    9.8    9.7    8.4    8.8    8.5   \n",
       "3    8.3    7.3    9.2    9.4    9.1    9.8    9.7    8.3    8.7    8.3   \n",
       "4    8.0    6.5    8.8    9.6    9.4   10.0   10.0    7.6    8.5    8.0   \n",
       "\n",
       "   p_q2f  p_q2g  p_q3a  p_q3b  p_q4b  p_q4c  p_q11c  p_q2a  p_q2c  p_q3c  \\\n",
       "0    8.1    7.5    7.3    6.7    7.6    7.9     7.5    8.0    7.4    8.7   \n",
       "1    8.0    7.2    7.0    6.9    8.0    8.4     7.4    7.6    7.1    9.0   \n",
       "2    8.3    8.0    7.6    7.3    8.6    8.7     8.4    8.8    8.2    8.8   \n",
       "3    8.1    7.6    7.6    7.5    8.6    8.6     8.4    8.5    8.1    8.8   \n",
       "4    8.2    7.4    7.8    7.2    8.1    7.9     7.8    8.2    8.3    9.2   \n",
       "\n",
       "   p_q6a  p_q6b  p_q11d  p_q11e  p_q5  p_q4a  p_q4d  p_q4e  p_q11a  p_q11b  \\\n",
       "0    6.3    6.6     7.6     7.6   7.4    7.8    7.4    NaN     8.3     7.5   \n",
       "1    6.4    6.5     7.6     7.8   8.6    7.7    7.8    NaN     8.3     7.6   \n",
       "2    7.9    6.8     8.5     8.5   8.7    8.6    8.5    NaN     8.9     8.4   \n",
       "3    7.3    6.8     8.2     8.3   8.0    8.7    8.3    NaN     8.8     8.3   \n",
       "4    7.4    7.0     7.9     7.9   8.8    8.2    7.8    NaN     8.1     7.6   \n",
       "\n",
       "   p_q11f  p_q1  p_q3d  p_q9  p_q10  p_q12aa  p_q12ab  p_q12ac  p_q12ad  \\\n",
       "0     7.7   NaN    NaN   NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "1     7.7   NaN    NaN   NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "2     8.6   NaN    NaN   NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "3     8.5   NaN    NaN   NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "4     7.7   NaN    NaN   NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q12ba  p_q12bb  p_q12bc  p_q12bd  p_q1_1  p_q1_2  p_q1_3  p_q1_4  p_q1_5  \\\n",
       "0      NaN      NaN      NaN      NaN     7.0     9.0    14.0    19.0    14.0   \n",
       "1      NaN      NaN      NaN      NaN    11.0    16.0    14.0    15.0    16.0   \n",
       "2      NaN      NaN      NaN      NaN    10.0    18.0    20.0    11.0    14.0   \n",
       "3      NaN      NaN      NaN      NaN     5.0     9.0     7.0    11.0     6.0   \n",
       "4      NaN      NaN      NaN      NaN     3.0    19.0     8.0    19.0    13.0   \n",
       "\n",
       "   p_q1_6  p_q1_7  p_q1_8  p_q1_9  p_q1_10  p_q1_11  p_q1_12  p_q1_13  \\\n",
       "0    19.0    16.0     0.0     1.0      0.0      0.0      0.0      0.0   \n",
       "1    11.0    16.0     1.0     0.0      1.0      0.0      0.0      0.0   \n",
       "2    12.0    17.0     0.0     0.0      0.0      0.0      0.0      0.0   \n",
       "3    11.0    13.0    14.0    13.0      9.0      0.0      1.0      0.0   \n",
       "4     9.0    29.0     0.0     0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   p_q1_14  p_q2a_1  p_q2a_2  p_q2a_3  p_q2a_4  p_q2a_5  p_q2b_1  p_q2b_2  \\\n",
       "0      0.0     47.0     48.0      6.0      0.0      0.0     40.0     50.0   \n",
       "1      0.0     44.0     43.0      9.0      4.0      0.0     42.0     42.0   \n",
       "2      0.0     64.0     36.0      0.0      0.0      0.0     54.0     45.0   \n",
       "3      0.0     57.0     41.0      1.0      1.0      1.0     53.0     45.0   \n",
       "4      0.0     57.0     37.0      1.0      4.0      0.0     41.0     51.0   \n",
       "\n",
       "   p_q2b_3  p_q2b_4  p_q2b_5  p_q2c_1  p_q2c_2  p_q2c_3  p_q2c_4  p_q2c_5  \\\n",
       "0      9.0      0.0      1.0     38.0     51.0      8.0      3.0      0.0   \n",
       "1     12.0      2.0      1.0     39.0     35.0     19.0      4.0      2.0   \n",
       "2      1.0      0.0      0.0     50.0     48.0      3.0      0.0      0.0   \n",
       "3      1.0      1.0      0.0     49.0     46.0      5.0      1.0      0.0   \n",
       "4      3.0      5.0      0.0     53.0     41.0      2.0      1.0      2.0   \n",
       "\n",
       "   p_q2d_1  p_q2d_2  p_q2d_3  p_q2d_4  p_q2d_5  p_q2e_1  p_q2e_2  p_q2e_3  \\\n",
       "0     53.0     43.0      1.0      2.0      0.0     46.0     47.0      5.0   \n",
       "1     50.0     46.0      3.0      0.0      1.0     45.0     48.0      5.0   \n",
       "2     66.0     34.0      1.0      0.0      0.0     58.0     41.0      2.0   \n",
       "3     62.0     35.0      1.0      1.0      1.0     55.0     40.0      2.0   \n",
       "4     61.0     35.0      2.0      1.0      0.0     52.0     40.0      5.0   \n",
       "\n",
       "   p_q2e_4  p_q2e_5  p_q2f_1  p_q2f_2  p_q2f_3  p_q2f_4  p_q2f_5  p_q2g_1  \\\n",
       "0      2.0      0.0     45.0     47.0      5.0      0.0      3.0     44.0   \n",
       "1      2.0      1.0     35.0     41.0      1.0      1.0     22.0     40.0   \n",
       "2      0.0      0.0     48.0     43.0      1.0      1.0      7.0     46.0   \n",
       "3      2.0      1.0     46.0     46.0      3.0      1.0      4.0     44.0   \n",
       "4      3.0      0.0     48.0     39.0      4.0      1.0      8.0     43.0   \n",
       "\n",
       "   p_q2g_2  p_q2g_3  p_q2g_4  p_q2g_5  p_q2h_1  p_q2h_2  p_q2h_3  p_q2h_4  \\\n",
       "0     35.0     16.0      2.0      2.0     49.0     43.0      6.0      1.0   \n",
       "1     38.0     15.0      5.0      3.0     42.0     43.0      6.0      3.0   \n",
       "2     46.0      5.0      1.0      2.0     46.0     48.0      3.0      1.0   \n",
       "3     41.0     14.0      1.0      0.0     48.0     44.0      4.0      1.0   \n",
       "4     40.0     10.0      6.0      1.0     46.0     46.0      4.0      2.0   \n",
       "\n",
       "   p_q2h_5  p_q3a_1  p_q3a_2  p_q3a_3  p_q3a_4  p_q3a_5  p_q3b_1  p_q3b_2  \\\n",
       "0      1.0     36.0     35.0     16.0      8.0      5.0     31.0     34.0   \n",
       "1      5.0     34.0     29.0     21.0     12.0      4.0     32.0     34.0   \n",
       "2      3.0     43.0     30.0     18.0      7.0      2.0     38.0     34.0   \n",
       "3      3.0     44.0     33.0     12.0      6.0      6.0     45.0     33.0   \n",
       "4      2.0     44.0     28.0     22.0      5.0      1.0     39.0     32.0   \n",
       "\n",
       "   p_q3b_3  p_q3b_4  p_q3b_5  p_q3c_1  p_q3c_2  p_q3c_3  p_q3c_4  p_q3c_5  \\\n",
       "0     15.0     14.0      6.0     44.0     31.0     15.0      8.0      1.0   \n",
       "1     18.0      9.0      6.0     43.0     39.0     11.0      5.0      2.0   \n",
       "2     14.0      8.0      5.0     41.0     39.0     13.0      5.0      3.0   \n",
       "3     10.0      5.0      8.0     47.0     33.0      7.0      8.0      5.0   \n",
       "4     16.0      6.0      8.0     57.0     29.0      9.0      2.0      2.0   \n",
       "\n",
       "   p_q3d_1  p_q3d_2  p_q3d_3  p_q3d_4  p_q3d_5  p_q4a_1  p_q4a_2  p_q4a_3  \\\n",
       "0     33.0     36.0     22.0      7.0      2.0     35.0     53.0      4.0   \n",
       "1     35.0     23.0     27.0     13.0      2.0     36.0     49.0     11.0   \n",
       "2     37.0     33.0     20.0      8.0      2.0     52.0     43.0      2.0   \n",
       "3     39.0     33.0     18.0      7.0      3.0     55.0     40.0      2.0   \n",
       "4     45.0     28.0     22.0      5.0      0.0     49.0     42.0      3.0   \n",
       "\n",
       "   p_q4a_4  p_q4a_5  p_q4b_1  p_q4b_2  p_q4b_3  p_q4b_4  p_q4b_5  p_q4c_1  \\\n",
       "0      2.0      6.0     33.0     49.0     14.0      0.0      3.0     36.0   \n",
       "1      1.0      3.0     39.0     50.0      9.0      1.0      1.0     49.0   \n",
       "2      1.0      2.0     51.0     46.0      1.0      1.0      1.0     56.0   \n",
       "3      0.0      3.0     55.0     40.0      3.0      1.0      1.0     57.0   \n",
       "4      3.0      2.0     43.0     48.0      7.0      2.0      0.0     45.0   \n",
       "\n",
       "   p_q4c_2  p_q4c_3  p_q4c_4  p_q4c_5  p_q4d_1  p_q4d_2  p_q4d_3  p_q4d_4  \\\n",
       "0     52.0      8.0      0.0      3.0     34.0     46.0     13.0      1.0   \n",
       "1     43.0      6.0      1.0      2.0     35.0     48.0      4.0      3.0   \n",
       "2     39.0      3.0      0.0      2.0     51.0     42.0      3.0      0.0   \n",
       "3     36.0      4.0      1.0      3.0     51.0     38.0      6.0      1.0   \n",
       "4     40.0      7.0      3.0      5.0     42.0     44.0      3.0      6.0   \n",
       "\n",
       "   p_q4d_5  p_q4e_1  p_q4e_2  p_q4e_3  p_q4e_4  p_q4e_5  p_q5a  p_q5b  p_q5c  \\\n",
       "0      6.0      NaN      NaN      NaN      NaN      NaN   34.0   18.0   21.0   \n",
       "1     10.0      NaN      NaN      NaN      NaN      NaN   39.0   60.0   12.0   \n",
       "2      4.0      NaN      NaN      NaN      NaN      NaN   59.0   31.0   49.0   \n",
       "3      3.0      NaN      NaN      NaN      NaN      NaN   23.0   62.0   17.0   \n",
       "4      5.0      NaN      NaN      NaN      NaN      NaN   69.0   77.0   24.0   \n",
       "\n",
       "   p_q5d  p_q5e  p_q5f  p_q5g  p_q5h  p_q5i  p_q5j  p_q6a_1  p_q6a_2  p_q6a_3  \\\n",
       "0    6.0    0.0   37.0    7.0   52.0   13.0   34.0     20.0     45.0     22.0   \n",
       "1    7.0    2.0   62.0   11.0   65.0   16.0   32.0     27.0     36.0     25.0   \n",
       "2   11.0   12.0   52.0   14.0   64.0   17.0   28.0     35.0     52.0      6.0   \n",
       "3    7.0   23.0   42.0    7.0   42.0   23.0   21.0     34.0     45.0     13.0   \n",
       "4    8.0    1.0   34.0   22.0   62.0   30.0   22.0     36.0     46.0     11.0   \n",
       "\n",
       "   p_q6a_4  p_q6a_5  p_q6b_1  p_q6b_2  p_q6b_3  p_q6b_4  p_q6b_5  p_q7a_1  \\\n",
       "0      6.0      7.0     16.0     38.0      5.0      0.0     41.0     39.0   \n",
       "1      6.0      6.0     18.0     27.0      1.0      0.0     54.0     41.0   \n",
       "2      1.0      5.0     24.0     32.0      5.0      1.0     39.0     55.0   \n",
       "3      3.0      5.0     23.0     31.0      4.0      1.0     42.0     49.0   \n",
       "4      6.0      1.0     28.0     33.0      0.0      5.0     34.0     45.0   \n",
       "\n",
       "   p_q7a_2  p_q7a_3  p_q7a_4  p_q7a_5  p_q7b_1  p_q7b_2  p_q7b_3  p_q7b_4  \\\n",
       "0     54.0      2.0      1.0      3.0     38.0     57.0      2.0      0.0   \n",
       "1     48.0      7.0      3.0      1.0     45.0     43.0      6.0      3.0   \n",
       "2     43.0      1.0      1.0      1.0     58.0     40.0      1.0      1.0   \n",
       "3     46.0      2.0      1.0      2.0     58.0     39.0      1.0      1.0   \n",
       "4     51.0      1.0      2.0      1.0     48.0     51.0      1.0      0.0   \n",
       "\n",
       "   p_q7b_5  p_q7c_1  p_q7c_2  p_q7c_3  p_q7c_4  p_q7c_5  p_q7d_1  p_q7d_2  \\\n",
       "0      2.0     33.0     51.0      5.0      6.0      6.0     43.0     50.0   \n",
       "1      3.0     33.0     43.0     11.0      3.0     10.0     42.0     37.0   \n",
       "2      0.0     50.0     42.0      1.0      1.0      5.0     53.0     41.0   \n",
       "3      0.0     51.0     37.0      5.0      2.0      5.0     52.0     41.0   \n",
       "4      0.0     41.0     49.0      3.0      3.0      2.0     49.0     39.0   \n",
       "\n",
       "   p_q7d_3  p_q7d_4  p_q7d_5  p_q8a_1  p_q8a_2  p_q8a_3  p_q8a_4  p_q8a_5  \\\n",
       "0      4.0      1.0      1.0     32.0     32.0     11.0      9.0     16.0   \n",
       "1     11.0      1.0      9.0     25.0     34.0      6.0     12.0     23.0   \n",
       "2      2.0      0.0      4.0     33.0     34.0      8.0      2.0     23.0   \n",
       "3      2.0      1.0      4.0     30.0     37.0      6.0      4.0     22.0   \n",
       "4      3.0      4.0      6.0     17.0     40.0     11.0      5.0     26.0   \n",
       "\n",
       "   p_q8b_1  p_q8b_2  p_q8b_3  p_q8b_4  p_q8b_5  p_q8c_1  p_q8c_2  p_q8c_3  \\\n",
       "0     61.0     17.0      2.0      3.0     16.0     71.0      5.0      1.0   \n",
       "1     59.0     13.0      3.0      3.0     23.0     69.0      5.0      3.0   \n",
       "2     71.0     11.0      3.0      1.0     14.0     74.0      3.0      2.0   \n",
       "3     63.0     13.0      1.0      1.0     21.0     68.0      5.0      2.0   \n",
       "4     69.0      8.0      4.0      5.0     14.0     80.0      3.0      3.0   \n",
       "\n",
       "   p_q8c_4  p_q8c_5  p_q8d_1  p_q8d_2  p_q8d_3  p_q8d_4  p_q8d_5  p_q8e_1  \\\n",
       "0      0.0     24.0     60.0     14.0      1.0      5.0     21.0     82.0   \n",
       "1      1.0     23.0     55.0      8.0      3.0      5.0     29.0     72.0   \n",
       "2      2.0     19.0     69.0      9.0      2.0      2.0     18.0     85.0   \n",
       "3      1.0     23.0     67.0      9.0      3.0      2.0     19.0     74.0   \n",
       "4      0.0     13.0     65.0     13.0      1.0      0.0     22.0     85.0   \n",
       "\n",
       "   p_q8e_2  p_q8e_3  p_q8e_4  p_q8e_5  p_q8f_1  p_q8f_2  p_q8f_3  p_q8f_4  \\\n",
       "0      2.0      0.0      0.0     15.0     83.0      4.0      0.0      0.0   \n",
       "1      0.0      0.0      0.0     28.0     69.0      1.0      1.0      0.0   \n",
       "2      1.0      1.0      1.0     12.0     82.0      2.0      1.0      2.0   \n",
       "3      1.0      1.0      0.0     24.0     73.0      1.0      1.0      1.0   \n",
       "4      0.0      0.0      0.0     15.0     84.0      0.0      0.0      0.0   \n",
       "\n",
       "   p_q8f_5  p_q9_1  p_q9_2  p_q9_3  p_q9_4  p_q9_5  p_q9_6  p_q9_7  p_q9_8  \\\n",
       "0     13.0    10.0    23.0    21.0    12.0     6.0     8.0     6.0     6.0   \n",
       "1     30.0     9.0    13.0    17.0    12.0     9.0     7.0     2.0    20.0   \n",
       "2     14.0     3.0    14.0    19.0    12.0     9.0     5.0     4.0    23.0   \n",
       "3     23.0     6.0     9.0    17.0    17.0    16.0     8.0     3.0    16.0   \n",
       "4     16.0     5.0    18.0    24.0     3.0     6.0     3.0    10.0    16.0   \n",
       "\n",
       "   p_q9_9  p_q9_10  p_q10a  p_q10b  p_q10c  p_q10d  p_q10e  p_q10f  p_q10g  \\\n",
       "0    10.0      0.0    62.0    33.0     7.0    54.0     3.0     2.0    62.0   \n",
       "1    10.0      1.0    47.0    65.0    14.0    61.0    14.0    10.0    47.0   \n",
       "2    10.0      1.0    48.0    47.0    11.0    60.0    12.0    10.0    57.0   \n",
       "3     8.0      0.0    63.0    49.0    13.0    52.0    11.0    10.0    68.0   \n",
       "4    15.0      0.0    50.0    60.0    14.0    63.0    16.0    14.0    70.0   \n",
       "\n",
       "   p_q10h  p_q10i  p_q10j  p_q10k  p_q10l  p_q11a_1  p_q11a_2  p_q11a_3  \\\n",
       "0    12.0     7.0    37.0     1.0     1.0      56.0      40.0       3.0   \n",
       "1    20.0    11.0    52.0     2.0     1.0      57.0      38.0       4.0   \n",
       "2    18.0    13.0    55.0     4.0     4.0      69.0      29.0       2.0   \n",
       "3    28.0    11.0    49.0     3.0     3.0      66.0      31.0       3.0   \n",
       "4    27.0    14.0    66.0     3.0     0.0      54.0      38.0       6.0   \n",
       "\n",
       "   p_q11a_4  p_q11b_1  p_q11b_2  p_q11b_3  p_q11b_4  p_q11c_1  p_q11c_2  \\\n",
       "0       1.0      38.0      49.0      12.0       1.0      43.0      43.0   \n",
       "1       1.0      42.0      46.0      10.0       2.0      39.0      47.0   \n",
       "2       0.0      55.0      42.0       3.0       0.0      56.0      42.0   \n",
       "3       0.0      55.0      39.0       6.0       0.0      56.0      39.0   \n",
       "4       2.0      44.0      42.0       9.0       4.0      48.0      40.0   \n",
       "\n",
       "   p_q11c_3  p_q11c_4  p_q11d_1  p_q11d_2  p_q11d_3  p_q11d_4  p_q11e_1  \\\n",
       "0      12.0       2.0      42.0      47.0      10.0       1.0      43.0   \n",
       "1      12.0       2.0      43.0      44.0      13.0       1.0      44.0   \n",
       "2       2.0       1.0      57.0      40.0       3.0       0.0      55.0   \n",
       "3       5.0       0.0      54.0      39.0       7.0       0.0      52.0   \n",
       "4       9.0       2.0      49.0      39.0      10.0       1.0      46.0   \n",
       "\n",
       "   p_q11e_2  p_q11e_3  p_q11e_4  p_q11f_1  p_q11f_2  p_q11f_3  p_q11f_4  \\\n",
       "0      46.0       9.0       2.0      47.0      43.0       6.0       5.0   \n",
       "1      47.0       8.0       1.0      44.0      45.0       9.0       1.0   \n",
       "2      43.0       1.0       0.0      58.0      40.0       1.0       0.0   \n",
       "3      45.0       3.0       0.0      58.0      38.0       4.0       0.0   \n",
       "4      47.0       7.0       1.0      49.0      37.0       9.0       4.0   \n",
       "\n",
       "   p_q12aa_1  p_q12aa_2  p_q12aa_3  p_q12aa_4  p_q12aa_5  p_q12ab_1  \\\n",
       "0       18.0       40.0       19.0        3.0       21.0       22.0   \n",
       "1       15.0       36.0       18.0        4.0       26.0       15.0   \n",
       "2       24.0       40.0       13.0        4.0       19.0       20.0   \n",
       "3       30.0       46.0        9.0        2.0       13.0       28.0   \n",
       "4       11.0       48.0       19.0        5.0       17.0       10.0   \n",
       "\n",
       "   p_q12ab_2  p_q12ab_3  p_q12ab_4  p_q12ab_5  p_q12ac_1  p_q12ac_2  \\\n",
       "0       33.0       11.0        3.0       30.0       20.0       48.0   \n",
       "1       36.0       11.0        6.0       33.0       18.0       40.0   \n",
       "2       45.0       10.0        2.0       23.0       22.0       52.0   \n",
       "3       47.0        6.0        2.0       17.0       31.0       49.0   \n",
       "4       49.0       15.0        2.0       24.0        8.0       54.0   \n",
       "\n",
       "   p_q12ac_3  p_q12ac_4  p_q12ac_5  p_q12ad_1  p_q12ad_2  p_q12ad_3  \\\n",
       "0        9.0        3.0       20.0       23.0       47.0        6.0   \n",
       "1       14.0        4.0       25.0       21.0       36.0       17.0   \n",
       "2        9.0        1.0       16.0       27.0       49.0        9.0   \n",
       "3        6.0        4.0       10.0       36.0       44.0        6.0   \n",
       "4       14.0        7.0       17.0       14.0       56.0       10.0   \n",
       "\n",
       "   p_q12ad_4  p_q12ad_5  p_q12ba_1  p_q12ba_2  p_q12ba_3  p_q12ba_4  \\\n",
       "0        3.0       21.0       19.0       30.0       14.0        6.0   \n",
       "1        3.0       23.0       12.0       28.0       23.0        6.0   \n",
       "2        1.0       14.0       21.0       42.0        9.0        7.0   \n",
       "3        2.0       11.0       21.0       38.0       14.0        5.0   \n",
       "4        6.0       14.0        8.0       29.0       25.0        5.0   \n",
       "\n",
       "   p_q12ba_5  p_q12bb_1  p_q12bb_2  p_q12bb_3  p_q12bb_4  p_q12bb_5  \\\n",
       "0       31.0       16.0       33.0       11.0        7.0       33.0   \n",
       "1       32.0       11.0       28.0       20.0        8.0       34.0   \n",
       "2       21.0       15.0       45.0       10.0        5.0       25.0   \n",
       "3       22.0       19.0       38.0       13.0        6.0       24.0   \n",
       "4       32.0        7.0       29.0       23.0        5.0       36.0   \n",
       "\n",
       "   p_q12bc_1  p_q12bc_2  p_q12bc_3  p_q12bc_4  p_q12bc_5  p_q12bd_1  \\\n",
       "0       18.0       35.0       10.0        6.0       31.0       19.0   \n",
       "1       11.0       34.0       17.0        5.0       32.0       15.0   \n",
       "2       18.0       45.0       11.0        5.0       21.0       18.0   \n",
       "3       22.0       41.0       11.0        5.0       20.0       22.0   \n",
       "4        7.0       29.0       21.0        7.0       36.0        7.0   \n",
       "\n",
       "   p_q12bd_2  p_q12bd_3  p_q12bd_4  p_q12bd_5  p_N_q1_1  p_N_q1_2  p_N_q1_3  \\\n",
       "0       34.0       10.0        8.0       29.0       6.0       8.0      12.0   \n",
       "1       28.0       21.0        5.0       31.0      17.0      25.0      23.0   \n",
       "2       47.0        9.0        5.0       21.0      33.0      62.0      68.0   \n",
       "3       40.0       12.0        5.0       20.0       7.0      13.0      10.0   \n",
       "4       31.0       22.0        7.0       33.0       3.0      16.0       7.0   \n",
       "\n",
       "   p_N_q1_4  p_N_q1_5  p_N_q1_6  p_N_q1_7  p_N_q1_8  p_N_q1_9  p_N_q1_10  \\\n",
       "0      16.0      12.0      16.0      14.0       0.0       1.0        0.0   \n",
       "1      24.0      25.0      17.0      26.0       1.0       0.0        1.0   \n",
       "2      37.0      47.0      42.0      58.0       0.0       0.0        0.0   \n",
       "3      16.0       9.0      15.0      18.0      20.0      19.0       13.0   \n",
       "4      16.0      11.0       8.0      25.0       0.0       0.0        0.0   \n",
       "\n",
       "   p_N_q1_11  p_N_q1_12  p_N_q1_13  p_N_q1_14  p_N_q2a_1  p_N_q2a_2  \\\n",
       "0        0.0        0.0        0.0        0.0       42.0       43.0   \n",
       "1        0.0        0.0        0.0        0.0       71.0       69.0   \n",
       "2        0.0        0.0        0.0        0.0      230.0      128.0   \n",
       "3        0.0        1.0        0.0        0.0       85.0       61.0   \n",
       "4        0.0        0.0        0.0        0.0       51.0       33.0   \n",
       "\n",
       "   p_N_q2a_3  p_N_q2a_4  p_N_q2a_5  p_N_q2b_1  p_N_q2b_2  p_N_q2b_3  \\\n",
       "0        5.0        0.0        0.0       34.0       43.0        8.0   \n",
       "1       14.0        7.0        0.0       68.0       68.0       19.0   \n",
       "2        1.0        0.0        1.0      192.0      158.0        3.0   \n",
       "3        2.0        1.0        1.0       79.0       68.0        2.0   \n",
       "4        1.0        4.0        0.0       36.0       45.0        3.0   \n",
       "\n",
       "   p_N_q2b_4  p_N_q2b_5  p_N_q2c_1  p_N_q2c_2  p_N_q2c_3  p_N_q2c_4  \\\n",
       "0        0.0        1.0       33.0       45.0        7.0        3.0   \n",
       "1        4.0        2.0       63.0       57.0       31.0        6.0   \n",
       "2        1.0        1.0      177.0      170.0        9.0        0.0   \n",
       "3        1.0        0.0       71.0       67.0        7.0        1.0   \n",
       "4        4.0        0.0       46.0       36.0        2.0        1.0   \n",
       "\n",
       "   p_N_q2c_5  p_N_q2d_1  p_N_q2d_2  p_N_q2d_3  p_N_q2d_4  p_N_q2d_5  \\\n",
       "0        0.0       46.0       37.0        1.0        2.0        0.0   \n",
       "1        4.0       80.0       73.0        5.0        0.0        2.0   \n",
       "2        0.0      230.0      118.0        2.0        0.0        1.0   \n",
       "3        0.0       92.0       52.0        2.0        1.0        1.0   \n",
       "4        2.0       52.0       30.0        2.0        1.0        0.0   \n",
       "\n",
       "   p_N_q2e_1  p_N_q2e_2  p_N_q2e_3  p_N_q2e_4  p_N_q2e_5  p_N_q2f_1  \\\n",
       "0       39.0       40.0        4.0        2.0        0.0       40.0   \n",
       "1       71.0       76.0        8.0        3.0        1.0       55.0   \n",
       "2      205.0      144.0        6.0        0.0        0.0      168.0   \n",
       "3       81.0       59.0        3.0        3.0        1.0       66.0   \n",
       "4       46.0       35.0        4.0        3.0        0.0       40.0   \n",
       "\n",
       "   p_N_q2f_2  p_N_q2f_3  p_N_q2f_4  p_N_q2f_5  p_N_q2g_1  p_N_q2g_2  \\\n",
       "0       41.0        4.0        0.0        3.0       38.0       30.0   \n",
       "1       65.0        2.0        2.0       34.0       63.0       59.0   \n",
       "2      150.0        4.0        3.0       25.0      161.0      163.0   \n",
       "3       67.0        5.0        1.0        6.0       64.0       59.0   \n",
       "4       33.0        3.0        1.0        7.0       38.0       35.0   \n",
       "\n",
       "   p_N_q2g_3  p_N_q2g_4  p_N_q2g_5  p_N_q2h_1  p_N_q2h_2  p_N_q2h_3  \\\n",
       "0       14.0        2.0        2.0       42.0       37.0        5.0   \n",
       "1       23.0        8.0        4.0       65.0       66.0        9.0   \n",
       "2       19.0        4.0        6.0      162.0      170.0       10.0   \n",
       "3       20.0        2.0        0.0       68.0       63.0        6.0   \n",
       "4        9.0        5.0        1.0       39.0       39.0        3.0   \n",
       "\n",
       "   p_N_q2h_4  p_N_q2h_5  p_N_q3a_1  p_N_q3a_2  p_N_q3a_3  p_N_q3a_4  \\\n",
       "0        1.0        1.0       32.0       31.0       14.0        7.0   \n",
       "1        5.0        8.0       55.0       47.0       34.0       19.0   \n",
       "2        2.0       10.0      154.0      106.0       64.0       24.0   \n",
       "3        2.0        4.0       63.0       47.0       17.0        8.0   \n",
       "4        2.0        2.0       39.0       25.0       19.0        4.0   \n",
       "\n",
       "   p_N_q3a_5  p_N_q3b_1  p_N_q3b_2  p_N_q3b_3  p_N_q3b_4  p_N_q3b_5  \\\n",
       "0        4.0       26.0       29.0       13.0       12.0        5.0   \n",
       "1        6.0       51.0       54.0       29.0       14.0       10.0   \n",
       "2        8.0      136.0      122.0       51.0       29.0       19.0   \n",
       "3        9.0       65.0       48.0       14.0        8.0       11.0   \n",
       "4        1.0       34.0       28.0       14.0        5.0        7.0   \n",
       "\n",
       "   p_N_q3c_1  p_N_q3c_2  p_N_q3c_3  p_N_q3c_4  p_N_q3c_5  p_N_q3d_1  \\\n",
       "0       38.0       27.0       13.0        7.0        1.0       29.0   \n",
       "1       68.0       62.0       18.0        8.0        3.0       56.0   \n",
       "2      144.0      137.0       44.0       16.0       11.0      131.0   \n",
       "3       68.0       48.0       10.0       11.0        7.0       56.0   \n",
       "4       50.0       25.0        8.0        2.0        2.0       40.0   \n",
       "\n",
       "   p_N_q3d_2  p_N_q3d_3  p_N_q3d_4  p_N_q3d_5  p_N_q4a_1  p_N_q4a_2  \\\n",
       "0       31.0       19.0        6.0        2.0       31.0       47.0   \n",
       "1       37.0       44.0       21.0        3.0       58.0       79.0   \n",
       "2      118.0       71.0       28.0        6.0      188.0      153.0   \n",
       "3       48.0       26.0       10.0        5.0       79.0       57.0   \n",
       "4       25.0       19.0        4.0        0.0       43.0       37.0   \n",
       "\n",
       "   p_N_q4a_3  p_N_q4a_4  p_N_q4a_5  p_N_q4b_1  p_N_q4b_2  p_N_q4b_3  \\\n",
       "0        4.0        2.0        5.0       29.0       43.0       12.0   \n",
       "1       17.0        2.0        5.0       62.0       80.0       14.0   \n",
       "2        8.0        2.0        8.0      181.0      165.0        5.0   \n",
       "3        3.0        0.0        5.0       81.0       58.0        5.0   \n",
       "4        3.0        3.0        2.0       38.0       42.0        6.0   \n",
       "\n",
       "   p_N_q4b_4  p_N_q4b_5  p_N_q4c_1  p_N_q4c_2  p_N_q4c_3  p_N_q4c_4  \\\n",
       "0        0.0        3.0       32.0       46.0        7.0        0.0   \n",
       "1        1.0        2.0       79.0       69.0        9.0        1.0   \n",
       "2        2.0        3.0      198.0      140.0       10.0        1.0   \n",
       "3        1.0        1.0       80.0       51.0        5.0        1.0   \n",
       "4        2.0        0.0       39.0       35.0        6.0        3.0   \n",
       "\n",
       "   p_N_q4c_5  p_N_q4d_1  p_N_q4d_2  p_N_q4d_3  p_N_q4d_4  p_N_q4d_5  \\\n",
       "0        3.0       28.0       38.0       11.0        1.0        5.0   \n",
       "1        3.0       56.0       77.0        6.0        4.0       16.0   \n",
       "2        6.0      178.0      146.0       11.0        1.0       14.0   \n",
       "3        4.0       73.0       55.0        9.0        1.0        5.0   \n",
       "4        4.0       37.0       39.0        3.0        5.0        4.0   \n",
       "\n",
       "   p_N_q4e_1  p_N_q4e_2  p_N_q4e_3  p_N_q4e_4  p_N_q4e_5  p_N_q5a  p_N_q5b  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN     31.0     16.0   \n",
       "1        NaN        NaN        NaN        NaN        NaN     62.0     97.0   \n",
       "2        NaN        NaN        NaN        NaN        NaN    216.0    113.0   \n",
       "3        NaN        NaN        NaN        NaN        NaN     34.0     93.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN     62.0     69.0   \n",
       "\n",
       "   p_N_q5c  p_N_q5d  p_N_q5e  p_N_q5f  p_N_q5g  p_N_q5h  p_N_q5i  p_N_q5j  \\\n",
       "0     19.0      5.0      0.0     33.0      6.0     47.0     12.0     31.0   \n",
       "1     20.0     12.0      4.0    100.0     17.0    104.0     26.0     52.0   \n",
       "2    180.0     40.0     44.0    192.0     52.0    234.0     63.0    103.0   \n",
       "3     25.0     10.0     35.0     64.0     10.0     64.0     34.0     32.0   \n",
       "4     22.0      7.0      1.0     31.0     20.0     56.0     27.0     20.0   \n",
       "\n",
       "   p_N_q6a_1  p_N_q6a_2  p_N_q6a_3  p_N_q6a_4  p_N_q6a_5  p_N_q6b_1  \\\n",
       "0       18.0       40.0       19.0        5.0        6.0       13.0   \n",
       "1       43.0       57.0       39.0        9.0       10.0       27.0   \n",
       "2      126.0      187.0       22.0        3.0       19.0       78.0   \n",
       "3       49.0       64.0       18.0        5.0        7.0       31.0   \n",
       "4       32.0       41.0       10.0        5.0        1.0       23.0   \n",
       "\n",
       "   p_N_q6b_2  p_N_q6b_3  p_N_q6b_4  p_N_q6b_5  p_N_q7a_1  p_N_q7a_2  \\\n",
       "0       31.0        4.0        0.0       33.0       35.0       49.0   \n",
       "1       41.0        2.0        0.0       82.0       64.0       75.0   \n",
       "2      104.0       16.0        3.0      129.0      197.0      152.0   \n",
       "3       41.0        5.0        1.0       56.0       72.0       67.0   \n",
       "4       27.0        0.0        4.0       28.0       40.0       45.0   \n",
       "\n",
       "   p_N_q7a_3  p_N_q7a_4  p_N_q7a_5  p_N_q7b_1  p_N_q7b_2  p_N_q7b_3  \\\n",
       "0        2.0        1.0        3.0       34.0       51.0        2.0   \n",
       "1       11.0        5.0        1.0       70.0       68.0       10.0   \n",
       "2        4.0        2.0        2.0      204.0      140.0        4.0   \n",
       "3        3.0        1.0        3.0       83.0       56.0        2.0   \n",
       "4        1.0        2.0        1.0       42.0       45.0        1.0   \n",
       "\n",
       "   p_N_q7b_4  p_N_q7b_5  p_N_q7c_1  p_N_q7c_2  p_N_q7c_3  p_N_q7c_4  \\\n",
       "0        0.0        2.0       28.0       44.0        4.0        5.0   \n",
       "1        5.0        4.0       52.0       67.0       17.0        5.0   \n",
       "2        2.0        0.0      176.0      148.0        4.0        4.0   \n",
       "3        1.0        0.0       72.0       53.0        7.0        3.0   \n",
       "4        0.0        0.0       36.0       43.0        3.0        3.0   \n",
       "\n",
       "   p_N_q7c_5  p_N_q7d_1  p_N_q7d_2  p_N_q7d_3  p_N_q7d_4  p_N_q7d_5  \\\n",
       "0        5.0       31.0       36.0        3.0        1.0        1.0   \n",
       "1       16.0       49.0       43.0       13.0        1.0       10.0   \n",
       "2       17.0      150.0      117.0        5.0        1.0       11.0   \n",
       "3        7.0       60.0       48.0        2.0        1.0        5.0   \n",
       "4        2.0       34.0       27.0        2.0        3.0        4.0   \n",
       "\n",
       "   p_N_q8a_1  p_N_q8a_2  p_N_q8a_3  p_N_q8a_4  p_N_q8a_5  p_N_q8b_1  \\\n",
       "0       27.0       27.0        9.0        8.0       14.0       53.0   \n",
       "1       39.0       53.0       10.0       18.0       36.0       92.0   \n",
       "2      107.0      113.0       25.0        6.0       77.0      229.0   \n",
       "3       43.0       53.0        9.0        6.0       32.0       88.0   \n",
       "4       15.0       35.0       10.0        4.0       23.0       58.0   \n",
       "\n",
       "   p_N_q8b_2  p_N_q8b_3  p_N_q8b_4  p_N_q8b_5  p_N_q8c_1  p_N_q8c_2  \\\n",
       "0       15.0        2.0        3.0       14.0       60.0        4.0   \n",
       "1       20.0        5.0        4.0       36.0      106.0        7.0   \n",
       "2       34.0        9.0        4.0       45.0      238.0        9.0   \n",
       "3       18.0        2.0        2.0       30.0       95.0        7.0   \n",
       "4        7.0        3.0        4.0       12.0       70.0        3.0   \n",
       "\n",
       "   p_N_q8c_3  p_N_q8c_4  p_N_q8c_5  p_N_q8d_1  p_N_q8d_2  p_N_q8d_3  \\\n",
       "0        1.0        0.0       20.0       52.0       12.0        1.0   \n",
       "1        5.0        1.0       35.0       86.0       13.0        4.0   \n",
       "2        8.0        7.0       60.0      222.0       28.0        7.0   \n",
       "3        3.0        2.0       32.0       94.0       13.0        4.0   \n",
       "4        3.0        0.0       11.0       57.0       11.0        1.0   \n",
       "\n",
       "   p_N_q8d_4  p_N_q8d_5  p_N_q8e_1  p_N_q8e_2  p_N_q8e_3  p_N_q8e_4  \\\n",
       "0        4.0       18.0       70.0        2.0        0.0        0.0   \n",
       "1        7.0       45.0      110.0        0.0        0.0        0.0   \n",
       "2        7.0       59.0      274.0        2.0        3.0        4.0   \n",
       "3        3.0       27.0      103.0        2.0        2.0        0.0   \n",
       "4        0.0       19.0       74.0        0.0        0.0        0.0   \n",
       "\n",
       "   p_N_q8e_5  p_N_q8f_1  p_N_q8f_2  p_N_q8f_3  p_N_q8f_4  p_N_q8f_5  p_N_q9_1  \\\n",
       "0       13.0       70.0        3.0        0.0        0.0       11.0       5.0   \n",
       "1       43.0      106.0        1.0        1.0        0.0       46.0      11.0   \n",
       "2       38.0      265.0        6.0        2.0        6.0       46.0       7.0   \n",
       "3       33.0      105.0        2.0        2.0        1.0       33.0       6.0   \n",
       "4       13.0       74.0        0.0        0.0        0.0       14.0       3.0   \n",
       "\n",
       "   p_N_q9_2  p_N_q9_3  p_N_q9_4  p_N_q9_5  p_N_q9_6  p_N_q9_7  p_N_q9_8  \\\n",
       "0      12.0      11.0       6.0       3.0       4.0       3.0       3.0   \n",
       "1      16.0      21.0      15.0      11.0       9.0       2.0      25.0   \n",
       "2      37.0      50.0      32.0      23.0      12.0      11.0      59.0   \n",
       "3       9.0      18.0      18.0      17.0       8.0       3.0      17.0   \n",
       "4      11.0      15.0       2.0       4.0       2.0       6.0      10.0   \n",
       "\n",
       "   p_N_q9_9  p_N_q9_10  p_N_q10a  p_N_q10b  p_N_q10c  p_N_q10d  p_N_q10e  \\\n",
       "0       5.0        0.0      56.0      30.0       6.0      49.0       3.0   \n",
       "1      12.0        1.0      76.0     105.0      22.0      99.0      23.0   \n",
       "2      26.0        2.0     176.0     173.0      40.0     219.0      43.0   \n",
       "3       8.0        0.0      95.0      74.0      19.0      79.0      17.0   \n",
       "4       9.0        0.0      45.0      54.0      13.0      57.0      14.0   \n",
       "\n",
       "   p_N_q10f  p_N_q10g  p_N_q10h  p_N_q10i  p_N_q10j  p_N_q10k  p_N_q10l  \\\n",
       "0       2.0      56.0      11.0       6.0      33.0       1.0       1.0   \n",
       "1      16.0      75.0      33.0      18.0      83.0       4.0       2.0   \n",
       "2      36.0     210.0      65.0      48.0     203.0      16.0      13.0   \n",
       "3      15.0     102.0      42.0      16.0      74.0       4.0       5.0   \n",
       "4      13.0      63.0      24.0      13.0      59.0       3.0       0.0   \n",
       "\n",
       "   p_N_q11a_1  p_N_q11a_2  p_N_q11a_3  p_N_q11a_4  p_N_q11b_1  p_N_q11b_2  \\\n",
       "0        50.0        36.0         3.0         1.0        33.0        42.0   \n",
       "1        89.0        59.0         7.0         2.0        66.0        72.0   \n",
       "2       246.0       105.0         6.0         0.0       193.0       147.0   \n",
       "3        97.0        45.0         5.0         0.0        78.0        56.0   \n",
       "4        49.0        34.0         5.0         2.0        40.0        38.0   \n",
       "\n",
       "   p_N_q11b_3  p_N_q11b_4  p_N_q11c_1  p_N_q11c_2  p_N_q11c_3  p_N_q11c_4  \\\n",
       "0        10.0         1.0        38.0        38.0        11.0         2.0   \n",
       "1        16.0         3.0        62.0        74.0        19.0         3.0   \n",
       "2        12.0         1.0       195.0       146.0         6.0         3.0   \n",
       "3         9.0         0.0        79.0        54.0         7.0         0.0   \n",
       "4         8.0         4.0        43.0        36.0         8.0         2.0   \n",
       "\n",
       "   p_N_q11d_1  p_N_q11d_2  p_N_q11d_3  p_N_q11d_4  p_N_q11e_1  p_N_q11e_2  \\\n",
       "0        37.0        42.0         9.0         1.0        38.0        41.0   \n",
       "1        68.0        70.0        20.0         1.0        69.0        75.0   \n",
       "2       201.0       140.0         9.0         1.0       191.0       149.0   \n",
       "3        75.0        55.0        10.0         0.0        73.0        64.0   \n",
       "4        44.0        35.0         9.0         1.0        41.0        42.0   \n",
       "\n",
       "   p_N_q11e_3  p_N_q11e_4  p_N_q11f_1  p_N_q11f_2  p_N_q11f_3  p_N_q11f_4  \\\n",
       "0         8.0         2.0        41.0        38.0         5.0         4.0   \n",
       "1        13.0         1.0        70.0        72.0        15.0         2.0   \n",
       "2         4.0         1.0       202.0       140.0         5.0         0.0   \n",
       "3         4.0         0.0        82.0        54.0         6.0         0.0   \n",
       "4         6.0         1.0        44.0        33.0         8.0         4.0   \n",
       "\n",
       "   p_N_q12aa_1  p_N_q12aa_2  p_N_q12aa_3  p_N_q12aa_4  p_N_q12aa_5  \\\n",
       "0         12.0         27.0         13.0          2.0         14.0   \n",
       "1         21.0         49.0         25.0          6.0         36.0   \n",
       "2         70.0        118.0         37.0         12.0         55.0   \n",
       "3         35.0         53.0         11.0          2.0         15.0   \n",
       "4          7.0         30.0         12.0          3.0         11.0   \n",
       "\n",
       "   p_N_q12ab_1  p_N_q12ab_2  p_N_q12ab_3  p_N_q12ab_4  p_N_q12ab_5  \\\n",
       "0         14.0         21.0          7.0          2.0         19.0   \n",
       "1         20.0         49.0         15.0          8.0         45.0   \n",
       "2         58.0        130.0         29.0          5.0         65.0   \n",
       "3         31.0         53.0          7.0          2.0         19.0   \n",
       "4          6.0         29.0          9.0          1.0         14.0   \n",
       "\n",
       "   p_N_q12ac_1  p_N_q12ac_2  p_N_q12ac_3  p_N_q12ac_4  p_N_q12ac_5  \\\n",
       "0         13.0         31.0          6.0          2.0         13.0   \n",
       "1         24.0         55.0         19.0          5.0         34.0   \n",
       "2         65.0        149.0         26.0          4.0         45.0   \n",
       "3         34.0         53.0          7.0          4.0         11.0   \n",
       "4          5.0         32.0          8.0          4.0         10.0   \n",
       "\n",
       "   p_N_q12ad_1  p_N_q12ad_2  p_N_q12ad_3  p_N_q12ad_4  p_N_q12ad_5  \\\n",
       "0         14.0         29.0          4.0          2.0         13.0   \n",
       "1         29.0         50.0         23.0          4.0         31.0   \n",
       "2         78.0        141.0         26.0          4.0         40.0   \n",
       "3         39.0         48.0          7.0          2.0         12.0   \n",
       "4          9.0         35.0          6.0          4.0          9.0   \n",
       "\n",
       "   p_N_q12ba_1  p_N_q12ba_2  p_N_q12ba_3  p_N_q12ba_4  p_N_q12ba_5  \\\n",
       "0         12.0         19.0          9.0          4.0         20.0   \n",
       "1         16.0         37.0         30.0          8.0         42.0   \n",
       "2         58.0        118.0         24.0         21.0         60.0   \n",
       "3         22.0         40.0         15.0          5.0         23.0   \n",
       "4          5.0         17.0         15.0          3.0         19.0   \n",
       "\n",
       "   p_N_q12bb_1  p_N_q12bb_2  p_N_q12bb_3  p_N_q12bb_4  p_N_q12bb_5  \\\n",
       "0         10.0         20.0          7.0          4.0         20.0   \n",
       "1         14.0         37.0         26.0         10.0         45.0   \n",
       "2         42.0        122.0         26.0         14.0         69.0   \n",
       "3         19.0         38.0         13.0          6.0         24.0   \n",
       "4          4.0         16.0         13.0          3.0         20.0   \n",
       "\n",
       "   p_N_q12bc_1  p_N_q12bc_2  p_N_q12bc_3  p_N_q12bc_4  p_N_q12bc_5  \\\n",
       "0         11.0         22.0          6.0          4.0         19.0   \n",
       "1         15.0         45.0         23.0          7.0         43.0   \n",
       "2         50.0        123.0         29.0         14.0         59.0   \n",
       "3         22.0         41.0         11.0          5.0         20.0   \n",
       "4          4.0         16.0         12.0          4.0         20.0   \n",
       "\n",
       "   p_N_q12bd_1  p_N_q12bd_2  p_N_q12bd_3  p_N_q12bd_4  p_N_q12bd_5  t_q6d  \\\n",
       "0         12.0         21.0          6.0          5.0         18.0    8.3   \n",
       "1         19.0         37.0         27.0          7.0         41.0    9.5   \n",
       "2         49.0        129.0         25.0         13.0         57.0    7.3   \n",
       "3         22.0         39.0         12.0          5.0         20.0    8.3   \n",
       "4          4.0         17.0         12.0          4.0         18.0    8.3   \n",
       "\n",
       "   t_q6e  t_q6f  t_q11a  t_q11b  t_q11c  t_q11d  t_q11e  t_q11f  t_q11g  \\\n",
       "0    7.9    8.1     6.0     6.3     7.9     6.8     5.2     8.1     6.5   \n",
       "1    9.1    9.2     8.0     8.5     9.0     8.1     7.5     9.0     7.1   \n",
       "2    6.2    6.9     6.3     5.9     8.1     8.9     6.5     7.7     7.8   \n",
       "3    7.0    7.7     5.3     5.5     7.2     7.5     5.9     7.4     5.2   \n",
       "4    8.4    8.6     8.0     8.8     8.9     8.9     6.5     8.8     7.4   \n",
       "\n",
       "   t_q11h  t_q11j  t_q11k  t_q11l  t_q11m  t_q11n  t_q11o  t_q1a  t_q1b  \\\n",
       "0     6.8     9.3     7.6     7.5     8.4     7.8     8.6    8.6    8.6   \n",
       "1     7.2     9.8     8.8     8.4     9.8     8.9     8.9    9.3    9.4   \n",
       "2     7.5     9.2     7.9     6.6     9.1     9.6     8.1    6.4    6.4   \n",
       "3     5.7     8.2     7.8     6.8     8.6     8.5     6.5    6.7    6.7   \n",
       "4     7.6     9.7     9.2     8.9     9.8     8.6     6.4    7.7    7.7   \n",
       "\n",
       "   t_q1c  t_q1f  t_q1g  t_q6h  t_q8c  t_q10b  t_q10d  t_q10e  t_q4  t_q5a  \\\n",
       "0    8.3    7.8    7.9    7.3    7.5     7.0     7.3     7.4   5.9    8.4   \n",
       "1    8.4    8.8    8.5    8.6    8.9     8.4     7.3     7.2   7.9    9.1   \n",
       "2    6.3    6.6    6.1    6.2    6.3     6.2     6.0     6.9   7.2    6.2   \n",
       "3    5.4    6.2    6.0    5.7    6.0     6.2     6.5     6.3   6.8    5.7   \n",
       "4    7.0    6.1    5.7    6.0    8.5     8.1     8.3     8.1   7.8    7.5   \n",
       "\n",
       "   t_q5b  t_q5c  t_q6b  t_q6c  t_q7a  t_q7d  t_q7e  t_q8a  t_q8b  t_q11i  \\\n",
       "0    NaN    8.4    8.9    8.7    7.3    6.3    6.8    7.0    7.0     8.6   \n",
       "1    9.6    9.5    8.6    8.8    8.8    8.8    8.6    8.8    9.0     9.2   \n",
       "2    6.0    7.7    6.4    8.0    5.9    6.2    6.7    6.4    6.3     8.0   \n",
       "3    6.3    8.5    6.7    7.2    7.2    6.9    7.3    5.6    5.6     7.2   \n",
       "4    6.7    8.9    7.1    7.8    7.7    8.5    7.5    7.1    8.0     8.8   \n",
       "\n",
       "   t_q1d  t_q1e  t_q2a  t_q2b  t_q2c  t_q2d  t_q2e  t_q2f  t_q3_1  t_q3_2  \\\n",
       "0    8.0    7.9    7.8    7.5    8.1    7.5    8.3    NaN     8.8     6.7   \n",
       "1    9.5    8.7    9.5    9.7    9.6    9.2    9.2    NaN     9.8     5.8   \n",
       "2    7.0    6.7    7.8    8.5    7.9    7.4    7.2    NaN     9.9     6.8   \n",
       "3    8.0    6.6    7.7    8.6    8.1    8.3    7.8    NaN    10.0     8.6   \n",
       "4    7.9    7.1    9.3    9.4    9.2    8.6    8.4    NaN     9.8     7.1   \n",
       "\n",
       "   t_q6a  t_q6g  t_q6i  t_q6j  t_q6k  t_q6l  t_q7b  t_q7c  t_q6m  t_q9  \\\n",
       "0    7.7    8.1    9.2    8.2    9.2    9.2    6.2    6.8    NaN   NaN   \n",
       "1    8.6    9.2    9.3    9.6    9.6    9.8    8.2    8.8    NaN   NaN   \n",
       "2    6.2    7.5    7.1    7.5    7.8    7.9    6.8    7.3    NaN   NaN   \n",
       "3    6.7    6.7    7.6    7.0    8.1    8.2    6.9    7.9    NaN   NaN   \n",
       "4    5.9    7.8    7.5    7.9    7.6    9.0    7.5    8.2    NaN   NaN   \n",
       "\n",
       "   t_q10a  t_q10c  t_q12a  t_q12b  t_q12c  t_q12d  t_q13a  t_q13b  t_q13c  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   t_q13d  t_q14  t_q15a  t_q15b  t_q15c  t_q15d  t_q15e  t_q15f  t_q1a_1  \\\n",
       "0     NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN     62.0   \n",
       "1     NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN     85.0   \n",
       "2     NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN     33.0   \n",
       "3     NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN     31.0   \n",
       "4     NaN    NaN     NaN     NaN     NaN     NaN     NaN     NaN     41.0   \n",
       "\n",
       "   t_q1a_2  t_q1a_3  t_q1a_4  t_q1b_1  t_q1b_2  t_q1b_3  t_q1b_4  t_q1c_1  \\\n",
       "0     33.0      5.0      0.0     62.0     33.0      5.0      0.0     62.0   \n",
       "1     12.0      0.0      3.0     91.0      3.0      3.0      3.0     68.0   \n",
       "2     43.0      7.0     17.0     36.0     36.0     14.0     14.0     29.0   \n",
       "3     48.0     10.0     10.0     31.0     45.0     17.0      7.0     21.0   \n",
       "4     55.0      0.0      5.0     36.0     59.0      5.0      0.0     30.0   \n",
       "\n",
       "   t_q1c_2  t_q1c_3  t_q1c_4  t_q1d_1  t_q1d_2  t_q1d_3  t_q1d_4  t_q1e_1  \\\n",
       "0     24.0     14.0      0.0     40.0     60.0      0.0      0.0     48.0   \n",
       "1     24.0      3.0      6.0     85.0     15.0      0.0      0.0     79.0   \n",
       "2     45.0     12.0     14.0     31.0     54.0     10.0      5.0     33.0   \n",
       "3     41.0     17.0     21.0     39.0     61.0      0.0      0.0     31.0   \n",
       "4     52.0     13.0      4.0     48.0     43.0     10.0      0.0     30.0   \n",
       "\n",
       "   t_q1e_2  t_q1e_3  t_q1e_4  t_q1f_1  t_q1f_2  t_q1f_3  t_q1f_4  t_q1g_1  \\\n",
       "0     43.0     10.0      0.0     43.0     48.0     10.0      0.0     48.0   \n",
       "1     12.0      0.0      9.0     79.0     12.0      3.0      6.0     68.0   \n",
       "2     43.0     14.0     10.0     40.0     33.0     10.0     17.0     38.0   \n",
       "3     41.0     21.0      7.0     28.0     45.0     14.0     14.0     24.0   \n",
       "4     57.0      9.0      4.0     30.0     39.0     13.0     17.0     22.0   \n",
       "\n",
       "   t_q1g_2  t_q1g_3  t_q1g_4  t_q2a_1  t_q2a_2  t_q2a_3  t_q2a_4  t_q2b_1  \\\n",
       "0     43.0     10.0      0.0     38.0     57.0      5.0      0.0     29.0   \n",
       "1     26.0      0.0      6.0     88.0      9.0      3.0      0.0     91.0   \n",
       "2     24.0     21.0     17.0     48.0     40.0     10.0      2.0     56.0   \n",
       "3     52.0      3.0     21.0     41.0     48.0     10.0      0.0     61.0   \n",
       "4     43.0     17.0     17.0     78.0     22.0      0.0      0.0     81.0   \n",
       "\n",
       "   t_q2b_2  t_q2b_3  t_q2b_4  t_q2c_1  t_q2c_2  t_q2c_3  t_q2c_4  t_q2d_1  \\\n",
       "0     67.0      5.0      0.0     43.0     57.0      0.0      0.0     38.0   \n",
       "1      9.0      0.0      0.0     91.0      6.0      3.0      0.0     82.0   \n",
       "2     44.0      0.0      0.0     44.0     51.0      5.0      0.0     29.0   \n",
       "3     36.0      4.0      0.0     54.0     36.0     11.0      0.0     55.0   \n",
       "4     19.0      0.0      0.0     76.0     24.0      0.0      0.0     65.0   \n",
       "\n",
       "   t_q2d_2  t_q2d_3  t_q2d_4  t_q2e_1  t_q2e_2  t_q2e_3  t_q2e_4  t_q2f_1  \\\n",
       "0     48.0     14.0      0.0     52.0     43.0      5.0      0.0      NaN   \n",
       "1     15.0      0.0      3.0     85.0      9.0      3.0      3.0      NaN   \n",
       "2     64.0      7.0      0.0     32.0     54.0     15.0      0.0      NaN   \n",
       "3     38.0      7.0      0.0     48.0     38.0     14.0      0.0      NaN   \n",
       "4     26.0      9.0      0.0     61.0     30.0      9.0      0.0      NaN   \n",
       "\n",
       "   t_q2f_2  t_q2f_3  t_q2f_4  t_q3a_1  t_q3a_2  t_q3a_3  t_q3b_1  t_q3b_2  \\\n",
       "0      NaN      NaN      NaN     45.0     27.0     18.0      5.0     14.0   \n",
       "1      NaN      NaN      NaN      9.0     65.0     24.0     88.0      6.0   \n",
       "2      NaN      NaN      NaN     90.0      7.0      0.0      2.0     19.0   \n",
       "3      NaN      NaN      NaN      3.0     34.0     55.0     97.0     38.0   \n",
       "4      NaN      NaN      NaN     83.0      0.0      9.0     87.0      4.0   \n",
       "\n",
       "   t_q3b_3  t_q3c_1  t_q3c_2  t_q3c_3  t_q3d_1  t_q3d_2  t_q3d_3  t_q3e_1  \\\n",
       "0     59.0     36.0     27.0     23.0     27.0     32.0     27.0      0.0   \n",
       "1      3.0      6.0     18.0     74.0     47.0      0.0     47.0      0.0   \n",
       "2     71.0     55.0     31.0     17.0     10.0     21.0     62.0     26.0   \n",
       "3      0.0     59.0     41.0     17.0     14.0     24.0     52.0     90.0   \n",
       "4      0.0     48.0     35.0     13.0      0.0      4.0     83.0      0.0   \n",
       "\n",
       "   t_q3e_2  t_q3e_3  t_q3f_1  t_q3f_2  t_q3f_3  t_q3g_1  t_q3g_2  t_q3g_3  \\\n",
       "0      0.0     82.0     50.0     14.0     32.0     27.0      9.0     59.0   \n",
       "1      0.0     97.0     94.0      3.0      0.0     59.0      0.0     38.0   \n",
       "2      5.0     64.0     90.0      2.0      0.0     10.0     10.0     74.0   \n",
       "3      0.0      7.0     90.0     28.0      3.0     24.0      7.0     62.0   \n",
       "4      0.0     87.0     17.0     48.0     26.0     61.0      0.0     30.0   \n",
       "\n",
       "   t_q3h_1  t_q3h_2  t_q3h_3  t_q3i_1  t_q3i_2  t_q3i_3  t_q3j_1  t_q3j_2  \\\n",
       "0     95.0      0.0      0.0      0.0     32.0     59.0     50.0     59.0   \n",
       "1     97.0      0.0      0.0      3.0     21.0     74.0     56.0     47.0   \n",
       "2     90.0      5.0      0.0     10.0     76.0     12.0     33.0     62.0   \n",
       "3     97.0     17.0      0.0     24.0     79.0      0.0     48.0     79.0   \n",
       "4     91.0      0.0      0.0      9.0     70.0     13.0     30.0     57.0   \n",
       "\n",
       "   t_q3j_3  t_q4_1  t_q4_2  t_q4_3  t_q4_4  t_q5a_1  t_q5a_2  t_q5a_3  \\\n",
       "0      0.0    10.0    57.0    33.0     0.0     67.0     19.0     14.0   \n",
       "1      6.0    48.0    42.0     6.0     3.0     85.0      6.0      6.0   \n",
       "2      7.0    26.0    64.0    10.0     0.0     31.0     40.0     12.0   \n",
       "3      3.0    30.0    48.0    19.0     4.0     21.0     45.0     21.0   \n",
       "4      9.0    50.0    40.0     5.0     5.0     50.0     27.0      9.0   \n",
       "\n",
       "   t_q5a_4  t_q5a_5  t_q5b_1  t_q5b_2  t_q5b_3  t_q5b_4  t_q5b_5  t_q5c_1  \\\n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0    100.0     62.0   \n",
       "1      3.0      0.0     91.0      6.0      3.0      0.0      0.0     88.0   \n",
       "2     17.0      0.0     26.0     40.0     19.0     14.0      0.0     38.0   \n",
       "3     14.0      0.0     31.0     38.0     21.0     10.0      0.0     66.0   \n",
       "4      9.0      5.0      0.0      4.0      0.0      0.0     96.0     77.0   \n",
       "\n",
       "   t_q5c_2  t_q5c_3  t_q5c_4  t_q5c_5  t_q6a_1  t_q6a_2  t_q6a_3  t_q6a_4  \\\n",
       "0     29.0     10.0      0.0      0.0     35.0     60.0      5.0      0.0   \n",
       "1      9.0      3.0      0.0      0.0     71.0     21.0      6.0      3.0   \n",
       "2     52.0      7.0      0.0      2.0     31.0     40.0     12.0     17.0   \n",
       "3     24.0     10.0      0.0      0.0     28.0     52.0     14.0      7.0   \n",
       "4     14.0      9.0      0.0      0.0     17.0     52.0     22.0      9.0   \n",
       "\n",
       "   t_q6b_1  t_q6b_2  t_q6b_3  t_q6b_4  t_q6c_1  t_q6c_2  t_q6c_3  t_q6c_4  \\\n",
       "0     67.0     33.0      0.0      0.0     67.0     29.0      5.0      0.0   \n",
       "1     74.0     15.0      9.0      3.0     74.0     21.0      3.0      3.0   \n",
       "2     33.0     38.0     17.0     12.0     52.0     40.0      2.0      5.0   \n",
       "3     31.0     48.0     10.0     10.0     45.0     41.0      0.0     14.0   \n",
       "4     30.0     57.0      9.0      4.0     43.0     48.0      9.0      0.0   \n",
       "\n",
       "   t_q6d_1  t_q6d_2  t_q6d_3  t_q6d_4  t_q6e_1  t_q6e_2  t_q6e_3  t_q6e_4  \\\n",
       "0     52.0     43.0      5.0      0.0     37.0     63.0      0.0      0.0   \n",
       "1     85.0     15.0      0.0      0.0     76.0     21.0      3.0      0.0   \n",
       "2     31.0     57.0     12.0      0.0     17.0     55.0     26.0      2.0   \n",
       "3     52.0     45.0      3.0      0.0     34.0     48.0     10.0      7.0   \n",
       "4     52.0     43.0      4.0      0.0     61.0     35.0      0.0      4.0   \n",
       "\n",
       "   t_q6f_1  t_q6f_2  t_q6f_3  t_q6f_4  t_q6g_1  t_q6g_2  t_q6g_3  t_q6g_4  \\\n",
       "0     47.0     47.0      5.0      0.0     48.0     48.0      5.0      0.0   \n",
       "1     79.0     18.0      3.0      0.0     79.0     18.0      3.0      0.0   \n",
       "2     31.0     48.0     19.0      2.0     38.0     51.0      8.0      3.0   \n",
       "3     45.0     45.0      7.0      3.0     14.0     75.0      7.0      4.0   \n",
       "4     61.0     35.0      4.0      0.0     52.0     29.0     19.0      0.0   \n",
       "\n",
       "   t_q6h_1  t_q6h_2  t_q6h_3  t_q6h_4  t_q6i_1  t_q6i_2  t_q6i_3  t_q6i_4  \\\n",
       "0     33.0     52.0     14.0      0.0     76.0     24.0      0.0      0.0   \n",
       "1     70.0     24.0      0.0      6.0     88.0      6.0      3.0      3.0   \n",
       "2     28.0     38.0     26.0      8.0     38.0     41.0     15.0      5.0   \n",
       "3     14.0     54.0     21.0     11.0     43.0     43.0     14.0      0.0   \n",
       "4     24.0     43.0     24.0     10.0     38.0     52.0      5.0      5.0   \n",
       "\n",
       "   t_q6j_1  t_q6j_2  t_q6j_3  t_q6j_4  t_q6k_1  t_q6k_2  t_q6k_3  t_q6k_4  \\\n",
       "0     45.0     55.0      0.0      0.0     80.0     15.0      5.0      0.0   \n",
       "1     91.0      6.0      3.0      0.0     91.0      6.0      3.0      0.0   \n",
       "2     49.0     38.0      3.0     10.0     33.0     67.0      0.0      0.0   \n",
       "3     36.0     46.0     11.0      7.0     54.0     36.0     11.0      0.0   \n",
       "4     48.0     48.0      0.0      5.0     52.0     33.0      5.0     10.0   \n",
       "\n",
       "   t_q6l_1  t_q6l_2  t_q6l_3  t_q6l_4  t_q6m_1  t_q6m_2  t_q6m_3  t_q6m_4  \\\n",
       "0     75.0     25.0      0.0      0.0     62.0     38.0      0.0      0.0   \n",
       "1     94.0      6.0      0.0      0.0     85.0      9.0      6.0      0.0   \n",
       "2     38.0     59.0      3.0      0.0     28.0     59.0     13.0      0.0   \n",
       "3     46.0     54.0      0.0      0.0     43.0     46.0     11.0      0.0   \n",
       "4     71.0     29.0      0.0      0.0     71.0     29.0      0.0      0.0   \n",
       "\n",
       "   t_q7a_1  t_q7a_2  t_q7a_3  t_q7a_4  t_q7a_5  t_q7b_1  t_q7b_2  t_q7b_3  \\\n",
       "0     33.0     57.0      5.0      5.0      0.0     24.0     43.0     29.0   \n",
       "1     70.0     21.0      3.0      3.0      3.0     67.0     15.0      9.0   \n",
       "2     16.0     50.0     29.0      5.0      0.0     26.0     58.0      5.0   \n",
       "3     39.0     39.0     11.0      7.0      4.0     32.0     46.0     18.0   \n",
       "4     50.0     35.0     10.0      5.0      0.0     40.0     45.0     15.0   \n",
       "\n",
       "   t_q7b_4  t_q7b_5  t_q7c_1  t_q7c_2  t_q7c_3  t_q7c_4  t_q7c_5  t_q7d_1  \\\n",
       "0      5.0      0.0     24.0     57.0     19.0      0.0      0.0     14.0   \n",
       "1      6.0      3.0     73.0     18.0      0.0      6.0      3.0     67.0   \n",
       "2      8.0      3.0     29.0     61.0      5.0      3.0      3.0     18.0   \n",
       "3      4.0      0.0     43.0     50.0      7.0      0.0      0.0     39.0   \n",
       "4      0.0      0.0     55.0     35.0     10.0      0.0      0.0     60.0   \n",
       "\n",
       "   t_q7d_2  t_q7d_3  t_q7d_4  t_q7d_5  t_q7e_1  t_q7e_2  t_q7e_3  t_q7e_4  \\\n",
       "0     57.0     24.0      0.0      5.0     19.0     71.0      5.0      5.0   \n",
       "1     24.0      6.0      0.0      3.0     67.0     21.0      9.0      0.0   \n",
       "2     53.0     26.0      3.0      0.0     24.0     55.0     18.0      3.0   \n",
       "3     36.0     11.0     11.0      4.0     36.0     43.0     11.0      4.0   \n",
       "4     35.0      5.0      0.0      0.0     45.0     40.0     10.0      5.0   \n",
       "\n",
       "   t_q7e_5  t_q8a_1  t_q8a_2  t_q8a_3  t_q8a_4  t_q8b_1  t_q8b_2  t_q8b_3  \\\n",
       "0      0.0     30.0     50.0     20.0      0.0     20.0     70.0     10.0   \n",
       "1      3.0     68.0     29.0      3.0      0.0     74.0     24.0      3.0   \n",
       "2      0.0     23.0     52.0     20.0      5.0     23.0     52.0     18.0   \n",
       "3      7.0     10.0     48.0     41.0      0.0     14.0     46.0     32.0   \n",
       "4      0.0     32.0     50.0     18.0      0.0     45.0     50.0      5.0   \n",
       "\n",
       "   t_q8b_4  t_q8c_1  t_q8c_2  t_q8c_3  t_q8c_4  t_q9_1  t_q9_2  t_q9_3  \\\n",
       "0      0.0     29.0     67.0      5.0      0.0     5.0    14.0    52.0   \n",
       "1      0.0     74.0     21.0      6.0      0.0     3.0     6.0     3.0   \n",
       "2      8.0     33.0     35.0     20.0     13.0     3.0     5.0    16.0   \n",
       "3      7.0     21.0     45.0     28.0      7.0     0.0    18.0    32.0   \n",
       "4      0.0     59.0     36.0      5.0      0.0    10.0     5.0    10.0   \n",
       "\n",
       "   t_q9_4  t_q9_5  t_q10a_1  t_q10a_2  t_q10a_3  t_q10a_4  t_q10a_5  t_q10a_6  \\\n",
       "0    24.0     5.0      62.0      14.0      14.0       5.0       0.0       5.0   \n",
       "1    78.0     9.0      74.0      21.0       3.0       0.0       0.0       3.0   \n",
       "2    70.0     5.0      48.0      18.0      20.0      10.0       0.0       5.0   \n",
       "3    39.0    11.0      41.0      28.0      21.0       7.0       0.0       3.0   \n",
       "4    60.0    15.0      50.0      27.0      14.0       0.0       5.0       5.0   \n",
       "\n",
       "   t_q10b_1  t_q10b_2  t_q10b_3  t_q10b_4  t_q10b_5  t_q10b_6  t_q10c_1  \\\n",
       "0      19.0      10.0      43.0      19.0       0.0      10.0      29.0   \n",
       "1      24.0      32.0      35.0       6.0       0.0       3.0      38.0   \n",
       "2      18.0      15.0      23.0      43.0       3.0       0.0      30.0   \n",
       "3       7.0      14.0      45.0      34.0       0.0       0.0      24.0   \n",
       "4      23.0      27.0      36.0       9.0       0.0       5.0      50.0   \n",
       "\n",
       "   t_q10c_2  t_q10c_3  t_q10c_4  t_q10c_5  t_q10c_6  t_q10d_1  t_q10d_2  \\\n",
       "0      24.0      24.0      14.0       5.0       5.0      14.0      19.0   \n",
       "1      18.0      32.0       6.0       0.0       6.0      21.0      12.0   \n",
       "2      28.0      23.0      15.0       5.0       0.0       5.0      23.0   \n",
       "3      28.0      38.0       3.0       7.0       0.0       7.0      14.0   \n",
       "4      23.0      18.0       0.0       9.0       0.0      14.0      36.0   \n",
       "\n",
       "   t_q10d_3  t_q10d_4  t_q10d_5  t_q10d_6  t_q10e_1  t_q10e_2  t_q10e_3  \\\n",
       "0      48.0      14.0       0.0       5.0       5.0      33.0      38.0   \n",
       "1      47.0      15.0       0.0       6.0      21.0      12.0      44.0   \n",
       "2      23.0      30.0       8.0      13.0      13.0      23.0      30.0   \n",
       "3      55.0      10.0       7.0       7.0       7.0      17.0      41.0   \n",
       "4      41.0       5.0       0.0       5.0      32.0      27.0      23.0   \n",
       "\n",
       "   t_q10e_4  t_q10e_5  t_q10e_6  t_q11a_1  t_q11a_2  t_q11a_3  t_q11a_4  \\\n",
       "0      10.0       5.0      10.0       5.0      70.0      25.0       0.0   \n",
       "1      12.0       3.0       9.0      50.0      41.0       9.0       0.0   \n",
       "2      23.0       3.0      10.0      25.0      50.0      15.0      10.0   \n",
       "3      28.0       3.0       3.0       7.0      48.0      41.0       3.0   \n",
       "4       9.0       5.0       5.0      59.0      23.0      18.0       0.0   \n",
       "\n",
       "   t_q11b_1  t_q11b_2  t_q11b_3  t_q11b_4  t_q11c_1  t_q11c_2  t_q11c_3  \\\n",
       "0      14.0      62.0      24.0       0.0      43.0      52.0       5.0   \n",
       "1      64.0      30.0       3.0       3.0      74.0      24.0       3.0   \n",
       "2      19.0      49.0      22.0      11.0      51.0      41.0       8.0   \n",
       "3      21.0      25.0      50.0       4.0      31.0      59.0       7.0   \n",
       "4      63.0      37.0       0.0       0.0      73.0      23.0       5.0   \n",
       "\n",
       "   t_q11c_4  t_q11d_1  t_q11d_2  t_q11d_3  t_q11d_4  t_q11e_1  t_q11e_2  \\\n",
       "0       0.0       0.0      19.0      57.0      24.0       0.0      52.0   \n",
       "1       0.0       3.0       6.0      35.0      56.0       0.0      12.0   \n",
       "2       0.0       0.0       3.0      29.0      68.0       3.0      23.0   \n",
       "3       3.0       0.0      10.0      55.0      34.0       0.0      48.0   \n",
       "4       0.0       0.0       9.0      14.0      77.0       5.0      18.0   \n",
       "\n",
       "   t_q11e_3  t_q11e_4  t_q11f_1  t_q11f_2  t_q11f_3  t_q11f_4  t_q11g_1  \\\n",
       "0      38.0      10.0       0.0       5.0      48.0      48.0       5.0   \n",
       "1      53.0      35.0       3.0       3.0      15.0      79.0      38.0   \n",
       "2      51.0      23.0       3.0       5.0      51.0      41.0      34.0   \n",
       "3      28.0      24.0       3.0      14.0      41.0      41.0       7.0   \n",
       "4      55.0      23.0       0.0       0.0      36.0      64.0      36.0   \n",
       "\n",
       "   t_q11g_2  t_q11g_3  t_q11g_4  t_q11h_1  t_q11h_2  t_q11h_3  t_q11h_4  \\\n",
       "0      85.0      10.0       0.0      14.0      76.0      10.0       0.0   \n",
       "1      41.0      15.0       6.0      29.0      59.0       9.0       3.0   \n",
       "2      66.0       0.0       0.0      24.0      76.0       0.0       0.0   \n",
       "3      52.0      31.0      10.0       7.0      66.0      21.0       7.0   \n",
       "4      55.0       5.0       5.0      36.0      55.0       9.0       0.0   \n",
       "\n",
       "   t_q11i_1  t_q11i_2  t_q11i_3  t_q11i_4  t_q11j_1  t_q11j_2  t_q11j_3  \\\n",
       "0      57.0      43.0       0.0       0.0       0.0       0.0      20.0   \n",
       "1      85.0       9.0       3.0       3.0       0.0       0.0       6.0   \n",
       "2      44.0      54.0       3.0       0.0       0.0       3.0      18.0   \n",
       "3      34.0      52.0      10.0       3.0       0.0       7.0      41.0   \n",
       "4      68.0      27.0       5.0       0.0       0.0       0.0       9.0   \n",
       "\n",
       "   t_q11j_4  t_q11k_1  t_q11k_2  t_q11k_3  t_q11k_4  t_q11l_1  t_q11l_2  \\\n",
       "0      80.0       0.0       5.0      62.0      33.0      25.0      75.0   \n",
       "1      94.0       0.0       3.0      29.0      68.0      68.0      24.0   \n",
       "2      79.0       3.0      15.0      26.0      56.0      26.0      56.0   \n",
       "3      52.0       0.0      10.0      45.0      45.0      21.0      62.0   \n",
       "4      91.0       0.0       0.0      23.0      77.0      73.0      23.0   \n",
       "\n",
       "   t_q11l_3  t_q11l_4  t_q11m_1  t_q11m_2  t_q11m_3  t_q11m_4  t_q11n_1  \\\n",
       "0       0.0       0.0       0.0       0.0      48.0      52.0      33.0   \n",
       "1       3.0       6.0       0.0       0.0       6.0      94.0      74.0   \n",
       "2       8.0      10.0       3.0       3.0      13.0      82.0      87.0   \n",
       "3      17.0       0.0       0.0       0.0      41.0      59.0      62.0   \n",
       "4       5.0       0.0       0.0       0.0       5.0      95.0      67.0   \n",
       "\n",
       "   t_q11n_2  t_q11n_3  t_q11n_4  t_q11o_1  t_q11o_2  t_q11o_3  t_q11o_4  \\\n",
       "0      67.0       0.0       0.0      59.0      41.0       0.0       0.0   \n",
       "1      24.0       0.0       3.0      72.0      21.0       7.0       0.0   \n",
       "2      13.0       0.0       0.0      57.0      36.0       0.0       7.0   \n",
       "3      31.0       7.0       0.0      19.0      57.0      24.0       0.0   \n",
       "4      29.0       0.0       5.0      40.0      27.0      20.0      13.0   \n",
       "\n",
       "   t_q12a_1  t_q12a_2  t_q12a_3  t_q12a_4  t_q12a_5  t_q12b_1  t_q12b_2  \\\n",
       "0       5.0      24.0      10.0      10.0      52.0       0.0      29.0   \n",
       "1      18.0      18.0       9.0      12.0      44.0      18.0      18.0   \n",
       "2       5.0      28.0      33.0      13.0      23.0       5.0      35.0   \n",
       "3       3.0      45.0      28.0      14.0      10.0       4.0      39.0   \n",
       "4       9.0      32.0       5.0       9.0      45.0       9.0      18.0   \n",
       "\n",
       "   t_q12b_3  t_q12b_4  t_q12b_5  t_q12c_1  t_q12c_2  t_q12c_3  t_q12c_4  \\\n",
       "0       5.0      14.0      52.0       0.0      29.0      10.0      14.0   \n",
       "1       9.0      15.0      41.0      21.0      21.0       6.0      15.0   \n",
       "2      28.0       8.0      25.0       3.0      43.0      25.0       8.0   \n",
       "3      18.0      18.0      21.0       3.0      45.0      17.0      21.0   \n",
       "4      14.0      14.0      45.0       9.0      23.0      14.0       9.0   \n",
       "\n",
       "   t_q12c_5  t_q12d_1  t_q12d_2  t_q12d_3  t_q12d_4  t_q12d_5  t_q13a_1  \\\n",
       "0      48.0       0.0      24.0      14.0      14.0      48.0       0.0   \n",
       "1      38.0      18.0      18.0       9.0      15.0      41.0       9.0   \n",
       "2      23.0       3.0      50.0      18.0      10.0      20.0       0.0   \n",
       "3      14.0       7.0      36.0      14.0      25.0      18.0       3.0   \n",
       "4      45.0       5.0      27.0      14.0       9.0      45.0       0.0   \n",
       "\n",
       "   t_q13a_2  t_q13a_3  t_q13a_4  t_q13a_5  t_q13b_1  t_q13b_2  t_q13b_3  \\\n",
       "0      19.0      19.0      33.0      29.0       0.0       5.0      29.0   \n",
       "1      12.0      18.0      32.0      29.0       9.0      15.0      15.0   \n",
       "2      23.0      35.0      25.0      18.0       0.0      23.0      38.0   \n",
       "3      24.0      41.0      28.0       3.0       4.0      21.0      32.0   \n",
       "4      23.0      18.0      36.0      23.0       0.0      14.0      18.0   \n",
       "\n",
       "   t_q13b_4  t_q13b_5  t_q13c_1  t_q13c_2  t_q13c_3  t_q13c_4  t_q13c_5  \\\n",
       "0      38.0      29.0       0.0      10.0      24.0      33.0      33.0   \n",
       "1      32.0      29.0       9.0      18.0      15.0      32.0      26.0   \n",
       "2      20.0      20.0       0.0      30.0      33.0      23.0      15.0   \n",
       "3      29.0      14.0       4.0      21.0      32.0      36.0       7.0   \n",
       "4      41.0      27.0       0.0      18.0      23.0      36.0      23.0   \n",
       "\n",
       "   t_q13d_1  t_q13d_2  t_q13d_3  t_q13d_4  t_q13d_5  t_q14_1  t_q14_2  \\\n",
       "0       0.0      14.0      29.0      33.0      24.0      0.0     15.0   \n",
       "1       6.0      15.0      21.0      32.0      26.0      6.0      3.0   \n",
       "2       0.0      28.0      33.0      20.0      20.0      5.0      8.0   \n",
       "3       4.0      21.0      39.0      25.0      11.0      4.0     18.0   \n",
       "4       0.0      18.0      23.0      32.0      27.0      5.0     15.0   \n",
       "\n",
       "   t_q14_3  t_q14_4  t_q14_5  t_q15a_1  t_q15a_2  t_q15a_3  t_q15a_4  \\\n",
       "0     50.0     25.0     10.0      33.0      57.0       5.0       5.0   \n",
       "1     55.0      9.0     27.0      47.0      44.0       0.0       9.0   \n",
       "2     46.0     14.0     27.0      20.0      33.0      30.0      18.0   \n",
       "3     50.0     11.0     18.0       7.0      38.0      34.0      21.0   \n",
       "4     55.0     10.0     15.0      50.0      23.0      23.0       5.0   \n",
       "\n",
       "   t_q15b_1  t_q15b_2  t_q15b_3  t_q15b_4  t_q15c_1  t_q15c_2  t_q15c_3  \\\n",
       "0      50.0      45.0       5.0       0.0      30.0      60.0      10.0   \n",
       "1      71.0      21.0       0.0       9.0      62.0      29.0       0.0   \n",
       "2      25.0      60.0      13.0       3.0      13.0      48.0      35.0   \n",
       "3      28.0      52.0       7.0      14.0      10.0      52.0      24.0   \n",
       "4      45.0      41.0      14.0       0.0      41.0      36.0      18.0   \n",
       "\n",
       "   t_q15c_4  t_q15d_1  t_q15d_2  t_q15d_3  t_q15d_4  t_q15e_1  t_q15e_2  \\\n",
       "0       0.0      52.0      48.0       0.0       0.0      57.0      43.0   \n",
       "1       9.0      88.0      12.0       0.0       0.0      88.0       9.0   \n",
       "2       5.0      35.0      55.0      10.0       0.0      31.0      59.0   \n",
       "3      14.0      45.0      55.0       0.0       0.0      48.0      48.0   \n",
       "4       5.0      73.0      27.0       0.0       0.0      68.0      32.0   \n",
       "\n",
       "   t_q15e_3  t_q15e_4  t_q15f_1  t_q15f_2  t_q15f_3  t_q15f_4  t_N_q1a_1  \\\n",
       "0       0.0       0.0      57.0      43.0       0.0       0.0       13.0   \n",
       "1       3.0       0.0      85.0      12.0       3.0       0.0       29.0   \n",
       "2      10.0       0.0      35.0      58.0       8.0       0.0       14.0   \n",
       "3       3.0       0.0      45.0      41.0      14.0       0.0        9.0   \n",
       "4       0.0       0.0      71.0      29.0       0.0       0.0        9.0   \n",
       "\n",
       "   t_N_q1a_2  t_N_q1a_3  t_N_q1a_4  t_N_q1b_1  t_N_q1b_2  t_N_q1b_3  \\\n",
       "0        7.0        1.0        0.0       13.0        7.0        1.0   \n",
       "1        4.0        0.0        1.0       31.0        1.0        1.0   \n",
       "2       18.0        3.0        7.0       15.0       15.0        6.0   \n",
       "3       14.0        3.0        3.0        9.0       13.0        5.0   \n",
       "4       12.0        0.0        1.0        8.0       13.0        1.0   \n",
       "\n",
       "   t_N_q1b_4  t_N_q1c_1  t_N_q1c_2  t_N_q1c_3  t_N_q1c_4  t_N_q1d_1  \\\n",
       "0        0.0       13.0        5.0        3.0        0.0        8.0   \n",
       "1        1.0       23.0        8.0        1.0        2.0       28.0   \n",
       "2        6.0       12.0       19.0        5.0        6.0       12.0   \n",
       "3        2.0        6.0       12.0        5.0        6.0       11.0   \n",
       "4        0.0        7.0       12.0        3.0        1.0       10.0   \n",
       "\n",
       "   t_N_q1d_2  t_N_q1d_3  t_N_q1d_4  t_N_q1e_1  t_N_q1e_2  t_N_q1e_3  \\\n",
       "0       12.0        0.0        0.0       10.0        9.0        2.0   \n",
       "1        5.0        0.0        0.0       27.0        4.0        0.0   \n",
       "2       21.0        4.0        2.0       14.0       18.0        6.0   \n",
       "3       17.0        0.0        0.0        9.0       12.0        6.0   \n",
       "4        9.0        2.0        0.0        7.0       13.0        2.0   \n",
       "\n",
       "   t_N_q1e_4  t_N_q1f_1  t_N_q1f_2  t_N_q1f_3  t_N_q1f_4  t_N_q1g_1  \\\n",
       "0        0.0        9.0       10.0        2.0        0.0       10.0   \n",
       "1        3.0       27.0        4.0        1.0        2.0       23.0   \n",
       "2        4.0       17.0       14.0        4.0        7.0       16.0   \n",
       "3        2.0        8.0       13.0        4.0        4.0        7.0   \n",
       "4        1.0        7.0        9.0        3.0        4.0        5.0   \n",
       "\n",
       "   t_N_q1g_2  t_N_q1g_3  t_N_q1g_4  t_N_q2a_1  t_N_q2a_2  t_N_q2a_3  \\\n",
       "0        9.0        2.0        0.0        8.0       12.0        1.0   \n",
       "1        9.0        0.0        2.0       30.0        3.0        1.0   \n",
       "2       10.0        9.0        7.0       20.0       17.0        4.0   \n",
       "3       15.0        1.0        6.0       12.0       14.0        3.0   \n",
       "4       10.0        4.0        4.0       18.0        5.0        0.0   \n",
       "\n",
       "   t_N_q2a_4  t_N_q2b_1  t_N_q2b_2  t_N_q2b_3  t_N_q2b_4  t_N_q2c_1  \\\n",
       "0        0.0        6.0       14.0        1.0        0.0        9.0   \n",
       "1        0.0       30.0        3.0        0.0        0.0       30.0   \n",
       "2        1.0       22.0       17.0        0.0        0.0       17.0   \n",
       "3        0.0       17.0       10.0        1.0        0.0       15.0   \n",
       "4        0.0       17.0        4.0        0.0        0.0       16.0   \n",
       "\n",
       "   t_N_q2c_2  t_N_q2c_3  t_N_q2c_4  t_N_q2d_1  t_N_q2d_2  t_N_q2d_3  \\\n",
       "0       12.0        0.0        0.0        8.0       10.0        3.0   \n",
       "1        2.0        1.0        0.0       28.0        5.0        0.0   \n",
       "2       20.0        2.0        0.0       12.0       27.0        3.0   \n",
       "3       10.0        3.0        0.0       16.0       11.0        2.0   \n",
       "4        5.0        0.0        0.0       15.0        6.0        2.0   \n",
       "\n",
       "   t_N_q2d_4  t_N_q2e_1  t_N_q2e_2  t_N_q2e_3  t_N_q2e_4  t_N_q2f_1  \\\n",
       "0        0.0       11.0        9.0        1.0        0.0        NaN   \n",
       "1        1.0       29.0        3.0        1.0        1.0        NaN   \n",
       "2        0.0       13.0       22.0        6.0        0.0        NaN   \n",
       "3        0.0       14.0       11.0        4.0        0.0        NaN   \n",
       "4        0.0       14.0        7.0        2.0        0.0        NaN   \n",
       "\n",
       "   t_N_q2f_2  t_N_q2f_3  t_N_q2f_4  t_N_q3a_1  t_N_q3a_2  t_N_q3a_3  \\\n",
       "0        NaN        NaN        NaN       10.0        6.0        4.0   \n",
       "1        NaN        NaN        NaN        3.0       22.0        8.0   \n",
       "2        NaN        NaN        NaN       38.0        3.0        0.0   \n",
       "3        NaN        NaN        NaN        1.0       10.0       16.0   \n",
       "4        NaN        NaN        NaN       19.0        0.0        2.0   \n",
       "\n",
       "   t_N_q3b_1  t_N_q3b_2  t_N_q3b_3  t_N_q3c_1  t_N_q3c_2  t_N_q3c_3  \\\n",
       "0        1.0        3.0       13.0        8.0        6.0        5.0   \n",
       "1       30.0        2.0        1.0        2.0        6.0       25.0   \n",
       "2        1.0        8.0       30.0       23.0       13.0        7.0   \n",
       "3       28.0       11.0        0.0       17.0       12.0        5.0   \n",
       "4       20.0        1.0        0.0       11.0        8.0        3.0   \n",
       "\n",
       "   t_N_q3d_1  t_N_q3d_2  t_N_q3d_3  t_N_q3e_1  t_N_q3e_2  t_N_q3e_3  \\\n",
       "0        6.0        7.0        6.0        0.0        0.0       18.0   \n",
       "1       16.0        0.0       16.0        0.0        0.0       33.0   \n",
       "2        4.0        9.0       26.0       11.0        2.0       27.0   \n",
       "3        4.0        7.0       15.0       26.0        0.0        2.0   \n",
       "4        0.0        1.0       19.0        0.0        0.0       20.0   \n",
       "\n",
       "   t_N_q3f_1  t_N_q3f_2  t_N_q3f_3  t_N_q3g_1  t_N_q3g_2  t_N_q3g_3  \\\n",
       "0       11.0        3.0        7.0        6.0        2.0       13.0   \n",
       "1       32.0        1.0        0.0       20.0        0.0       13.0   \n",
       "2       38.0        1.0        0.0        4.0        4.0       31.0   \n",
       "3       26.0        8.0        1.0        7.0        2.0       18.0   \n",
       "4        4.0       11.0        6.0       14.0        0.0        7.0   \n",
       "\n",
       "   t_N_q3h_1  t_N_q3h_2  t_N_q3h_3  t_N_q3i_1  t_N_q3i_2  t_N_q3i_3  \\\n",
       "0       21.0        0.0        0.0        0.0        7.0       13.0   \n",
       "1       33.0        0.0        0.0        1.0        7.0       25.0   \n",
       "2       38.0        2.0        0.0        4.0       32.0        5.0   \n",
       "3       28.0        5.0        0.0        7.0       23.0        0.0   \n",
       "4       21.0        0.0        0.0        2.0       16.0        3.0   \n",
       "\n",
       "   t_N_q3j_1  t_N_q3j_2  t_N_q3j_3  t_N_q4_1  t_N_q4_2  t_N_q4_3  t_N_q4_4  \\\n",
       "0       11.0       13.0        0.0       2.0      12.0       7.0       0.0   \n",
       "1       19.0       16.0        2.0      16.0      14.0       2.0       1.0   \n",
       "2       14.0       26.0        3.0      10.0      25.0       4.0       0.0   \n",
       "3       14.0       23.0        1.0       8.0      13.0       5.0       1.0   \n",
       "4        7.0       13.0        2.0      10.0       8.0       1.0       1.0   \n",
       "\n",
       "   t_N_q5a_1  t_N_q5a_2  t_N_q5a_3  t_N_q5a_4  t_N_q5a_5  t_N_q5b_1  \\\n",
       "0       14.0        4.0        3.0        0.0        0.0        0.0   \n",
       "1       29.0        2.0        2.0        1.0        0.0       30.0   \n",
       "2       13.0       17.0        5.0        7.0        0.0       11.0   \n",
       "3        6.0       13.0        6.0        4.0        0.0        9.0   \n",
       "4       11.0        6.0        2.0        2.0        1.0        0.0   \n",
       "\n",
       "   t_N_q5b_2  t_N_q5b_3  t_N_q5b_4  t_N_q5b_5  t_N_q5c_1  t_N_q5c_2  \\\n",
       "0        0.0        0.0        0.0       21.0       13.0        6.0   \n",
       "1        2.0        1.0        0.0        0.0       30.0        3.0   \n",
       "2       17.0        8.0        6.0        0.0       16.0       22.0   \n",
       "3       11.0        6.0        3.0        0.0       19.0        7.0   \n",
       "4        1.0        0.0        0.0       22.0       17.0        3.0   \n",
       "\n",
       "   t_N_q5c_3  t_N_q5c_4  t_N_q5c_5  t_N_q6a_1  t_N_q6a_2  t_N_q6a_3  \\\n",
       "0        2.0        0.0        0.0        7.0       12.0        1.0   \n",
       "1        1.0        0.0        0.0       24.0        7.0        2.0   \n",
       "2        3.0        0.0        1.0       13.0       17.0        5.0   \n",
       "3        3.0        0.0        0.0        8.0       15.0        4.0   \n",
       "4        2.0        0.0        0.0        4.0       12.0        5.0   \n",
       "\n",
       "   t_N_q6a_4  t_N_q6b_1  t_N_q6b_2  t_N_q6b_3  t_N_q6b_4  t_N_q6c_1  \\\n",
       "0        0.0       14.0        7.0        0.0        0.0       14.0   \n",
       "1        1.0       25.0        5.0        3.0        1.0       25.0   \n",
       "2        7.0       14.0       16.0        7.0        5.0       22.0   \n",
       "3        2.0        9.0       14.0        3.0        3.0       13.0   \n",
       "4        2.0        7.0       13.0        2.0        1.0       10.0   \n",
       "\n",
       "   t_N_q6c_2  t_N_q6c_3  t_N_q6c_4  t_N_q6d_1  t_N_q6d_2  t_N_q6d_3  \\\n",
       "0        6.0        1.0        0.0       11.0        9.0        1.0   \n",
       "1        7.0        1.0        1.0       29.0        5.0        0.0   \n",
       "2       17.0        1.0        2.0       13.0       24.0        5.0   \n",
       "3       12.0        0.0        4.0       15.0       13.0        1.0   \n",
       "4       11.0        2.0        0.0       12.0       10.0        1.0   \n",
       "\n",
       "   t_N_q6d_4  t_N_q6e_1  t_N_q6e_2  t_N_q6e_3  t_N_q6e_4  t_N_q6f_1  \\\n",
       "0        0.0        7.0       12.0        0.0        0.0        9.0   \n",
       "1        0.0       26.0        7.0        1.0        0.0       27.0   \n",
       "2        0.0        7.0       23.0       11.0        1.0       13.0   \n",
       "3        0.0       10.0       14.0        3.0        2.0       13.0   \n",
       "4        0.0       14.0        8.0        0.0        1.0       14.0   \n",
       "\n",
       "   t_N_q6f_2  t_N_q6f_3  t_N_q6f_4  t_N_q6g_1  t_N_q6g_2  t_N_q6g_3  \\\n",
       "0        9.0        1.0        0.0       10.0       10.0        1.0   \n",
       "1        6.0        1.0        0.0       26.0        6.0        1.0   \n",
       "2       20.0        8.0        1.0       15.0       20.0        3.0   \n",
       "3       13.0        2.0        1.0        4.0       21.0        2.0   \n",
       "4        8.0        1.0        0.0       11.0        6.0        4.0   \n",
       "\n",
       "   t_N_q6g_4  t_N_q6h_1  t_N_q6h_2  t_N_q6h_3  t_N_q6h_4  t_N_q6i_1  \\\n",
       "0        0.0        7.0       11.0        3.0        0.0       16.0   \n",
       "1        0.0       23.0        8.0        0.0        2.0       29.0   \n",
       "2        1.0       11.0       15.0       10.0        3.0       15.0   \n",
       "3        1.0        4.0       15.0        6.0        3.0       12.0   \n",
       "4        0.0        5.0        9.0        5.0        2.0        8.0   \n",
       "\n",
       "   t_N_q6i_2  t_N_q6i_3  t_N_q6i_4  t_N_q6j_1  t_N_q6j_2  t_N_q6j_3  \\\n",
       "0        5.0        0.0        0.0        9.0       11.0        0.0   \n",
       "1        2.0        1.0        1.0       30.0        2.0        1.0   \n",
       "2       16.0        6.0        2.0       19.0       15.0        1.0   \n",
       "3       12.0        4.0        0.0       10.0       13.0        3.0   \n",
       "4       11.0        1.0        1.0       10.0       10.0        0.0   \n",
       "\n",
       "   t_N_q6j_4  t_N_q6k_1  t_N_q6k_2  t_N_q6k_3  t_N_q6k_4  t_N_q6l_1  \\\n",
       "0        0.0       16.0        3.0        1.0        0.0       15.0   \n",
       "1        0.0       30.0        2.0        1.0        0.0       31.0   \n",
       "2        4.0       13.0       26.0        0.0        0.0       15.0   \n",
       "3        2.0       15.0       10.0        3.0        0.0       13.0   \n",
       "4        1.0       11.0        7.0        1.0        2.0       15.0   \n",
       "\n",
       "   t_N_q6l_2  t_N_q6l_3  t_N_q6l_4  t_N_q6m_1  t_N_q6m_2  t_N_q6m_3  \\\n",
       "0        5.0        0.0        0.0       13.0        8.0        0.0   \n",
       "1        2.0        0.0        0.0       28.0        3.0        2.0   \n",
       "2       23.0        1.0        0.0       11.0       23.0        5.0   \n",
       "3       15.0        0.0        0.0       12.0       13.0        3.0   \n",
       "4        6.0        0.0        0.0       15.0        6.0        0.0   \n",
       "\n",
       "   t_N_q6m_4  t_N_q7a_1  t_N_q7a_2  t_N_q7a_3  t_N_q7a_4  t_N_q7a_5  \\\n",
       "0        0.0        7.0       12.0        1.0        1.0        0.0   \n",
       "1        0.0       23.0        7.0        1.0        1.0        1.0   \n",
       "2        0.0        6.0       19.0       11.0        2.0        0.0   \n",
       "3        0.0       11.0       11.0        3.0        2.0        1.0   \n",
       "4        0.0       10.0        7.0        2.0        1.0        0.0   \n",
       "\n",
       "   t_N_q7b_1  t_N_q7b_2  t_N_q7b_3  t_N_q7b_4  t_N_q7b_5  t_N_q7c_1  \\\n",
       "0        5.0        9.0        6.0        1.0        0.0        5.0   \n",
       "1       22.0        5.0        3.0        2.0        1.0       24.0   \n",
       "2       10.0       22.0        2.0        3.0        1.0       11.0   \n",
       "3        9.0       13.0        5.0        1.0        0.0       12.0   \n",
       "4        8.0        9.0        3.0        0.0        0.0       11.0   \n",
       "\n",
       "   t_N_q7c_2  t_N_q7c_3  t_N_q7c_4  t_N_q7c_5  t_N_q7d_1  t_N_q7d_2  \\\n",
       "0       12.0        4.0        0.0        0.0        3.0       12.0   \n",
       "1        6.0        0.0        2.0        1.0       22.0        8.0   \n",
       "2       23.0        2.0        1.0        1.0        7.0       20.0   \n",
       "3       14.0        2.0        0.0        0.0       11.0       10.0   \n",
       "4        7.0        2.0        0.0        0.0       12.0        7.0   \n",
       "\n",
       "   t_N_q7d_3  t_N_q7d_4  t_N_q7d_5  t_N_q7e_1  t_N_q7e_2  t_N_q7e_3  \\\n",
       "0        5.0        0.0        1.0        4.0       15.0        1.0   \n",
       "1        2.0        0.0        1.0       22.0        7.0        3.0   \n",
       "2       10.0        1.0        0.0        9.0       21.0        7.0   \n",
       "3        3.0        3.0        1.0       10.0       12.0        3.0   \n",
       "4        1.0        0.0        0.0        9.0        8.0        2.0   \n",
       "\n",
       "   t_N_q7e_4  t_N_q7e_5  t_N_q8a_1  t_N_q8a_2  t_N_q8a_3  t_N_q8a_4  \\\n",
       "0        1.0        0.0        6.0       10.0        4.0        0.0   \n",
       "1        0.0        1.0       23.0       10.0        1.0        0.0   \n",
       "2        1.0        0.0        9.0       21.0        8.0        2.0   \n",
       "3        1.0        2.0        3.0       14.0       12.0        0.0   \n",
       "4        1.0        0.0        7.0       11.0        4.0        0.0   \n",
       "\n",
       "   t_N_q8b_1  t_N_q8b_2  t_N_q8b_3  t_N_q8b_4  t_N_q8c_1  t_N_q8c_2  \\\n",
       "0        4.0       14.0        2.0        0.0        6.0       14.0   \n",
       "1       25.0        8.0        1.0        0.0       25.0        7.0   \n",
       "2        9.0       21.0        7.0        3.0       13.0       14.0   \n",
       "3        4.0       13.0        9.0        2.0        6.0       13.0   \n",
       "4       10.0       11.0        1.0        0.0       13.0        8.0   \n",
       "\n",
       "   t_N_q8c_3  t_N_q8c_4  t_N_q9_1  t_N_q9_2  t_N_q9_3  t_N_q9_4  t_N_q9_5  \\\n",
       "0        1.0        0.0       1.0       3.0      11.0       5.0       1.0   \n",
       "1        2.0        0.0       1.0       2.0       1.0      25.0       3.0   \n",
       "2        8.0        5.0       1.0       2.0       6.0      26.0       2.0   \n",
       "3        8.0        2.0       0.0       5.0       9.0      11.0       3.0   \n",
       "4        1.0        0.0       2.0       1.0       2.0      12.0       3.0   \n",
       "\n",
       "   t_N_q10a_1  t_N_q10a_2  t_N_q10a_3  t_N_q10a_4  t_N_q10a_5  t_N_q10a_6  \\\n",
       "0        13.0         3.0         3.0         1.0         0.0         1.0   \n",
       "1        25.0         7.0         1.0         0.0         0.0         1.0   \n",
       "2        19.0         7.0         8.0         4.0         0.0         2.0   \n",
       "3        12.0         8.0         6.0         2.0         0.0         1.0   \n",
       "4        11.0         6.0         3.0         0.0         1.0         1.0   \n",
       "\n",
       "   t_N_q10b_1  t_N_q10b_2  t_N_q10b_3  t_N_q10b_4  t_N_q10b_5  t_N_q10b_6  \\\n",
       "0         4.0         2.0         9.0         4.0         0.0         2.0   \n",
       "1         8.0        11.0        12.0         2.0         0.0         1.0   \n",
       "2         7.0         6.0         9.0        17.0         1.0         0.0   \n",
       "3         2.0         4.0        13.0        10.0         0.0         0.0   \n",
       "4         5.0         6.0         8.0         2.0         0.0         1.0   \n",
       "\n",
       "   t_N_q10c_1  t_N_q10c_2  t_N_q10c_3  t_N_q10c_4  t_N_q10c_5  t_N_q10c_6  \\\n",
       "0         6.0         5.0         5.0         3.0         1.0         1.0   \n",
       "1        13.0         6.0        11.0         2.0         0.0         2.0   \n",
       "2        12.0        11.0         9.0         6.0         2.0         0.0   \n",
       "3         7.0         8.0        11.0         1.0         2.0         0.0   \n",
       "4        11.0         5.0         4.0         0.0         2.0         0.0   \n",
       "\n",
       "   t_N_q10d_1  t_N_q10d_2  t_N_q10d_3  t_N_q10d_4  t_N_q10d_5  t_N_q10d_6  \\\n",
       "0         3.0         4.0        10.0         3.0         0.0         1.0   \n",
       "1         7.0         4.0        16.0         5.0         0.0         2.0   \n",
       "2         2.0         9.0         9.0        12.0         3.0         5.0   \n",
       "3         2.0         4.0        16.0         3.0         2.0         2.0   \n",
       "4         3.0         8.0         9.0         1.0         0.0         1.0   \n",
       "\n",
       "   t_N_q10e_1  t_N_q10e_2  t_N_q10e_3  t_N_q10e_4  t_N_q10e_5  t_N_q10e_6  \\\n",
       "0         1.0         7.0         8.0         2.0         1.0         2.0   \n",
       "1         7.0         4.0        15.0         4.0         1.0         3.0   \n",
       "2         5.0         9.0        12.0         9.0         1.0         4.0   \n",
       "3         2.0         5.0        12.0         8.0         1.0         1.0   \n",
       "4         7.0         6.0         5.0         2.0         1.0         1.0   \n",
       "\n",
       "   t_N_q11a_1  t_N_q11a_2  t_N_q11a_3  t_N_q11a_4  t_N_q11b_1  t_N_q11b_2  \\\n",
       "0         1.0        14.0         5.0         0.0         3.0        13.0   \n",
       "1        17.0        14.0         3.0         0.0        21.0        10.0   \n",
       "2        10.0        20.0         6.0         4.0         7.0        18.0   \n",
       "3         2.0        14.0        12.0         1.0         6.0         7.0   \n",
       "4        13.0         5.0         4.0         0.0        12.0         7.0   \n",
       "\n",
       "   t_N_q11b_3  t_N_q11b_4  t_N_q11c_1  t_N_q11c_2  t_N_q11c_3  t_N_q11c_4  \\\n",
       "0         5.0         0.0         9.0        11.0         1.0         0.0   \n",
       "1         1.0         1.0        25.0         8.0         1.0         0.0   \n",
       "2         8.0         4.0        20.0        16.0         3.0         0.0   \n",
       "3        14.0         1.0         9.0        17.0         2.0         1.0   \n",
       "4         0.0         0.0        16.0         5.0         1.0         0.0   \n",
       "\n",
       "   t_N_q11d_1  t_N_q11d_2  t_N_q11d_3  t_N_q11d_4  t_N_q11e_1  t_N_q11e_2  \\\n",
       "0         0.0         4.0        12.0         5.0         0.0        11.0   \n",
       "1         1.0         2.0        12.0        19.0         0.0         4.0   \n",
       "2         0.0         1.0        11.0        26.0         1.0         9.0   \n",
       "3         0.0         3.0        16.0        10.0         0.0        14.0   \n",
       "4         0.0         2.0         3.0        17.0         1.0         4.0   \n",
       "\n",
       "   t_N_q11e_3  t_N_q11e_4  t_N_q11f_1  t_N_q11f_2  t_N_q11f_3  t_N_q11f_4  \\\n",
       "0         8.0         2.0         0.0         1.0        10.0        10.0   \n",
       "1        18.0        12.0         1.0         1.0         5.0        27.0   \n",
       "2        20.0         9.0         1.0         2.0        20.0        16.0   \n",
       "3         8.0         7.0         1.0         4.0        12.0        12.0   \n",
       "4        12.0         5.0         0.0         0.0         8.0        14.0   \n",
       "\n",
       "   t_N_q11g_1  t_N_q11g_2  t_N_q11g_3  t_N_q11g_4  t_N_q11h_1  t_N_q11h_2  \\\n",
       "0         1.0        17.0         2.0         0.0         3.0        16.0   \n",
       "1        13.0        14.0         5.0         2.0        10.0        20.0   \n",
       "2        13.0        25.0         0.0         0.0         9.0        29.0   \n",
       "3         2.0        15.0         9.0         3.0         2.0        19.0   \n",
       "4         8.0        12.0         1.0         1.0         8.0        12.0   \n",
       "\n",
       "   t_N_q11h_3  t_N_q11h_4  t_N_q11i_1  t_N_q11i_2  t_N_q11i_3  t_N_q11i_4  \\\n",
       "0         2.0         0.0        12.0         9.0         0.0         0.0   \n",
       "1         3.0         1.0        29.0         3.0         1.0         1.0   \n",
       "2         0.0         0.0        17.0        21.0         1.0         0.0   \n",
       "3         6.0         2.0        10.0        15.0         3.0         1.0   \n",
       "4         2.0         0.0        15.0         6.0         1.0         0.0   \n",
       "\n",
       "   t_N_q11j_1  t_N_q11j_2  t_N_q11j_3  t_N_q11j_4  t_N_q11k_1  t_N_q11k_2  \\\n",
       "0         0.0         0.0         4.0        16.0         0.0         1.0   \n",
       "1         0.0         0.0         2.0        32.0         0.0         1.0   \n",
       "2         0.0         1.0         7.0        31.0         1.0         6.0   \n",
       "3         0.0         2.0        12.0        15.0         0.0         3.0   \n",
       "4         0.0         0.0         2.0        20.0         0.0         0.0   \n",
       "\n",
       "   t_N_q11k_3  t_N_q11k_4  t_N_q11l_1  t_N_q11l_2  t_N_q11l_3  t_N_q11l_4  \\\n",
       "0        13.0         7.0         5.0        15.0         0.0         0.0   \n",
       "1        10.0        23.0        23.0         8.0         1.0         2.0   \n",
       "2        10.0        22.0        10.0        22.0         3.0         4.0   \n",
       "3        13.0        13.0         6.0        18.0         5.0         0.0   \n",
       "4         5.0        17.0        16.0         5.0         1.0         0.0   \n",
       "\n",
       "   t_N_q11m_1  t_N_q11m_2  t_N_q11m_3  t_N_q11m_4  t_N_q11n_1  t_N_q11n_2  \\\n",
       "0         0.0         0.0        10.0        11.0         7.0        14.0   \n",
       "1         0.0         0.0         2.0        32.0        25.0         8.0   \n",
       "2         1.0         1.0         5.0        31.0        33.0         5.0   \n",
       "3         0.0         0.0        12.0        17.0        18.0         9.0   \n",
       "4         0.0         0.0         1.0        21.0        14.0         6.0   \n",
       "\n",
       "   t_N_q11n_3  t_N_q11n_4  t_N_q11o_1  t_N_q11o_2  t_N_q11o_3  t_N_q11o_4  \\\n",
       "0         0.0         0.0        10.0         7.0         0.0         0.0   \n",
       "1         0.0         1.0        21.0         6.0         2.0         0.0   \n",
       "2         0.0         0.0        16.0        10.0         0.0         2.0   \n",
       "3         2.0         0.0         4.0        12.0         5.0         0.0   \n",
       "4         0.0         1.0         6.0         4.0         3.0         2.0   \n",
       "\n",
       "   t_N_q12a_1  t_N_q12a_2  t_N_q12a_3  t_N_q12a_4  t_N_q12a_5  t_N_q12b_1  \\\n",
       "0         1.0         5.0         2.0         2.0        11.0         0.0   \n",
       "1         6.0         6.0         3.0         4.0        15.0         6.0   \n",
       "2         2.0        11.0        13.0         5.0         9.0         2.0   \n",
       "3         1.0        13.0         8.0         4.0         3.0         1.0   \n",
       "4         2.0         7.0         1.0         2.0        10.0         2.0   \n",
       "\n",
       "   t_N_q12b_2  t_N_q12b_3  t_N_q12b_4  t_N_q12b_5  t_N_q12c_1  t_N_q12c_2  \\\n",
       "0         6.0         1.0         3.0        11.0         0.0         6.0   \n",
       "1         6.0         3.0         5.0        14.0         7.0         7.0   \n",
       "2        14.0        11.0         3.0        10.0         1.0        17.0   \n",
       "3        11.0         5.0         5.0         6.0         1.0        13.0   \n",
       "4         4.0         3.0         3.0        10.0         2.0         5.0   \n",
       "\n",
       "   t_N_q12c_3  t_N_q12c_4  t_N_q12c_5  t_N_q12d_1  t_N_q12d_2  t_N_q12d_3  \\\n",
       "0         2.0         3.0        10.0         0.0         5.0         3.0   \n",
       "1         2.0         5.0        13.0         6.0         6.0         3.0   \n",
       "2        10.0         3.0         9.0         1.0        20.0         7.0   \n",
       "3         5.0         6.0         4.0         2.0        10.0         4.0   \n",
       "4         3.0         2.0        10.0         1.0         6.0         3.0   \n",
       "\n",
       "   t_N_q12d_4  t_N_q12d_5  t_N_q13a_1  t_N_q13a_2  t_N_q13a_3  t_N_q13a_4  \\\n",
       "0         3.0        10.0         0.0         4.0         4.0         7.0   \n",
       "1         5.0        14.0         3.0         4.0         6.0        11.0   \n",
       "2         4.0         8.0         0.0         9.0        14.0        10.0   \n",
       "3         7.0         5.0         1.0         7.0        12.0         8.0   \n",
       "4         2.0        10.0         0.0         5.0         4.0         8.0   \n",
       "\n",
       "   t_N_q13a_5  t_N_q13b_1  t_N_q13b_2  t_N_q13b_3  t_N_q13b_4  t_N_q13b_5  \\\n",
       "0         6.0         0.0         1.0         6.0         8.0         6.0   \n",
       "1        10.0         3.0         5.0         5.0        11.0        10.0   \n",
       "2         7.0         0.0         9.0        15.0         8.0         8.0   \n",
       "3         1.0         1.0         6.0         9.0         8.0         4.0   \n",
       "4         5.0         0.0         3.0         4.0         9.0         6.0   \n",
       "\n",
       "   t_N_q13c_1  t_N_q13c_2  t_N_q13c_3  t_N_q13c_4  t_N_q13c_5  t_N_q13d_1  \\\n",
       "0         0.0         2.0         5.0         7.0         7.0         0.0   \n",
       "1         3.0         6.0         5.0        11.0         9.0         2.0   \n",
       "2         0.0        12.0        13.0         9.0         6.0         0.0   \n",
       "3         1.0         6.0         9.0        10.0         2.0         1.0   \n",
       "4         0.0         4.0         5.0         8.0         5.0         0.0   \n",
       "\n",
       "   t_N_q13d_2  t_N_q13d_3  t_N_q13d_4  t_N_q13d_5  t_N_q14_1  t_N_q14_2  \\\n",
       "0         3.0         6.0         7.0         5.0        0.0        3.0   \n",
       "1         5.0         7.0        11.0         9.0        2.0        1.0   \n",
       "2        11.0        13.0         8.0         8.0        2.0        3.0   \n",
       "3         6.0        11.0         7.0         3.0        1.0        5.0   \n",
       "4         4.0         5.0         7.0         6.0        1.0        3.0   \n",
       "\n",
       "   t_N_q14_3  t_N_q14_4  t_N_q14_5  t_N_q15a_1  t_N_q15a_2  t_N_q15a_3  \\\n",
       "0       10.0        5.0        2.0         7.0        12.0         1.0   \n",
       "1       18.0        3.0        9.0        16.0        15.0         0.0   \n",
       "2       17.0        5.0       10.0         8.0        13.0        12.0   \n",
       "3       14.0        3.0        5.0         2.0        11.0        10.0   \n",
       "4       11.0        2.0        3.0        11.0         5.0         5.0   \n",
       "\n",
       "   t_N_q15a_4  t_N_q15b_1  t_N_q15b_2  t_N_q15b_3  t_N_q15b_4  t_N_q15c_1  \\\n",
       "0         1.0        10.0         9.0         1.0         0.0         6.0   \n",
       "1         3.0        24.0         7.0         0.0         3.0        21.0   \n",
       "2         7.0        10.0        24.0         5.0         1.0         5.0   \n",
       "3         6.0         8.0        15.0         2.0         4.0         3.0   \n",
       "4         1.0        10.0         9.0         3.0         0.0         9.0   \n",
       "\n",
       "   t_N_q15c_2  t_N_q15c_3  t_N_q15c_4  t_N_q15d_1  t_N_q15d_2  t_N_q15d_3  \\\n",
       "0        12.0         2.0         0.0        11.0        10.0         0.0   \n",
       "1        10.0         0.0         3.0        30.0         4.0         0.0   \n",
       "2        19.0        14.0         2.0        14.0        22.0         4.0   \n",
       "3        15.0         7.0         4.0        13.0        16.0         0.0   \n",
       "4         8.0         4.0         1.0        16.0         6.0         0.0   \n",
       "\n",
       "   t_N_q15d_4  t_N_q15e_1  t_N_q15e_2  t_N_q15e_3  t_N_q15e_4  t_N_q15f_1  \\\n",
       "0         0.0        12.0         9.0         0.0         0.0        12.0   \n",
       "1         0.0        30.0         3.0         1.0         0.0        29.0   \n",
       "2         0.0        12.0        23.0         4.0         0.0        14.0   \n",
       "3         0.0        14.0        14.0         1.0         0.0        13.0   \n",
       "4         0.0        15.0         7.0         0.0         0.0        15.0   \n",
       "\n",
       "   t_N_q15f_2  t_N_q15f_3  t_N_q15f_4  s_q6a  s_q6b  s_q6c  s_q12a  s_q12b  \\\n",
       "0         9.0         0.0         0.0    NaN    NaN    NaN     NaN     NaN   \n",
       "1         4.0         1.0         0.0    NaN    NaN    NaN     NaN     NaN   \n",
       "2        23.0         3.0         0.0    NaN    NaN    NaN     NaN     NaN   \n",
       "3        12.0         4.0         0.0    5.9    4.8    7.4     4.8     3.5   \n",
       "4         6.0         0.0         0.0    NaN    NaN    NaN     NaN     NaN   \n",
       "\n",
       "   s_q12c  s_q13a  s_q13b  s_q13c  s_q13d  s_q13e  s_q13f  s_q13g  s_q14a  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3     4.6     9.4     6.7     5.6     4.4     7.3     8.7     8.0     6.2   \n",
       "4     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   s_q14b  s_q14c  s_q14d  s_q14e  s_q14f  s_q14g  s_q2b  s_q4a  s_q4b  s_q8b  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "3     5.3     6.7     6.4     6.3     5.0     6.6    7.1    5.4    4.7    6.5   \n",
       "4     NaN     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   s_q8c  s_q8d  s_q2a  s_q2c  s_q5a  s_q5b  s_q6d  s_q6e  s_q6f  s_q6g  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3    6.9    4.9    6.7    6.3    5.4    4.9    6.1    6.9    6.9    7.2   \n",
       "4    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   s_q6h  s_q9  s_q10  s_q11  s_q3a  s_q3b  s_q3c  s_q3d  s_q3e  s_q3f  s_q3g  \\\n",
       "0    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3    5.9   8.7    7.7    5.7    7.2    7.6    8.6    5.6    6.7    7.0    7.6   \n",
       "4    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   s_q3h  s_q3i  s_q7a  s_q7b  s_q1  s_q8a  s_q1_1  s_q1_2  s_q1_3  s_q1_4  \\\n",
       "0    NaN    NaN    NaN    NaN   NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "1    NaN    NaN    NaN    NaN   NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "2    NaN    NaN    NaN    NaN   NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "3    NaN    NaN    8.1    7.7   NaN    NaN    30.0    33.0    36.0     0.0   \n",
       "4    NaN    NaN    NaN    NaN   NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   s_q1_5  s_q1_6  s_q1_7  s_q2a_1  s_q2a_2  s_q2a_3  s_q2a_4  s_q2b_1  \\\n",
       "0     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     0.0     0.0     0.0     23.0     60.0     11.0      6.0     29.0   \n",
       "4     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q2b_2  s_q2b_3  s_q2b_4  s_q2c_1  s_q2c_2  s_q2c_3  s_q2c_4  s_q3a_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     55.0     13.0      2.0     23.0     50.0     18.0      9.0     30.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q3a_2  s_q3a_3  s_q3a_4  s_q3b_1  s_q3b_2  s_q3b_3  s_q3b_4  s_q3c_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     60.0      6.0      4.0     43.0     45.0      8.0      4.0     65.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q3c_2  s_q3c_3  s_q3c_4  s_q3d_1  s_q3d_2  s_q3d_3  s_q3d_4  s_q3e_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     32.0      1.0      2.0     17.0     46.0     25.0     12.0     29.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q3e_2  s_q3e_3  s_q3e_4  s_q3f_1  s_q3f_2  s_q3f_3  s_q3f_4  s_q3g_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     47.0     20.0      4.0     34.0     47.0     14.0      4.0     47.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q3g_2  s_q3g_3  s_q3g_4  s_q3h_1  s_q3h_2  s_q3h_3  s_q3h_4  s_q3i_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     39.0      8.0      5.0      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q3i_2  s_q3i_3  s_q3i_4  s_q4a_1  s_q4a_2  s_q4a_3  s_q4a_4  s_q4b_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN     19.0     26.0     27.0     27.0     26.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q4b_2  s_q4b_3  s_q4b_4  s_q5a_1  s_q5a_2  s_q5a_3  s_q5a_4  s_q5b_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     27.0     29.0     19.0     17.0     31.0     27.0     26.0     23.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q5b_2  s_q5b_3  s_q5b_4  s_q6a_1  s_q6a_2  s_q6a_3  s_q6a_4  s_q6a_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     29.0     27.0     21.0     21.0     39.0     19.0     12.0      9.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q6b_1  s_q6b_2  s_q6b_3  s_q6b_4  s_q6b_5  s_q6c_1  s_q6c_2  s_q6c_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     12.0     37.0     22.0     23.0      6.0     36.0     45.0      5.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q6c_4  s_q6c_5  s_q6d_1  s_q6d_2  s_q6d_3  s_q6d_4  s_q6d_5  s_q6e_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      7.0      8.0     20.0     29.0     12.0      7.0     33.0     30.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q6e_2  s_q6e_3  s_q6e_4  s_q6e_5  s_q6f_1  s_q6f_2  s_q6f_3  s_q6f_4  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     39.0      8.0      7.0     17.0     31.0     42.0     14.0      7.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q6f_5  s_q6g_1  s_q6g_2  s_q6g_3  s_q6g_4  s_q6g_5  s_q6h_1  s_q6h_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      7.0     35.0     45.0     12.0      7.0      1.0     18.0     42.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q6h_3  s_q6h_4  s_q6h_5  s_q7a_1  s_q7a_2  s_q7a_3  s_q7a_4  s_q7b_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     18.0     13.0      9.0      4.0     30.0     29.0     37.0      6.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q7b_2  s_q7b_3  s_q7b_4  s_q8a_1  s_q8a_2  s_q8a_3  s_q8a_4  s_q8a_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     35.0     33.0     27.0      5.0     11.0     31.0     36.0     18.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q8b_1  s_q8b_2  s_q8b_3  s_q8b_4  s_q8b_5  s_q8c_1  s_q8c_2  s_q8c_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      6.0     21.0     46.0     19.0      9.0     10.0     18.0     27.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q8c_4  s_q8c_5  s_q8d_1  s_q8d_2  s_q8d_3  s_q8d_4  s_q8d_5  s_q9a_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     19.0     25.0     10.0     49.0     27.0      7.0      7.0     21.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q9a_2  s_q9a_3  s_q9b_1  s_q9b_2  s_q9b_3  s_q9c_1  s_q9c_2  s_q9c_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     14.0     65.0     77.0     12.0     11.0     27.0     34.0     39.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q9d_1  s_q9d_2  s_q9d_3  s_q9e_1  s_q9e_2  s_q9e_3  s_q9f_1  s_q9f_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     40.0      8.0     52.0     61.0      6.0     34.0     72.0     14.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q9f_3  s_q9g_1  s_q9g_2  s_q9g_3  s_q9h_1  s_q9h_2  s_q9h_3  s_q10a_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "3     14.0     15.0     12.0     73.0     77.0     11.0     11.0      25.0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "\n",
       "   s_q10a_2  s_q10a_3  s_q10b_1  s_q10b_2  s_q10b_3  s_q10c_1  s_q10c_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      20.0      55.0      69.0      14.0      17.0      24.0      33.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q10c_3  s_q10d_1  s_q10d_2  s_q10d_3  s_q10e_1  s_q10e_2  s_q10e_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      42.0      33.0      19.0      48.0      46.0      10.0      44.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q10f_1  s_q10f_2  s_q10f_3  s_q10g_1  s_q10g_2  s_q10g_3  s_q10h_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      64.0      16.0      20.0      56.0      28.0      16.0      34.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q10h_2  s_q10h_3  s_q11_1  s_q11_2  s_q11_3  s_q11_4  s_q12a_1  s_q12a_2  \\\n",
       "0       NaN       NaN      NaN      NaN      NaN      NaN       NaN       NaN   \n",
       "1       NaN       NaN      NaN      NaN      NaN      NaN       NaN       NaN   \n",
       "2       NaN       NaN      NaN      NaN      NaN      NaN       NaN       NaN   \n",
       "3      22.0      44.0     19.0     47.0     21.0     13.0       9.0      48.0   \n",
       "4       NaN       NaN      NaN      NaN      NaN      NaN       NaN       NaN   \n",
       "\n",
       "   s_q12a_3  s_q12a_4  s_q12b_1  s_q12b_2  s_q12b_3  s_q12b_4  s_q12c_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      24.0      20.0      26.0      49.0      19.0       6.0      10.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q12c_2  s_q12c_3  s_q12c_4  s_q13a_1  s_q13a_2  s_q13a_3  s_q13a_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      42.0      23.0      25.0      85.0      13.0       3.0       0.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q13b_1  s_q13b_2  s_q13b_3  s_q13b_4  s_q13c_1  s_q13c_2  s_q13c_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      36.0      39.0      14.0      11.0      17.0      52.0      14.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q13c_4  s_q13d_1  s_q13d_2  s_q13d_3  s_q13d_4  s_q13e_1  s_q13e_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      17.0      15.0      35.0      18.0      32.0      54.0      25.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q13e_3  s_q13e_4  s_q13f_1  s_q13f_2  s_q13f_3  s_q13f_4  s_q13g_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      10.0      12.0      71.0      21.0       4.0       3.0      64.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q13g_2  s_q13g_3  s_q13g_4  s_q14a_1  s_q14a_2  s_q14a_3  s_q14a_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      20.0       7.0       9.0      23.0      50.0      15.0      12.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q14b_1  s_q14b_2  s_q14b_3  s_q14b_4  s_q14c_1  s_q14c_2  s_q14c_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      16.0      43.0      23.0      18.0      31.0      47.0      14.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q14c_4  s_q14d_1  s_q14d_2  s_q14d_3  s_q14d_4  s_q14e_1  s_q14e_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       9.0      28.0      48.0      13.0      11.0      25.0      50.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q14e_3  s_q14e_4  s_q14f_1  s_q14f_2  s_q14f_3  s_q14f_4  s_q14g_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      14.0      11.0      17.0      39.0      24.0      21.0      31.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q14g_2  s_q14g_3  s_q14g_4  s_N_q1_1  s_N_q1_2  s_N_q1_3  s_N_q1_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      46.0      16.0       8.0      42.0      46.0      50.0       0.0   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_N_q1_5  s_N_q1_6  s_N_q1_7  s_N_q2a_1  s_N_q2a_2  s_N_q2a_3  s_N_q2a_4  \\\n",
       "0       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "1       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "2       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "3       0.0       0.0       0.0       32.0       84.0       15.0        8.0   \n",
       "4       NaN       NaN       NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q2b_1  s_N_q2b_2  s_N_q2b_3  s_N_q2b_4  s_N_q2c_1  s_N_q2c_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       41.0       77.0       18.0        3.0       32.0       68.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q2c_3  s_N_q2c_4  s_N_q3a_1  s_N_q3a_2  s_N_q3a_3  s_N_q3a_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       25.0       12.0       42.0       84.0        8.0        6.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3b_1  s_N_q3b_2  s_N_q3b_3  s_N_q3b_4  s_N_q3c_1  s_N_q3c_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       60.0       63.0       11.0        5.0       90.0       44.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3c_3  s_N_q3c_4  s_N_q3d_1  s_N_q3d_2  s_N_q3d_3  s_N_q3d_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        2.0        3.0       23.0       64.0       35.0       16.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3e_1  s_N_q3e_2  s_N_q3e_3  s_N_q3e_4  s_N_q3f_1  s_N_q3f_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       41.0       65.0       28.0        5.0       47.0       66.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3f_3  s_N_q3f_4  s_N_q3g_1  s_N_q3g_2  s_N_q3g_3  s_N_q3g_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       20.0        6.0       62.0       52.0       11.0        7.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3h_1  s_N_q3h_2  s_N_q3h_3  s_N_q3h_4  s_N_q3i_1  s_N_q3i_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q3i_3  s_N_q3i_4  s_N_q4a_1  s_N_q4a_2  s_N_q4a_3  s_N_q4a_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN       26.0       35.0       37.0       37.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q4b_1  s_N_q4b_2  s_N_q4b_3  s_N_q4b_4  s_N_q5a_1  s_N_q5a_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       34.0       36.0       38.0       25.0       23.0       42.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q5a_3  s_N_q5a_4  s_N_q5b_1  s_N_q5b_2  s_N_q5b_3  s_N_q5b_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       37.0       35.0       31.0       39.0       37.0       29.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6a_1  s_N_q6a_2  s_N_q6a_3  s_N_q6a_4  s_N_q6a_5  s_N_q6b_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       29.0       54.0       26.0       17.0       12.0       16.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6b_2  s_N_q6b_3  s_N_q6b_4  s_N_q6b_5  s_N_q6c_1  s_N_q6c_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       51.0       30.0       32.0        8.0       49.0       62.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6c_3  s_N_q6c_4  s_N_q6c_5  s_N_q6d_1  s_N_q6d_2  s_N_q6d_3  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        7.0        9.0       11.0       27.0       40.0       16.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6d_4  s_N_q6d_5  s_N_q6e_1  s_N_q6e_2  s_N_q6e_3  s_N_q6e_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       10.0       45.0       41.0       53.0       11.0        9.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6e_5  s_N_q6f_1  s_N_q6f_2  s_N_q6f_3  s_N_q6f_4  s_N_q6f_5  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       23.0       42.0       57.0       19.0       10.0        9.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6g_1  s_N_q6g_2  s_N_q6g_3  s_N_q6g_4  s_N_q6g_5  s_N_q6h_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       48.0       62.0       17.0        9.0        1.0       25.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q6h_2  s_N_q6h_3  s_N_q6h_4  s_N_q6h_5  s_N_q7a_1  s_N_q7a_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       57.0       24.0       18.0       13.0        6.0       42.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q7a_3  s_N_q7a_4  s_N_q7b_1  s_N_q7b_2  s_N_q7b_3  s_N_q7b_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       41.0       52.0        8.0       48.0       46.0       37.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q8a_1  s_N_q8a_2  s_N_q8a_3  s_N_q8a_4  s_N_q8a_5  s_N_q8b_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        7.0       15.0       43.0       50.0       25.0        8.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q8b_2  s_N_q8b_3  s_N_q8b_4  s_N_q8b_5  s_N_q8c_1  s_N_q8c_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       29.0       63.0       26.0       12.0       14.0       25.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q8c_3  s_N_q8c_4  s_N_q8c_5  s_N_q8d_1  s_N_q8d_2  s_N_q8d_3  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       38.0       27.0       35.0       14.0       67.0       37.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q8d_4  s_N_q8d_5  s_N_q9a_1  s_N_q9a_2  s_N_q9a_3  s_N_q9b_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       10.0       10.0       28.0       18.0       87.0      107.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q9b_2  s_N_q9b_3  s_N_q9c_1  s_N_q9c_2  s_N_q9c_3  s_N_q9d_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       17.0       15.0       36.0       46.0       53.0       55.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q9d_2  s_N_q9d_3  s_N_q9e_1  s_N_q9e_2  s_N_q9e_3  s_N_q9f_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       11.0       72.0       83.0        8.0       46.0      101.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q9f_2  s_N_q9f_3  s_N_q9g_1  s_N_q9g_2  s_N_q9g_3  s_N_q9h_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       20.0       19.0       20.0       16.0       96.0      108.0   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q9h_2  s_N_q9h_3  s_N_q10a_1  s_N_q10a_2  s_N_q10a_3  s_N_q10b_1  \\\n",
       "0        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "1        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "2        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "3       16.0       16.0        33.0        26.0        73.0        93.0   \n",
       "4        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q10b_2  s_N_q10b_3  s_N_q10c_1  s_N_q10c_2  s_N_q10c_3  s_N_q10d_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        19.0        23.0        32.0        44.0        56.0        43.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q10d_2  s_N_q10d_3  s_N_q10e_1  s_N_q10e_2  s_N_q10e_3  s_N_q10f_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        25.0        63.0        60.0        13.0        58.0        84.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q10f_2  s_N_q10f_3  s_N_q10g_1  s_N_q10g_2  s_N_q10g_3  s_N_q10h_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        21.0        26.0        75.0        37.0        21.0        44.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q10h_2  s_N_q10h_3  s_N_q11_1  s_N_q11_2  s_N_q11_3  s_N_q11_4  \\\n",
       "0         NaN         NaN        NaN        NaN        NaN        NaN   \n",
       "1         NaN         NaN        NaN        NaN        NaN        NaN   \n",
       "2         NaN         NaN        NaN        NaN        NaN        NaN   \n",
       "3        29.0        57.0       25.0       63.0       29.0       18.0   \n",
       "4         NaN         NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   s_N_q12a_1  s_N_q12a_2  s_N_q12a_3  s_N_q12a_4  s_N_q12b_1  s_N_q12b_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        12.0        67.0        34.0        28.0        36.0        68.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q12b_3  s_N_q12b_4  s_N_q12c_1  s_N_q12c_2  s_N_q12c_3  s_N_q12c_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        27.0         9.0        14.0        59.0        32.0        35.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q13a_1  s_N_q13a_2  s_N_q13a_3  s_N_q13a_4  s_N_q13b_1  s_N_q13b_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3       120.0        18.0         4.0         0.0        51.0        54.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q13b_3  s_N_q13b_4  s_N_q13c_1  s_N_q13c_2  s_N_q13c_3  s_N_q13c_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        19.0        16.0        24.0        74.0        20.0        24.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q13d_1  s_N_q13d_2  s_N_q13d_3  s_N_q13d_4  s_N_q13e_1  s_N_q13e_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        21.0        50.0        26.0        45.0        76.0        35.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q13e_3  s_N_q13e_4  s_N_q13f_1  s_N_q13f_2  s_N_q13f_3  s_N_q13f_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        14.0        17.0       100.0        30.0         6.0         4.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q13g_1  s_N_q13g_2  s_N_q13g_3  s_N_q13g_4  s_N_q14a_1  s_N_q14a_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        89.0        28.0         9.0        12.0        32.0        68.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q14a_3  s_N_q14a_4  s_N_q14b_1  s_N_q14b_2  s_N_q14b_3  s_N_q14b_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        21.0        16.0        23.0        60.0        32.0        25.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q14c_1  s_N_q14c_2  s_N_q14c_3  s_N_q14c_4  s_N_q14d_1  s_N_q14d_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        43.0        66.0        19.0        12.0        39.0        68.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q14d_3  s_N_q14d_4  s_N_q14e_1  s_N_q14e_2  s_N_q14e_3  s_N_q14e_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        18.0        16.0        35.0        70.0        20.0        16.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q14f_1  s_N_q14f_2  s_N_q14f_3  s_N_q14f_4  s_N_q14g_1  s_N_q14g_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3        23.0        54.0        33.0        29.0        31.0        46.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   s_N_q14g_3  s_N_q14g_4  p_q1c  p_q1b  p_q1e  p_q1f  p_q6c  p_q12c  p_q1a  \\\n",
       "0         NaN         NaN    NaN    NaN    NaN    NaN    NaN     NaN    NaN   \n",
       "1         NaN         NaN    NaN    NaN    NaN    NaN    NaN     NaN    NaN   \n",
       "2         NaN         NaN    NaN    NaN    NaN    NaN    NaN     NaN    NaN   \n",
       "3        16.0         8.0    NaN    NaN    NaN    NaN    NaN     NaN    NaN   \n",
       "4         NaN         NaN    NaN    NaN    NaN    NaN    NaN     NaN    NaN   \n",
       "\n",
       "   p_q1d  p_q3e  p_q6e  p_q7  p_q12d  p_q1g  p_q6d  p_q6f  p_q6g  p_q6h  \\\n",
       "0    NaN    NaN    NaN   NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN    NaN    NaN   NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN    NaN    NaN   NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3    NaN    NaN    NaN   NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN    NaN    NaN   NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   p_q12a  p_q12b  p_q12e  p_q12f  p_q13a  p_q13b  p_q13c  p_q13d  p_q14a  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   p_q14b  p_q14c  p_q14d  p_q1a_1  p_q1a_2  p_q1a_3  p_q1a_4  p_q1a_5  \\\n",
       "0     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4     NaN     NaN     NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q1b_1  p_q1b_2  p_q1b_3  p_q1b_4  p_q1b_5  p_q1c_1  p_q1c_2  p_q1c_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q1c_4  p_q1c_5  p_q1d_1  p_q1d_2  p_q1d_3  p_q1d_4  p_q1d_5  p_q1e_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q1e_2  p_q1e_3  p_q1e_4  p_q1e_5  p_q1f_1  p_q1f_2  p_q1f_3  p_q1f_4  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q1f_5  p_q1g_1  p_q1g_2  p_q1g_3  p_q1g_4  p_q1g_5  p_q3e_1  p_q3e_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q3e_3  p_q3e_4  p_q3e_5  p_q5k  p_q5l  p_q5m  p_q5n  p_q6c_1  p_q6c_2  \\\n",
       "0      NaN      NaN      NaN    NaN    NaN    NaN    NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN    NaN    NaN    NaN    NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN    NaN    NaN    NaN    NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN    NaN    NaN    NaN    NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN    NaN    NaN    NaN    NaN      NaN      NaN   \n",
       "\n",
       "   p_q6c_3  p_q6c_4  p_q6c_5  p_q6d_1  p_q6d_2  p_q6d_3  p_q6d_4  p_q6d_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q6e_1  p_q6e_2  p_q6e_3  p_q6e_4  p_q6e_5  p_q6f_1  p_q6f_2  p_q6f_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q6f_4  p_q6f_5  p_q6g_1  p_q6g_2  p_q6g_3  p_q6g_4  p_q6g_5  p_q6h_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q6h_2  p_q6h_3  p_q6h_4  p_q6h_5  p_q7e  p_q7f  p_q7g  p_q7h  p_q7i  \\\n",
       "0      NaN      NaN      NaN      NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1      NaN      NaN      NaN      NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2      NaN      NaN      NaN      NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3      NaN      NaN      NaN      NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4      NaN      NaN      NaN      NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   p_q7j  p_q7k  p_q7l  p_q7m  p_q7n  p_q9_11  p_q9_12  p_q9_13  p_q9_14  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "1    NaN    NaN    NaN    NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "2    NaN    NaN    NaN    NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "3    NaN    NaN    NaN    NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "4    NaN    NaN    NaN    NaN    NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_q9_15  p_q10a_1  p_q10a_2  p_q10a_3  p_q10a_4  p_q10a_5  p_q10b_1  \\\n",
       "0      NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1      NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2      NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4      NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q10b_2  p_q10b_3  p_q10b_4  p_q10b_5  p_q10c_1  p_q10c_2  p_q10c_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q10c_4  p_q10c_5  p_q10d_1  p_q10d_2  p_q10d_3  p_q10d_4  p_q10d_5  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q10e_1  p_q10e_2  p_q10e_3  p_q10e_4  p_q10e_5  p_q10f_1  p_q10f_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q10f_3  p_q10f_4  p_q10f_5  p_q11a_5  p_q11b_5  p_q11c_5  p_q11d_5  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q11e_5  p_q12a_1  p_q12a_2  p_q12a_3  p_q12a_4  p_q12b_1  p_q12b_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q12b_3  p_q12b_4  p_q12c_1  p_q12c_2  p_q12c_3  p_q12c_4  p_q12d_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q12d_2  p_q12d_3  p_q12d_4  p_q12e_1  p_q12e_2  p_q12e_3  p_q12e_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q12f_1  p_q12f_2  p_q12f_3  p_q12f_4  p_q13a_1  p_q13a_2  p_q13a_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q13a_4  p_q13a_5  p_q13b_1  p_q13b_2  p_q13b_3  p_q13b_4  p_q13b_5  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q13c_1  p_q13c_2  p_q13c_3  p_q13c_4  p_q13c_5  p_q13d_1  p_q13d_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q13d_3  p_q13d_4  p_q13d_5  p_q14a_1  p_q14a_2  p_q14a_3  p_q14a_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q14a_5  p_q14b_1  p_q14b_2  p_q14b_3  p_q14b_4  p_q14b_5  p_q14c_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q14c_2  p_q14c_3  p_q14c_4  p_q14c_5  p_q14d_1  p_q14d_2  p_q14d_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   p_q14d_4  p_q14d_5  p_N_q1a_1  p_N_q1a_2  p_N_q1a_3  p_N_q1a_4  p_N_q1a_5  \\\n",
       "0       NaN       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1       NaN       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2       NaN       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       NaN       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4       NaN       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q1b_1  p_N_q1b_2  p_N_q1b_3  p_N_q1b_4  p_N_q1b_5  p_N_q1c_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q1c_2  p_N_q1c_3  p_N_q1c_4  p_N_q1c_5  p_N_q1d_1  p_N_q1d_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q1d_3  p_N_q1d_4  p_N_q1d_5  p_N_q1e_1  p_N_q1e_2  p_N_q1e_3  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q1e_4  p_N_q1e_5  p_N_q1f_1  p_N_q1f_2  p_N_q1f_3  p_N_q1f_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q1f_5  p_N_q1g_1  p_N_q1g_2  p_N_q1g_3  p_N_q1g_4  p_N_q1g_5  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q3e_1  p_N_q3e_2  p_N_q3e_3  p_N_q3e_4  p_N_q3e_5  p_N_q5k  p_N_q5l  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN      NaN      NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN      NaN      NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN      NaN      NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN      NaN      NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN      NaN      NaN   \n",
       "\n",
       "   p_N_q5m  p_N_q5n  p_N_q6c_1  p_N_q6c_2  p_N_q6c_3  p_N_q6c_4  p_N_q6c_5  \\\n",
       "0      NaN      NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1      NaN      NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2      NaN      NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3      NaN      NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4      NaN      NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q6d_1  p_N_q6d_2  p_N_q6d_3  p_N_q6d_4  p_N_q6d_5  p_N_q6e_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q6e_2  p_N_q6e_3  p_N_q6e_4  p_N_q6e_5  p_N_q6f_1  p_N_q6f_2  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q6f_3  p_N_q6f_4  p_N_q6f_5  p_N_q6g_1  p_N_q6g_2  p_N_q6g_3  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q6g_4  p_N_q6g_5  p_N_q6h_1  p_N_q6h_2  p_N_q6h_3  p_N_q6h_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   p_N_q6h_5  p_N_q7a  p_N_q7b  p_N_q7c  p_N_q7d  p_N_q7e  p_N_q7f  p_N_q7g  \\\n",
       "0        NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1        NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2        NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3        NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4        NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   p_N_q7h  p_N_q7i  p_N_q7j  p_N_q7k  p_N_q7l  p_N_q7m  p_N_q7n  p_N_q9_11  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN        NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN        NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN        NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN        NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN        NaN   \n",
       "\n",
       "   p_N_q9_12  p_N_q9_13  p_N_q9_14  p_N_q9_15  p_N_q10a_1  p_N_q10a_2  \\\n",
       "0        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "1        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "2        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "3        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "4        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q10a_3  p_N_q10a_4  p_N_q10a_5  p_N_q10b_1  p_N_q10b_2  p_N_q10b_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q10b_4  p_N_q10b_5  p_N_q10c_1  p_N_q10c_2  p_N_q10c_3  p_N_q10c_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q10c_5  p_N_q10d_1  p_N_q10d_2  p_N_q10d_3  p_N_q10d_4  p_N_q10d_5  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q10e_1  p_N_q10e_2  p_N_q10e_3  p_N_q10e_4  p_N_q10e_5  p_N_q10f_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q10f_2  p_N_q10f_3  p_N_q10f_4  p_N_q10f_5  p_N_q11a_5  p_N_q11b_5  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q11c_5  p_N_q11d_5  p_N_q11e_5  p_N_q12a_1  p_N_q12a_2  p_N_q12a_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q12a_4  p_N_q12b_1  p_N_q12b_2  p_N_q12b_3  p_N_q12b_4  p_N_q12c_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q12c_2  p_N_q12c_3  p_N_q12c_4  p_N_q12d_1  p_N_q12d_2  p_N_q12d_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q12d_4  p_N_q12e_1  p_N_q12e_2  p_N_q12e_3  p_N_q12e_4  p_N_q12f_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q12f_2  p_N_q12f_3  p_N_q12f_4  p_N_q13a_1  p_N_q13a_2  p_N_q13a_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q13a_4  p_N_q13a_5  p_N_q13b_1  p_N_q13b_2  p_N_q13b_3  p_N_q13b_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q13b_5  p_N_q13c_1  p_N_q13c_2  p_N_q13c_3  p_N_q13c_4  p_N_q13c_5  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q13d_1  p_N_q13d_2  p_N_q13d_3  p_N_q13d_4  p_N_q13d_5  p_N_q14a_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q14a_2  p_N_q14a_3  p_N_q14a_4  p_N_q14a_5  p_N_q14b_1  p_N_q14b_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q14b_3  p_N_q14b_4  p_N_q14b_5  p_N_q14c_1  p_N_q14c_2  p_N_q14c_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q14c_4  p_N_q14c_5  p_N_q14d_1  p_N_q14d_2  p_N_q14d_3  p_N_q14d_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   p_N_q14d_5  t_q13e  t_q13f  t_q13g  t_q13h  t_q13i  t_q13j  t_q13k  t_q13l  \\\n",
       "0         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   t_q13m  t_q13n  t_q12  t_q10  t_q7f  t_q16a  t_q16b  t_q16c  t_q16d  \\\n",
       "0     NaN     NaN    NaN    NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN    NaN    NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "2     NaN     NaN    NaN    NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "3     NaN     NaN    NaN    NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "4     NaN     NaN    NaN    NaN    NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   t_q17a  t_q17b  t_q17c  t_q17d  t_q17e  t_q17f  t_q3k_1  t_q3k_2  t_q3k_3  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "3     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "4     NaN     NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "\n",
       "   t_q3l_1  t_q3l_2  t_q3l_3  t_q7f_1  t_q7f_2  t_q7f_3  t_q7f_4  t_q7f_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   t_q10_1  t_q10_2  t_q10_3  t_q10_4  t_q10_5  t_q11a_5  t_q11b_5  t_q11c_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q11d_5  t_q12_1  t_q12_2  t_q12_3  t_q12_4  t_q12_5  t_q12_6  t_q13e_1  \\\n",
       "0       NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "1       NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "2       NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "3       NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "4       NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "\n",
       "   t_q13e_2  t_q13e_3  t_q13e_4  t_q13f_1  t_q13f_2  t_q13f_3  t_q13f_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q13g_1  t_q13g_2  t_q13g_3  t_q13g_4  t_q13h_1  t_q13h_2  t_q13h_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q13h_4  t_q13i_1  t_q13i_2  t_q13i_3  t_q13i_4  t_q13j_1  t_q13j_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q13j_3  t_q13j_4  t_q13k_1  t_q13k_2  t_q13k_3  t_q13k_4  t_q13l_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q13l_2  t_q13l_3  t_q13l_4  t_q13m_1  t_q13m_2  t_q13m_3  t_q13m_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q13n_1  t_q13n_2  t_q13n_3  t_q13n_4  t_q14_6  t_q15a_5  t_q15b_5  \\\n",
       "0       NaN       NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "\n",
       "   t_q15c_5  t_q15d_5  t_q16a_1  t_q16a_2  t_q16a_3  t_q16a_4  t_q16a_5  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q16b_1  t_q16b_2  t_q16b_3  t_q16b_4  t_q16b_5  t_q16c_1  t_q16c_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q16c_3  t_q16c_4  t_q16c_5  t_q16d_1  t_q16d_2  t_q16d_3  t_q16d_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q16d_5  t_q17a_1  t_q17a_2  t_q17a_3  t_q17a_4  t_q17b_1  t_q17b_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q17b_3  t_q17b_4  t_q17c_1  t_q17c_2  t_q17c_3  t_q17c_4  t_q17d_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q17d_2  t_q17d_3  t_q17d_4  t_q17e_1  t_q17e_2  t_q17e_3  t_q17e_4  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   t_q17f_1  t_q17f_2  t_q17f_3  t_q17f_4  t_N_q3k_1  t_N_q3k_2  t_N_q3k_3  \\\n",
       "0       NaN       NaN       NaN       NaN        NaN        NaN        NaN   \n",
       "1       NaN       NaN       NaN       NaN        NaN        NaN        NaN   \n",
       "2       NaN       NaN       NaN       NaN        NaN        NaN        NaN   \n",
       "3       NaN       NaN       NaN       NaN        NaN        NaN        NaN   \n",
       "4       NaN       NaN       NaN       NaN        NaN        NaN        NaN   \n",
       "\n",
       "   t_N_q3l_1  t_N_q3l_2  t_N_q3l_3  t_N_q7f_1  t_N_q7f_2  t_N_q7f_3  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   t_N_q7f_4  t_N_q7f_5  t_N_q10_1  t_N_q10_2  t_N_q10_3  t_N_q10_4  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   t_N_q10_5  t_N_q11a_5  t_N_q11b_5  t_N_q11c_5  t_N_q11d_5  t_N_q12_1  \\\n",
       "0        NaN         NaN         NaN         NaN         NaN        NaN   \n",
       "1        NaN         NaN         NaN         NaN         NaN        NaN   \n",
       "2        NaN         NaN         NaN         NaN         NaN        NaN   \n",
       "3        NaN         NaN         NaN         NaN         NaN        NaN   \n",
       "4        NaN         NaN         NaN         NaN         NaN        NaN   \n",
       "\n",
       "   t_N_q12_2  t_N_q12_3  t_N_q12_4  t_N_q12_5  t_N_q12_6  t_N_q13e_1  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "\n",
       "   t_N_q13e_2  t_N_q13e_3  t_N_q13e_4  t_N_q13f_1  t_N_q13f_2  t_N_q13f_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13f_4  t_N_q13g_1  t_N_q13g_2  t_N_q13g_3  t_N_q13g_4  t_N_q13h_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13h_2  t_N_q13h_3  t_N_q13h_4  t_N_q13i_1  t_N_q13i_2  t_N_q13i_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13i_4  t_N_q13j_1  t_N_q13j_2  t_N_q13j_3  t_N_q13j_4  t_N_q13k_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13k_2  t_N_q13k_3  t_N_q13k_4  t_N_q13l_1  t_N_q13l_2  t_N_q13l_3  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13l_4  t_N_q13m_1  t_N_q13m_2  t_N_q13m_3  t_N_q13m_4  t_N_q13n_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q13n_2  t_N_q13n_3  t_N_q13n_4  t_N_q14_6  t_N_q15a_5  t_N_q15b_5  \\\n",
       "0         NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN        NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q15c_5  t_N_q15d_5  t_N_q16a_1  t_N_q16a_2  t_N_q16a_3  t_N_q16a_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q16a_5  t_N_q16b_1  t_N_q16b_2  t_N_q16b_3  t_N_q16b_4  t_N_q16b_5  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q16c_1  t_N_q16c_2  t_N_q16c_3  t_N_q16c_4  t_N_q16c_5  t_N_q16d_1  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q16d_2  t_N_q16d_3  t_N_q16d_4  t_N_q16d_5  t_N_q17a_1  t_N_q17a_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q17a_3  t_N_q17a_4  t_N_q17b_1  t_N_q17b_2  t_N_q17b_3  t_N_q17b_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q17c_1  t_N_q17c_2  t_N_q17c_3  t_N_q17c_4  t_N_q17d_1  t_N_q17d_2  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q17d_3  t_N_q17d_4  t_N_q17e_1  t_N_q17e_2  t_N_q17e_3  t_N_q17e_4  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   t_N_q17f_1  t_N_q17f_2  t_N_q17f_3  t_N_q17f_4  s_q5c  s_q11a  s_q11b  \\\n",
       "0         NaN         NaN         NaN         NaN    NaN     NaN     NaN   \n",
       "1         NaN         NaN         NaN         NaN    NaN     NaN     NaN   \n",
       "2         NaN         NaN         NaN         NaN    NaN     NaN     NaN   \n",
       "3         NaN         NaN         NaN         NaN    NaN     NaN     NaN   \n",
       "4         NaN         NaN         NaN         NaN    NaN     NaN     NaN   \n",
       "\n",
       "   s_q11c  s_q12d  s_q12e  s_q12f  s_q12g  s_q1b  s_q7c  s_q7d  s_q1a  s_q1c  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4     NaN     NaN     NaN     NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   s_q5d  s_q5e  s_q5g  s_q8  s_q2d  s_q2e  s_q2f  s_q2g  s_q5f  s_q14  \\\n",
       "0    NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3    NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN    NaN    NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   s_q1a_1  s_q1a_2  s_q1a_3  s_q1a_4  s_q1b_1  s_q1b_2  s_q1b_3  s_q1b_4  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q1c_1  s_q1c_2  s_q1c_3  s_q1c_4  s_q2d_1  s_q2d_2  s_q2d_3  s_q2d_4  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q2e_1  s_q2e_2  s_q2e_3  s_q2e_4  s_q2f_1  s_q2f_2  s_q2f_3  s_q2f_4  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q2g_1  s_q2g_2  s_q2g_3  s_q2g_4  s_q5a_5  s_q5b_5  s_q5c_1  s_q5c_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q5c_3  s_q5c_4  s_q5c_5  s_q5d_1  s_q5d_2  s_q5d_3  s_q5d_4  s_q5d_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q5e_1  s_q5e_2  s_q5e_3  s_q5e_4  s_q5e_5  s_q5f_1  s_q5f_2  s_q5f_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q5f_4  s_q5f_5  s_q5g_1  s_q5g_2  s_q5g_3  s_q5g_4  s_q5g_5  s_q7a_5  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q7b_5  s_q7c_1  s_q7c_2  s_q7c_3  s_q7c_4  s_q7c_5  s_q7d_1  s_q7d_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q7d_3  s_q7d_4  s_q7d_5  s_q8e_1  s_q8e_2  s_q8e_3  s_q8f_1  s_q8f_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q8f_3  s_q8g_1  s_q8g_2  s_q8g_3  s_q8h_1  s_q8h_2  s_q8h_3  s_q8i_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q8i_2  s_q8i_3  s_q8j_1  s_q8j_2  s_q8j_3  s_q8k_1  s_q8k_2  s_q8k_3  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q8l_1  s_q8l_2  s_q8l_3  s_q9i_1  s_q9i_2  s_q9i_3  s_q9j_1  s_q9j_2  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q9j_3  s_q9k_1  s_q9k_2  s_q9k_3  s_q9l_1  s_q9l_2  s_q9l_3  s_q10_1  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q10_2  s_q10_3  s_q10_4  s_q11a_1  s_q11a_2  s_q11a_3  s_q11a_4  \\\n",
       "0      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n",
       "1      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n",
       "2      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n",
       "3      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n",
       "4      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q11b_1  s_q11b_2  s_q11b_3  s_q11b_4  s_q11c_1  s_q11c_2  s_q11c_3  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q11c_4  s_q12d_1  s_q12d_2  s_q12d_3  s_q12d_4  s_q12e_1  s_q12e_2  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q12e_3  s_q12e_4  s_q12f_1  s_q12f_2  s_q12f_3  s_q12f_4  s_q12g_1  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   s_q12g_2  s_q12g_3  s_q12g_4  s_q14_1  s_q14_2  s_q14_3  s_q14_4  s_q14_5  \\\n",
       "0       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4       NaN       NaN       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   s_q14_6  s_q14_7  s_q14_8  s_q14_9  s_q14_10  s_q14_11  \n",
       "0      NaN      NaN      NaN      NaN       NaN       NaN  \n",
       "1      NaN      NaN      NaN      NaN       NaN       NaN  \n",
       "2      NaN      NaN      NaN      NaN       NaN       NaN  \n",
       "3      NaN      NaN      NaN      NaN       NaN       NaN  \n",
       "4      NaN      NaN      NaN      NaN       NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the files into dataframes\n",
    "all_survey = pd.read_csv(\"https://raw.githubusercontent.com/nathpignaton/guided_projects/main/nyc-sat-analysis/survey_all.txt\", \n",
    "                         delimiter=\"\\t\", encoding=\"windows-1252\")\n",
    "\n",
    "d75_survey = pd.read_csv(\"https://raw.githubusercontent.com/nathpignaton/guided_projects/main/nyc-sat-analysis/survey_d75.txt\", \n",
    "                         delimiter=\"\\t\", encoding=\"windows-1252\")\n",
    "\n",
    "# concatenating both\n",
    "survey = pd.concat([all_survey, d75_survey], axis=0)\n",
    "\n",
    "print(survey.shape)\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we can see above, the resulting dataframe has more than 2000 columns, so we see that we need to reduce it, since we do not need all of them and its easier to work with less columns.  \n",
    "Thanks to the [dictionary of the survey data](https://data.cityofnewyork.us/Education/2011-NYC-School-Survey/mnz3-dyi8) we can keep the columns we know we'll need and remove the other ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1702, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copying the data from `DBN` column to `dbn` column to fit the name pattern\n",
    "survey['DBN'] = survey['dbn']\n",
    "\n",
    "# selecting the columns we want\n",
    "columns_filter = [\"DBN\", \"rr_s\", \"rr_t\", \"rr_p\", \"N_s\", \"N_t\", \"N_p\", \"saf_p_11\", \"com_p_11\", \"eng_p_11\", \n",
    "                  \"aca_p_11\", \"saf_t_11\", \"com_t_11\", \"eng_t_11\", \"aca_t_11\", \"saf_s_11\", \"com_s_11\", \"eng_s_11\", \n",
    "                  \"aca_s_11\", \"saf_tot_11\", \"com_tot_11\", \"eng_tot_11\", \"aca_tot_11\"]\n",
    "\n",
    "# removing the other columns\n",
    "survey = survey.loc[:, columns_filter]\n",
    "\n",
    "# adding it to the dictionary\n",
    "data['survey'] = survey\n",
    "\n",
    "data['survey'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Done that, we need to start cleaning and preparing the information do be displayed and analyzed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Data Preprocessing\n",
    "\n",
    "First thing will be to unify our data based on a common column, in our case this common column is `DBN`. DBN is the District Borough Number and it is the combination of the District Number, the letter code for the borough and the number of the school.   \n",
    "To unify the datasets using DBN, we need to be sure that all of them are treated and in the same format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3.1 Treating the `DBN` columns\n",
    "\n",
    "We're going to need to change the names of the columns in every dataset but `hs_directory` dataframe, so they're all lowercase, and create the `dbn` column in the `class_size` dataset too.  \n",
    "\n",
    "To help us understand what happened in the change, the loop has some specific phrases that tell us what happened to the entry: **name successfully changed**, **name already in lowercase** or **no DBN column**.  \n",
    "We are also going to use a cool class that makes easy to print text with different colors in python. Bellow we are going to create it, the topic about different font printing in Python was found in StackOverflow [here](https://stackoverflow.com/questions/8924173/how-do-i-print-bold-text-in-python/8930747). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class color:\n",
    "   PURPLE = '\\033[95m'\n",
    "   CYAN = '\\033[96m'\n",
    "   DARKCYAN = '\\033[36m'\n",
    "   BLUE = '\\033[94m'\n",
    "   GREEN = '\\033[92m'\n",
    "   YELLOW = '\\033[93m'\n",
    "   RED = '\\033[91m'\n",
    "   BOLD = '\\033[1m'\n",
    "   UNDERLINE = '\\033[4m'\n",
    "   END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ap_2010 DBN column was changed to lowercase.\n",
      "\u001b[94m\u001b[1mThe class_size has no DBN.\u001b[0m\n",
      "The demographics DBN column was changed to lowercase.\n",
      "The graduation DBN column was changed to lowercase.\n",
      "The hs_directory DBN column is already lowercase.\n",
      "The math_test_results DBN column was changed to lowercase.\n",
      "The sat_results DBN column was changed to lowercase.\n",
      "\u001b[94m\u001b[1mThe school_attendence has no DBN.\u001b[0m\n",
      "The survey DBN column was changed to lowercase.\n"
     ]
    }
   ],
   "source": [
    "# changin the name of the DBN columns to lower case (all of them)\n",
    "for item in data.keys():\n",
    "    if ('DBN') in data[item].columns: \n",
    "        data[item].rename({'DBN':'dbn'}, axis=1, inplace=True)\n",
    "        print(\"The {} DBN column was changed to lowercase.\".format(item))\n",
    "    elif ('dbn') in data[item].columns:\n",
    "        print(\"The {} DBN column is already lowercase.\".format(item))\n",
    "    else:\n",
    "        # let's make this information a litte bit more visible\n",
    "        print(color.BLUE + color.BOLD + \"The {} has no DBN.\".format(item) + color.END)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we can see above, the `class_size` and `school_attendence` have no `DBN` or `dbn` column, let's look at these specific dataframes to see if the information is there, in any other form. But first, let's look at the `dbn` values to see how they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['01M015', '01M019', '01M020', ..., '32K554', '32K556', '32K564'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a sample of what the dbn looks like\n",
    "data['demographics']['dbn'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we can see above, the `dbn` values are made of two numbers, followed by a letter and other three numbers. With that in mind we can see that the information we need is already in `class_size` and `school attendence`. But the `school attendence` dataset is not separated by schools, it's separated by District. We are going to leave it as it is for now and treat it later.  \n",
    "\n",
    "Let's start looking at the `class_size` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CSD</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>SCHOOL CODE</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>PROGRAM TYPE</th>\n",
       "      <th>CORE SUBJECT (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>CORE COURSE (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>SERVICE CATEGORY(K-9* ONLY)</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>DATA SOURCE</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>0K</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>0K</td>\n",
       "      <td>CTT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>01</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>01</td>\n",
       "      <td>CTT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>02</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CSD BOROUGH SCHOOL CODE                SCHOOL NAME GRADE  PROGRAM TYPE  \\\n",
       "0    1       M        M015  P.S. 015 Roberto Clemente     0K       GEN ED   \n",
       "1    1       M        M015  P.S. 015 Roberto Clemente     0K          CTT   \n",
       "2    1       M        M015  P.S. 015 Roberto Clemente     01       GEN ED   \n",
       "3    1       M        M015  P.S. 015 Roberto Clemente     01          CTT   \n",
       "4    1       M        M015  P.S. 015 Roberto Clemente     02       GEN ED   \n",
       "\n",
       "  CORE SUBJECT (MS CORE and 9-12 ONLY) CORE COURSE (MS CORE and 9-12 ONLY)  \\\n",
       "0                                    -                                   -   \n",
       "1                                    -                                   -   \n",
       "2                                    -                                   -   \n",
       "3                                    -                                   -   \n",
       "4                                    -                                   -   \n",
       "\n",
       "  SERVICE CATEGORY(K-9* ONLY)  NUMBER OF STUDENTS / SEATS FILLED  \\\n",
       "0                           -                               19.0   \n",
       "1                           -                               21.0   \n",
       "2                           -                               17.0   \n",
       "3                           -                               17.0   \n",
       "4                           -                               15.0   \n",
       "\n",
       "   NUMBER OF SECTIONS  AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  \\\n",
       "0                 1.0                19.0                    19.0   \n",
       "1                 1.0                21.0                    21.0   \n",
       "2                 1.0                17.0                    17.0   \n",
       "3                 1.0                17.0                    17.0   \n",
       "4                 1.0                15.0                    15.0   \n",
       "\n",
       "   SIZE OF LARGEST CLASS DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO  \n",
       "0                   19.0         ATS                             NaN  \n",
       "1                   21.0         ATS                             NaN  \n",
       "2                   17.0         ATS                             NaN  \n",
       "3                   17.0         ATS                             NaN  \n",
       "4                   15.0         ATS                             NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class_size'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we can see above, the `DBN` is just the union of the `CSD` and `SCHOOL CODE` columns. But to do so we need to insert an extra 0 to the number when needed, since the `DBN` is composed by two numbers in the beginning, not one. After it we can concatenate the other values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>SCHOOL NAME</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>PROGRAM TYPE</th>\n",
       "      <th>CORE SUBJECT (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>CORE COURSE (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>SERVICE CATEGORY(K-9* ONLY)</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
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       "      <th>DATA SOURCE</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "      <th>two_digit_csd</th>\n",
       "      <th>dbn</th>\n",
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       "  </thead>\n",
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       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>01</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>01</td>\n",
       "      <td>CTT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M015</td>\n",
       "      <td>P.S. 015 Roberto Clemente</td>\n",
       "      <td>02</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ATS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CSD BOROUGH SCHOOL CODE                SCHOOL NAME GRADE  PROGRAM TYPE  \\\n",
       "0    1       M        M015  P.S. 015 Roberto Clemente     0K       GEN ED   \n",
       "1    1       M        M015  P.S. 015 Roberto Clemente     0K          CTT   \n",
       "2    1       M        M015  P.S. 015 Roberto Clemente     01       GEN ED   \n",
       "3    1       M        M015  P.S. 015 Roberto Clemente     01          CTT   \n",
       "4    1       M        M015  P.S. 015 Roberto Clemente     02       GEN ED   \n",
       "\n",
       "  CORE SUBJECT (MS CORE and 9-12 ONLY) CORE COURSE (MS CORE and 9-12 ONLY)  \\\n",
       "0                                    -                                   -   \n",
       "1                                    -                                   -   \n",
       "2                                    -                                   -   \n",
       "3                                    -                                   -   \n",
       "4                                    -                                   -   \n",
       "\n",
       "  SERVICE CATEGORY(K-9* ONLY)  NUMBER OF STUDENTS / SEATS FILLED  \\\n",
       "0                           -                               19.0   \n",
       "1                           -                               21.0   \n",
       "2                           -                               17.0   \n",
       "3                           -                               17.0   \n",
       "4                           -                               15.0   \n",
       "\n",
       "   NUMBER OF SECTIONS  AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  \\\n",
       "0                 1.0                19.0                    19.0   \n",
       "1                 1.0                21.0                    21.0   \n",
       "2                 1.0                17.0                    17.0   \n",
       "3                 1.0                17.0                    17.0   \n",
       "4                 1.0                15.0                    15.0   \n",
       "\n",
       "   SIZE OF LARGEST CLASS DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO  \\\n",
       "0                   19.0         ATS                             NaN   \n",
       "1                   21.0         ATS                             NaN   \n",
       "2                   17.0         ATS                             NaN   \n",
       "3                   17.0         ATS                             NaN   \n",
       "4                   15.0         ATS                             NaN   \n",
       "\n",
       "  two_digit_csd     dbn  \n",
       "0            01  01M015  \n",
       "1            01  01M015  \n",
       "2            01  01M015  \n",
       "3            01  01M015  \n",
       "4            01  01M015  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a function to do it to the whole column\n",
    "def insert_zero(number):\n",
    "    # the number need to be a string to be concatenated\n",
    "    string = str(number)\n",
    "    size = len(string)\n",
    "    # if it is a one digit number, it will turn it into a two digit number\n",
    "    if size == 1:\n",
    "        return string.zfill(2)\n",
    "    # if it is a two digit number, nothing happens\n",
    "    if size == 2:\n",
    "        return string\n",
    "    \n",
    "# applying to the column\n",
    "data['class_size']['two_digit_csd'] = data['class_size']['CSD'].apply(insert_zero)\n",
    "\n",
    "# concateneting the columns\n",
    "data['class_size']['dbn'] = data['class_size']['two_digit_csd'] + data['class_size']['SCHOOL CODE']\n",
    "\n",
    "data['class_size'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Following our treatment, we are going to analyze and look into the `sat_score` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Creating the main `sat_score` column in the `sat_results` dataframe \n",
    "To see what our problem looks like, let's print the dataset to see the columns and the information they store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355</td>\n",
       "      <td>404</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383</td>\n",
       "      <td>423</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377</td>\n",
       "      <td>402</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414</td>\n",
       "      <td>401</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390</td>\n",
       "      <td>433</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn                                    SCHOOL NAME  \\\n",
       "0  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1  01M448            UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2  01M450                     EAST SIDE COMMUNITY SCHOOL   \n",
       "3  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
       "\n",
       "  Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score  \\\n",
       "0                     29                             355                 404   \n",
       "1                     91                             383                 423   \n",
       "2                     70                             377                 402   \n",
       "3                      7                             414                 401   \n",
       "4                     44                             390                 433   \n",
       "\n",
       "  SAT Writing Avg. Score  \n",
       "0                    363  \n",
       "1                    366  \n",
       "2                    370  \n",
       "3                    359  \n",
       "4                    384  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking out our data\n",
    "data['sat_results'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>sat_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>1122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1207.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn                                    SCHOOL NAME  \\\n",
       "0  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1  01M448            UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2  01M450                     EAST SIDE COMMUNITY SCHOOL   \n",
       "3  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
       "\n",
       "  Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "0                     29                            355.0   \n",
       "1                     91                            383.0   \n",
       "2                     70                            377.0   \n",
       "3                      7                            414.0   \n",
       "4                     44                            390.0   \n",
       "\n",
       "   SAT Math Avg. Score  SAT Writing Avg. Score  sat_score  \n",
       "0                404.0                   363.0     1122.0  \n",
       "1                423.0                   366.0     1172.0  \n",
       "2                402.0                   370.0     1149.0  \n",
       "3                401.0                   359.0     1174.0  \n",
       "4                433.0                   384.0     1207.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the columns\n",
    "data['sat_results']['SAT Math Avg. Score'] = pd.to_numeric(data['sat_results']['SAT Math Avg. Score'], \n",
    "                                                           errors='coerce')\n",
    "\n",
    "data['sat_results']['SAT Critical Reading Avg. Score'] = pd.to_numeric(data['sat_results']['SAT Critical Reading Avg. Score'], \n",
    "                                                                       errors='coerce')\n",
    "\n",
    "data['sat_results']['SAT Writing Avg. Score'] = pd.to_numeric(data['sat_results']['SAT Writing Avg. Score'],\n",
    "                                                              errors='coerce')\n",
    "\n",
    "# calculating the sat_score column\n",
    "data['sat_results']['sat_score'] = data['sat_results']['SAT Math Avg. Score'] + data['sat_results']['SAT Critical Reading Avg. Score'] + data['sat_results']['SAT Writing Avg. Score']\n",
    "\n",
    "data['sat_results'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done that, we're going to extract the latitude and longitude values for each school in the `hs_directory`, so we can map the schools and uncover any geographic patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Extracting the Latitude and Longitude from `hs_directory`\n",
    "This will be done using a function that uses a regular expression to take the exact numbers we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lat\n",
      " 0    40.601989336\n",
      "1    40.593593811\n",
      "2    40.692133704\n",
      "3    40.822303765\n",
      "4    40.773670507\n",
      "Name: lat, dtype: object \n",
      "\n",
      " long\n",
      " 0     -73.762834323\n",
      "1     -73.984729232\n",
      "2     -73.931503172\n",
      "3      -73.85596139\n",
      "4     -73.985268558\n",
      "Name: long, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# latitude extractor\n",
    "def extractor_lat(string):\n",
    "    coords = re.findall(\"\\(.+\\)\", string)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "# longitude extractor\n",
    "def extractor_long(string):\n",
    "    coords = re.findall(\"\\(.+\\)\", string)\n",
    "    long = coords[0].split(\",\")[1].replace(\")\", \"\")\n",
    "    return long\n",
    "\n",
    "# using the functions to extract the latitude and longitude\n",
    "data['hs_directory']['lat'] = data['hs_directory']['Location 1'].apply(extractor_lat)\n",
    "data['hs_directory']['long'] = data['hs_directory']['Location 1'].apply(extractor_long)\n",
    "\n",
    "print(\"lat\\n\", data['hs_directory']['lat'].head(), \"\\n\\n\", \"long\\n\", data['hs_directory']['long'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we've done that, we still have to do a few things before combining the data. If we look close enough, we can see that the DBN values in the `class_size` dataframe are not unique.    \n",
    "\n",
    "\n",
    "Since we are looking for the average SAT score per school, we need only the data correspondent to the later grades. We are also going to look the program types, each school has multiple program types, we are going to choose the largest category. Beyond that we need to check the class size, that has multiple entries due to the different subjects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3.4 Working the `class_size` dataframe to contain unique DBNs\n",
    "\n",
    "To do so, we are going to filter the `class_size` data so the `GRADE` column has only `09-12` grades. Next lets take a look into the `PROGRAM TYPE` column, only after it we can move on to the `CORE SUBJECT` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GEN ED     6513\n",
       "CTT        2953\n",
       "SPEC ED    1178\n",
       "Name: PROGRAM TYPE, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a new variable to store the information\n",
    "class_size = data['class_size']\n",
    "\n",
    "# filtering GRADE values\n",
    "class_size = class_size[class_size['GRADE '] == '09-12']\n",
    "\n",
    "# checking the PROGAM TYPE values\n",
    "class_size['PROGRAM TYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we saw above, `GEN ED` is the category with the most amount of entries, with that in mind, we are going to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CSD</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>SCHOOL CODE</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>PROGRAM TYPE</th>\n",
       "      <th>CORE SUBJECT (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>CORE COURSE (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>SERVICE CATEGORY(K-9* ONLY)</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>DATA SOURCE</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "      <th>two_digit_csd</th>\n",
       "      <th>dbn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513.000000</td>\n",
       "      <td>6513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>583</td>\n",
       "      <td>583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>K</td>\n",
       "      <td>K505</td>\n",
       "      <td>Bronx School for Law, Government and Justice</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>Integrated Algebra</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02</td>\n",
       "      <td>09X505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1927</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>6513</td>\n",
       "      <td>6513</td>\n",
       "      <td>1977</td>\n",
       "      <td>449</td>\n",
       "      <td>6513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>814</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.266697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.045755</td>\n",
       "      <td>6.682788</td>\n",
       "      <td>24.748549</td>\n",
       "      <td>19.675725</td>\n",
       "      <td>28.739751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.275239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>253.464004</td>\n",
       "      <td>8.506092</td>\n",
       "      <td>5.499972</td>\n",
       "      <td>6.473547</td>\n",
       "      <td>6.315539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2965.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CSD BOROUGH SCHOOL CODE  \\\n",
       "count   6513.000000    6513        6513   \n",
       "unique          NaN       5         583   \n",
       "top             NaN       K        K505   \n",
       "freq            NaN    1927          21   \n",
       "mean      14.266697     NaN         NaN   \n",
       "std        9.275239     NaN         NaN   \n",
       "min        1.000000     NaN         NaN   \n",
       "25%        7.000000     NaN         NaN   \n",
       "50%       13.000000     NaN         NaN   \n",
       "75%       22.000000     NaN         NaN   \n",
       "max       32.000000     NaN         NaN   \n",
       "\n",
       "                                         SCHOOL NAME GRADE  PROGRAM TYPE  \\\n",
       "count                                           6513   6513         6513   \n",
       "unique                                           583      1            1   \n",
       "top     Bronx School for Law, Government and Justice  09-12       GEN ED   \n",
       "freq                                              21   6513         6513   \n",
       "mean                                             NaN    NaN          NaN   \n",
       "std                                              NaN    NaN          NaN   \n",
       "min                                              NaN    NaN          NaN   \n",
       "25%                                              NaN    NaN          NaN   \n",
       "50%                                              NaN    NaN          NaN   \n",
       "75%                                              NaN    NaN          NaN   \n",
       "max                                              NaN    NaN          NaN   \n",
       "\n",
       "       CORE SUBJECT (MS CORE and 9-12 ONLY)  \\\n",
       "count                                  6513   \n",
       "unique                                    4   \n",
       "top                                 ENGLISH   \n",
       "freq                                   1977   \n",
       "mean                                    NaN   \n",
       "std                                     NaN   \n",
       "min                                     NaN   \n",
       "25%                                     NaN   \n",
       "50%                                     NaN   \n",
       "75%                                     NaN   \n",
       "max                                     NaN   \n",
       "\n",
       "       CORE COURSE (MS CORE and 9-12 ONLY) SERVICE CATEGORY(K-9* ONLY)  \\\n",
       "count                                 6513                        6513   \n",
       "unique                                  22                           1   \n",
       "top                     Integrated Algebra                           -   \n",
       "freq                                   449                        6513   \n",
       "mean                                   NaN                         NaN   \n",
       "std                                    NaN                         NaN   \n",
       "min                                    NaN                         NaN   \n",
       "25%                                    NaN                         NaN   \n",
       "50%                                    NaN                         NaN   \n",
       "75%                                    NaN                         NaN   \n",
       "max                                    NaN                         NaN   \n",
       "\n",
       "        NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  \\\n",
       "count                         6513.000000         6513.000000   \n",
       "unique                                NaN                 NaN   \n",
       "top                                   NaN                 NaN   \n",
       "freq                                  NaN                 NaN   \n",
       "mean                           177.045755            6.682788   \n",
       "std                            253.464004            8.506092   \n",
       "min                              6.000000            1.000000   \n",
       "25%                             54.000000            2.000000   \n",
       "50%                             95.000000            4.000000   \n",
       "75%                            184.000000            7.000000   \n",
       "max                           2965.000000          100.000000   \n",
       "\n",
       "        AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "count          6513.000000             6513.000000            6513.000000   \n",
       "unique                 NaN                     NaN                    NaN   \n",
       "top                    NaN                     NaN                    NaN   \n",
       "freq                   NaN                     NaN                    NaN   \n",
       "mean             24.748549               19.675725              28.739751   \n",
       "std               5.499972                6.473547               6.315539   \n",
       "min               6.000000                5.000000               6.000000   \n",
       "25%              21.200000               14.000000              25.000000   \n",
       "50%              25.000000               19.000000              30.000000   \n",
       "75%              28.800000               24.000000              34.000000   \n",
       "max              95.000000               95.000000              95.000000   \n",
       "\n",
       "       DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO two_digit_csd     dbn  \n",
       "count         6513                             0.0          6513    6513  \n",
       "unique           1                             NaN            32     583  \n",
       "top          STARS                             NaN            02  09X505  \n",
       "freq          6513                             NaN           814      21  \n",
       "mean           NaN                             NaN           NaN     NaN  \n",
       "std            NaN                             NaN           NaN     NaN  \n",
       "min            NaN                             NaN           NaN     NaN  \n",
       "25%            NaN                             NaN           NaN     NaN  \n",
       "50%            NaN                             NaN           NaN     NaN  \n",
       "75%            NaN                             NaN           NaN     NaN  \n",
       "max            NaN                             NaN           NaN     NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering the PROGRAM TYPE\n",
    "class_size = class_size[class_size['PROGRAM TYPE'] == 'GEN ED']\n",
    "\n",
    "# checking the new df\n",
    "class_size.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now that we've filtered the information, we still need to group the information to show it per DBN. Looking closely into the columns, we can see that the class sizes are separated into major subjects and other specific subjects related. This isn't relevant for us, since our objective is to analyze data per school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CSD</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>SCHOOL CODE</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>PROGRAM TYPE</th>\n",
       "      <th>CORE SUBJECT (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>CORE COURSE (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>SERVICE CATEGORY(K-9* ONLY)</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>DATA SOURCE</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "      <th>two_digit_csd</th>\n",
       "      <th>dbn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 9</td>\n",
       "      <td>-</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 10</td>\n",
       "      <td>-</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 11</td>\n",
       "      <td>-</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 12</td>\n",
       "      <td>-</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>MATH</td>\n",
       "      <td>Integrated Algebra</td>\n",
       "      <td>-</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CSD BOROUGH SCHOOL CODE                                    SCHOOL NAME  \\\n",
       "225    1       M        M292  Henry Street School for International Studies   \n",
       "226    1       M        M292  Henry Street School for International Studies   \n",
       "227    1       M        M292  Henry Street School for International Studies   \n",
       "228    1       M        M292  Henry Street School for International Studies   \n",
       "229    1       M        M292  Henry Street School for International Studies   \n",
       "\n",
       "    GRADE  PROGRAM TYPE CORE SUBJECT (MS CORE and 9-12 ONLY)  \\\n",
       "225  09-12       GEN ED                              ENGLISH   \n",
       "226  09-12       GEN ED                              ENGLISH   \n",
       "227  09-12       GEN ED                              ENGLISH   \n",
       "228  09-12       GEN ED                              ENGLISH   \n",
       "229  09-12       GEN ED                                 MATH   \n",
       "\n",
       "    CORE COURSE (MS CORE and 9-12 ONLY) SERVICE CATEGORY(K-9* ONLY)  \\\n",
       "225                           English 9                           -   \n",
       "226                          English 10                           -   \n",
       "227                          English 11                           -   \n",
       "228                          English 12                           -   \n",
       "229                  Integrated Algebra                           -   \n",
       "\n",
       "     NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  \\\n",
       "225                               63.0                 3.0   \n",
       "226                               79.0                 3.0   \n",
       "227                               38.0                 2.0   \n",
       "228                               69.0                 3.0   \n",
       "229                               53.0                 3.0   \n",
       "\n",
       "     AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "225                21.0                    19.0                   25.0   \n",
       "226                26.3                    24.0                   31.0   \n",
       "227                19.0                    16.0                   22.0   \n",
       "228                23.0                    13.0                   30.0   \n",
       "229                17.7                    16.0                   21.0   \n",
       "\n",
       "    DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO two_digit_csd     dbn  \n",
       "225       STARS                             NaN            01  01M292  \n",
       "226       STARS                             NaN            01  01M292  \n",
       "227       STARS                             NaN            01  01M292  \n",
       "228       STARS                             NaN            01  01M292  \n",
       "229       STARS                             NaN            01  01M292  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking into the CORE SUBJECT column\n",
    "class_size.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "With that in mind, we are going to group the dataset calculating the averages per DBN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>CSD</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>1</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>22.564286</td>\n",
       "      <td>18.50</td>\n",
       "      <td>26.571429</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M332</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>21.00</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M378</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.00</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M448</td>\n",
       "      <td>1</td>\n",
       "      <td>105.6875</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>22.231250</td>\n",
       "      <td>18.25</td>\n",
       "      <td>27.062500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M450</td>\n",
       "      <td>1</td>\n",
       "      <td>57.6000</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>22.866667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn  CSD  NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  \\\n",
       "0  01M292    1                            88.0000            4.000000   \n",
       "1  01M332    1                            46.0000            2.000000   \n",
       "2  01M378    1                            33.0000            1.000000   \n",
       "3  01M448    1                           105.6875            4.750000   \n",
       "4  01M450    1                            57.6000            2.733333   \n",
       "\n",
       "   AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "0           22.564286                   18.50              26.571429   \n",
       "1           22.000000                   21.00              23.500000   \n",
       "2           33.000000                   33.00              33.000000   \n",
       "3           22.231250                   18.25              27.062500   \n",
       "4           21.200000                   19.40              22.866667   \n",
       "\n",
       "   SCHOOLWIDE PUPIL-TEACHER RATIO  \n",
       "0                             NaN  \n",
       "1                             NaN  \n",
       "2                             NaN  \n",
       "3                             NaN  \n",
       "4                             NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the dataset into a new variable\n",
    "grouped = class_size.groupby('dbn')\n",
    "\n",
    "# calculating the mean \n",
    "class_size = grouped.agg(np.mean)\n",
    "\n",
    "# reseting index and turning dbn into a column again\n",
    "class_size = class_size.reset_index() \n",
    "\n",
    "# assigning it back to the dictionary\n",
    "data['class_size'] = class_size\n",
    "\n",
    "data['class_size'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Next we are going to treat the `demographics` dataframe to contain only unique DBNs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3.5 Working the `demographics`  dataframe to contain unique DBNs \n",
    "In this dataframe, the main problem is related to the school year. The dataset has information from various years. In our analysis we are going to use only the most recent data, so we are going to filter the `20112012` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>Name</th>\n",
       "      <th>schoolyear</th>\n",
       "      <th>fl_percent</th>\n",
       "      <th>frl_percent</th>\n",
       "      <th>total_enrollment</th>\n",
       "      <th>prek</th>\n",
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       "      <th>grade11</th>\n",
       "      <th>grade12</th>\n",
       "      <th>ell_num</th>\n",
       "      <th>ell_percent</th>\n",
       "      <th>sped_num</th>\n",
       "      <th>sped_percent</th>\n",
       "      <th>ctt_num</th>\n",
       "      <th>selfcontained_num</th>\n",
       "      <th>asian_num</th>\n",
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       "      <th>6</th>\n",
       "      <td>01M015</td>\n",
       "      <td>P.S. 015 ROBERTO CLEMENTE</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.4</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6.3</td>\n",
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       "      <td>92.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01M019</td>\n",
       "      <td>P.S. 019 ASHER LEVY</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.5</td>\n",
       "      <td>328</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>51</td>\n",
       "      <td>15.5</td>\n",
       "      <td>81</td>\n",
       "      <td>24.7</td>\n",
       "      <td>158</td>\n",
       "      <td>48.2</td>\n",
       "      <td>28</td>\n",
       "      <td>8.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01M020</td>\n",
       "      <td>PS 020 ANNA SILVER</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.5</td>\n",
       "      <td>626</td>\n",
       "      <td>52</td>\n",
       "      <td>102</td>\n",
       "      <td>121</td>\n",
       "      <td>87</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>49</td>\n",
       "      <td>31</td>\n",
       "      <td>190</td>\n",
       "      <td>30.4</td>\n",
       "      <td>55</td>\n",
       "      <td>8.8</td>\n",
       "      <td>357</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>330.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>296.0</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>01M034</td>\n",
       "      <td>PS 034 FRANKLIN D ROOSEVELT</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.7</td>\n",
       "      <td>401</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
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       "      <td></td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>5.5</td>\n",
       "      <td>90</td>\n",
       "      <td>22.4</td>\n",
       "      <td>275</td>\n",
       "      <td>68.6</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>50.9</td>\n",
       "      <td>197.0</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>01M063</td>\n",
       "      <td>PS 063 WILLIAM MCKINLEY</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.9</td>\n",
       "      <td>176</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>45.0</td>\n",
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       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>41</td>\n",
       "      <td>23.3</td>\n",
       "      <td>110</td>\n",
       "      <td>62.5</td>\n",
       "      <td>15</td>\n",
       "      <td>8.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>55.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>44.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dbn                                              Name  schoolyear  \\\n",
       "6   01M015  P.S. 015 ROBERTO CLEMENTE                           20112012   \n",
       "13  01M019  P.S. 019 ASHER LEVY                                 20112012   \n",
       "20  01M020  PS 020 ANNA SILVER                                  20112012   \n",
       "27  01M034  PS 034 FRANKLIN D ROOSEVELT                         20112012   \n",
       "35  01M063  PS 063 WILLIAM MCKINLEY                             20112012   \n",
       "\n",
       "   fl_percent  frl_percent  total_enrollment prek    k grade1 grade2 grade3  \\\n",
       "6         NaN         89.4               189   13   31     35     28     25   \n",
       "13        NaN         61.5               328   32   46     52     54     52   \n",
       "20        NaN         92.5               626   52  102    121     87     88   \n",
       "27        NaN         99.7               401   14   34     38     36     45   \n",
       "35        NaN         78.9               176   18   20     30     21     31   \n",
       "\n",
       "   grade4 grade5 grade6 grade7 grade8 grade9 grade10 grade11 grade12  ell_num  \\\n",
       "6      28     29                                                         20.0   \n",
       "13     46     46                                                         33.0   \n",
       "20     85     91                                                        128.0   \n",
       "27     28     40     55     55     56                                    34.0   \n",
       "35     26     30                                                          6.0   \n",
       "\n",
       "    ell_percent  sped_num  sped_percent ctt_num selfcontained_num  asian_num  \\\n",
       "6          10.6      40.0          21.2      23                 7         12   \n",
       "13         10.1      59.0          18.0      16                16         51   \n",
       "20         20.4      97.0          15.5      49                31        190   \n",
       "27          8.5     106.0          26.4      59                16         22   \n",
       "35          3.4      45.0          25.6      34                 4          9   \n",
       "\n",
       "    asian_per  black_num  black_per  hispanic_num  hispanic_per  white_num  \\\n",
       "6         6.3         63       33.3           109          57.7          4   \n",
       "13       15.5         81       24.7           158          48.2         28   \n",
       "20       30.4         55        8.8           357          57.0         16   \n",
       "27        5.5         90       22.4           275          68.6          8   \n",
       "35        5.1         41       23.3           110          62.5         15   \n",
       "\n",
       "    white_per  male_num  male_per  female_num  female_per  \n",
       "6         2.1      97.0      51.3        92.0        48.7  \n",
       "13        8.5     147.0      44.8       181.0        55.2  \n",
       "20        2.6     330.0      52.7       296.0        47.3  \n",
       "27        2.0     204.0      50.9       197.0        49.1  \n",
       "35        8.5      97.0      55.1        79.0        44.9  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the dataset into a new variable\n",
    "demographics = data['demographics']\n",
    "\n",
    "# filtering the entries\n",
    "demographics = demographics[demographics['schoolyear'] == 20112012]\n",
    "\n",
    "# assigning it back to the dictionary\n",
    "data['demographics'] = demographics\n",
    "\n",
    "data['demographics'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Done that we will move to the last dataframe to condense `gradutation`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3.6 Working the `graduation`  dataframe to contain unique DBNs\n",
    "In the graduation dataframe, the `Demographic` and `Cohort` are the ones preventing the DBN to be unique. `Cohort` appears to refer to the year the data represents, and the `Demographic` appears to refer to a specific demographic group. In this case, we want to pick data from the most recent Cohort available, which is **2006**. We also want data from the full cohort, so we'll only pick rows where Demographic is **Total Cohort**.  \n",
    "Let's filter the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Demographic</th>\n",
       "      <th>dbn</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Cohort</th>\n",
       "      <th>Total Cohort</th>\n",
       "      <th>Total Grads - n</th>\n",
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       "      <th>Total Regents - n</th>\n",
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       "      <th>Regents w/o Advanced - n</th>\n",
       "      <th>Regents w/o Advanced - % of cohort</th>\n",
       "      <th>Regents w/o Advanced - % of grads</th>\n",
       "      <th>Local - n</th>\n",
       "      <th>Local - % of cohort</th>\n",
       "      <th>Local - % of grads</th>\n",
       "      <th>Still Enrolled - n</th>\n",
       "      <th>Still Enrolled - % of cohort</th>\n",
       "      <th>Dropped Out - n</th>\n",
       "      <th>Dropped Out - % of cohort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL</td>\n",
       "      <td>2006</td>\n",
       "      <td>78</td>\n",
       "      <td>43</td>\n",
       "      <td>55.1</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>16</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>124</td>\n",
       "      <td>53</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42</td>\n",
       "      <td>33.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>34</td>\n",
       "      <td>27.4</td>\n",
       "      <td>64.2</td>\n",
       "      <td>11</td>\n",
       "      <td>8.9</td>\n",
       "      <td>20.8</td>\n",
       "      <td>46</td>\n",
       "      <td>37.1</td>\n",
       "      <td>20</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>77.8</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>15</td>\n",
       "      <td>16.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40</td>\n",
       "      <td>47.6</td>\n",
       "      <td>85.1</td>\n",
       "      <td>17</td>\n",
       "      <td>20.2</td>\n",
       "      <td>36.2</td>\n",
       "      <td>23</td>\n",
       "      <td>27.4</td>\n",
       "      <td>48.9</td>\n",
       "      <td>7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>25</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M515</td>\n",
       "      <td>LOWER EAST SIDE PREPARATORY HIGH SCHO</td>\n",
       "      <td>2006</td>\n",
       "      <td>193</td>\n",
       "      <td>105</td>\n",
       "      <td>54.4</td>\n",
       "      <td>91</td>\n",
       "      <td>47.2</td>\n",
       "      <td>86.7</td>\n",
       "      <td>69</td>\n",
       "      <td>35.8</td>\n",
       "      <td>65.7</td>\n",
       "      <td>22</td>\n",
       "      <td>11.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14</td>\n",
       "      <td>7.3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>53</td>\n",
       "      <td>27.5</td>\n",
       "      <td>35</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Demographic     dbn                            School Name Cohort  \\\n",
       "3   Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL   2006   \n",
       "10  Total Cohort  01M448    UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   2006   \n",
       "17  Total Cohort  01M450             EAST SIDE COMMUNITY SCHOOL   2006   \n",
       "24  Total Cohort  01M509                MARTA VALLE HIGH SCHOOL   2006   \n",
       "31  Total Cohort  01M515  LOWER EAST SIDE PREPARATORY HIGH SCHO   2006   \n",
       "\n",
       "    Total Cohort Total Grads - n  Total Grads - % of cohort Total Regents - n  \\\n",
       "3             78              43                       55.1                36   \n",
       "10           124              53                       42.7                42   \n",
       "17            90              70                       77.8                67   \n",
       "24            84              47                       56.0                40   \n",
       "31           193             105                       54.4                91   \n",
       "\n",
       "    Total Regents - % of cohort  Total Regents - % of grads  \\\n",
       "3                          46.2                        83.7   \n",
       "10                         33.9                        79.2   \n",
       "17                         74.4                        95.7   \n",
       "24                         47.6                        85.1   \n",
       "31                         47.2                        86.7   \n",
       "\n",
       "   Advanced Regents - n  Advanced Regents - % of cohort  \\\n",
       "3                     0                             0.0   \n",
       "10                    8                             6.5   \n",
       "17                    0                             0.0   \n",
       "24                   17                            20.2   \n",
       "31                   69                            35.8   \n",
       "\n",
       "    Advanced Regents - % of grads Regents w/o Advanced - n  \\\n",
       "3                             0.0                       36   \n",
       "10                           15.1                       34   \n",
       "17                            0.0                       67   \n",
       "24                           36.2                       23   \n",
       "31                           65.7                       22   \n",
       "\n",
       "    Regents w/o Advanced - % of cohort  Regents w/o Advanced - % of grads  \\\n",
       "3                                 46.2                               83.7   \n",
       "10                                27.4                               64.2   \n",
       "17                                74.4                               95.7   \n",
       "24                                27.4                               48.9   \n",
       "31                                11.4                               21.0   \n",
       "\n",
       "   Local - n  Local - % of cohort  Local - % of grads Still Enrolled - n  \\\n",
       "3          7                  9.0                16.3                 16   \n",
       "10        11                  8.9                20.8                 46   \n",
       "17         3                  3.3                 4.3                 15   \n",
       "24         7                  8.3                14.9                 25   \n",
       "31        14                  7.3                13.3                 53   \n",
       "\n",
       "    Still Enrolled - % of cohort Dropped Out - n  Dropped Out - % of cohort  \n",
       "3                           20.5              11                       14.1  \n",
       "10                          37.1              20                       16.1  \n",
       "17                          16.7               5                        5.6  \n",
       "24                          29.8               5                        6.0  \n",
       "31                          27.5              35                       18.1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the dataset into a new variable\n",
    "graduation = data['graduation']\n",
    "\n",
    "# filtering both columns\n",
    "graduation = graduation[graduation['Cohort'] == '2006']\n",
    "graduation = graduation[graduation['Demographic'] == 'Total Cohort']\n",
    "\n",
    "# assigning it back to the dictionary\n",
    "data['graduation'] = graduation\n",
    "\n",
    "data['graduation'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "After treating the unique DBNs, we still need to treat the `ap_2010` dataframe, because it has numeric values stored as strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3.7 Converting string numeric values to the right datatype\n",
    "There are three columns we need this change to happen: `AP Test Takers `, `Total Exams Taken` and `Number of Exams with scores 3 4 or 5`. We'll do a loop to convert all of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dbn                                      object\n",
       "SchoolName                               object\n",
       "AP Test Takers                          float64\n",
       "Total Exams Taken                       float64\n",
       "Number of Exams with scores 3 4 or 5    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a list with the columns we need to convert\n",
    "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n",
    "\n",
    "# loop to conversion\n",
    "for col in cols:\n",
    "    data['ap_2010'][col] = pd.to_numeric(data['ap_2010'][col], errors='coerce')\n",
    "    \n",
    "data['ap_2010'].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we are finally ready do merge our datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Combining the Data\n",
    "To do so, we are going to merge the data in a strategic way to always keep the most amount of DBNs possible, not removing any entry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Merging the datasets one by one\n",
    "With the objective of not losing any important value, we are going to merge the datasets using two different approaches, left and inner join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(479, 33)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>sat_score</th>\n",
       "      <th>SchoolName</th>\n",
       "      <th>AP Test Takers</th>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <th>Number of Exams with scores 3 4 or 5</th>\n",
       "      <th>Demographic</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Cohort</th>\n",
       "      <th>Total Cohort</th>\n",
       "      <th>Total Grads - n</th>\n",
       "      <th>Total Grads - % of cohort</th>\n",
       "      <th>Total Regents - n</th>\n",
       "      <th>Total Regents - % of cohort</th>\n",
       "      <th>Total Regents - % of grads</th>\n",
       "      <th>Advanced Regents - n</th>\n",
       "      <th>Advanced Regents - % of cohort</th>\n",
       "      <th>Advanced Regents - % of grads</th>\n",
       "      <th>Regents w/o Advanced - n</th>\n",
       "      <th>Regents w/o Advanced - % of cohort</th>\n",
       "      <th>Regents w/o Advanced - % of grads</th>\n",
       "      <th>Local - n</th>\n",
       "      <th>Local - % of cohort</th>\n",
       "      <th>Local - % of grads</th>\n",
       "      <th>Still Enrolled - n</th>\n",
       "      <th>Still Enrolled - % of cohort</th>\n",
       "      <th>Dropped Out - n</th>\n",
       "      <th>Dropped Out - % of cohort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL</td>\n",
       "      <td>2006</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43</td>\n",
       "      <td>55.1</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>16</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD H.S.</td>\n",
       "      <td>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>124.0</td>\n",
       "      <td>53</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42</td>\n",
       "      <td>33.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>34</td>\n",
       "      <td>27.4</td>\n",
       "      <td>64.2</td>\n",
       "      <td>11</td>\n",
       "      <td>8.9</td>\n",
       "      <td>20.8</td>\n",
       "      <td>46</td>\n",
       "      <td>37.1</td>\n",
       "      <td>20</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>EAST SIDE COMMUNITY HS</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70</td>\n",
       "      <td>77.8</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>15</td>\n",
       "      <td>16.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>84.0</td>\n",
       "      <td>47</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40</td>\n",
       "      <td>47.6</td>\n",
       "      <td>85.1</td>\n",
       "      <td>17</td>\n",
       "      <td>20.2</td>\n",
       "      <td>36.2</td>\n",
       "      <td>23</td>\n",
       "      <td>27.4</td>\n",
       "      <td>48.9</td>\n",
       "      <td>7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>25</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn                                    SCHOOL NAME  \\\n",
       "0  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1  01M448            UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2  01M450                     EAST SIDE COMMUNITY SCHOOL   \n",
       "3  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
       "\n",
       "  Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "0                     29                            355.0   \n",
       "1                     91                            383.0   \n",
       "2                     70                            377.0   \n",
       "3                      7                            414.0   \n",
       "4                     44                            390.0   \n",
       "\n",
       "   SAT Math Avg. Score  SAT Writing Avg. Score  sat_score  \\\n",
       "0                404.0                   363.0     1122.0   \n",
       "1                423.0                   366.0     1172.0   \n",
       "2                402.0                   370.0     1149.0   \n",
       "3                401.0                   359.0     1174.0   \n",
       "4                433.0                   384.0     1207.0   \n",
       "\n",
       "                     SchoolName  AP Test Takers   Total Exams Taken  \\\n",
       "0                           NaN              NaN                NaN   \n",
       "1  UNIVERSITY NEIGHBORHOOD H.S.             39.0               49.0   \n",
       "2        EAST SIDE COMMUNITY HS             19.0               21.0   \n",
       "3                           NaN              NaN                NaN   \n",
       "4                           NaN              NaN                NaN   \n",
       "\n",
       "   Number of Exams with scores 3 4 or 5   Demographic  \\\n",
       "0                                   NaN  Total Cohort   \n",
       "1                                  10.0  Total Cohort   \n",
       "2                                   NaN  Total Cohort   \n",
       "3                                   NaN           NaN   \n",
       "4                                   NaN  Total Cohort   \n",
       "\n",
       "                             School Name Cohort  Total Cohort Total Grads - n  \\\n",
       "0  HENRY STREET SCHOOL FOR INTERNATIONAL   2006          78.0              43   \n",
       "1    UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   2006         124.0              53   \n",
       "2             EAST SIDE COMMUNITY SCHOOL   2006          90.0              70   \n",
       "3                                    NaN    NaN           NaN             NaN   \n",
       "4                MARTA VALLE HIGH SCHOOL   2006          84.0              47   \n",
       "\n",
       "   Total Grads - % of cohort Total Regents - n  Total Regents - % of cohort  \\\n",
       "0                       55.1                36                         46.2   \n",
       "1                       42.7                42                         33.9   \n",
       "2                       77.8                67                         74.4   \n",
       "3                        NaN               NaN                          NaN   \n",
       "4                       56.0                40                         47.6   \n",
       "\n",
       "   Total Regents - % of grads Advanced Regents - n  \\\n",
       "0                        83.7                    0   \n",
       "1                        79.2                    8   \n",
       "2                        95.7                    0   \n",
       "3                         NaN                  NaN   \n",
       "4                        85.1                   17   \n",
       "\n",
       "   Advanced Regents - % of cohort  Advanced Regents - % of grads  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             6.5                           15.1   \n",
       "2                             0.0                            0.0   \n",
       "3                             NaN                            NaN   \n",
       "4                            20.2                           36.2   \n",
       "\n",
       "  Regents w/o Advanced - n  Regents w/o Advanced - % of cohort  \\\n",
       "0                       36                                46.2   \n",
       "1                       34                                27.4   \n",
       "2                       67                                74.4   \n",
       "3                      NaN                                 NaN   \n",
       "4                       23                                27.4   \n",
       "\n",
       "   Regents w/o Advanced - % of grads Local - n  Local - % of cohort  \\\n",
       "0                               83.7         7                  9.0   \n",
       "1                               64.2        11                  8.9   \n",
       "2                               95.7         3                  3.3   \n",
       "3                                NaN       NaN                  NaN   \n",
       "4                               48.9         7                  8.3   \n",
       "\n",
       "   Local - % of grads Still Enrolled - n  Still Enrolled - % of cohort  \\\n",
       "0                16.3                 16                          20.5   \n",
       "1                20.8                 46                          37.1   \n",
       "2                 4.3                 15                          16.7   \n",
       "3                 NaN                NaN                           NaN   \n",
       "4                14.9                 25                          29.8   \n",
       "\n",
       "  Dropped Out - n  Dropped Out - % of cohort  \n",
       "0              11                       14.1  \n",
       "1              20                       16.1  \n",
       "2               5                        5.6  \n",
       "3             NaN                        NaN  \n",
       "4               5                        6.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the dataset into a new variable\n",
    "combined = data['sat_results']\n",
    "\n",
    "# combining dataset per dataset, based on the DBNs in the sat_results\n",
    "combined = combined.merge(data['ap_2010'], how='left', on='dbn')\n",
    "combined = combined.merge(data['graduation'], how='left', on='dbn')\n",
    "\n",
    "print(combined.shape)\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(363, 164)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbn</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
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       "      <th>Total Regents - % of cohort</th>\n",
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       "      <th>Advanced Regents - n</th>\n",
       "      <th>Advanced Regents - % of cohort</th>\n",
       "      <th>Advanced Regents - % of grads</th>\n",
       "      <th>Regents w/o Advanced - n</th>\n",
       "      <th>Regents w/o Advanced - % of cohort</th>\n",
       "      <th>Regents w/o Advanced - % of grads</th>\n",
       "      <th>Local - n</th>\n",
       "      <th>Local - % of cohort</th>\n",
       "      <th>Local - % of grads</th>\n",
       "      <th>Still Enrolled - n</th>\n",
       "      <th>Still Enrolled - % of cohort</th>\n",
       "      <th>Dropped Out - n</th>\n",
       "      <th>Dropped Out - % of cohort</th>\n",
       "      <th>CSD</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "      <th>Name</th>\n",
       "      <th>schoolyear</th>\n",
       "      <th>fl_percent</th>\n",
       "      <th>frl_percent</th>\n",
       "      <th>total_enrollment</th>\n",
       "      <th>prek</th>\n",
       "      <th>k</th>\n",
       "      <th>grade1</th>\n",
       "      <th>grade2</th>\n",
       "      <th>grade3</th>\n",
       "      <th>grade4</th>\n",
       "      <th>grade5</th>\n",
       "      <th>grade6</th>\n",
       "      <th>grade7</th>\n",
       "      <th>grade8</th>\n",
       "      <th>grade9</th>\n",
       "      <th>grade10</th>\n",
       "      <th>grade11</th>\n",
       "      <th>grade12</th>\n",
       "      <th>ell_num</th>\n",
       "      <th>ell_percent</th>\n",
       "      <th>sped_num</th>\n",
       "      <th>sped_percent</th>\n",
       "      <th>ctt_num</th>\n",
       "      <th>selfcontained_num</th>\n",
       "      <th>asian_num</th>\n",
       "      <th>asian_per</th>\n",
       "      <th>black_num</th>\n",
       "      <th>black_per</th>\n",
       "      <th>hispanic_num</th>\n",
       "      <th>hispanic_per</th>\n",
       "      <th>white_num</th>\n",
       "      <th>white_per</th>\n",
       "      <th>male_num</th>\n",
       "      <th>male_per</th>\n",
       "      <th>female_num</th>\n",
       "      <th>female_per</th>\n",
       "      <th>rr_s</th>\n",
       "      <th>rr_t</th>\n",
       "      <th>rr_p</th>\n",
       "      <th>N_s</th>\n",
       "      <th>N_t</th>\n",
       "      <th>N_p</th>\n",
       "      <th>saf_p_11</th>\n",
       "      <th>com_p_11</th>\n",
       "      <th>eng_p_11</th>\n",
       "      <th>aca_p_11</th>\n",
       "      <th>saf_t_11</th>\n",
       "      <th>com_t_11</th>\n",
       "      <th>eng_t_11</th>\n",
       "      <th>aca_t_11</th>\n",
       "      <th>saf_s_11</th>\n",
       "      <th>com_s_11</th>\n",
       "      <th>eng_s_11</th>\n",
       "      <th>aca_s_11</th>\n",
       "      <th>saf_tot_11</th>\n",
       "      <th>com_tot_11</th>\n",
       "      <th>eng_tot_11</th>\n",
       "      <th>aca_tot_11</th>\n",
       "      <th>school_name</th>\n",
       "      <th>borough</th>\n",
       "      <th>building_code</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>fax_number</th>\n",
       "      <th>grade_span_min</th>\n",
       "      <th>grade_span_max</th>\n",
       "      <th>expgrade_span_min</th>\n",
       "      <th>expgrade_span_max</th>\n",
       "      <th>bus</th>\n",
       "      <th>subway</th>\n",
       "      <th>primary_address_line_1</th>\n",
       "      <th>city</th>\n",
       "      <th>state_code</th>\n",
       "      <th>postcode</th>\n",
       "      <th>website</th>\n",
       "      <th>total_students</th>\n",
       "      <th>campus_name</th>\n",
       "      <th>school_type</th>\n",
       "      <th>overview_paragraph</th>\n",
       "      <th>program_highlights</th>\n",
       "      <th>language_classes</th>\n",
       "      <th>advancedplacement_courses</th>\n",
       "      <th>online_ap_courses</th>\n",
       "      <th>online_language_courses</th>\n",
       "      <th>extracurricular_activities</th>\n",
       "      <th>psal_sports_boys</th>\n",
       "      <th>psal_sports_girls</th>\n",
       "      <th>psal_sports_coed</th>\n",
       "      <th>school_sports</th>\n",
       "      <th>partner_cbo</th>\n",
       "      <th>partner_hospital</th>\n",
       "      <th>partner_highered</th>\n",
       "      <th>partner_cultural</th>\n",
       "      <th>partner_nonprofit</th>\n",
       "      <th>partner_corporate</th>\n",
       "      <th>partner_financial</th>\n",
       "      <th>partner_other</th>\n",
       "      <th>addtl_info1</th>\n",
       "      <th>addtl_info2</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>se_services</th>\n",
       "      <th>ell_programs</th>\n",
       "      <th>school_accessibility_description</th>\n",
       "      <th>number_programs</th>\n",
       "      <th>priority01</th>\n",
       "      <th>priority02</th>\n",
       "      <th>priority03</th>\n",
       "      <th>priority04</th>\n",
       "      <th>priority05</th>\n",
       "      <th>priority06</th>\n",
       "      <th>priority07</th>\n",
       "      <th>priority08</th>\n",
       "      <th>priority09</th>\n",
       "      <th>priority10</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Community Board</th>\n",
       "      <th>Council District</th>\n",
       "      <th>Census Tract</th>\n",
       "      <th>BIN</th>\n",
       "      <th>BBL</th>\n",
       "      <th>NTA</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL</td>\n",
       "      <td>2006</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43</td>\n",
       "      <td>55.1</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>16</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1</td>\n",
       "      <td>1</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>22.564286</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>26.571429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>422</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>50</td>\n",
       "      <td>98</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>94.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>105.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>59</td>\n",
       "      <td>14.0</td>\n",
       "      <td>123</td>\n",
       "      <td>29.1</td>\n",
       "      <td>227</td>\n",
       "      <td>53.8</td>\n",
       "      <td>7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>259.0</td>\n",
       "      <td>61.4</td>\n",
       "      <td>163.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>89.0</td>\n",
       "      <td>70</td>\n",
       "      <td>39</td>\n",
       "      <td>379.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M056</td>\n",
       "      <td>212-406-9411</td>\n",
       "      <td>212-406-9417</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B39, M14A, M14D, M15, M15-SBS, M21, M22, M9</td>\n",
       "      <td>B, D to Grand St ; F to East Broadway ; J, M, ...</td>\n",
       "      <td>220 Henry Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>http://schools.nyc.gov/schoolportals/01/M292</td>\n",
       "      <td>323.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Henry Street School for International Studies ...</td>\n",
       "      <td>Global/International Studies in core subjects,...</td>\n",
       "      <td>Chinese (Mandarin), Spanish</td>\n",
       "      <td>Psychology</td>\n",
       "      <td>Chinese Language and Culture, Spanish Literatu...</td>\n",
       "      <td>Chinese (Mandarin), Spanish</td>\n",
       "      <td>Math through Card Play; Art, Poetry/Spoken Wor...</td>\n",
       "      <td>Softball</td>\n",
       "      <td>Softball</td>\n",
       "      <td>Soccer</td>\n",
       "      <td>Boxing, Track, CHAMPS, Tennis, Flag Football, ...</td>\n",
       "      <td>The Henry Street Settlement; Asia Society; Ame...</td>\n",
       "      <td>Gouverneur Hospital (Turning Points)</td>\n",
       "      <td>New York University</td>\n",
       "      <td>Asia Society</td>\n",
       "      <td>Heart of America Foundation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8:30 AM</td>\n",
       "      <td>3:30 PM</td>\n",
       "      <td>This school will provide students with disabil...</td>\n",
       "      <td>ESL</td>\n",
       "      <td>Functionally Accessible</td>\n",
       "      <td>1</td>\n",
       "      <td>Priority to continuing 8th graders</td>\n",
       "      <td>Then to Manhattan students or residents who at...</td>\n",
       "      <td>Then to New York City residents who attend an ...</td>\n",
       "      <td>Then to Manhattan students or residents</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220 Henry Street\\nNew York, NY 10002\\n(40.7137...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1003223.0</td>\n",
       "      <td>1.002690e+09</td>\n",
       "      <td>Lower East Side                               ...</td>\n",
       "      <td>40.713763947</td>\n",
       "      <td>-73.98526004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD H.S.</td>\n",
       "      <td>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>124.0</td>\n",
       "      <td>53</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42</td>\n",
       "      <td>33.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>34</td>\n",
       "      <td>27.4</td>\n",
       "      <td>64.2</td>\n",
       "      <td>11</td>\n",
       "      <td>8.9</td>\n",
       "      <td>20.8</td>\n",
       "      <td>46</td>\n",
       "      <td>37.1</td>\n",
       "      <td>20</td>\n",
       "      <td>16.1</td>\n",
       "      <td>1</td>\n",
       "      <td>105.687500</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>22.231250</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>27.062500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.8</td>\n",
       "      <td>394</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>109</td>\n",
       "      <td>97</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>29.2</td>\n",
       "      <td>89</td>\n",
       "      <td>22.6</td>\n",
       "      <td>181</td>\n",
       "      <td>45.9</td>\n",
       "      <td>9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>226.0</td>\n",
       "      <td>57.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>84.0</td>\n",
       "      <td>95</td>\n",
       "      <td>10</td>\n",
       "      <td>385.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>University Neighborhood High School</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M446</td>\n",
       "      <td>212-962-4341</td>\n",
       "      <td>212-267-5611</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M14A, M14D, M15, M21, M22, M9</td>\n",
       "      <td>F to East Broadway ; J, M, Z to Delancey St-Es...</td>\n",
       "      <td>200 Monroe Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>www.universityneighborhoodhs.com</td>\n",
       "      <td>299.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>University Neighborhood High School (UNHS) is ...</td>\n",
       "      <td>While attending UNHS, students can earn up to ...</td>\n",
       "      <td>Chinese, Spanish</td>\n",
       "      <td>Calculus AB, Chinese Language and Culture, Eng...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chinese (Cantonese), Chinese (Mandarin), Spanish</td>\n",
       "      <td>Basketball, Badminton, Handball, Glee, Dance, ...</td>\n",
       "      <td>Basketball, Bowling, Cross Country, Softball, ...</td>\n",
       "      <td>Basketball, Bowling, Cross Country, Softball, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grand Street Settlement, Henry Street Settleme...</td>\n",
       "      <td>Gouverneur Hospital, The Door, The Mount Sinai...</td>\n",
       "      <td>New York University, CUNY Baruch College, Pars...</td>\n",
       "      <td>Dance Film Association, Dance Makers Film Work...</td>\n",
       "      <td>W!SE, Big Brothers Big Sisters, Peer Health Ex...</td>\n",
       "      <td>Deloitte LLP Consulting and Financial Services...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Movement Research</td>\n",
       "      <td>Incoming students are expected to attend schoo...</td>\n",
       "      <td>Community Service Requirement, Dress Code Requ...</td>\n",
       "      <td>8:15 AM</td>\n",
       "      <td>3:15 PM</td>\n",
       "      <td>This school will provide students with disabil...</td>\n",
       "      <td>ESL</td>\n",
       "      <td>Not Functionally Accessible</td>\n",
       "      <td>3</td>\n",
       "      <td>Open to New York City residents</td>\n",
       "      <td>For M35B only: Open only to students whose hom...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 Monroe Street\\nNew York, NY 10002\\n(40.712...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>1003214.0</td>\n",
       "      <td>1.002590e+09</td>\n",
       "      <td>Lower East Side                               ...</td>\n",
       "      <td>40.712331851001</td>\n",
       "      <td>-73.984796625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>EAST SIDE COMMUNITY HS</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70</td>\n",
       "      <td>77.8</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>74.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>15</td>\n",
       "      <td>16.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>22.866667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST SIDE COMMUNITY HIGH SCHOOL</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.8</td>\n",
       "      <td>598</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>101</td>\n",
       "      <td>93</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>91</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>9.7</td>\n",
       "      <td>143</td>\n",
       "      <td>23.9</td>\n",
       "      <td>331</td>\n",
       "      <td>55.4</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>327.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>271.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>East Side Community School</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M060</td>\n",
       "      <td>212-460-8467</td>\n",
       "      <td>212-260-9657</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M101, M102, M103, M14A, M14D, M15, M15-SBS, M2...</td>\n",
       "      <td>6 to Astor Place ; L to 1st Ave</td>\n",
       "      <td>420 East 12 Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10009</td>\n",
       "      <td>www.eschs.org</td>\n",
       "      <td>649.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consortium School</td>\n",
       "      <td>We are a small 6-12 secondary school that prep...</td>\n",
       "      <td>Our Advisory System ensures that we can effect...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Calculus AB, English Literature and Composition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Sign Language, Arabic, Chinese (Manda...</td>\n",
       "      <td>After-School Tutoring, Art Portfolio Classes, ...</td>\n",
       "      <td>Basketball, Soccer, Softball</td>\n",
       "      <td>Basketball, Soccer, Softball</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basketball, Bicycling, Fitness, Flag Football,...</td>\n",
       "      <td>University Settlement, Big Brothers Big Sister...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Columbia Teachers College, New York University...</td>\n",
       "      <td>, Internship Program, Loisaida Art Gallery loc...</td>\n",
       "      <td>College Bound Initiative, Center for Collabora...</td>\n",
       "      <td>Prudential Securities, Moore Capital, Morgan S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn Boulders (Rock Climbing)</td>\n",
       "      <td>Students present and defend their work to comm...</td>\n",
       "      <td>Our school requires an Academic Portfolio for ...</td>\n",
       "      <td>8:30 AM</td>\n",
       "      <td>3:30 PM</td>\n",
       "      <td>This school will provide students with disabil...</td>\n",
       "      <td>ESL</td>\n",
       "      <td>Not Functionally Accessible</td>\n",
       "      <td>1</td>\n",
       "      <td>Priority to continuing 8th graders</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420 East 12 Street\\nNew York, NY 10009\\n(40.72...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1005974.0</td>\n",
       "      <td>1.004390e+09</td>\n",
       "      <td>East Village                                  ...</td>\n",
       "      <td>40.729782687</td>\n",
       "      <td>-73.983041441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>84.0</td>\n",
       "      <td>47</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40</td>\n",
       "      <td>47.6</td>\n",
       "      <td>85.1</td>\n",
       "      <td>17</td>\n",
       "      <td>20.2</td>\n",
       "      <td>36.2</td>\n",
       "      <td>23</td>\n",
       "      <td>27.4</td>\n",
       "      <td>48.9</td>\n",
       "      <td>7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>25</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.642857</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.571429</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.357143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MARTA VALLE SECONDARY SCHOOL</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.7</td>\n",
       "      <td>367</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143</td>\n",
       "      <td>100</td>\n",
       "      <td>51</td>\n",
       "      <td>73</td>\n",
       "      <td>41.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>9.3</td>\n",
       "      <td>116</td>\n",
       "      <td>31.6</td>\n",
       "      <td>209</td>\n",
       "      <td>56.9</td>\n",
       "      <td>6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>170.0</td>\n",
       "      <td>46.3</td>\n",
       "      <td>197.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>306.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Marta Valle High School</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M025</td>\n",
       "      <td>212-473-8152</td>\n",
       "      <td>212-475-7588</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B39, M103, M14A, M14D, M15, M15-SBS, M21, M22,...</td>\n",
       "      <td>B, D to Grand St ; F, J, M, Z to Delancey St-E...</td>\n",
       "      <td>145 Stanton Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>www.martavalle.org</td>\n",
       "      <td>401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marta Valle High School (MVHS) offers a strong...</td>\n",
       "      <td>Advanced Regents Diploma, Early Graduation, up...</td>\n",
       "      <td>French, Spanish</td>\n",
       "      <td>English Literature and Composition, Studio Art...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>Model Peer Leadership Program, 'The Vine' Stud...</td>\n",
       "      <td>Rugby, Volleyball</td>\n",
       "      <td>Rugby, Volleyball</td>\n",
       "      <td>Rugby</td>\n",
       "      <td>Volleyball, Zumba</td>\n",
       "      <td>NYCDOE Innovation Zone Lab Site, Grand Street ...</td>\n",
       "      <td>Gouvenuer's Hospital</td>\n",
       "      <td>New York University (NYU), Sarah Lawrence Coll...</td>\n",
       "      <td>Young Audiences, The National Arts Club, Educa...</td>\n",
       "      <td>College for Every Student (CFES), Morningside ...</td>\n",
       "      <td>Estée Lauder</td>\n",
       "      <td>Bank of America</td>\n",
       "      <td>CASALEAP, Beacon</td>\n",
       "      <td>Students Dress for Success, Summer Bridge to S...</td>\n",
       "      <td>Community Service Requirement, Extended Day Pr...</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>3:30 PM</td>\n",
       "      <td>This school will provide students with disabil...</td>\n",
       "      <td>ESL</td>\n",
       "      <td>Functionally Accessible</td>\n",
       "      <td>1</td>\n",
       "      <td>Priority to District 1 students or residents</td>\n",
       "      <td>Then to Manhattan students or residents</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145 Stanton Street\\nNew York, NY 10002\\n(40.72...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>1004323.0</td>\n",
       "      <td>1.003540e+09</td>\n",
       "      <td>Chinatown                                     ...</td>\n",
       "      <td>40.720569079</td>\n",
       "      <td>-73.985672691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M539</td>\n",
       "      <td>NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...</td>\n",
       "      <td>159</td>\n",
       "      <td>522.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>NEW EXPLORATIONS SCI,TECH,MATH</td>\n",
       "      <td>255.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>NEW EXPLORATIONS INTO SCIENCE  TECHNO</td>\n",
       "      <td>2006</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46</td>\n",
       "      <td>100.0</td>\n",
       "      <td>46</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31</td>\n",
       "      <td>67.4</td>\n",
       "      <td>67.4</td>\n",
       "      <td>15</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>156.368421</td>\n",
       "      <td>6.157895</td>\n",
       "      <td>25.510526</td>\n",
       "      <td>19.473684</td>\n",
       "      <td>31.210526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEW EXPLORATIONS INTO SCIENCE TECH AND MATH</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1613</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>107</td>\n",
       "      <td>139</td>\n",
       "      <td>110</td>\n",
       "      <td>114</td>\n",
       "      <td>107</td>\n",
       "      <td>149</td>\n",
       "      <td>126</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>123</td>\n",
       "      <td>147</td>\n",
       "      <td>157</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>448</td>\n",
       "      <td>27.8</td>\n",
       "      <td>189</td>\n",
       "      <td>11.7</td>\n",
       "      <td>229</td>\n",
       "      <td>14.2</td>\n",
       "      <td>725</td>\n",
       "      <td>44.9</td>\n",
       "      <td>794.0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>819.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>98.0</td>\n",
       "      <td>68</td>\n",
       "      <td>51</td>\n",
       "      <td>923.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>New Explorations into Science, Technology and ...</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M022</td>\n",
       "      <td>212-677-5190</td>\n",
       "      <td>212-260-8124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B39, M14A, M14D, M21, M22, M8, M9</td>\n",
       "      <td>F, J, M, Z to Delancey St-Essex St</td>\n",
       "      <td>111 Columbia Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>www.nestmk12.net</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Explorations into Science, Technology and ...</td>\n",
       "      <td>1st level science sequence - 9th grade: Regent...</td>\n",
       "      <td>Chinese (Mandarin), French, Italian, Latin, Sp...</td>\n",
       "      <td>Biology, Calculus AB, Calculus BC, Chemistry, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>After-school Jazz Band, Annual Coffee House Co...</td>\n",
       "      <td>Basketball, Fencing, Indoor Track</td>\n",
       "      <td>Basketball, Fencing, Indoor Track</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Badminton, Baseball, Cross-Country, Dance, Out...</td>\n",
       "      <td>7th Precinct Community Affairs, NYCWastele$$, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hunter College, New York University, Cornell U...</td>\n",
       "      <td>VH1, Dancing Classrooms, Center for Arts Educa...</td>\n",
       "      <td>After 3</td>\n",
       "      <td>Time Warner Cable, Google, IBM, MET Project, S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dress Code Required: Business Casual - shirt/b...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8:15 AM</td>\n",
       "      <td>4:00 PM</td>\n",
       "      <td>This school will provide students with disabil...</td>\n",
       "      <td>ESL</td>\n",
       "      <td>Not Functionally Accessible</td>\n",
       "      <td>1</td>\n",
       "      <td>Priority to continuing 8th graders</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111 Columbia Street\\nNew York, NY 10002\\n(40.7...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>1004070.0</td>\n",
       "      <td>1.003350e+09</td>\n",
       "      <td>Lower East Side                               ...</td>\n",
       "      <td>40.718725451</td>\n",
       "      <td>-73.979426386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn                                        SCHOOL NAME  \\\n",
       "0  01M292      HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1  01M448                UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2  01M450                         EAST SIDE COMMUNITY SCHOOL   \n",
       "3  01M509                            MARTA VALLE HIGH SCHOOL   \n",
       "4  01M539  NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...   \n",
       "\n",
       "  Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "0                     29                            355.0   \n",
       "1                     91                            383.0   \n",
       "2                     70                            377.0   \n",
       "3                     44                            390.0   \n",
       "4                    159                            522.0   \n",
       "\n",
       "   SAT Math Avg. Score  SAT Writing Avg. Score  sat_score  \\\n",
       "0                404.0                   363.0     1122.0   \n",
       "1                423.0                   366.0     1172.0   \n",
       "2                402.0                   370.0     1149.0   \n",
       "3                433.0                   384.0     1207.0   \n",
       "4                574.0                   525.0     1621.0   \n",
       "\n",
       "                       SchoolName  AP Test Takers   Total Exams Taken  \\\n",
       "0                             NaN              NaN                NaN   \n",
       "1    UNIVERSITY NEIGHBORHOOD H.S.             39.0               49.0   \n",
       "2          EAST SIDE COMMUNITY HS             19.0               21.0   \n",
       "3                             NaN              NaN                NaN   \n",
       "4  NEW EXPLORATIONS SCI,TECH,MATH            255.0              377.0   \n",
       "\n",
       "   Number of Exams with scores 3 4 or 5   Demographic  \\\n",
       "0                                   NaN  Total Cohort   \n",
       "1                                  10.0  Total Cohort   \n",
       "2                                   NaN  Total Cohort   \n",
       "3                                   NaN  Total Cohort   \n",
       "4                                 191.0  Total Cohort   \n",
       "\n",
       "                             School Name Cohort  Total Cohort Total Grads - n  \\\n",
       "0  HENRY STREET SCHOOL FOR INTERNATIONAL   2006          78.0              43   \n",
       "1    UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   2006         124.0              53   \n",
       "2             EAST SIDE COMMUNITY SCHOOL   2006          90.0              70   \n",
       "3                MARTA VALLE HIGH SCHOOL   2006          84.0              47   \n",
       "4  NEW EXPLORATIONS INTO SCIENCE  TECHNO   2006          46.0              46   \n",
       "\n",
       "   Total Grads - % of cohort Total Regents - n  Total Regents - % of cohort  \\\n",
       "0                       55.1                36                         46.2   \n",
       "1                       42.7                42                         33.9   \n",
       "2                       77.8                67                         74.4   \n",
       "3                       56.0                40                         47.6   \n",
       "4                      100.0                46                        100.0   \n",
       "\n",
       "   Total Regents - % of grads Advanced Regents - n  \\\n",
       "0                        83.7                    0   \n",
       "1                        79.2                    8   \n",
       "2                        95.7                    0   \n",
       "3                        85.1                   17   \n",
       "4                       100.0                   31   \n",
       "\n",
       "   Advanced Regents - % of cohort  Advanced Regents - % of grads  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             6.5                           15.1   \n",
       "2                             0.0                            0.0   \n",
       "3                            20.2                           36.2   \n",
       "4                            67.4                           67.4   \n",
       "\n",
       "  Regents w/o Advanced - n  Regents w/o Advanced - % of cohort  \\\n",
       "0                       36                                46.2   \n",
       "1                       34                                27.4   \n",
       "2                       67                                74.4   \n",
       "3                       23                                27.4   \n",
       "4                       15                                32.6   \n",
       "\n",
       "   Regents w/o Advanced - % of grads Local - n  Local - % of cohort  \\\n",
       "0                               83.7         7                  9.0   \n",
       "1                               64.2        11                  8.9   \n",
       "2                               95.7         3                  3.3   \n",
       "3                               48.9         7                  8.3   \n",
       "4                               32.6         0                  0.0   \n",
       "\n",
       "   Local - % of grads Still Enrolled - n  Still Enrolled - % of cohort  \\\n",
       "0                16.3                 16                          20.5   \n",
       "1                20.8                 46                          37.1   \n",
       "2                 4.3                 15                          16.7   \n",
       "3                14.9                 25                          29.8   \n",
       "4                 0.0                  0                           0.0   \n",
       "\n",
       "  Dropped Out - n  Dropped Out - % of cohort  CSD  \\\n",
       "0              11                       14.1    1   \n",
       "1              20                       16.1    1   \n",
       "2               5                        5.6    1   \n",
       "3               5                        6.0    1   \n",
       "4               0                        0.0    1   \n",
       "\n",
       "   NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  AVERAGE CLASS SIZE  \\\n",
       "0                          88.000000            4.000000           22.564286   \n",
       "1                         105.687500            4.750000           22.231250   \n",
       "2                          57.600000            2.733333           21.200000   \n",
       "3                          69.642857            3.000000           23.571429   \n",
       "4                         156.368421            6.157895           25.510526   \n",
       "\n",
       "   SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "0               18.500000              26.571429   \n",
       "1               18.250000              27.062500   \n",
       "2               19.400000              22.866667   \n",
       "3               20.000000              27.357143   \n",
       "4               19.473684              31.210526   \n",
       "\n",
       "   SCHOOLWIDE PUPIL-TEACHER RATIO  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "                                               Name  schoolyear fl_percent  \\\n",
       "0  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES       20112012        NaN   \n",
       "1  UNIVERSITY NEIGHBORHOOD HIGH SCHOOL                 20112012        NaN   \n",
       "2  EAST SIDE COMMUNITY HIGH SCHOOL                     20112012        NaN   \n",
       "3  MARTA VALLE SECONDARY SCHOOL                        20112012        NaN   \n",
       "4  NEW EXPLORATIONS INTO SCIENCE TECH AND MATH         20112012        NaN   \n",
       "\n",
       "   frl_percent  total_enrollment  prek     k grade1 grade2 grade3 grade4  \\\n",
       "0         88.6               422                                           \n",
       "1         71.8               394                                           \n",
       "2         71.8               598                                           \n",
       "3         80.7               367                                           \n",
       "4         23.0              1613         100    107    139    110    114   \n",
       "\n",
       "  grade5 grade6 grade7 grade8 grade9 grade10 grade11 grade12  ell_num  \\\n",
       "0            32     33     50     98      79      80      50     94.0   \n",
       "1                                109      97      93      95     83.0   \n",
       "2            92     73     76    101      93      77      86     30.0   \n",
       "3                                143     100      51      73     41.0   \n",
       "4    107    149    126    117    117     123     147     157      4.0   \n",
       "\n",
       "   ell_percent  sped_num  sped_percent ctt_num selfcontained_num  asian_num  \\\n",
       "0         22.3     105.0          24.9      34                35         59   \n",
       "1         21.1      86.0          21.8      55                10        115   \n",
       "2          5.0     158.0          26.4      91                19         58   \n",
       "3         11.2      95.0          25.9      28                36         34   \n",
       "4          0.2      43.0           2.7       2                 0        448   \n",
       "\n",
       "   asian_per  black_num  black_per  hispanic_num  hispanic_per  white_num  \\\n",
       "0       14.0        123       29.1           227          53.8          7   \n",
       "1       29.2         89       22.6           181          45.9          9   \n",
       "2        9.7        143       23.9           331          55.4         62   \n",
       "3        9.3        116       31.6           209          56.9          6   \n",
       "4       27.8        189       11.7           229          14.2        725   \n",
       "\n",
       "   white_per  male_num  male_per  female_num  female_per  rr_s  rr_t  rr_p  \\\n",
       "0        1.7     259.0      61.4       163.0        38.6  89.0    70    39   \n",
       "1        2.3     226.0      57.4       168.0        42.6  84.0    95    10   \n",
       "2       10.4     327.0      54.7       271.0        45.3   0.0    98    28   \n",
       "3        1.6     170.0      46.3       197.0        53.7  90.0   100    21   \n",
       "4       44.9     794.0      49.2       819.0        50.8  98.0    68    51   \n",
       "\n",
       "     N_s   N_t    N_p  saf_p_11  com_p_11  eng_p_11  aca_p_11  saf_t_11  \\\n",
       "0  379.0  26.0  151.0       7.8       7.7       7.4       7.6       6.3   \n",
       "1  385.0  37.0   46.0       7.9       7.4       7.2       7.3       6.6   \n",
       "2    NaN  42.0  150.0       8.7       8.2       8.1       8.4       7.3   \n",
       "3  306.0  29.0   69.0       7.7       7.4       7.2       7.3       6.4   \n",
       "4  923.0  67.0  736.0       8.5       7.9       7.9       8.4       7.6   \n",
       "\n",
       "   com_t_11  eng_t_11  aca_t_11  saf_s_11  com_s_11  eng_s_11  aca_s_11  \\\n",
       "0       5.3       6.1       6.5       6.0       5.6       6.1       6.7   \n",
       "1       5.8       6.6       7.3       6.0       5.7       6.3       7.0   \n",
       "2       8.0       8.0       8.8       NaN       NaN       NaN       NaN   \n",
       "3       5.3       6.1       6.8       6.4       5.9       6.4       7.0   \n",
       "4       5.6       5.9       7.3       7.3       6.4       7.0       7.7   \n",
       "\n",
       "   saf_tot_11  com_tot_11  eng_tot_11  aca_tot_11  \\\n",
       "0         6.7         6.2         6.6         7.0   \n",
       "1         6.8         6.3         6.7         7.2   \n",
       "2         7.9         7.9         7.9         8.4   \n",
       "3         6.9         6.2         6.6         7.0   \n",
       "4         7.8         6.7         6.9         7.8   \n",
       "\n",
       "                                         school_name    borough building_code  \\\n",
       "0      Henry Street School for International Studies  Manhattan          M056   \n",
       "1                University Neighborhood High School  Manhattan          M446   \n",
       "2                         East Side Community School  Manhattan          M060   \n",
       "3                            Marta Valle High School  Manhattan          M025   \n",
       "4  New Explorations into Science, Technology and ...  Manhattan          M022   \n",
       "\n",
       "   phone_number    fax_number  grade_span_min  grade_span_max  \\\n",
       "0  212-406-9411  212-406-9417             6.0              12   \n",
       "1  212-962-4341  212-267-5611             9.0              12   \n",
       "2  212-460-8467  212-260-9657             6.0              12   \n",
       "3  212-473-8152  212-475-7588             9.0              12   \n",
       "4  212-677-5190  212-260-8124             NaN              12   \n",
       "\n",
       "   expgrade_span_min  expgrade_span_max  \\\n",
       "0                NaN                NaN   \n",
       "1                NaN                NaN   \n",
       "2                NaN                NaN   \n",
       "3                NaN                NaN   \n",
       "4                NaN                NaN   \n",
       "\n",
       "                                                 bus  \\\n",
       "0        B39, M14A, M14D, M15, M15-SBS, M21, M22, M9   \n",
       "1                      M14A, M14D, M15, M21, M22, M9   \n",
       "2  M101, M102, M103, M14A, M14D, M15, M15-SBS, M2...   \n",
       "3  B39, M103, M14A, M14D, M15, M15-SBS, M21, M22,...   \n",
       "4                  B39, M14A, M14D, M21, M22, M8, M9   \n",
       "\n",
       "                                              subway primary_address_line_1  \\\n",
       "0  B, D to Grand St ; F to East Broadway ; J, M, ...       220 Henry Street   \n",
       "1  F to East Broadway ; J, M, Z to Delancey St-Es...      200 Monroe Street   \n",
       "2                    6 to Astor Place ; L to 1st Ave     420 East 12 Street   \n",
       "3  B, D to Grand St ; F, J, M, Z to Delancey St-E...     145 Stanton Street   \n",
       "4                 F, J, M, Z to Delancey St-Essex St    111 Columbia Street   \n",
       "\n",
       "       city state_code  postcode  \\\n",
       "0  New York         NY     10002   \n",
       "1  New York         NY     10002   \n",
       "2  New York         NY     10009   \n",
       "3  New York         NY     10002   \n",
       "4  New York         NY     10002   \n",
       "\n",
       "                                        website  total_students campus_name  \\\n",
       "0  http://schools.nyc.gov/schoolportals/01/M292           323.0         NaN   \n",
       "1              www.universityneighborhoodhs.com           299.0         NaN   \n",
       "2                                 www.eschs.org           649.0         NaN   \n",
       "3                            www.martavalle.org           401.0         NaN   \n",
       "4                              www.nestmk12.net          1725.0         NaN   \n",
       "\n",
       "         school_type                                 overview_paragraph  \\\n",
       "0                NaN  Henry Street School for International Studies ...   \n",
       "1                NaN  University Neighborhood High School (UNHS) is ...   \n",
       "2  Consortium School  We are a small 6-12 secondary school that prep...   \n",
       "3                NaN  Marta Valle High School (MVHS) offers a strong...   \n",
       "4                NaN  New Explorations into Science, Technology and ...   \n",
       "\n",
       "                                  program_highlights  \\\n",
       "0  Global/International Studies in core subjects,...   \n",
       "1  While attending UNHS, students can earn up to ...   \n",
       "2  Our Advisory System ensures that we can effect...   \n",
       "3  Advanced Regents Diploma, Early Graduation, up...   \n",
       "4  1st level science sequence - 9th grade: Regent...   \n",
       "\n",
       "                                    language_classes  \\\n",
       "0                        Chinese (Mandarin), Spanish   \n",
       "1                                   Chinese, Spanish   \n",
       "2                                                NaN   \n",
       "3                                    French, Spanish   \n",
       "4  Chinese (Mandarin), French, Italian, Latin, Sp...   \n",
       "\n",
       "                           advancedplacement_courses  \\\n",
       "0                                         Psychology   \n",
       "1  Calculus AB, Chinese Language and Culture, Eng...   \n",
       "2    Calculus AB, English Literature and Composition   \n",
       "3  English Literature and Composition, Studio Art...   \n",
       "4  Biology, Calculus AB, Calculus BC, Chemistry, ...   \n",
       "\n",
       "                                   online_ap_courses  \\\n",
       "0  Chinese Language and Culture, Spanish Literatu...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                             online_language_courses  \\\n",
       "0                        Chinese (Mandarin), Spanish   \n",
       "1   Chinese (Cantonese), Chinese (Mandarin), Spanish   \n",
       "2  American Sign Language, Arabic, Chinese (Manda...   \n",
       "3                                            Spanish   \n",
       "4                                                NaN   \n",
       "\n",
       "                          extracurricular_activities  \\\n",
       "0  Math through Card Play; Art, Poetry/Spoken Wor...   \n",
       "1  Basketball, Badminton, Handball, Glee, Dance, ...   \n",
       "2  After-School Tutoring, Art Portfolio Classes, ...   \n",
       "3  Model Peer Leadership Program, 'The Vine' Stud...   \n",
       "4  After-school Jazz Band, Annual Coffee House Co...   \n",
       "\n",
       "                                    psal_sports_boys  \\\n",
       "0                                           Softball   \n",
       "1  Basketball, Bowling, Cross Country, Softball, ...   \n",
       "2                       Basketball, Soccer, Softball   \n",
       "3                                  Rugby, Volleyball   \n",
       "4                  Basketball, Fencing, Indoor Track   \n",
       "\n",
       "                                   psal_sports_girls psal_sports_coed  \\\n",
       "0                                           Softball           Soccer   \n",
       "1  Basketball, Bowling, Cross Country, Softball, ...              NaN   \n",
       "2                       Basketball, Soccer, Softball              NaN   \n",
       "3                                  Rugby, Volleyball            Rugby   \n",
       "4                  Basketball, Fencing, Indoor Track              NaN   \n",
       "\n",
       "                                       school_sports  \\\n",
       "0  Boxing, Track, CHAMPS, Tennis, Flag Football, ...   \n",
       "1                                                NaN   \n",
       "2  Basketball, Bicycling, Fitness, Flag Football,...   \n",
       "3                                  Volleyball, Zumba   \n",
       "4  Badminton, Baseball, Cross-Country, Dance, Out...   \n",
       "\n",
       "                                         partner_cbo  \\\n",
       "0  The Henry Street Settlement; Asia Society; Ame...   \n",
       "1  Grand Street Settlement, Henry Street Settleme...   \n",
       "2  University Settlement, Big Brothers Big Sister...   \n",
       "3  NYCDOE Innovation Zone Lab Site, Grand Street ...   \n",
       "4  7th Precinct Community Affairs, NYCWastele$$, ...   \n",
       "\n",
       "                                    partner_hospital  \\\n",
       "0               Gouverneur Hospital (Turning Points)   \n",
       "1  Gouverneur Hospital, The Door, The Mount Sinai...   \n",
       "2                                                NaN   \n",
       "3                               Gouvenuer's Hospital   \n",
       "4                                                NaN   \n",
       "\n",
       "                                    partner_highered  \\\n",
       "0                                New York University   \n",
       "1  New York University, CUNY Baruch College, Pars...   \n",
       "2  Columbia Teachers College, New York University...   \n",
       "3  New York University (NYU), Sarah Lawrence Coll...   \n",
       "4  Hunter College, New York University, Cornell U...   \n",
       "\n",
       "                                    partner_cultural  \\\n",
       "0                                       Asia Society   \n",
       "1  Dance Film Association, Dance Makers Film Work...   \n",
       "2  , Internship Program, Loisaida Art Gallery loc...   \n",
       "3  Young Audiences, The National Arts Club, Educa...   \n",
       "4  VH1, Dancing Classrooms, Center for Arts Educa...   \n",
       "\n",
       "                                   partner_nonprofit  \\\n",
       "0                        Heart of America Foundation   \n",
       "1  W!SE, Big Brothers Big Sisters, Peer Health Ex...   \n",
       "2  College Bound Initiative, Center for Collabora...   \n",
       "3  College for Every Student (CFES), Morningside ...   \n",
       "4                                            After 3   \n",
       "\n",
       "                                   partner_corporate partner_financial  \\\n",
       "0                                                NaN               NaN   \n",
       "1  Deloitte LLP Consulting and Financial Services...               NaN   \n",
       "2  Prudential Securities, Moore Capital, Morgan S...               NaN   \n",
       "3                                       Estée Lauder   Bank of America   \n",
       "4  Time Warner Cable, Google, IBM, MET Project, S...               NaN   \n",
       "\n",
       "                       partner_other  \\\n",
       "0                     United Nations   \n",
       "1                  Movement Research   \n",
       "2  Brooklyn Boulders (Rock Climbing)   \n",
       "3                   CASALEAP, Beacon   \n",
       "4                                NaN   \n",
       "\n",
       "                                         addtl_info1  \\\n",
       "0                                                NaN   \n",
       "1  Incoming students are expected to attend schoo...   \n",
       "2  Students present and defend their work to comm...   \n",
       "3  Students Dress for Success, Summer Bridge to S...   \n",
       "4  Dress Code Required: Business Casual - shirt/b...   \n",
       "\n",
       "                                         addtl_info2 start_time end_time  \\\n",
       "0                                                NaN    8:30 AM  3:30 PM   \n",
       "1  Community Service Requirement, Dress Code Requ...    8:15 AM  3:15 PM   \n",
       "2  Our school requires an Academic Portfolio for ...    8:30 AM  3:30 PM   \n",
       "3  Community Service Requirement, Extended Day Pr...    8:00 AM  3:30 PM   \n",
       "4                                                NaN    8:15 AM  4:00 PM   \n",
       "\n",
       "                                         se_services ell_programs  \\\n",
       "0  This school will provide students with disabil...          ESL   \n",
       "1  This school will provide students with disabil...          ESL   \n",
       "2  This school will provide students with disabil...          ESL   \n",
       "3  This school will provide students with disabil...          ESL   \n",
       "4  This school will provide students with disabil...          ESL   \n",
       "\n",
       "  school_accessibility_description  number_programs  \\\n",
       "0          Functionally Accessible                1   \n",
       "1      Not Functionally Accessible                3   \n",
       "2      Not Functionally Accessible                1   \n",
       "3          Functionally Accessible                1   \n",
       "4      Not Functionally Accessible                1   \n",
       "\n",
       "                                     priority01  \\\n",
       "0            Priority to continuing 8th graders   \n",
       "1               Open to New York City residents   \n",
       "2            Priority to continuing 8th graders   \n",
       "3  Priority to District 1 students or residents   \n",
       "4            Priority to continuing 8th graders   \n",
       "\n",
       "                                          priority02  \\\n",
       "0  Then to Manhattan students or residents who at...   \n",
       "1  For M35B only: Open only to students whose hom...   \n",
       "2                    Then to New York City residents   \n",
       "3            Then to Manhattan students or residents   \n",
       "4                    Then to New York City residents   \n",
       "\n",
       "                                          priority03  \\\n",
       "0  Then to New York City residents who attend an ...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                    Then to New York City residents   \n",
       "4                                                NaN   \n",
       "\n",
       "                                priority04                       priority05  \\\n",
       "0  Then to Manhattan students or residents  Then to New York City residents   \n",
       "1                                      NaN                              NaN   \n",
       "2                                      NaN                              NaN   \n",
       "3                                      NaN                              NaN   \n",
       "4                                      NaN                              NaN   \n",
       "\n",
       "  priority06 priority07 priority08 priority09 priority10  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "                                          Location 1  Community Board  \\\n",
       "0  220 Henry Street\\nNew York, NY 10002\\n(40.7137...              3.0   \n",
       "1  200 Monroe Street\\nNew York, NY 10002\\n(40.712...              3.0   \n",
       "2  420 East 12 Street\\nNew York, NY 10009\\n(40.72...              3.0   \n",
       "3  145 Stanton Street\\nNew York, NY 10002\\n(40.72...              3.0   \n",
       "4  111 Columbia Street\\nNew York, NY 10002\\n(40.7...              3.0   \n",
       "\n",
       "   Council District  Census Tract        BIN           BBL  \\\n",
       "0               1.0         201.0  1003223.0  1.002690e+09   \n",
       "1               1.0         202.0  1003214.0  1.002590e+09   \n",
       "2               2.0          34.0  1005974.0  1.004390e+09   \n",
       "3               1.0        3001.0  1004323.0  1.003540e+09   \n",
       "4               2.0        2201.0  1004070.0  1.003350e+09   \n",
       "\n",
       "                                                 NTA              lat  \\\n",
       "0  Lower East Side                               ...     40.713763947   \n",
       "1  Lower East Side                               ...  40.712331851001   \n",
       "2  East Village                                  ...     40.729782687   \n",
       "3  Chinatown                                     ...     40.720569079   \n",
       "4  Lower East Side                               ...     40.718725451   \n",
       "\n",
       "             long  \n",
       "0    -73.98526004  \n",
       "1   -73.984796625  \n",
       "2   -73.983041441  \n",
       "3   -73.985672691  \n",
       "4   -73.979426386  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining the datasets keeping only the entries in both dataframes\n",
    "combined = combined.merge(data['class_size'], how='inner', on='dbn')\n",
    "combined = combined.merge(data['demographics'], how='inner', on='dbn')\n",
    "combined = combined.merge(data['survey'], how='inner', on='dbn')\n",
    "combined = combined.merge(data['hs_directory'], how='inner', on='dbn')\n",
    "\n",
    "print(combined.shape)\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've combined the data we can see that a lot of entries are missing, this is the result of the left joins we did. It happened because some DBNs did not have that information. To follow our analysis, we are going to fill this values with the mean of the correspondent column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 4.2 Dealing with missing values\n",
    "We are going to use some methods to calculate the means and to fill the blank values with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# calculating the means\n",
    "means = combined.mean()\n",
    "\n",
    "# filling the missing values\n",
    "combined = combined.fillna(means)\n",
    "combined = combined.fillna(0)\n",
    "\n",
    "# printing null values per column if they exist\n",
    "for column in combined.columns:\n",
    "    if combined[column].isnull().sum() != 0:\n",
    "        print(\"{}: {}\".format(column, combined[column].isnull().sum()))\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Finished with the null values, we'll create a new column in our dataset to make an interesting analysis of the information we have, looking at in from a different angle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 4.3 Creating a column to store the district\n",
    "Before analyzing our data, we are going to create a new column in the combined dataset with the two first characters of the schools DBN, correspondent to the district. This will be done with the objective of analyzing the information in a district level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    01\n",
       "1    01\n",
       "2    01\n",
       "3    01\n",
       "4    01\n",
       "Name: school_dist, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a function that extracts the first two chars\n",
    "def two_chars(string):\n",
    "    return string[0:2]\n",
    "\n",
    "# applying it to the whole column of dbn, storing in a new column\n",
    "combined['school_dist'] = combined['dbn'].apply(two_chars)\n",
    "\n",
    "combined['school_dist'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Once we've done that, we can start to look for insights in the dataset we've built. First thin we are going to do is look for correlations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Looking for correlations\n",
    "Searching for correlations in a table manner would be very inconvenient for our data, since it has too many columns. With that in mind, we are going to plot heatmaps per group containing the correlations.  \n",
    "We will start treating the dataframe to plot the heatmaps, after it we will create variables containing the columns of each group. We will start the analysis with the surveys of the parents, students and teachers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sat_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sat_score</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <td>0.987771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <td>0.986820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <td>0.972643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advanced Regents - % of cohort</th>\n",
       "      <td>0.771566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advanced Regents - % of grads</th>\n",
       "      <td>0.739927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Regents - % of cohort</th>\n",
       "      <td>0.667603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white_per</th>\n",
       "      <td>0.620718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Grads - % of cohort</th>\n",
       "      <td>0.584234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_per</th>\n",
       "      <td>0.570730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP Test Takers</th>\n",
       "      <td>0.523140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <td>0.514333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Regents - % of grads</th>\n",
       "      <td>0.494732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_num</th>\n",
       "      <td>0.475445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Exams with scores 3 4 or 5</th>\n",
       "      <td>0.463245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      sat_score\n",
       "sat_score                              1.000000\n",
       "SAT Writing Avg. Score                 0.987771\n",
       "SAT Critical Reading Avg. Score        0.986820\n",
       "SAT Math Avg. Score                    0.972643\n",
       "Advanced Regents - % of cohort         0.771566\n",
       "Advanced Regents - % of grads          0.739927\n",
       "Total Regents - % of cohort            0.667603\n",
       "white_per                              0.620718\n",
       "Total Grads - % of cohort              0.584234\n",
       "asian_per                              0.570730\n",
       "AP Test Takers                         0.523140\n",
       "Total Exams Taken                      0.514333\n",
       "Total Regents - % of grads             0.494732\n",
       "asian_num                              0.475445\n",
       "Number of Exams with scores 3 4 or 5   0.463245"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = pd.DataFrame(combined.corr()['sat_score'])\n",
    "corr.sort_values('sat_score', ascending=False, inplace=True)\n",
    "corr.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After treating the data to be plotted, we can start the analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Survey Correlations\n",
    "The columns related to surveys were easy, since we've selected them before when we were merging them to the main dataframe. After plotting the map, we will be able to see which ones are correlated to the `sat_results`, with the name of the column, we can consult the dictionary of the data to understand the mean of that number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group of columns related to parents, students and teachers surveys\n",
    "surveys = ['rr_s', 'rr_t', 'rr_p', 'N_s', 'N_t', 'N_p', 'saf_p_11', 'com_p_11', 'eng_p_11', 'aca_p_11', 'saf_t_11', \n",
    "           'com_t_11', 'eng_t_11', 'aca_t_11', 'saf_s_11', 'com_s_11', 'eng_s_11', 'aca_s_11', 'saf_tot_11', \n",
    "           'com_tot_11', 'eng_tot_11', 'aca_tot_11']\n",
    "\n",
    "# configurating the size of the plot\n",
    "fig, ax = plt.subplots(figsize=(18, 8))\n",
    "\n",
    "# plotting a bar graph\n",
    "barplot = sns.barplot(x=surveys, y='sat_score', data=corr.loc[surveys], ax=ax)\n",
    "\n",
    "# configurating the plot\n",
    "plt.setp(fig.axes, yticks=[])\n",
    "plt.xticks(rotation=45, size=12)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "ax.axhline(0, color=\"k\", clip_on=False)\n",
    "ax.set_ylabel(\"\", size=12)\n",
    "ax.set_xlabel(\"Survey correlations per column\", size=12)\n",
    "ax.set_title(\"Correlations between Surveys and SAT scores\", size=18, pad=30)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# annotating the values in the bars\n",
    "for bar in barplot.patches:\n",
    "    barplot.annotate(format(bar.get_height(), '.2f'),\n",
    "                    (bar.get_x() + bar.get_width()/2., bar.get_height()), \n",
    "                    ha='center', va='center', size=12, xytext=(0,12),\n",
    "                    textcoords='offset points')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking our graph ant the data dictionary, we can see summarize the information in a table:  \n",
    "\n",
    "| Column | Correlation | Information |  \n",
    "| :--: | :--: | :--: |  \n",
    "| N_s | 0.42 | Number of students respondents. |  \n",
    "| N_p | 0.42 | Number of parents respondents. |\n",
    "| saf_s_11 | 0.34 | Safety and respect score based on student responses. |\n",
    "| aca_s_11 | 0.34 | Academic expectations score based on teachers responses. |\n",
    "| saf_tot_11 | 0.32 | Safety and respect total score. |\n",
    "| saf_t_11 | 0.31 | Safety and respect score based on teachers responses. |  \n",
    "\n",
    "\n",
    "Above we can see no strong correlations, just moderate ones. The ones that are higher: Number of students and number of parents respondents, don't tell us much about how the sat score is influenced by the surveys.  \n",
    "On the other hand, it is very interesting to see how the **safety scores** of teachers and students relate to the sat. Let's plot some graphs to see how they behave per district, checking for different patterns in different locations.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing scores per district\n",
    "To do so, we are going to do scatter plots per column, with the objective of looking closely to the differences between the safety scores, then we can plot another bar plot to show the correlation with the sat scores. After it we are going to look closer to each Borough's average safety scores and sat_scores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configurating the size of the image\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "# creating the scatter plot\n",
    "ax = sns.scatterplot(data=combined, x='sat_score', y='saf_s_11', hue='borough')\n",
    "\n",
    "# configurating the plot\n",
    "ax.set_ylabel(\"\", size=12)\n",
    "ax.set_xlabel(\"SAT scores\", size=12)\n",
    "ax.set_title(\"Correlation between safety scores by students and SAT scores\", size=18, pad=30)\n",
    "\n",
    "plt.setp(fig.axes, yticks=[])\n",
    "plt.xticks(size=12)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configurating the size of the image\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "# creating the scatter plot\n",
    "ax = sns.scatterplot(data=combined, x='sat_score', y='saf_tot_11', hue='borough')\n",
    "\n",
    "# configurating the plot\n",
    "ax.set_ylabel(\"total safety scores\", size=12)\n",
    "ax.set_xlabel(\"SAT scores\", size=12)\n",
    "ax.set_title(\"Correlation between total safety scores and SAT scores\", size=18, pad=30)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graphs above, we can see that in the SAT scores until 1400 there's no visible correlation, making us believe that the safety scores doesn't make a significant difference. On the other hand, between 1400 and 2000 we see no schools with safety scores lower than 7, also there's a small positive correlation, showing that the safer could have greater grades. It is a little bit tricky to get, maybe looking closer into the borough's overall safety, we can find more significant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>saf_s_11</th>\n",
       "      <th>saf_t_11</th>\n",
       "      <th>saf_tot_11</th>\n",
       "      <th>sat_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>borough</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>6.606577</td>\n",
       "      <td>7.026882</td>\n",
       "      <td>7.322581</td>\n",
       "      <td>1157.598203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>6.370755</td>\n",
       "      <td>6.985849</td>\n",
       "      <td>7.129245</td>\n",
       "      <td>1181.364461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>6.831370</td>\n",
       "      <td>7.287778</td>\n",
       "      <td>7.473333</td>\n",
       "      <td>1278.331410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>6.721875</td>\n",
       "      <td>7.365625</td>\n",
       "      <td>7.387500</td>\n",
       "      <td>1286.753032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>6.530000</td>\n",
       "      <td>7.210000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>1382.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               saf_s_11  saf_t_11  saf_tot_11    sat_score\n",
       "borough                                                   \n",
       "Bronx          6.606577  7.026882    7.322581  1157.598203\n",
       "Brooklyn       6.370755  6.985849    7.129245  1181.364461\n",
       "Manhattan      6.831370  7.287778    7.473333  1278.331410\n",
       "Queens         6.721875  7.365625    7.387500  1286.753032\n",
       "Staten Island  6.530000  7.210000    7.200000  1382.500000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping per district\n",
    "group_by = ['saf_s_11', 'saf_tot_11', 'saf_t_11', 'sat_score']\n",
    "borough_group = combined.pivot_table(values=group_by, columns = 'borough', aggfunc=np.mean)\n",
    "borough_group = borough_group.T\n",
    "borough_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking the grouped information, we can see that **Brooklyn** has the overall smallest safety scores, followed by **Staten Island** and **Bronx**. This makes us believe that people that live in this boroughs have a sense of insecurity and it, somehow, relates to the SAT score of the students.  \n",
    "After this analysis, we are going to investigate the relation between the race of students and their SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Races Correlations\n",
    "Following our project, we are going to dig deeper into the races of the students, analyzing the correlations between them and the scores. To start we will make a bar plot with the correlations between the percentage of each race in each school and their respective sat scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of columns we are going to use\n",
    "race_percentage = ['white_per', 'asian_per', 'black_per', 'hispanic_per']\n",
    "\n",
    "# configurating the size of the plot\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# plotting a bar graph\n",
    "barplot = sns.barplot(x=race_percentage, y='sat_score', data=corr.loc[race_percentage], ax=ax, palette=\"husl\")\n",
    "\n",
    "# configurating the plot\n",
    "plt.setp(fig.axes, yticks=[])\n",
    "plt.xticks(rotation=45, size=12)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "ax.axhline(0, color=\"k\", clip_on=False)\n",
    "ax.set_ylabel(\"\", size=12)\n",
    "ax.set_xlabel(\"Race percentge correlations per column\", size=12)\n",
    "ax.set_title(\"Correlations between race percentages and SAT scores\", size=18, pad=30)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# annotating the values in the bars\n",
    "for bar in barplot.patches:\n",
    "    barplot.annotate(format(bar.get_height(), '.2f'),\n",
    "                    (bar.get_x() + bar.get_width()/2., bar.get_height()), \n",
    "                    ha='center', va='center', size=12, xytext=(0,12),\n",
    "                    textcoords='offset points')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, white and Asian correlate positively with the sat scores, showing that schools with a high amount of white and Asian students have overall better grades than those with smaller percentages. While Black and Hispanic show a negative correlation, indicating that the greater the percentage is, the smaller the average of SAT scores are. This doesn't mean causation, the SAT scores are not necessarily smaller because of this percentage, the correlations show only a possibility based on the numbers. \n",
    "With that in mind, we can see a clear lack of balance between the races. To investigate it better, let's look closer the schools with high percentages of Hispanic students creating a scatter plot between `hispanic_per` and `sat_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configurating the size of the image\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "# creating the scatter plot\n",
    "ax = sns.scatterplot(data=combined, x='sat_score', y='hispanic_per')\n",
    "\n",
    "# configurating the plot\n",
    "ax.set_ylabel(\"hispanic_per\", size=12)\n",
    "ax.set_xlabel(\"sat_scores\", size=12)\n",
    "ax.set_title(\"Correlation between hispanic percentages and SAT scores\", size=18, pad=30)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, it is difficult to see a clear correlation, but it is interesting to see that schools with **more than 30%** of hispanic students **don't seem to reach scores greater than 1500**, while the schools with **less than 30%** have a **wider range of grades**.    \n",
    "\n",
    "With than in mind we are going to look closer to the schools with the greater and the smaller hispanic percentages, looking for other characteristics that may make them similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>hispanic_per</th>\n",
       "      <th>asian_per</th>\n",
       "      <th>black_per</th>\n",
       "      <th>white_per</th>\n",
       "      <th>borough</th>\n",
       "      <th>sat_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>MANHATTAN BRIDGES HIGH SCHOOL</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL</td>\n",
       "      <td>96.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>ACADEMY FOR LANGUAGE AND TECHNOLOGY</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>INTERNATIONAL SCHOOL FOR LIBERAL ARTS</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>MULTICULTURAL HIGH SCHOOL</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>951.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           SCHOOL NAME  hispanic_per  \\\n",
       "44                       MANHATTAN BRIDGES HIGH SCHOOL          99.8   \n",
       "82    WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL          96.7   \n",
       "89   GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...          99.8   \n",
       "125                ACADEMY FOR LANGUAGE AND TECHNOLOGY          99.4   \n",
       "141              INTERNATIONAL SCHOOL FOR LIBERAL ARTS          99.8   \n",
       "176   PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE          99.8   \n",
       "253                          MULTICULTURAL HIGH SCHOOL          99.8   \n",
       "286             PAN AMERICAN INTERNATIONAL HIGH SCHOOL         100.0   \n",
       "\n",
       "     asian_per  black_per  white_per    borough  sat_score  \n",
       "44         0.2        0.0        0.0  Manhattan     1058.0  \n",
       "82         0.0        2.3        0.3  Manhattan     1174.0  \n",
       "89         0.0        0.0        0.0  Manhattan     1014.0  \n",
       "125        0.0        0.6        0.0      Bronx      951.0  \n",
       "141        0.2        0.0        0.0      Bronx      934.0  \n",
       "176        0.0        0.0        0.0      Bronx      970.0  \n",
       "253        0.0        0.2        0.0   Brooklyn      887.0  \n",
       "286        0.0        0.0        0.0     Queens      951.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# schools with more than 95% of hispanic students\n",
    "combined.loc[combined['hispanic_per']>95,\n",
    "            ['SCHOOL NAME', 'hispanic_per', 'asian_per', 'black_per', 'white_per', 'borough', 'sat_score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe above shows us that almost all schools with more than 95% of hispanic students, have almost all of them with a range of 99.4 to 99.8, one school with 100% and another with 96.7%. By the name of the schools we can see that the reason behind this percentage being high is probably the international motivation of the schools.\n",
    "\n",
    "Other important observations are that the **Pan American International High School at Monroe** accepts only Latino immigrants and that there is only one school with this high percentage in Brooklyn and Queens, on the other hand there are three in Manhattan and Bronx. That could be a reflex of the population characteristics in these boroughs.    \n",
    "\n",
    "\n",
    "After that we are going to take a look at the schools with percentage of hispanic students less than 10% and with SAT scores lower to 1800."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>hispanic_per</th>\n",
       "      <th>asian_per</th>\n",
       "      <th>black_per</th>\n",
       "      <th>white_per</th>\n",
       "      <th>borough</th>\n",
       "      <th>sat_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES</td>\n",
       "      <td>4.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1424.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>BEDFORD ACADEMY HIGH SCHOOL</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>89.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>BENJAMIN BANNEKER ACADEMY</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1391.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>BOYS AND GIRLS HIGH SCHOOL</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1097.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>ACADEMY FOR COLLEGE PREPARATION AND CAREER EXP...</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>THE HIGH SCHOOL FOR GLOBAL CITIZENSHIP</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>89.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1176.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>SCHOOL FOR HUMAN RIGHTS, THE</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>88.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1088.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>SCHOOL FOR DEMOCRACY AND LEADERSHIP</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>89.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>HIGH SCHOOL FOR YOUTH AND COMMUNITY DEVELOPMEN...</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1027.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>HIGH SCHOOL FOR SERVICE &amp; LEARNING AT ERASMUS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>SCIENCE, TECHNOLOGY AND RESEARCH EARLY COLLEGE...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>83.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>MEDGAR EVERS COLLEGE PREPARATORY SCHOOL</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>95.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1436.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>CLARA BARTON HIGH SCHOOL</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>91.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>IT TAKES A VILLAGE ACADEMY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>92.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>963.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>BROOKLYN GENERATION SCHOOL</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>88.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>KURT HAHN EXPEDITIONARY LEARNING SCHOOL</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1092.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>VICTORY COLLEGIATE HIGH SCHOOL</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>93.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>ARTS &amp; MEDIA PREPARATORY ACADEMY</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>89.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>BROOKLYN COLLEGIATE: A COLLEGE BOARD SCHOOL</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>88.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1185.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>QUEENS PREPARATORY ACADEMY</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1099.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>PATHWAYS COLLEGE PREPARATORY SCHOOL: A COLLEGE...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>94.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>EXCELSIOR PREPARATORY HIGH SCHOOL</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>82.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>CAMBRIA HEIGHTS ACADEMY</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>87.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1223.438806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>HUMANITIES &amp; ARTS MAGNET HIGH SCHOOL</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>85.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>TOTTENVILLE HIGH SCHOOL</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>82.1</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1418.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           SCHOOL NAME  hispanic_per  \\\n",
       "46     HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES           4.0   \n",
       "192                        BEDFORD ACADEMY HIGH SCHOOL           7.1   \n",
       "194                          BENJAMIN BANNEKER ACADEMY           8.8   \n",
       "220                         BOYS AND GIRLS HIGH SCHOOL           7.8   \n",
       "223  ACADEMY FOR COLLEGE PREPARATION AND CAREER EXP...           6.7   \n",
       "226             THE HIGH SCHOOL FOR GLOBAL CITIZENSHIP           7.3   \n",
       "227                       SCHOOL FOR HUMAN RIGHTS, THE           8.3   \n",
       "228                SCHOOL FOR DEMOCRACY AND LEADERSHIP           8.4   \n",
       "229  HIGH SCHOOL FOR YOUTH AND COMMUNITY DEVELOPMEN...           9.1   \n",
       "230      HIGH SCHOOL FOR SERVICE & LEARNING AT ERASMUS           9.0   \n",
       "231  SCIENCE, TECHNOLOGY AND RESEARCH EARLY COLLEGE...           9.4   \n",
       "235            MEDGAR EVERS COLLEGE PREPARATORY SCHOOL           2.8   \n",
       "236                           CLARA BARTON HIGH SCHOOL           5.8   \n",
       "237                         IT TAKES A VILLAGE ACADEMY           4.5   \n",
       "238                         BROOKLYN GENERATION SCHOOL           8.5   \n",
       "240            KURT HAHN EXPEDITIONARY LEARNING SCHOOL           8.8   \n",
       "241                     VICTORY COLLEGIATE HIGH SCHOOL           5.8   \n",
       "242                   ARTS & MEDIA PREPARATORY ACADEMY           9.6   \n",
       "280        BROOKLYN COLLEGIATE: A COLLEGE BOARD SCHOOL           8.3   \n",
       "330                         QUEENS PREPARATORY ACADEMY           8.2   \n",
       "331  PATHWAYS COLLEGE PREPARATORY SCHOOL: A COLLEGE...           2.9   \n",
       "332                  EXCELSIOR PREPARATORY HIGH SCHOOL           9.7   \n",
       "335                            CAMBRIA HEIGHTS ACADEMY           7.5   \n",
       "337               HUMANITIES & ARTS MAGNET HIGH SCHOOL           8.5   \n",
       "353                            TOTTENVILLE HIGH SCHOOL           9.9   \n",
       "\n",
       "     asian_per  black_per  white_per        borough    sat_score  \n",
       "46        89.5        3.4        2.3      Manhattan  1424.000000  \n",
       "192        2.7       89.4        0.3       Brooklyn  1312.000000  \n",
       "194        2.7       87.0        0.4       Brooklyn  1391.000000  \n",
       "220        0.5       90.0        0.7       Brooklyn  1097.000000  \n",
       "223        0.2       92.0        1.1       Brooklyn  1139.000000  \n",
       "226        1.5       89.2        1.5       Brooklyn  1176.000000  \n",
       "227        0.8       88.9        1.6       Brooklyn  1088.000000  \n",
       "228        0.5       89.4        1.5       Brooklyn  1153.000000  \n",
       "229        1.0       87.3        1.0       Brooklyn  1027.000000  \n",
       "230        2.1       87.0        1.7       Brooklyn  1105.000000  \n",
       "231        3.1       83.9        1.3       Brooklyn  1360.000000  \n",
       "235        0.9       95.7        0.0       Brooklyn  1436.000000  \n",
       "236        1.8       91.2        0.7       Brooklyn  1251.000000  \n",
       "237        1.8       92.9        0.5       Brooklyn   963.000000  \n",
       "238        1.5       88.8        0.9       Brooklyn  1145.000000  \n",
       "240        0.0       88.1        1.7       Brooklyn  1092.000000  \n",
       "241        0.3       93.5        0.0       Brooklyn  1143.000000  \n",
       "242        0.7       89.4        0.0       Brooklyn  1080.000000  \n",
       "280        1.9       88.1        0.5       Brooklyn  1185.000000  \n",
       "330        5.2       84.4        0.9         Queens  1099.000000  \n",
       "331        2.2       94.2        0.7         Queens  1173.000000  \n",
       "332        4.3       82.8        0.5         Queens  1202.000000  \n",
       "335        3.4       87.1        2.0         Queens  1223.438806  \n",
       "337        4.0       85.9        1.5         Queens  1151.000000  \n",
       "353        5.4        2.1       82.1  Staten Island  1418.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the schools we need\n",
    "combined.loc[(combined['hispanic_per']<10) & (combined['sat_score']<1800), \n",
    "            ['SCHOOL NAME', 'hispanic_per', 'asian_per', 'black_per', 'white_per', 'borough', 'sat_score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can see that the schools are **mainly located in** two boroughs: **Brooklyn and Queens**, both of them may be correlated to the reality of the borough population. **Staten Island** shows a **very small amount of other races that are not white**, so it may not be correlated specifically with hispanic, and the school located in Manhattan with 4.0 percent of hispanic students is **HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES**, this shows that probably the smaller amout of other races is related to the emphasis in the asian students.   \n",
    "\n",
    "\n",
    "To illustrate the difference between the borough population, we can take a look at the information taken from the [US Census of 2010](https://www.census.gov/quickfacts/fact/table/newyorkcitynewyork,bronxcountybronxboroughnewyork,kingscountybrooklynboroughnewyork,newyorkcountymanhattanboroughnewyork,queenscountyqueensboroughnewyork,richmondcountystatenislandboroughnewyork/POP010210) that specifies the races percentages in the NYC Boroughs in that year, after it no census were made, just predictions, the next is from 2019 and is doesn't help us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fact</th>\n",
       "      <th>Bronx</th>\n",
       "      <th>Brooklyn</th>\n",
       "      <th>Manhattan</th>\n",
       "      <th>Queens</th>\n",
       "      <th>Staten Island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>White alone, percent</td>\n",
       "      <td>44.7%</td>\n",
       "      <td>49.8%</td>\n",
       "      <td>64.6%</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>74.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Black or African American alone, percent</td>\n",
       "      <td>43.6%</td>\n",
       "      <td>33.8%</td>\n",
       "      <td>17.8%</td>\n",
       "      <td>20.7%</td>\n",
       "      <td>11.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Indian and Alaska Native alone, percent</td>\n",
       "      <td>2.9%</td>\n",
       "      <td>0.9%</td>\n",
       "      <td>1.2%</td>\n",
       "      <td>1.3%</td>\n",
       "      <td>0.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Asian alone, percent</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>12.7%</td>\n",
       "      <td>12.8%</td>\n",
       "      <td>26.9%</td>\n",
       "      <td>10.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Native Hawaiian and Other Pacific Islander alo...</td>\n",
       "      <td>0.4%</td>\n",
       "      <td>0.1%</td>\n",
       "      <td>0.2%</td>\n",
       "      <td>0.2%</td>\n",
       "      <td>0.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Two or More Races, percent</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>2.7%</td>\n",
       "      <td>3.4%</td>\n",
       "      <td>3.0%</td>\n",
       "      <td>2.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hispanic or Latino, percent</td>\n",
       "      <td>56.4%</td>\n",
       "      <td>18.9%</td>\n",
       "      <td>25.6%</td>\n",
       "      <td>28.2%</td>\n",
       "      <td>18.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>White alone, not Hispanic or Latino, percent</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>24.9%</td>\n",
       "      <td>59.6%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Fact  Bronx Brooklyn  \\\n",
       "8                                White alone, percent  44.7%    49.8%   \n",
       "9            Black or African American alone, percent  43.6%    33.8%   \n",
       "10   American Indian and Alaska Native alone, percent   2.9%     0.9%   \n",
       "11                               Asian alone, percent   4.6%    12.7%   \n",
       "12  Native Hawaiian and Other Pacific Islander alo...   0.4%     0.1%   \n",
       "13                         Two or More Races, percent   3.8%     2.7%   \n",
       "14                        Hispanic or Latino, percent  56.4%    18.9%   \n",
       "15       White alone, not Hispanic or Latino, percent   9.0%    36.8%   \n",
       "\n",
       "   Manhattan Queens Staten Island  \n",
       "8      64.6%  47.8%         74.5%  \n",
       "9      17.8%  20.7%         11.6%  \n",
       "10      1.2%   1.3%          0.7%  \n",
       "11     12.8%  26.9%         10.9%  \n",
       "12      0.2%   0.2%          0.1%  \n",
       "13      3.4%   3.0%          2.2%  \n",
       "14     25.6%  28.2%         18.6%  \n",
       "15     47.2%  24.9%         59.6%  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the csv with raw data\n",
    "demographics_raw = pd.read_csv('https://raw.githubusercontent.com/nathpignaton/guided_projects/main/nyc-sat-analysis/QuickFactsNY.csv')\n",
    "\n",
    "# renaming the columns\n",
    "new_columns = {'Bronx County (Bronx Borough), New York': 'Bronx', 'Kings County (Brooklyn Borough), New York': 'Brooklyn', \n",
    "               'New York County (Manhattan Borough), New York': 'Manhattan', \n",
    "               'Queens County (Queens Borough), New York': 'Queens',\n",
    "               'Richmond County (Staten Island Borough), New York': 'Staten Island'}\n",
    "\n",
    "demographics_raw.rename(columns=new_columns, inplace=True)\n",
    "\n",
    "# creating a df with the columns we need\n",
    "demographics = demographics_raw[['Fact', 'Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island']]\n",
    "\n",
    "# filtering the info\n",
    "demographics = demographics[(demographics['Fact'] == 'White alone, percent') |  \n",
    "                            (demographics['Fact'] == 'Black or African American alone, percent') |\n",
    "                            (demographics['Fact'] == 'American Indian and Alaska Native alone, percent') |\n",
    "                            (demographics['Fact'] == 'Asian alone, percent') |\n",
    "                            (demographics['Fact'] == 'Native Hawaiian and Other Pacific Islander alone, percent') |\n",
    "                            (demographics['Fact'] == 'Two or More Races, percent') |\n",
    "                            (demographics['Fact'] == 'Hispanic or Latino, percent') |\n",
    "                            (demographics['Fact'] == 'White alone, not Hispanic or Latino, percent')]\n",
    "\n",
    "demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the information shown in the schools databases are highly related to the population in the school borough. Makes sense that the schools in Brooklyn and Staten Island have such a low percentage of hispanic students, since the percentage of hispanic or latinos in Staten Island is 18.6% and in Brooklyn 18.9%. The same logic follows Manhattan and Queens, that have (both) a higher percentage of hispanic or latinos, 25.6% in Manhattan and 28.2% in Queens.   \n",
    "Coming back to the SATs, we are going to see the influence of gender in the grades, looking for patterns that could be interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Gender Correlation\n",
    "After the analysis made before, we are going to check the relation between male and female students and the SAT scores. Let's plot a bar graph with the correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configurating the size of the plot\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "# plotting a bar graph\n",
    "barplot = sns.barplot(x=['male_per', 'female_per'], y='sat_score', \n",
    "                      data=corr.loc[['male_per', 'female_per']], \n",
    "                      ax=ax, palette='pastel')\n",
    "\n",
    "# configurating the plot\n",
    "plt.setp(fig.axes, yticks=[])\n",
    "plt.xticks(size=12)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "ax.axhline(0, color=\"k\", clip_on=False)\n",
    "ax.set_ylabel(\"\", size=12)\n",
    "ax.set_xlabel(\"correlation between percentage and SAT scores\", size=12)\n",
    "ax.set_title(\"Correlations between male and female student percentages and average SAT scores of the schools\", size=18, pad=30)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# annotating the values in the bars\n",
    "for bar in barplot.patches:\n",
    "    barplot.annotate(format(bar.get_height(), '.2f'),\n",
    "                    (bar.get_x() + bar.get_width()/2., bar.get_height()), \n",
    "                    ha='center', va='center', size=12, xytext=(0,12),\n",
    "                    textcoords='offset points')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, we can see the very interesting information that schools with a higher percentage of female students show, also, a higher average SAT Score, let's look closer at this information using a scatter plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configurating the size of the image\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "# creating the scatter plot\n",
    "ax = sns.scatterplot(data=combined, x='sat_score', y='male_per')\n",
    "\n",
    "# configurating the plot\n",
    "ax.set_ylabel(\"female percentage\", size=12)\n",
    "ax.set_xlabel(\"average of SAT scores\", size=12)\n",
    "ax.set_title(\"Correlation between female students percentages and average SAT scores\", size=18, pad=30)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, we can see that the school with the higher average of SAT scores have, also, a specific percentage of female students, this percentages sits in the range of 20% to 70%. To investigate it further, we are going to look at the schools with more than 60% of female students and SAT score higher than 1700. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>sat_score</th>\n",
       "      <th>female_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BARD HIGH SCHOOL EARLY COLLEGE</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ELEANOR ROOSEVELT HIGH SCHOOL</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>BEACON HIGH SCHOOL</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC &amp; A...</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>TOWNSEND HARRIS HIGH SCHOOL</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>71.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           SCHOOL NAME  sat_score  female_per\n",
       "5                       BARD HIGH SCHOOL EARLY COLLEGE     1856.0        68.7\n",
       "26                       ELEANOR ROOSEVELT HIGH SCHOOL     1758.0        67.5\n",
       "60                                  BEACON HIGH SCHOOL     1744.0        61.0\n",
       "61   FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...     1707.0        73.6\n",
       "302                        TOWNSEND HARRIS HIGH SCHOOL     1910.0        71.1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.loc[(combined['female_per']>60) & (combined['sat_score']>1700), ['SCHOOL NAME', 'sat_score', 'female_per']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking closely to the schools above, we can see that the schools are all highly competitive, with Advanced Placement programs and a small amount of students.    \n",
    "Since this schools have Advanced Placement programs, let's see the influence of it in the average SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Advanced Placement exams Correlation\n",
    "To see if the two informations correlate somehow we need to, first, calculate the percentage of students in each school that took an AP exam. After it we can plot the scatter plot to see the behavior of this relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculating the percentage\n",
    "ap_per = combined['AP Test Takers ']/combined['total_enrollment']\n",
    "\n",
    "combined['ap_per'] = ap_per\n",
    "\n",
    "# configurating the size of the image\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "# creating the scatter plot\n",
    "ax = sns.scatterplot(data=combined, x='sat_score', y='ap_per')\n",
    "\n",
    "# configurating the plot\n",
    "ax.set_ylabel(\"advanced placement percentage\", size=12)\n",
    "ax.set_xlabel(\"average of SAT scores\", size=12)\n",
    "ax.set_title(\"Correlation between AP takers percentages and average SAT scores\", size=18, pad=30)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.tick_params(bottom=False)\n",
    "\n",
    "sns.despine(bottom=True, left=True, right=True)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, we can see that the schools with average of SAT scores higher than 1400 correlate more with the percentage of students that did the advanced placement tests, showing that it makes difference in the grades higher than 1400."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 Conclusion\n",
    "\n",
    "Through the analysis above, we saw that several aspects influence the average SAT of the schools and that, some of them, have a bigger and smaller influence. In a nutshell:  \n",
    "\n",
    "**Survey Correlations** showed us that `safety and respect scores of students` and `academic expectation scores` have a higher correlation than other survey aspects. This led us to investigate the SAT scores per district, looking at the social information of each one of them. We'll talk more about that below.  \n",
    "\n",
    "\n",
    "Surprisingly **Gender Correlation** showed us that the amount of female students is positively correlated to the higher scores. \n",
    "\n",
    "\n",
    "On the other hand, the existence of **Advanced Placement exams** told us that, as we expected, schools that give this resource to their students, have a higher chance of getting a higher SAT score.\n",
    "\n",
    "Talking about the SAT average scores by **District** we can see that in the ones where the safety scores are lower, the SAT scores tend to be smaller, showing that the sense of security influences how great the average of the SAT score can be.\n",
    "\n",
    "When it comes to **Races**, the average of the SAT scores shows us that when a school has a percentage above 30% of Hispanic students, it will fall, probably, into the range of 0 to 1500 average. We saw, after that, the relation of small SAT scores and low Hispanic students percentage, showing that the schools with less Hispanic and less averages, have a high percentage of Black students, this also relates to the **District** where the schools are.  \n",
    "\n",
    "To conclude we can say that **races** and **location** being badly correlated to the average of the SAT scores show us that the allegations of the tests being unfair to specific racial groups make sense, showing that there is room to improve to make smaller the differences."
   ]
  }
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