{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *Analyzing NYC High School Data*\n", "\n", "One of the most controversial issues in the U.S. educational system is the effectiveness of standardized tests, and whether they're unfair to certain groups. \n", "\n", "One of such exam is SAT. The SAT, or Scholastic Aptitude Test, is an exam that U.S. high school students take before applying to college. Colleges take the test scores into account when deciding who to admit, so it's fairly important to perform well on it.\n", "\n", "In this project, we will collect data from various sources and determine whether SAT scores are unfair to certain groups.\n", "\n", "**New York City** has a significant immigrant population and is very diverse, so comparing demographic factors such as race, income, and gender with SAT scores is a good way to determine whether the SAT is a fair test. For example, if certain racial groups consistently perform better on the SAT, we would have some evidence that the SAT is unfair.\n", "\n", "**For the purposes of this project, we'll be using data about New York City public schools, which can be found here.
Below are the datasets that we will be using:**\n", "\n", "SAT scores by school - SAT scores for each high school in New York City
\n", "School attendance - Attendance information for each school in New York City
\n", "Class size - Information on class size for each school
\n", "AP test results - Advanced Placement (AP) exam results for each high school (passing an optional AP exam in a particular subject can earn a student college credit in that subject)
\n", "Graduation outcomes - The percentage of students who graduated, and other outcome information
\n", "Demographics - Demographic information for each school
\n", "School survey - Surveys of parents, teachers, and students at each school
\n", "\n", "**All of these data sets are interrelated. We'll combine all of them into a single data set *combined* which will be used in our analysis, to find correlations.**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read in the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy\n", "import re\n", "\n", "data_files = [\n", " \"ap_2010.csv\",\n", " \"class_size.csv\",\n", " \"demographics.csv\",\n", " \"graduation.csv\",\n", " \"hs_directory.csv\",\n", " \"sat_results.csv\"\n", "]\n", "\n", "data = {}\n", "\n", "for f in data_files:\n", " d = pd.read_csv(\"schools/{0}\".format(f))\n", " data[f.replace(\".csv\", \"\")] = d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Print Data Keys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ap_2010\n", "class_size\n", "demographics\n", "graduation\n", "hs_directory\n", "sat_results\n" ] } ], "source": [ "for item in data:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Print Data Values (Datasets)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNSchoolNameAP Test TakersTotal Exams TakenNumber of Exams with scores 3 4 or 5
001M448UNIVERSITY NEIGHBORHOOD H.S.394910
101M450EAST SIDE COMMUNITY HS1921s
201M515LOWER EASTSIDE PREP242624
301M539NEW EXPLORATIONS SCI,TECH,MATH255377191
402M296High School of Hospitality Managementsss
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" ], "text/plain": [ " DBN SchoolName AP Test Takers \\\n", "0 01M448 UNIVERSITY NEIGHBORHOOD H.S. 39 \n", "1 01M450 EAST SIDE COMMUNITY HS 19 \n", "2 01M515 LOWER EASTSIDE PREP 24 \n", "3 01M539 NEW EXPLORATIONS SCI,TECH,MATH 255 \n", "4 02M296 High School of Hospitality Management s \n", "\n", " Total Exams Taken Number of Exams with scores 3 4 or 5 \n", "0 49 10 \n", "1 21 s \n", "2 26 24 \n", "3 377 191 \n", "4 s s " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['ap_2010'].head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CSDBOROUGHSCHOOL CODESCHOOL NAMEGRADEPROGRAM TYPECORE SUBJECT (MS CORE and 9-12 ONLY)CORE COURSE (MS CORE and 9-12 ONLY)SERVICE CATEGORY(K-9* ONLY)NUMBER OF STUDENTS / SEATS FILLEDNUMBER OF SECTIONSAVERAGE CLASS SIZESIZE OF SMALLEST CLASSSIZE OF LARGEST CLASSDATA SOURCESCHOOLWIDE PUPIL-TEACHER RATIO
01MM015P.S. 015 Roberto Clemente0KGEN ED---19.01.019.019.019.0ATSNaN
11MM015P.S. 015 Roberto Clemente0KCTT---21.01.021.021.021.0ATSNaN
21MM015P.S. 015 Roberto Clemente01GEN ED---17.01.017.017.017.0ATSNaN
31MM015P.S. 015 Roberto Clemente01CTT---17.01.017.017.017.0ATSNaN
41MM015P.S. 015 Roberto Clemente02GEN ED---15.01.015.015.015.0ATSNaN
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" ], "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['class_size'].head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNNameschoolyearfl_percentfrl_percenttotal_enrollmentprekkgrade1grade2...black_numblack_perhispanic_numhispanic_perwhite_numwhite_permale_nummale_perfemale_numfemale_per
001M015P.S. 015 ROBERTO CLEMENTE2005200689.4NaN28115364033...7426.318967.351.8158.056.2123.043.8
101M015P.S. 015 ROBERTO CLEMENTE2006200789.4NaN24315293938...6828.015363.041.6140.057.6103.042.4
201M015P.S. 015 ROBERTO CLEMENTE2007200889.4NaN26118433936...7729.515760.272.7143.054.8118.045.2
301M015P.S. 015 ROBERTO CLEMENTE2008200989.4NaN25217374432...7529.814959.172.8149.059.1103.040.9
401M015P.S. 015 ROBERTO CLEMENTE2009201096.520816402832...6732.211856.762.9124.059.684.040.4
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5 rows × 38 columns

\n", "
" ], "text/plain": [ " DBN Name schoolyear fl_percent frl_percent \\\n", "0 01M015 P.S. 015 ROBERTO CLEMENTE 20052006 89.4 NaN \n", "1 01M015 P.S. 015 ROBERTO CLEMENTE 20062007 89.4 NaN \n", "2 01M015 P.S. 015 ROBERTO CLEMENTE 20072008 89.4 NaN \n", "3 01M015 P.S. 015 ROBERTO CLEMENTE 20082009 89.4 NaN \n", "4 01M015 P.S. 015 ROBERTO CLEMENTE 20092010 96.5 \n", "\n", " total_enrollment prek k grade1 grade2 ... black_num black_per \\\n", "0 281 15 36 40 33 ... 74 26.3 \n", "1 243 15 29 39 38 ... 68 28.0 \n", "2 261 18 43 39 36 ... 77 29.5 \n", "3 252 17 37 44 32 ... 75 29.8 \n", "4 208 16 40 28 32 ... 67 32.2 \n", "\n", " hispanic_num hispanic_per white_num white_per male_num male_per female_num \\\n", "0 189 67.3 5 1.8 158.0 56.2 123.0 \n", "1 153 63.0 4 1.6 140.0 57.6 103.0 \n", "2 157 60.2 7 2.7 143.0 54.8 118.0 \n", "3 149 59.1 7 2.8 149.0 59.1 103.0 \n", "4 118 56.7 6 2.9 124.0 59.6 84.0 \n", "\n", " female_per \n", "0 43.8 \n", "1 42.4 \n", "2 45.2 \n", "3 40.9 \n", "4 40.4 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['demographics'].head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DemographicDBNSchool NameCohortTotal CohortTotal Grads - nTotal Grads - % of cohortTotal Regents - nTotal Regents - % of cohortTotal Regents - % of grads...Regents w/o Advanced - nRegents w/o Advanced - % of cohortRegents w/o Advanced - % of gradsLocal - nLocal - % of cohortLocal - % of gradsStill Enrolled - nStill Enrolled - % of cohortDropped Out - nDropped Out - % of cohort
0Total Cohort01M292HENRY STREET SCHOOL FOR INTERNATIONAL20035sssss...ssssssssss
1Total Cohort01M292HENRY STREET SCHOOL FOR INTERNATIONAL2004553767.3%1730.9%45.9%...1730.9%45.9%2036.4%54.1%1527.3%35.5%
2Total Cohort01M292HENRY STREET SCHOOL FOR INTERNATIONAL2005644367.2%2742.2%62.8%...2742.2%62.8%1625%37.200000000000003%914.1%914.1%
3Total Cohort01M292HENRY STREET SCHOOL FOR INTERNATIONAL2006784355.1%3646.2%83.7%...3646.2%83.7%79%16.3%1620.5%1114.1%
4Total Cohort01M292HENRY STREET SCHOOL FOR INTERNATIONAL2006 Aug784456.4%3747.4%84.1%...3747.4%84.1%79%15.9%1519.2%1114.1%
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5 rows × 23 columns

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" ], "text/plain": [ " Demographic DBN School Name Cohort \\\n", "0 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2003 \n", "1 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2004 \n", "2 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2005 \n", "3 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2006 \n", "4 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2006 Aug \n", "\n", " Total Cohort Total Grads - n Total Grads - % of cohort Total Regents - n \\\n", "0 5 s s s \n", "1 55 37 67.3% 17 \n", "2 64 43 67.2% 27 \n", "3 78 43 55.1% 36 \n", "4 78 44 56.4% 37 \n", "\n", " Total Regents - % of cohort Total Regents - % of grads \\\n", "0 s s \n", "1 30.9% 45.9% \n", "2 42.2% 62.8% \n", "3 46.2% 83.7% \n", "4 47.4% 84.1% \n", "\n", " ... Regents w/o Advanced - n \\\n", "0 ... s \n", "1 ... 17 \n", "2 ... 27 \n", "3 ... 36 \n", "4 ... 37 \n", "\n", " Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads \\\n", "0 s s \n", "1 30.9% 45.9% \n", "2 42.2% 62.8% \n", "3 46.2% 83.7% \n", "4 47.4% 84.1% \n", "\n", " Local - n Local - % of cohort Local - % of grads Still Enrolled - n \\\n", "0 s s s s \n", "1 20 36.4% 54.1% 15 \n", "2 16 25% 37.200000000000003% 9 \n", "3 7 9% 16.3% 16 \n", "4 7 9% 15.9% 15 \n", "\n", " Still Enrolled - % of cohort Dropped Out - n Dropped Out - % of cohort \n", "0 s s s \n", "1 27.3% 3 5.5% \n", "2 14.1% 9 14.1% \n", "3 20.5% 11 14.1% \n", "4 19.2% 11 14.1% \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['graduation'].head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dbnschool_nameborobuilding_codephone_numberfax_numbergrade_span_mingrade_span_maxexpgrade_span_minexpgrade_span_max...priority02priority03priority04priority05priority06priority07priority08priority09priority10Location 1
017K548Brooklyn School for Music & TheatreBrooklynK440718-230-6250718-230-6262912NaNNaN...Then to New York City residentsNaNNaNNaNNaNNaNNaNNaNNaN883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67...
109X543High School for Violin and DanceBronxX400718-842-0687718-589-9849912NaNNaN...Then to New York City residents who attend an ...Then to Bronx students or residentsThen to New York City residentsNaNNaNNaNNaNNaNNaN1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...
209X327Comprehensive Model School Project M.S. 327BronxX240718-294-8111718-294-8109612NaNNaN...Then to Bronx students or residents who attend...Then to New York City residents who attend an ...Then to Bronx students or residentsThen to New York City residentsNaNNaNNaNNaNNaN1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241...
302M280Manhattan Early College School for AdvertisingManhattanM520718-935-3477NaN910914.0...Then to New York City residents who attend an ...Then to Manhattan students or residentsThen to New York City residentsNaNNaNNaNNaNNaNNaN411 Pearl Street\\nNew York, NY 10038\\n(40.7106...
428Q680Queens Gateway to Health Sciences Secondary Sc...QueensQ695718-969-3155718-969-3552612NaNNaN...Then to Districts 28 and 29 students or residentsThen to Queens students or residentsThen to New York City residentsNaNNaNNaNNaNNaNNaN160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40...
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5 rows × 58 columns

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" ], "text/plain": [ " dbn school_name boro \\\n", "0 17K548 Brooklyn School for Music & Theatre Brooklyn \n", "1 09X543 High School for Violin and Dance Bronx \n", "2 09X327 Comprehensive Model School Project M.S. 327 Bronx \n", "3 02M280 Manhattan Early College School for Advertising Manhattan \n", "4 28Q680 Queens Gateway to Health Sciences Secondary Sc... Queens \n", "\n", " building_code phone_number fax_number grade_span_min grade_span_max \\\n", "0 K440 718-230-6250 718-230-6262 9 12 \n", "1 X400 718-842-0687 718-589-9849 9 12 \n", "2 X240 718-294-8111 718-294-8109 6 12 \n", "3 M520 718-935-3477 NaN 9 10 \n", "4 Q695 718-969-3155 718-969-3552 6 12 \n", "\n", " expgrade_span_min expgrade_span_max \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 9 14.0 \n", "4 NaN NaN \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", "\n", " priority02 \\\n", "0 Then to New York City residents \n", "1 Then to New York City residents who attend an ... \n", "2 Then to Bronx students or residents who attend... \n", "3 Then to New York City residents who attend an ... \n", "4 Then to Districts 28 and 29 students or residents \n", "\n", " priority03 \\\n", "0 NaN \n", "1 Then to Bronx students or residents \n", "2 Then to New York City residents who attend an ... \n", "3 Then to Manhattan students or residents \n", "4 Then to Queens students or residents \n", "\n", " priority04 priority05 \\\n", "0 NaN NaN \n", "1 Then to New York City residents NaN \n", "2 Then to Bronx students or residents Then to New York City residents \n", "3 Then to New York City residents NaN \n", "4 Then to New York City residents 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 \n", "0 883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67... \n", "1 1110 Boston Road\\nBronx, NY 10456\\n(40.8276026... \n", "2 1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241... \n", "3 411 Pearl Street\\nNew York, NY 10038\\n(40.7106... \n", "4 160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40... \n", "\n", "[5 rows x 58 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['hs_directory'].head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNSCHOOL NAMENum of SAT Test TakersSAT Critical Reading Avg. ScoreSAT Math Avg. ScoreSAT Writing Avg. Score
001M292HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES29355404363
101M448UNIVERSITY NEIGHBORHOOD HIGH SCHOOL91383423366
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301M458FORSYTH SATELLITE ACADEMY7414401359
401M509MARTA VALLE HIGH SCHOOL44390433384
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" ], "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['sat_results'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each data set appears to either have a DBN column, or the information we need to create one. After some processing and making `DBN` column in each dataset, we can combine columns of each dataset together in a single data set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read in the surveys" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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N_pN_sN_taca_p_11aca_s_11aca_t_11aca_tot_11bncom_p_11com_s_11...t_q8c_1t_q8c_2t_q8c_3t_q8c_4t_q9t_q9_1t_q9_2t_q9_3t_q9_4t_q9_5
090.0NaN22.07.8NaN7.97.9M0157.6NaN...29.067.05.00.0NaN5.014.052.024.05.0
1161.0NaN34.07.8NaN9.18.4M0197.6NaN...74.021.06.00.0NaN3.06.03.078.09.0
2367.0NaN42.08.6NaN7.58.0M0208.3NaN...33.035.020.013.0NaN3.05.016.070.05.0
3151.0145.029.08.57.47.87.9M0348.25.9...21.045.028.07.0NaN0.018.032.039.011.0
490.0NaN23.07.9NaN8.18.0M0637.9NaN...59.036.05.00.0NaN10.05.010.060.015.0
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5 rows × 2773 columns

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" ], "text/plain": [ " N_p N_s N_t aca_p_11 aca_s_11 aca_t_11 aca_tot_11 bn \\\n", "0 90.0 NaN 22.0 7.8 NaN 7.9 7.9 M015 \n", "1 161.0 NaN 34.0 7.8 NaN 9.1 8.4 M019 \n", "2 367.0 NaN 42.0 8.6 NaN 7.5 8.0 M020 \n", "3 151.0 145.0 29.0 8.5 7.4 7.8 7.9 M034 \n", "4 90.0 NaN 23.0 7.9 NaN 8.1 8.0 M063 \n", "\n", " com_p_11 com_s_11 ... t_q8c_1 t_q8c_2 t_q8c_3 t_q8c_4 t_q9 \\\n", "0 7.6 NaN ... 29.0 67.0 5.0 0.0 NaN \n", "1 7.6 NaN ... 74.0 21.0 6.0 0.0 NaN \n", "2 8.3 NaN ... 33.0 35.0 20.0 13.0 NaN \n", "3 8.2 5.9 ... 21.0 45.0 28.0 7.0 NaN \n", "4 7.9 NaN ... 59.0 36.0 5.0 0.0 NaN \n", "\n", " t_q9_1 t_q9_2 t_q9_3 t_q9_4 t_q9_5 \n", "0 5.0 14.0 52.0 24.0 5.0 \n", "1 3.0 6.0 3.0 78.0 9.0 \n", "2 3.0 5.0 16.0 70.0 5.0 \n", "3 0.0 18.0 32.0 39.0 11.0 \n", "4 10.0 5.0 10.0 60.0 15.0 \n", "\n", "[5 rows x 2773 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_survey = pd.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", "d75_survey = pd.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", "survey = pd.concat([all_survey, d75_survey], axis=0, sort=True)\n", "survey.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 01M015\n", "1 01M019\n", "2 01M020\n", "3 01M034\n", "4 01M063\n", "Name: dbn, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey['dbn'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two immediate facts that we can see in the data:\n", "- There are over 2000 columns, nearly all of which we don't need. We'll have to filter the data to remove the unnecessary ones. Working with fewer columns will make it easier to print the dataframe out and find correlations within it.\n", "> We'll need to filter the columns to remove the ones we don't need. Luckily, there's a data dictionary at the [original data download location.](https://data.cityofnewyork.us/Education/NYC-School-Survey-2011/mnz3-dyi8) The dictionary tells us what each column represents. We'll pick columns having aggregate survey data about how parents, teachers, and students feel about school safety, academic performance, and more.\n", "- The survey data has a dbn column that we'll want to convert to uppercase (DBN). The conversion will make the column name consistent with the other data sets." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNrr_srr_trr_pN_sN_tN_psaf_p_11com_p_11eng_p_11...eng_t_11aca_t_11saf_s_11com_s_11eng_s_11aca_s_11saf_tot_11com_tot_11eng_tot_11aca_tot_11
001M015NaN8860NaN22.090.08.57.67.5...7.67.9NaNNaNNaNNaN8.07.77.57.9
101M019NaN10060NaN34.0161.08.47.67.6...8.99.1NaNNaNNaNNaN8.58.18.28.4
201M020NaN8873NaN42.0367.08.98.38.3...6.87.5NaNNaNNaNNaN8.27.37.58.0
301M03489.07350145.029.0151.08.88.28.0...6.87.86.25.96.57.47.36.77.17.9
401M063NaN10060NaN23.090.08.77.98.1...7.88.1NaNNaNNaNNaN8.57.67.98.0
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5 rows × 23 columns

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" ], "text/plain": [ " DBN rr_s rr_t rr_p N_s N_t N_p saf_p_11 com_p_11 eng_p_11 \\\n", "0 01M015 NaN 88 60 NaN 22.0 90.0 8.5 7.6 7.5 \n", "1 01M019 NaN 100 60 NaN 34.0 161.0 8.4 7.6 7.6 \n", "2 01M020 NaN 88 73 NaN 42.0 367.0 8.9 8.3 8.3 \n", "3 01M034 89.0 73 50 145.0 29.0 151.0 8.8 8.2 8.0 \n", "4 01M063 NaN 100 60 NaN 23.0 90.0 8.7 7.9 8.1 \n", "\n", " ... eng_t_11 aca_t_11 saf_s_11 com_s_11 eng_s_11 aca_s_11 \\\n", "0 ... 7.6 7.9 NaN NaN NaN NaN \n", "1 ... 8.9 9.1 NaN NaN NaN NaN \n", "2 ... 6.8 7.5 NaN NaN NaN NaN \n", "3 ... 6.8 7.8 6.2 5.9 6.5 7.4 \n", "4 ... 7.8 8.1 NaN NaN NaN NaN \n", "\n", " saf_tot_11 com_tot_11 eng_tot_11 aca_tot_11 \n", "0 8.0 7.7 7.5 7.9 \n", "1 8.5 8.1 8.2 8.4 \n", "2 8.2 7.3 7.5 8.0 \n", "3 7.3 6.7 7.1 7.9 \n", "4 8.5 7.6 7.9 8.0 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey[\"DBN\"] = survey[\"dbn\"]\n", "\n", "survey_fields = [\n", " \"DBN\", \n", " \"rr_s\", \n", " \"rr_t\", \n", " \"rr_p\", \n", " \"N_s\", \n", " \"N_t\", \n", " \"N_p\", \n", " \"saf_p_11\", \n", " \"com_p_11\", \n", " \"eng_p_11\", \n", " \"aca_p_11\", \n", " \"saf_t_11\", \n", " \"com_t_11\", \n", " \"eng_t_11\", \n", " \"aca_t_11\", \n", " \"saf_s_11\", \n", " \"com_s_11\", \n", " \"eng_s_11\", \n", " \"aca_s_11\", \n", " \"saf_tot_11\", \n", " \"com_tot_11\", \n", " \"eng_tot_11\", \n", " \"aca_tot_11\",\n", "]\n", "survey = survey.loc[:,survey_fields]\n", "data[\"survey\"] = survey\n", "\n", "data['survey'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add/Modify DBN columns in class_size and hs_directory\n", "\n", "When we explored all of the data sets, we noticed that some of them, like `class_size` and `hs_directory`, don't have a `DBN` column. \n", "- `hs_directory` does have a `dbn` column, though, so we can just rename it.\n", "- However, `class_size` doesn't appear to have the column at all. For making up `DBN` column, we will add `CSD` and `SCHOOL CODE` columns and add a leading 0 to the `CSD` if the `CSD` is less than two digits long" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 17K548\n", "1 09X543\n", "2 09X327\n", "3 02M280\n", "4 28Q680\n", "Name: DBN, dtype: object\n" ] }, { "data": { "text/plain": [ "0 01M015\n", "1 01M015\n", "2 01M015\n", "3 01M015\n", "4 01M015\n", "Name: DBN, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n", "\n", "def pad_csd(num):\n", " string_representation = str(num)\n", " if len(string_representation) > 1:\n", " return string_representation\n", " else:\n", " return \"0\" + string_representation\n", " \n", "data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n", "data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]\n", "\n", "print(data['hs_directory']['DBN'].head())\n", "data[\"class_size\"]['DBN'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert numeric columns from Object type to numeric and Add Longitude and Latitude Columns\n", "\n", "- Before procedding further, we need to change columns in `ap_2010` from object (string) data type to numeric data type.\n", "- Same needs to be done with SAT scores columns in `sat_results`. And then make up a new column that totals up the SAT scores.\n", "- We also want to parse the latitude and longitude coordinates for each school. This will enable us to map the schools and uncover any geographic patterns in the data. The coordinates are currently in the text field `Location 1` in the `hs_directory` data set. We will need to use some regex alongwith string manipulation to extract them in two new columns. And then convert them to numeric format." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n", "for c in cols:\n", " data[\"sat_results\"][c] = pd.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n", "\n", "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n", " \n", "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n", "for col in cols:\n", " data[\"ap_2010\"][col] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")\n", "\n", "def find_lat(loc):\n", " coords = re.findall(\"\\(.+, .+\\)\", loc)\n", " lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n", " return lat\n", "\n", "def find_lon(loc):\n", " coords = re.findall(\"\\(.+, .+\\)\", loc)\n", " lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n", " return lon\n", "\n", "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n", "data[\"hs_directory\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n", "\n", "data[\"hs_directory\"][\"lat\"] = pd.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n", "data[\"hs_directory\"][\"lon\"] = pd.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Condense datasets\n", "\n", "- Further, we will clean our datasets more to have a single row for each `DBN` value per dataset. This will help us when while combining the data sets.\n", "- We'll perform cleaning for three datasets:\n", "\n", "> **`class_size`** : each school has multiple values for `GRADE, PROGRAM TYPE`, `CORE SUBJECT (MS CORE and 9-12 ONLY)`, and `CORE COURSE (MS CORE and 9-12 ONLY)`. \n", " - We will filter on `GRADE(09-12)` and `PROGRAM TYPE('GEN ED')` to get information about only classes where SAT has been taken. \n", " - And, then we will use groupby method on `DBN` to get unique groups per school. Taking average on the resulting groupby object using agg() method will give unique values per `DBN`. \n", "> **`demographics`**: Here, only column that prevents a given DBN from being unique is `schoolyear`. We only want to select rows where `schoolyear` is `20112012`. This will give us the most recent year of data, and also match our SAT results data.

\n", "> **`graduation`**: The `Demographic` and `Cohort` columns are what prevent `DBN` from being unique in the graduation data. A `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`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class_size = data[\"class_size\"]\n", "class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n", "class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n", "\n", "class_size = class_size.groupby(\"DBN\").agg(numpy.mean)\n", "class_size.reset_index(inplace=True)\n", "data[\"class_size\"] = class_size\n", "\n", "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n", "\n", "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n", "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine the datasets\n", "\n", "- We'll merge two data sets at a time. For example, we'll merge `sat_results` with one dataset, then with another, then the result of that with another dataset. We'll continue combining data sets in this way until we've merged all of them. Afterwards, we'll have roughly the same number of rows, but each row will have columns from all of the data sets.\n", "- Because we're concerned with determing demographic factors correlating with SAT score, we want to preserve as many rows as possible from `sat_results` while minimizing null values. Therefore, we will use different merge strategies. \n", "- Data sets having lot of missing DBN values will be merged using a left join.\n", "- Data sets having DBN values identical to those in sat_results will be merged using inner join. These data sets also have information we need to keep. Most of our analysis would be impossible if a significant number of rows were missing from `demographics`, for example." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "combined = data[\"sat_results\"]\n", "\n", "combined = combined.merge(data[\"ap_2010\"], on=\"DBN\", how=\"left\")\n", "combined = combined.merge(data[\"graduation\"], on=\"DBN\", how=\"left\")\n", "\n", "to_merge = [\"class_size\", \"demographics\", \"survey\", \"hs_directory\"]\n", "\n", "for m in to_merge:\n", " combined = combined.merge(data[m], on=\"DBN\", how=\"inner\")\n", "\n", "combined = combined.fillna(combined.mean())\n", "combined = combined.fillna(0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNSCHOOL NAMENum of SAT Test TakersSAT Critical Reading Avg. ScoreSAT Math Avg. ScoreSAT Writing Avg. Scoresat_scoreSchoolNameAP Test TakersTotal Exams TakenNumber of Exams with scores 3 4 or 5DemographicSchool NameCohortTotal CohortTotal Grads - nTotal Grads - % of cohortTotal Regents - nTotal Regents - % of cohortTotal Regents - % of gradsAdvanced Regents - nAdvanced Regents - % of cohortAdvanced Regents - % of gradsRegents w/o Advanced - nRegents w/o Advanced - % of cohortRegents w/o Advanced - % of gradsLocal - nLocal - % of cohortLocal - % of gradsStill Enrolled - nStill Enrolled - % of cohortDropped Out - nDropped Out - % of cohortCSDNUMBER OF STUDENTS / SEATS FILLEDNUMBER OF SECTIONSAVERAGE CLASS SIZESIZE OF SMALLEST CLASSSIZE OF LARGEST CLASSSCHOOLWIDE PUPIL-TEACHER RATIONameschoolyearfl_percentfrl_percenttotal_enrollmentprekkgrade1grade2grade3grade4grade5grade6grade7grade8grade9grade10grade11grade12ell_numell_percentsped_numsped_percentctt_numselfcontained_numasian_numasian_perblack_numblack_perhispanic_numhispanic_perwhite_numwhite_permale_nummale_perfemale_numfemale_perrr_srr_trr_pN_sN_tN_psaf_p_11com_p_11eng_p_11aca_p_11saf_t_11com_t_11eng_t_11aca_t_11saf_s_11com_s_11eng_s_11aca_s_11saf_tot_11com_tot_11eng_tot_11aca_tot_11dbnschool_nameborobuilding_codephone_numberfax_numbergrade_span_mingrade_span_maxexpgrade_span_minexpgrade_span_maxbussubwayprimary_address_line_1citystate_codezipwebsitetotal_studentscampus_nameschool_typeoverview_paragraphprogram_highlightslanguage_classesadvancedplacement_coursesonline_ap_coursesonline_language_coursesextracurricular_activitiespsal_sports_boyspsal_sports_girlspsal_sports_coedschool_sportspartner_cbopartner_hospitalpartner_higheredpartner_culturalpartner_nonprofitpartner_corporatepartner_financialpartner_otheraddtl_info1addtl_info2start_timeend_timese_servicesell_programsschool_accessibility_descriptionnumber_programspriority01priority02priority03priority04priority05priority06priority07priority08priority09priority10Location 1latlon
001M292HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES29355.0404.0363.01122.00129.028846197.038462153.45Total CohortHENRY STREET SCHOOL FOR INTERNATIONAL200678.04355.1%3646.2%83.7%00%0%3646.2%83.7%79%16.3%1620.5%1114.1%188.0000004.00000022.56428618.50000026.5714290.0HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES20112012088.64223233509879805094.022.3105.024.934355914.012329.122753.871.7259.061.4163.038.689.07039379.00000026.0151.07.87.77.47.66.35.36.16.56.0000005.6000006.1000006.7000006.76.26.67.001M292Henry Street School for International StudiesManhattanM056212-406-9411212-406-9417612012.0B39, M14A, M14D, M15, M15-SBS, M21, M22, M9B, D to Grand St ; F to East Broadway ; J, M, ...220 Henry StreetNew YorkNY10002http://schools.nyc.gov/schoolportals/01/M292323.000Henry Street School for International Studies ...Global/International Studies in core subjects,...Chinese (Mandarin), SpanishPsychologyChinese Language and Culture, Spanish Literatu...Chinese (Mandarin), SpanishMath through Card Play; Art, Poetry/Spoken Wor...BasketballSoftballSoccerBoxing, Track, CHAMPS, Tennis, Flag Football, ...The Henry Street Settlement; Asia Society; Ame...Gouverneur Hospital (Turning Points)New York UniversityAsia SocietyHeart of America Foundation00United Nations008:30 AM3:30 PMThis school will provide students with disabil...ESLFunctionally Accessible1Priority to continuing 8th gradersThen to Manhattan students or residents who at...Then to New York City residents who attend an ...Then to Manhattan students or residentsThen to New York City residents00000220 Henry Street\\nNew York, NY 10002\\n(40.7137...40.713764-73.985260
101M448UNIVERSITY NEIGHBORHOOD HIGH SCHOOL91383.0423.0366.01172.0UNIVERSITY NEIGHBORHOOD H.S.39.00000049.00000010.00Total CohortUNIVERSITY NEIGHBORHOOD HIGH SCHOOL2006124.05342.7%4233.9%79.2%86.5%15.1%3427.4%64.2%118.9%20.8%4637.1%2016.100000000000001%1105.6875004.75000022.23125018.25000027.0625000.0UNIVERSITY NEIGHBORHOOD HIGH SCHOOL20112012071.839410997939583.021.186.021.8551011529.28922.618145.992.3226.057.4168.042.684.09510385.00000037.046.07.97.47.27.36.65.86.67.36.0000005.7000006.3000007.0000006.86.36.77.201M448University Neighborhood High SchoolManhattanM446212-962-4341212-267-5611912012.0M14A, M14D, M15, M21, M22, M9F to East Broadway ; J, M, Z to Delancey St-Es...200 Monroe StreetNew YorkNY10002www.universityneighborhoodhs.com299.000University Neighborhood High School (UNHS) is ...While attending UNHS, students can earn up to ...Chinese, SpanishCalculus AB, Chinese Language and Culture, Eng...0Chinese (Cantonese), Chinese (Mandarin), SpanishBasketball, Badminton, Handball, Glee, Dance, ...Baseball, Basketball, Bowling, Cross Country, ...Basketball, Bowling, Cross Country, Softball, ...00Grand Street Settlement, Henry Street Settleme...Gouverneur Hospital, The Door, The Mount Sinai...New York University, CUNY Baruch College, Pars...Dance Film Association, Dance Makers Film Work...W!SE, Big Brothers Big Sisters, Peer Health Ex...Deloitte LLP Consulting and Financial Services...0Movement ResearchIncoming students are expected to attend schoo...Community Service Requirement, Dress Code Requ...8:15 AM3:15 PMThis school will provide students with disabil...ESLNot Functionally Accessible3Open to New York City residentsFor M35B only: Open only to students whose hom...00000000200 Monroe Street\\nNew York, NY 10002\\n(40.712...40.712332-73.984797
201M450EAST SIDE COMMUNITY SCHOOL70377.0402.0370.01149.0EAST SIDE COMMUNITY HS19.00000021.000000153.45Total CohortEAST SIDE COMMUNITY SCHOOL200690.07077.8%6774.400000000000006%95.7%00%0%6774.400000000000006%95.7%33.3%4.3%1516.7%55.6%157.6000002.73333321.20000019.40000022.8666670.0EAST SIDE COMMUNITY HIGH SCHOOL20112012071.859892737610193778630.05.0158.026.49119589.714323.933155.46210.4327.054.7271.045.30.09828598.20833342.0150.08.78.28.18.47.38.08.08.86.6116676.0947226.6202787.3813897.97.97.98.401M450East Side Community SchoolManhattanM060212-460-8467212-260-9657612012.0M101, M102, M103, M14A, M14D, M15, M15-SBS, M2...6 to Astor Place ; L to 1st Ave420 East 12 StreetNew YorkNY10009www.eschs.org649.00Consortium SchoolWe are a small 6-12 secondary school that prep...Our Advisory System ensures that we can effect...0Calculus AB, English Literature and Composition0American Sign Language, Arabic, Chinese (Manda...After-School Tutoring, Art Portfolio Classes, ...Baseball, Basketball, SoccerBasketball, Soccer, Softball0Basketball, Bicycling, Fitness, Flag Football,...University Settlement, Big Brothers Big Sister...0Columbia Teachers College, New York University..., Internship Program, Loisaida Art Gallery loc...College Bound Initiative, Center for Collabora...Prudential Securities, Moore Capital, Morgan S...0Brooklyn Boulders (Rock Climbing)Students present and defend their work to comm...Our school requires an Academic Portfolio for ...8:30 AM3:30 PMThis school will provide students with disabil...ESLNot Functionally Accessible1Priority to continuing 8th gradersThen to New York City residents00000000420 East 12 Street\\nNew York, NY 10009\\n(40.72...40.729783-73.983041
301M509MARTA VALLE HIGH SCHOOL44390.0433.0384.01207.00129.028846197.038462153.45Total CohortMARTA VALLE HIGH SCHOOL200684.04756%4047.6%85.1%1720.2%36.200000000000003%2327.4%48.9%78.300000000000001%14.9%2529.8%56%169.6428573.00000023.57142920.00000027.3571430.0MARTA VALLE SECONDARY SCHOOL20112012080.7367143100517341.011.295.025.92836349.311631.620956.961.6170.046.3197.053.790.010021306.00000029.069.07.77.47.27.36.45.36.16.86.4000005.9000006.4000007.0000006.96.26.67.001M509Marta Valle High SchoolManhattanM025212-473-8152212-475-7588912012.0B39, M103, M14A, M14D, M15, M15-SBS, M21, M22,...B, D to Grand St ; F, J, M, Z to Delancey St-E...145 Stanton StreetNew YorkNY10002www.martavalle.org401.000Marta Valle High School (MVHS) offers a strong...Advanced Regents Diploma, Early Graduation, up...French, SpanishEnglish Literature and Composition, Studio Art...0SpanishModel Peer Leadership Program, 'The Vine' Stud...Basketball, RugbyRugby, VolleyballRugbyVolleyball, ZumbaNYCDOE Innovation Zone Lab Site, Grand Street ...Gouvenuer's HospitalNew York University (NYU), Sarah Lawrence Coll...Young Audiences, The National Arts Club, Educa...College for Every Student (CFES), Morningside ...Estée LauderBank of AmericaCASALEAP, BeaconStudents Dress for Success, Summer Bridge to S...Community Service Requirement, Extended Day Pr...8:00 AM3:30 PMThis school will provide students with disabil...ESLFunctionally Accessible1Priority to District 1 students or residentsThen to Manhattan students or residentsThen to New York City residents0000000145 Stanton Street\\nNew York, NY 10002\\n(40.72...40.720569-73.985673
401M539NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...159522.0574.0525.01621.0NEW EXPLORATIONS SCI,TECH,MATH255.000000377.000000191.00Total CohortNEW EXPLORATIONS INTO SCIENCE TECHNO200646.046100%46100%100%3167.400000000000006%67.400000000000006%1532.6%32.6%00%0%00%00%1156.3684216.15789525.51052619.47368431.2105260.0NEW EXPLORATIONS INTO SCIENCE TECH AND MATH20112012023.016131001071391101141071491261171171231471574.00.243.02.72044827.818911.722914.272544.9794.049.2819.050.898.06851923.00000067.0736.08.57.97.98.47.65.65.97.37.3000006.4000007.0000007.7000007.86.76.97.801M539New Explorations into Science, Technology and ...ManhattanM022212-677-5190212-260-8124K12012.0B39, M14A, M14D, M21, M22, M8, M9F, J, M, Z to Delancey St-Essex St111 Columbia StreetNew YorkNY10002www.nestmk12.net1725.000New Explorations into Science, Technology and ...1st level science sequence - 9th grade: Regent...Chinese (Mandarin), French, Italian, Latin, Sp...Biology, Calculus AB, Calculus BC, Chemistry, ...00After-school Jazz Band, Annual Coffee House Co...Basketball, Fencing, Indoor TrackBasketball, Fencing, Indoor Track0Badminton, Baseball, Cross-Country, Dance, Out...7th Precinct Community Affairs, NYCWastele$$, ...0Hunter College, New York University, Cornell U...VH1, Dancing Classrooms, Center for Arts Educa...After 3Time Warner Cable, Google, IBM, MET Project, S...00Dress Code Required: Business Casual - shirt/b...08:15 AM4:00 PMThis school will provide students with disabil...ESLNot Functionally Accessible1Priority to continuing 8th gradersThen to New York City residents00000000111 Columbia Street\\nNew York, NY 10002\\n(40.7...40.718725-73.979426
\n", "
" ], "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 0 129.028846 197.038462 \n", "1 UNIVERSITY NEIGHBORHOOD H.S. 39.000000 49.000000 \n", "2 EAST SIDE COMMUNITY HS 19.000000 21.000000 \n", "3 0 129.028846 197.038462 \n", "4 NEW EXPLORATIONS SCI,TECH,MATH 255.000000 377.000000 \n", "\n", " Number of Exams with scores 3 4 or 5 Demographic \\\n", "0 153.45 Total Cohort \n", "1 10.00 Total Cohort \n", "2 153.45 Total Cohort \n", "3 153.45 Total Cohort \n", "4 191.00 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 74.400000000000006% \n", "3 56% 40 47.6% \n", "4 100% 46 100% \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% 31 \n", "\n", " Advanced Regents - % of cohort Advanced Regents - % of grads \\\n", "0 0% 0% \n", "1 6.5% 15.1% \n", "2 0% 0% \n", "3 20.2% 36.200000000000003% \n", "4 67.400000000000006% 67.400000000000006% \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 74.400000000000006% \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% \n", "1 64.2% 11 8.9% \n", "2 95.7% 3 3.3% \n", "3 48.9% 7 8.300000000000001% \n", "4 32.6% 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% \n", "\n", " Dropped Out - n Dropped Out - % of cohort CSD \\\n", "0 11 14.1% 1 \n", "1 20 16.100000000000001% 1 \n", "2 5 5.6% 1 \n", "3 5 6% 1 \n", "4 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 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", " Name schoolyear fl_percent \\\n", "0 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 20112012 0 \n", "1 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 20112012 0 \n", "2 EAST SIDE COMMUNITY HIGH SCHOOL 20112012 0 \n", "3 MARTA VALLE SECONDARY SCHOOL 20112012 0 \n", "4 NEW EXPLORATIONS INTO SCIENCE TECH AND MATH 20112012 0 \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", "0 379.000000 26.0 151.0 7.8 7.7 7.4 7.6 6.3 \n", "1 385.000000 37.0 46.0 7.9 7.4 7.2 7.3 6.6 \n", "2 598.208333 42.0 150.0 8.7 8.2 8.1 8.4 7.3 \n", "3 306.000000 29.0 69.0 7.7 7.4 7.2 7.3 6.4 \n", "4 923.000000 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 6.000000 5.600000 6.100000 6.700000 \n", "1 5.8 6.6 7.3 6.000000 5.700000 6.300000 7.000000 \n", "2 8.0 8.0 8.8 6.611667 6.094722 6.620278 7.381389 \n", "3 5.3 6.1 6.8 6.400000 5.900000 6.400000 7.000000 \n", "4 5.6 5.9 7.3 7.300000 6.400000 7.000000 7.700000 \n", "\n", " saf_tot_11 com_tot_11 eng_tot_11 aca_tot_11 dbn \\\n", "0 6.7 6.2 6.6 7.0 01M292 \n", "1 6.8 6.3 6.7 7.2 01M448 \n", "2 7.9 7.9 7.9 8.4 01M450 \n", "3 6.9 6.2 6.6 7.0 01M509 \n", "4 7.8 6.7 6.9 7.8 01M539 \n", "\n", " school_name boro 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 12 \n", "1 212-962-4341 212-267-5611 9 12 \n", "2 212-460-8467 212-260-9657 6 12 \n", "3 212-473-8152 212-475-7588 9 12 \n", "4 212-677-5190 212-260-8124 K 12 \n", "\n", " expgrade_span_min expgrade_span_max \\\n", "0 0 12.0 \n", "1 0 12.0 \n", "2 0 12.0 \n", "3 0 12.0 \n", "4 0 12.0 \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 zip website \\\n", "0 New York NY 10002 http://schools.nyc.gov/schoolportals/01/M292 \n", "1 New York NY 10002 www.universityneighborhoodhs.com \n", "2 New York NY 10009 www.eschs.org \n", "3 New York NY 10002 www.martavalle.org \n", "4 New York NY 10002 www.nestmk12.net \n", "\n", " total_students campus_name school_type \\\n", "0 323.0 0 0 \n", "1 299.0 0 0 \n", "2 649.0 0 Consortium School \n", "3 401.0 0 0 \n", "4 1725.0 0 0 \n", "\n", " overview_paragraph \\\n", "0 Henry Street School for International Studies ... \n", "1 University Neighborhood High School (UNHS) is ... \n", "2 We are a small 6-12 secondary school that prep... \n", "3 Marta Valle High School (MVHS) offers a strong... \n", "4 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 0 \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 0 \n", "2 0 \n", "3 0 \n", "4 0 \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 0 \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 Basketball \n", "1 Baseball, Basketball, Bowling, Cross Country, ... \n", "2 Baseball, Basketball, Soccer \n", "3 Basketball, Rugby \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, ... 0 \n", "2 Basketball, Soccer, Softball 0 \n", "3 Rugby, Volleyball Rugby \n", "4 Basketball, Fencing, Indoor Track 0 \n", "\n", " school_sports \\\n", "0 Boxing, Track, CHAMPS, Tennis, Flag Football, ... \n", "1 0 \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 0 \n", "3 Gouvenuer's Hospital \n", "4 0 \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 0 0 \n", "1 Deloitte LLP Consulting and Financial Services... 0 \n", "2 Prudential Securities, Moore Capital, Morgan S... 0 \n", "3 Estée Lauder Bank of America \n", "4 Time Warner Cable, Google, IBM, MET Project, S... 0 \n", "\n", " partner_other \\\n", "0 United Nations \n", "1 Movement Research \n", "2 Brooklyn Boulders (Rock Climbing) \n", "3 CASALEAP, Beacon \n", "4 0 \n", "\n", " addtl_info1 \\\n", "0 0 \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 0 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 0 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 0 \n", "2 0 \n", "3 Then to New York City residents \n", "4 0 \n", "\n", " priority04 priority05 \\\n", "0 Then to Manhattan students or residents Then to New York City residents \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " priority06 priority07 priority08 priority09 priority10 \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Location 1 lat lon \n", "0 220 Henry Street\\nNew York, NY 10002\\n(40.7137... 40.713764 -73.985260 \n", "1 200 Monroe Street\\nNew York, NY 10002\\n(40.712... 40.712332 -73.984797 \n", "2 420 East 12 Street\\nNew York, NY 10009\\n(40.72... 40.729783 -73.983041 \n", "3 145 Stanton Street\\nNew York, NY 10002\\n(40.72... 40.720569 -73.985673 \n", "4 111 Columbia Street\\nNew York, NY 10002\\n(40.7... 40.718725 -73.979426 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('display.max_columns', 500)\n", "combined.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add a school district column for mapping\n", "\n", "Now, we have a clean data set on which we can base our analysis. Mapping the statistics out on a school district level might be an interesting way to analyze them. Adding a column to the data set that specifies the school district will help us accomplish this." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DBNSCHOOL NAMENum of SAT Test TakersSAT Critical Reading Avg. ScoreSAT Math Avg. ScoreSAT Writing Avg. Scoresat_scoreSchoolNameAP Test TakersTotal Exams TakenNumber of Exams with scores 3 4 or 5DemographicSchool NameCohortTotal CohortTotal Grads - nTotal Grads - % of cohortTotal Regents - nTotal Regents - % of cohortTotal Regents - % of gradsAdvanced Regents - nAdvanced Regents - % of cohortAdvanced Regents - % of gradsRegents w/o Advanced - nRegents w/o Advanced - % of cohortRegents w/o Advanced - % of gradsLocal - nLocal - % of cohortLocal - % of gradsStill Enrolled - nStill Enrolled - % of cohortDropped Out - nDropped Out - % of cohortCSDNUMBER OF STUDENTS / SEATS FILLEDNUMBER OF SECTIONSAVERAGE CLASS SIZESIZE OF SMALLEST CLASSSIZE OF LARGEST CLASSSCHOOLWIDE PUPIL-TEACHER RATIONameschoolyearfl_percentfrl_percenttotal_enrollmentprekkgrade1grade2grade3grade4grade5grade6grade7grade8grade9grade10grade11grade12ell_numell_percentsped_numsped_percentctt_numselfcontained_numasian_numasian_perblack_numblack_perhispanic_numhispanic_perwhite_numwhite_permale_nummale_perfemale_numfemale_perrr_srr_trr_pN_sN_tN_psaf_p_11com_p_11eng_p_11aca_p_11saf_t_11com_t_11eng_t_11aca_t_11saf_s_11com_s_11eng_s_11aca_s_11saf_tot_11com_tot_11eng_tot_11aca_tot_11dbnschool_nameborobuilding_codephone_numberfax_numbergrade_span_mingrade_span_maxexpgrade_span_minexpgrade_span_maxbussubwayprimary_address_line_1citystate_codezipwebsitetotal_studentscampus_nameschool_typeoverview_paragraphprogram_highlightslanguage_classesadvancedplacement_coursesonline_ap_coursesonline_language_coursesextracurricular_activitiespsal_sports_boyspsal_sports_girlspsal_sports_coedschool_sportspartner_cbopartner_hospitalpartner_higheredpartner_culturalpartner_nonprofitpartner_corporatepartner_financialpartner_otheraddtl_info1addtl_info2start_timeend_timese_servicesell_programsschool_accessibility_descriptionnumber_programspriority01priority02priority03priority04priority05priority06priority07priority08priority09priority10Location 1latlonschool_dist
001M292HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES29355.0404.0363.01122.00129.028846197.038462153.45Total CohortHENRY STREET SCHOOL FOR INTERNATIONAL200678.04355.1%3646.2%83.7%00%0%3646.2%83.7%79%16.3%1620.5%1114.1%188.00004.00000022.56428618.5026.5714290.0HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES20112012088.64223233509879805094.022.3105.024.934355914.012329.122753.871.7259.061.4163.038.689.07039379.00000026.0151.07.87.77.47.66.35.36.16.56.0000005.6000006.1000006.7000006.76.26.67.001M292Henry Street School for International StudiesManhattanM056212-406-9411212-406-9417612012.0B39, M14A, M14D, M15, M15-SBS, M21, M22, M9B, D to Grand St ; F to East Broadway ; J, M, ...220 Henry StreetNew YorkNY10002http://schools.nyc.gov/schoolportals/01/M292323.000Henry Street School for International Studies ...Global/International Studies in core subjects,...Chinese (Mandarin), SpanishPsychologyChinese Language and Culture, Spanish Literatu...Chinese (Mandarin), SpanishMath through Card Play; Art, Poetry/Spoken Wor...BasketballSoftballSoccerBoxing, Track, CHAMPS, Tennis, Flag Football, ...The Henry Street Settlement; Asia Society; Ame...Gouverneur Hospital (Turning Points)New York UniversityAsia SocietyHeart of America Foundation00United Nations008:30 AM3:30 PMThis school will provide students with disabil...ESLFunctionally Accessible1Priority to continuing 8th gradersThen to Manhattan students or residents who at...Then to New York City residents who attend an ...Then to Manhattan students or residentsThen to New York City residents00000220 Henry Street\\nNew York, NY 10002\\n(40.7137...40.713764-73.98526001
101M448UNIVERSITY NEIGHBORHOOD HIGH SCHOOL91383.0423.0366.01172.0UNIVERSITY NEIGHBORHOOD H.S.39.00000049.00000010.00Total CohortUNIVERSITY NEIGHBORHOOD HIGH SCHOOL2006124.05342.7%4233.9%79.2%86.5%15.1%3427.4%64.2%118.9%20.8%4637.1%2016.100000000000001%1105.68754.75000022.23125018.2527.0625000.0UNIVERSITY NEIGHBORHOOD HIGH SCHOOL20112012071.839410997939583.021.186.021.8551011529.28922.618145.992.3226.057.4168.042.684.09510385.00000037.046.07.97.47.27.36.65.86.67.36.0000005.7000006.3000007.0000006.86.36.77.201M448University Neighborhood High SchoolManhattanM446212-962-4341212-267-5611912012.0M14A, M14D, M15, M21, M22, M9F to East Broadway ; J, M, Z to Delancey St-Es...200 Monroe StreetNew YorkNY10002www.universityneighborhoodhs.com299.000University Neighborhood High School (UNHS) is ...While attending UNHS, students can earn up to ...Chinese, SpanishCalculus AB, Chinese Language and Culture, Eng...0Chinese (Cantonese), Chinese (Mandarin), SpanishBasketball, Badminton, Handball, Glee, Dance, ...Baseball, Basketball, Bowling, Cross Country, ...Basketball, Bowling, Cross Country, Softball, ...00Grand Street Settlement, Henry Street Settleme...Gouverneur Hospital, The Door, The Mount Sinai...New York University, CUNY Baruch College, Pars...Dance Film Association, Dance Makers Film Work...W!SE, Big Brothers Big Sisters, Peer Health Ex...Deloitte LLP Consulting and Financial Services...0Movement ResearchIncoming students are expected to attend schoo...Community Service Requirement, Dress Code Requ...8:15 AM3:15 PMThis school will provide students with disabil...ESLNot Functionally Accessible3Open to New York City residentsFor M35B only: Open only to students whose hom...00000000200 Monroe Street\\nNew York, NY 10002\\n(40.712...40.712332-73.98479701
201M450EAST SIDE COMMUNITY SCHOOL70377.0402.0370.01149.0EAST SIDE COMMUNITY HS19.00000021.000000153.45Total CohortEAST SIDE COMMUNITY SCHOOL200690.07077.8%6774.400000000000006%95.7%00%0%6774.400000000000006%95.7%33.3%4.3%1516.7%55.6%157.60002.73333321.20000019.4022.8666670.0EAST SIDE COMMUNITY HIGH SCHOOL20112012071.859892737610193778630.05.0158.026.49119589.714323.933155.46210.4327.054.7271.045.30.09828598.20833342.0150.08.78.28.18.47.38.08.08.86.6116676.0947226.6202787.3813897.97.97.98.401M450East Side Community SchoolManhattanM060212-460-8467212-260-9657612012.0M101, M102, M103, M14A, M14D, M15, M15-SBS, M2...6 to Astor Place ; L to 1st Ave420 East 12 StreetNew YorkNY10009www.eschs.org649.00Consortium SchoolWe are a small 6-12 secondary school that prep...Our Advisory System ensures that we can effect...0Calculus AB, English Literature and Composition0American Sign Language, Arabic, Chinese (Manda...After-School Tutoring, Art Portfolio Classes, ...Baseball, Basketball, SoccerBasketball, Soccer, Softball0Basketball, Bicycling, Fitness, Flag Football,...University Settlement, Big Brothers Big Sister...0Columbia Teachers College, New York University..., Internship Program, Loisaida Art Gallery loc...College Bound Initiative, Center for Collabora...Prudential Securities, Moore Capital, Morgan S...0Brooklyn Boulders (Rock Climbing)Students present and defend their work to comm...Our school requires an Academic Portfolio for ...8:30 AM3:30 PMThis school will provide students with disabil...ESLNot Functionally Accessible1Priority to continuing 8th gradersThen to New York City residents00000000420 East 12 Street\\nNew York, NY 10009\\n(40.72...40.729783-73.98304101
\n", "
" ], "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", "\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", "\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", "\n", " SchoolName AP Test Takers Total Exams Taken \\\n", "0 0 129.028846 197.038462 \n", "1 UNIVERSITY NEIGHBORHOOD H.S. 39.000000 49.000000 \n", "2 EAST SIDE COMMUNITY HS 19.000000 21.000000 \n", "\n", " Number of Exams with scores 3 4 or 5 Demographic \\\n", "0 153.45 Total Cohort \n", "1 10.00 Total Cohort \n", "2 153.45 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", "\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 74.400000000000006% \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", "\n", " Advanced Regents - % of cohort Advanced Regents - % of grads \\\n", "0 0% 0% \n", "1 6.5% 15.1% \n", "2 0% 0% \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 74.400000000000006% \n", "\n", " Regents w/o Advanced - % of grads Local - n Local - % of cohort \\\n", "0 83.7% 7 9% \n", "1 64.2% 11 8.9% \n", "2 95.7% 3 3.3% \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", "\n", " Dropped Out - n Dropped Out - % of cohort CSD \\\n", "0 11 14.1% 1 \n", "1 20 16.100000000000001% 1 \n", "2 5 5.6% 1 \n", "\n", " NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE \\\n", "0 88.0000 4.000000 22.564286 \n", "1 105.6875 4.750000 22.231250 \n", "2 57.6000 2.733333 21.200000 \n", "\n", " SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS \\\n", "0 18.50 26.571429 \n", "1 18.25 27.062500 \n", "2 19.40 22.866667 \n", "\n", " SCHOOLWIDE PUPIL-TEACHER RATIO \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " Name schoolyear fl_percent \\\n", "0 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 20112012 0 \n", "1 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 20112012 0 \n", "2 EAST SIDE COMMUNITY HIGH SCHOOL 20112012 0 \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", "\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", "\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", "\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", "\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", "\n", " N_s N_t N_p saf_p_11 com_p_11 eng_p_11 aca_p_11 saf_t_11 \\\n", "0 379.000000 26.0 151.0 7.8 7.7 7.4 7.6 6.3 \n", "1 385.000000 37.0 46.0 7.9 7.4 7.2 7.3 6.6 \n", "2 598.208333 42.0 150.0 8.7 8.2 8.1 8.4 7.3 \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 6.000000 5.600000 6.100000 6.700000 \n", "1 5.8 6.6 7.3 6.000000 5.700000 6.300000 7.000000 \n", "2 8.0 8.0 8.8 6.611667 6.094722 6.620278 7.381389 \n", "\n", " saf_tot_11 com_tot_11 eng_tot_11 aca_tot_11 dbn \\\n", "0 6.7 6.2 6.6 7.0 01M292 \n", "1 6.8 6.3 6.7 7.2 01M448 \n", "2 7.9 7.9 7.9 8.4 01M450 \n", "\n", " school_name boro 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", "\n", " phone_number fax_number grade_span_min grade_span_max \\\n", "0 212-406-9411 212-406-9417 6 12 \n", "1 212-962-4341 212-267-5611 9 12 \n", "2 212-460-8467 212-260-9657 6 12 \n", "\n", " expgrade_span_min expgrade_span_max \\\n", "0 0 12.0 \n", "1 0 12.0 \n", "2 0 12.0 \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", "\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", "\n", " city state_code zip website \\\n", "0 New York NY 10002 http://schools.nyc.gov/schoolportals/01/M292 \n", "1 New York NY 10002 www.universityneighborhoodhs.com \n", "2 New York NY 10009 www.eschs.org \n", "\n", " total_students campus_name school_type \\\n", "0 323.0 0 0 \n", "1 299.0 0 0 \n", "2 649.0 0 Consortium School \n", "\n", " overview_paragraph \\\n", "0 Henry Street School for International Studies ... \n", "1 University Neighborhood High School (UNHS) is ... \n", "2 We are a small 6-12 secondary school that prep... \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", "\n", " language_classes \\\n", "0 Chinese (Mandarin), Spanish \n", "1 Chinese, Spanish \n", "2 0 \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", "\n", " online_ap_courses \\\n", "0 Chinese Language and Culture, Spanish Literatu... \n", "1 0 \n", "2 0 \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", "\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", "\n", " psal_sports_boys \\\n", "0 Basketball \n", "1 Baseball, Basketball, Bowling, Cross Country, ... \n", "2 Baseball, Basketball, Soccer \n", "\n", " psal_sports_girls psal_sports_coed \\\n", "0 Softball Soccer \n", "1 Basketball, Bowling, Cross Country, Softball, ... 0 \n", "2 Basketball, Soccer, Softball 0 \n", "\n", " school_sports \\\n", "0 Boxing, Track, CHAMPS, Tennis, Flag Football, ... \n", "1 0 \n", "2 Basketball, Bicycling, Fitness, Flag Football,... \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", "\n", " partner_hospital \\\n", "0 Gouverneur Hospital (Turning Points) \n", "1 Gouverneur Hospital, The Door, The Mount Sinai... \n", "2 0 \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", "\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", "\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", "\n", " partner_corporate partner_financial \\\n", "0 0 0 \n", "1 Deloitte LLP Consulting and Financial Services... 0 \n", "2 Prudential Securities, Moore Capital, Morgan S... 0 \n", "\n", " partner_other \\\n", "0 United Nations \n", "1 Movement Research \n", "2 Brooklyn Boulders (Rock Climbing) \n", "\n", " addtl_info1 \\\n", "0 0 \n", "1 Incoming students are expected to attend schoo... \n", "2 Students present and defend their work to comm... \n", "\n", " addtl_info2 start_time end_time \\\n", "0 0 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", "\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", "\n", " school_accessibility_description number_programs \\\n", "0 Functionally Accessible 1 \n", "1 Not Functionally Accessible 3 \n", "2 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", "\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", "\n", " priority03 \\\n", "0 Then to New York City residents who attend an ... \n", "1 0 \n", "2 0 \n", "\n", " priority04 priority05 \\\n", "0 Then to Manhattan students or residents Then to New York City residents \n", "1 0 0 \n", "2 0 0 \n", "\n", " priority06 priority07 priority08 priority09 priority10 \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "\n", " Location 1 lat lon \\\n", "0 220 Henry Street\\nNew York, NY 10002\\n(40.7137... 40.713764 -73.985260 \n", "1 200 Monroe Street\\nNew York, NY 10002\\n(40.712... 40.712332 -73.984797 \n", "2 420 East 12 Street\\nNew York, NY 10009\\n(40.72... 40.729783 -73.983041 \n", "\n", " school_dist \n", "0 01 \n", "1 01 \n", "2 01 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_first_two_chars(dbn):\n", " return dbn[0:2]\n", "\n", "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)\n", "\n", "combined.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find correlations" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAT Critical Reading Avg. Score 0.986820\n", "SAT Math Avg. Score 0.972643\n", "SAT Writing Avg. Score 0.987771\n", "sat_score 1.000000\n", "AP Test Takers 0.523140\n", "Total Exams Taken 0.514333\n", "Number of Exams with scores 3 4 or 5 0.463245\n", "Total Cohort 0.325144\n", "CSD 0.042948\n", "NUMBER OF STUDENTS / SEATS FILLED 0.394626\n", "NUMBER OF SECTIONS 0.362673\n", "AVERAGE CLASS SIZE 0.381014\n", "SIZE OF SMALLEST CLASS 0.249949\n", "SIZE OF LARGEST CLASS 0.314434\n", "SCHOOLWIDE PUPIL-TEACHER RATIO NaN\n", "schoolyear NaN\n", "fl_percent NaN\n", "frl_percent -0.722225\n", "total_enrollment 0.367857\n", "ell_num -0.153778\n", "ell_percent -0.398750\n", "sped_num 0.034933\n", "sped_percent -0.448170\n", "asian_num 0.475445\n", "asian_per 0.570730\n", "black_num 0.027979\n", "black_per -0.284139\n", "hispanic_num 0.025744\n", "hispanic_per -0.396985\n", "white_num 0.449559\n", " ... \n", "rr_p 0.047925\n", "N_s 0.423463\n", "N_t 0.291463\n", "N_p 0.421530\n", "saf_p_11 0.122913\n", "com_p_11 -0.115073\n", "eng_p_11 0.020254\n", "aca_p_11 0.035155\n", "saf_t_11 0.313810\n", "com_t_11 0.082419\n", "eng_t_11 0.036906\n", "aca_t_11 0.132348\n", "saf_s_11 0.337639\n", "com_s_11 0.187370\n", "eng_s_11 0.213822\n", "aca_s_11 0.339435\n", "saf_tot_11 0.318753\n", "com_tot_11 0.077310\n", "eng_tot_11 0.100102\n", "aca_tot_11 0.190966\n", "grade_span_max NaN\n", "expgrade_span_max NaN\n", "zip -0.063977\n", "total_students 0.407827\n", "number_programs 0.117012\n", "priority08 NaN\n", "priority09 NaN\n", "priority10 NaN\n", "lat -0.121029\n", "lon -0.132222\n", "Name: sat_score, Length: 67, dtype: float64\n" ] } ], "source": [ "correlations = combined.corr()\n", "correlations = correlations[\"sat_score\"]\n", "print(correlations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting survey correlations\n", "\n", "- There are several fields in combined that originally came from a survey of parents, teachers, and students. We will make a bar plot of the correlations between these fields and sat_score." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n", "survey_fields.remove(\"DBN\")\n", "\n", "import matplotlib.pyplot as plt\n", "combined.corr()['sat_score'][survey_fields].plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Key Observations:**\n", "\n", "- `N_s`, `N_t`, `N_p` correlate highly with `sat_score`. Since these fields are directly related to total enrollment, this can be understood.\n", "- `rr_s` (Student Response Rate) however is the more interesting point. Since, students who excel academically are more likely to respond to a survey regarding `sat_score`.\n", "- `saf_t_11`, `saf_s_11`, `saf_tot_11` gives us another good perspective about the relationship between a safe environment and academic brilliance. Schools where Students and Teachers feel safe are more likely to have good `sat_score`.\n", "- `aca_s_11` i.e. how students percieve academic standards correlates highly for `sat_score`. However, same is not true for `aca_p_11` and `aca_t_11`, i.e. how Parents and Teachers perceive academic standards. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exploring Safety and SAT Scores\n", "\n", "- We will investigating safety scores by making a scatter plot of the saf_s_11 column vs. the sat_score in combined.\n", "- Map out safety scores : \n", " - Compute the average safety score for each district.\n", " - Make a map that shows safety scores by district." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "combined.plot.scatter('saf_s_11', 'sat_score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From the Scatter Plot, we can observe that there seems to be a positive correlation between the `sat_score` and `saf_s_11`, although its not that strong.**" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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school_distSAT Critical Reading Avg. ScoreSAT Math Avg. ScoreSAT Writing Avg. Scoresat_scoreAP Test TakersTotal Exams TakenNumber of Exams with scores 3 4 or 5Total CohortCSDNUMBER OF STUDENTS / SEATS FILLEDNUMBER OF SECTIONSAVERAGE CLASS SIZESIZE OF SMALLEST CLASSSIZE OF LARGEST CLASSSCHOOLWIDE PUPIL-TEACHER RATIOschoolyearfl_percentfrl_percenttotal_enrollmentell_numell_percentsped_numsped_percentasian_numasian_perblack_numblack_perhispanic_numhispanic_perwhite_numwhite_permale_nummale_perfemale_numfemale_perrr_srr_trr_pN_sN_tN_psaf_p_11com_p_11eng_p_11aca_p_11saf_t_11com_t_11eng_t_11aca_t_11saf_s_11com_s_11eng_s_11aca_s_11saf_tot_11com_tot_11eng_tot_11aca_tot_11grade_span_maxexpgrade_span_maxziptotal_studentsnumber_programspriority08priority09priority10latlon
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102426.619092444.186256424.8328361295.638184128.908454201.516827157.495833158.6478492.0149.8189495.68636025.03811820.66266728.5938610.020112012.00.063.164583605.60416751.62500013.00625070.77083313.212500118.39583314.479167141.14583324.733333271.45833349.55416768.54166710.056250266.50000044.718750339.10416755.28125083.31250086.41666738.333333495.17100731.687500190.0000008.2541677.4958337.3791677.7041677.4041676.5208337.1208337.5645836.9106606.2040576.6504227.3850297.5208336.7208337.0375007.54166712.012.010023.770833621.3958331.4166670.00.00.040.739699-73.991386
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405427.159915438.236674419.6660981285.06268785.722527115.725275142.464286143.6774195.0120.6239014.84510124.29024720.44752727.7467030.020112012.00.058.671429605.85714321.5714294.15714359.71428610.42857132.7142867.300000342.57142949.000000195.57142936.21428631.2857146.714286300.00000048.471429305.85714351.52857182.85714379.28571440.428571442.42857127.714286184.8571438.0857147.3428577.3571437.6857146.8571435.8857146.2428576.9571436.3142866.0000006.4285717.2857147.0857146.4142866.6714297.31428612.012.010030.142857609.8571431.1428570.00.00.040.817077-73.949251
506382.011940400.565672382.0662691164.643881108.711538159.715385105.425000180.8483876.0139.0417095.28586025.32419920.55612629.2178720.020112012.00.084.250000621.800000165.70000030.04000080.10000014.9500007.8000000.81000082.00000011.470000523.90000086.3800005.0000000.720000333.60000053.790000288.20000046.21000085.00000083.50000054.600000482.30000033.000000261.5000008.5300007.9900007.7500008.0500007.4900006.6100007.0600007.6600006.9500006.1500006.7000007.5100007.6600006.9200007.1700007.76000012.012.010036.200000628.9000001.3000000.00.00.040.848970-73.932502
607376.461538380.461538371.9230771128.84615473.703402112.476331105.276923105.6054597.097.5974164.14661023.56003719.39446527.0040520.020112012.00.079.630769481.00000072.00000016.66153884.84615417.4461548.0000001.707692144.46153829.253846322.00000067.6076923.7692310.800000261.76923153.138462219.23076946.86153880.53846277.15384642.923077376.23076925.846154187.6153858.4230777.7615387.6000007.9076926.9692316.3153856.7846157.2846156.8000006.1846156.6461547.4538467.3923086.7461547.0076927.55384612.012.010452.692308465.8461541.4615380.00.00.040.816815-73.919971
708386.214383395.542741377.9080051159.665129118.379371168.020979144.731818215.5102648.0129.7650995.16571023.81319219.38535527.3621410.020112012.00.076.663636684.54545572.90909115.790909140.54545519.60909130.2727272.809091179.81818227.809091433.54545566.10909136.1818182.554545355.00000045.454545329.54545554.54545569.45454577.63636436.636364459.83712134.000000148.5454558.1727277.6363647.4545457.7181827.1090916.2545456.7818187.3909096.3646975.9540666.4018437.1437637.2181826.6454556.8636367.40909112.012.010467.000000547.6363641.2727270.00.00.040.823803-73.866087
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2122473.500000502.750000474.2500001450.500000391.007212614.509615370.362500580.25000022.0495.27936915.77739130.07763622.50967533.4647400.020112012.00.044.2250002142.500000138.2500004.400000166.2500006.650000507.75000017.975000622.25000035.100000271.00000012.350000732.00000033.9750001015.75000044.3000001126.75000055.70000090.25000087.50000048.7500001887.25000089.5000001106.2500008.3000007.3250007.5250007.7750008.1750007.4000007.6000008.2000007.0250006.0500006.7750007.5000007.8500006.9000007.3000007.85000012.012.011223.0000002149.0000002.2500000.00.00.040.618285-73.952288
2223380.666667398.666667378.0000001157.33333329.00000031.000000153.45000087.00000023.0120.1130954.98511924.29970218.65476228.9940480.020112012.00.070.400000503.6666678.0000001.60000068.66666714.4666676.0000001.133333438.00000087.13333353.33333310.6000003.3333330.566667221.33333344.433333282.33333355.56666784.00000073.66666740.666667420.33333325.333333175.0000007.9000007.5333337.5666677.7000007.0333336.4333336.6666677.3666676.2333336.0666676.7666677.5666677.0666676.7000007.0000007.56666712.012.011219.000000391.0000001.3333330.00.00.040.668586-73.912298
2324405.846154434.000000402.1538461242.000000126.474852179.094675115.165385234.68238224.0213.4719038.61352923.86593618.56504028.2514710.020112012.00.062.584615991.000000179.76923121.06923197.0769239.607692182.69230817.17692374.7692317.800000616.61538563.415385113.46153811.284615572.76923151.646154418.23076948.35384683.84615483.23076935.692308765.46153847.769231247.6923088.4615387.6846157.5692317.8692317.6692316.5692317.2307697.7230777.1076926.1692316.7846157.5000007.7538466.8076927.1923087.70769212.012.011206.153846962.4615382.2307690.00.00.040.740621-73.911518
2425437.250000483.500000436.2500001357.000000205.260817279.889423174.793750268.73387125.0280.5760079.99038527.10627021.21318931.3712200.020112012.00.052.9500001356.750000257.50000019.737500136.7500009.975000465.25000040.600000240.37500013.062500494.00000029.237500148.87500016.537500669.50000048.500000687.25000051.50000087.00000078.50000037.6250001028.25000056.000000370.8750008.1750007.5375007.4125007.6875007.4875006.8250007.2000007.7125006.9125006.1500006.7125007.4000007.5250006.8375007.1125007.61250012.012.011361.0000001288.8750001.8750000.00.00.040.745414-73.815558
2526445.200000487.600000444.8000001377.600000410.605769632.407692392.090000825.60000026.0595.95321619.24011729.97114020.31578933.5543270.020112012.00.043.8000002991.600000248.6000007.480000350.00000011.9600001259.20000038.140000676.40000028.420000604.80000019.380000432.40000013.4200001475.00000049.2200001516.60000050.78000067.00000083.40000023.8000001930.800000129.800000684.2000007.7200006.9800007.2200007.2600007.0000006.5800006.8400007.2600006.7600006.0600006.6600007.3800007.1400006.5400006.9000007.30000012.012.011388.6000002837.4000004.6000000.00.00.040.748507-73.759176
2627407.800000422.200000394.3000001224.300000100.611538145.31538595.125000288.96129027.0249.3245369.01904326.64965920.75753530.9380810.020112012.00.061.3400001148.200000117.8000006.820000134.60000011.950000263.10000016.890000365.70000039.760000394.10000031.380000103.90000010.750000610.70000052.400000537.50000047.60000076.90000086.50000040.900000775.10000059.200000276.8000007.8300007.4000007.3700007.6400006.9300006.7800007.0600007.6300006.3900006.0000006.5000007.3100007.0100006.7100006.9500007.51000012.012.011556.3000001072.0000002.5000000.00.00.040.638828-73.807823
2728445.941655465.997286435.9080051347.846947182.010490273.559441175.336364351.21407628.0255.3811648.71905826.77080721.34981630.1720380.020112012.00.053.7909091210.63636487.3636364.963636123.4545459.827273403.72727331.345455343.72727337.509091283.45454519.954545165.6363649.400000590.36363645.863636620.27272754.13636485.09090985.54545540.545455872.54545554.727273362.6363648.1727277.5909097.4727277.7818187.6000007.1454557.3636367.9727276.6272736.0818186.6181827.4545457.4636366.9363647.1454557.74545512.012.011422.0000001304.2727272.5454550.00.00.040.709344-73.806367
2829395.764925399.457090386.7078361181.92985163.38581796.514423135.26875098.10887129.088.3721553.45419325.35563221.40920128.6727300.020112012.00.052.362500428.25000012.6250002.91250056.12500013.03750020.7500004.825000359.87500083.98750039.0000009.1500004.1250001.050000219.87500051.862500208.37500048.13750077.62500090.62500044.750000294.50000021.375000146.5000007.7625007.3625007.3250007.5625007.0500006.9125007.1625007.5750006.0750005.9625006.4875007.2500006.9625006.7375007.0000007.47500012.012.011413.625000474.1250001.2500000.00.00.040.685276-73.752740
2930430.679934465.961857429.7402991326.382090157.231838252.123932115.150000310.52688230.0251.8037449.26048625.71564419.31227329.7607260.020112012.00.054.2111111221.555556220.55555616.133333125.1111117.988889281.77777824.866667124.22222210.444444607.22222243.522222204.44444420.788889604.11111144.777778617.44444455.22222289.00000085.44444442.333333963.44444463.111111318.7777788.2222227.4444447.4333337.7000007.5000006.6555566.9111117.5333337.0333336.1666676.8444447.5111117.5777786.7444447.0777787.58888912.012.011103.0000001123.3333332.5555560.00.00.040.755398-73.932306
3031457.500000472.500000452.5000001382.500000228.908654355.111538194.435000450.78709731.0380.52831913.25128428.11972919.99549432.7856410.020112012.00.038.3100001850.60000064.2000002.860000301.50000016.710000177.70000010.300000307.30000018.270000418.70000022.170000938.70000048.810000948.50000052.090000902.10000047.91000088.90000091.30000043.5000001476.60000088.900000593.3000007.8000007.3900007.4600007.6200007.2100007.1400007.3900007.8600006.5300006.0700006.7800007.3300007.2000006.8700007.2000007.61000012.012.010307.1000001847.5000005.0000000.00.00.040.595680-74.125726
3132371.500000385.833333362.1666671119.50000070.342949100.17948783.558333105.33333332.0100.5256134.45057222.47980417.93652026.8160130.020112012.00.082.866667420.83333379.50000018.53333367.50000015.6500006.3333331.75000082.00000019.833333326.16666776.8500003.8333330.966667217.83333351.716667203.00000048.28333375.66666772.33333334.666667312.83333322.333333137.8333338.3500008.0500007.8833338.2166677.0500006.1666676.9000007.4833336.7666676.0333336.7833337.4833337.3833336.7500007.2000007.73333312.012.011231.666667381.5000001.0000000.00.00.040.696295-73.917124
\n", "
" ], "text/plain": [ " school_dist SAT Critical Reading Avg. Score SAT Math Avg. Score \\\n", "0 01 441.833333 473.333333 \n", "1 02 426.619092 444.186256 \n", "2 03 428.529851 437.997512 \n", "3 04 402.142857 416.285714 \n", "4 05 427.159915 438.236674 \n", "5 06 382.011940 400.565672 \n", "6 07 376.461538 380.461538 \n", "7 08 386.214383 395.542741 \n", "8 09 373.755970 383.582836 \n", "9 10 403.363636 418.000000 \n", "10 11 389.866667 394.533333 \n", "11 12 364.769900 379.109453 \n", "12 13 409.393800 424.127440 \n", "13 14 395.937100 398.189765 \n", "14 15 395.679934 404.628524 \n", "15 16 371.529851 379.164179 \n", "16 17 386.571429 394.071429 \n", "17 18 373.454545 373.090909 \n", "18 19 367.083333 377.583333 \n", "19 20 406.223881 465.731343 \n", "20 21 395.283582 421.786974 \n", "21 22 473.500000 502.750000 \n", "22 23 380.666667 398.666667 \n", "23 24 405.846154 434.000000 \n", "24 25 437.250000 483.500000 \n", "25 26 445.200000 487.600000 \n", "26 27 407.800000 422.200000 \n", "27 28 445.941655 465.997286 \n", "28 29 395.764925 399.457090 \n", "29 30 430.679934 465.961857 \n", "30 31 457.500000 472.500000 \n", "31 32 371.500000 385.833333 \n", "\n", " SAT Writing Avg. Score sat_score AP Test Takers Total Exams Taken \\\n", "0 439.333333 1354.500000 116.681090 173.019231 \n", "1 424.832836 1295.638184 128.908454 201.516827 \n", "2 426.915672 1293.443035 156.183494 244.522436 \n", "3 405.714286 1224.142857 129.016484 183.879121 \n", "4 419.666098 1285.062687 85.722527 115.725275 \n", "5 382.066269 1164.643881 108.711538 159.715385 \n", "6 371.923077 1128.846154 73.703402 112.476331 \n", "7 377.908005 1159.665129 118.379371 168.020979 \n", "8 374.633134 1131.971940 71.411538 104.265385 \n", "9 400.863636 1222.227273 132.231206 226.914336 \n", "10 380.600000 1165.000000 83.813462 122.484615 \n", "11 357.943781 1101.823134 93.102564 139.442308 \n", "12 403.666361 1237.187600 232.931953 382.704142 \n", "13 385.333049 1179.459915 77.798077 114.873626 \n", "14 390.295854 1190.604312 94.574786 141.581197 \n", "15 369.415672 1120.109701 82.264423 126.519231 \n", "16 380.785714 1161.428571 105.583791 163.087912 \n", "17 371.454545 1118.000000 129.028846 197.038462 \n", "18 359.166667 1103.833333 88.097756 124.769231 \n", "19 401.732537 1273.687761 227.805769 359.407692 \n", "20 389.242062 1206.312619 135.467657 203.835664 \n", "21 474.250000 1450.500000 391.007212 614.509615 \n", "22 378.000000 1157.333333 29.000000 31.000000 \n", "23 402.153846 1242.000000 126.474852 179.094675 \n", "24 436.250000 1357.000000 205.260817 279.889423 \n", "25 444.800000 1377.600000 410.605769 632.407692 \n", "26 394.300000 1224.300000 100.611538 145.315385 \n", "27 435.908005 1347.846947 182.010490 273.559441 \n", "28 386.707836 1181.929851 63.385817 96.514423 \n", "29 429.740299 1326.382090 157.231838 252.123932 \n", "30 452.500000 1382.500000 228.908654 355.111538 \n", "31 362.166667 1119.500000 70.342949 100.179487 \n", "\n", " Number of Exams with scores 3 4 or 5 Total Cohort CSD \\\n", "0 135.800000 93.500000 1.0 \n", "1 157.495833 158.647849 2.0 \n", "2 193.087500 183.384409 3.0 \n", "3 151.035714 113.857143 4.0 \n", "4 142.464286 143.677419 5.0 \n", "5 105.425000 180.848387 6.0 \n", "6 105.276923 105.605459 7.0 \n", "7 144.731818 215.510264 8.0 \n", "8 98.470000 113.330645 9.0 \n", "9 191.618182 161.318182 10.0 \n", "10 108.833333 122.866667 11.0 \n", "11 153.450000 110.467742 12.0 \n", "12 320.773077 224.595533 13.0 \n", "13 123.282143 112.347926 14.0 \n", "14 153.450000 104.207885 15.0 \n", "15 153.450000 247.185484 16.0 \n", "16 111.360714 121.357143 17.0 \n", "17 153.450000 72.771261 18.0 \n", "18 120.670833 114.322581 19.0 \n", "19 177.690000 591.374194 20.0 \n", "20 142.377273 275.351906 21.0 \n", "21 370.362500 580.250000 22.0 \n", "22 153.450000 87.000000 23.0 \n", "23 115.165385 234.682382 24.0 \n", "24 174.793750 268.733871 25.0 \n", "25 392.090000 825.600000 26.0 \n", "26 95.125000 288.961290 27.0 \n", "27 175.336364 351.214076 28.0 \n", "28 135.268750 98.108871 29.0 \n", "29 115.150000 310.526882 30.0 \n", "30 194.435000 450.787097 31.0 \n", "31 83.558333 105.333333 32.0 \n", "\n", " NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE \\\n", "0 115.244241 5.148538 22.675415 \n", "1 149.818949 5.686360 25.038118 \n", "2 156.005994 5.839200 23.716311 \n", "3 132.362265 5.192610 24.101048 \n", "4 120.623901 4.845101 24.290247 \n", "5 139.041709 5.285860 25.324199 \n", "6 97.597416 4.146610 23.560037 \n", "7 129.765099 5.165710 23.813192 \n", "8 100.118588 4.335504 23.986379 \n", "9 168.876526 6.512691 24.244350 \n", "10 129.031031 5.037052 24.956523 \n", "11 91.684504 4.151865 22.212078 \n", "12 218.306055 8.003842 24.621784 \n", "13 123.643728 4.905127 24.311291 \n", "14 135.707319 5.236588 25.423643 \n", "15 177.501282 6.970513 24.234006 \n", "16 130.246192 5.135694 25.408854 \n", "17 72.209438 3.317955 22.102538 \n", "18 105.752625 4.247924 24.314442 \n", "19 420.029766 14.721019 25.423366 \n", "20 224.702989 8.191627 25.284294 \n", "21 495.279369 15.777391 30.077636 \n", "22 120.113095 4.985119 24.299702 \n", "23 213.471903 8.613529 23.865936 \n", "24 280.576007 9.990385 27.106270 \n", "25 595.953216 19.240117 29.971140 \n", "26 249.324536 9.019043 26.649659 \n", "27 255.381164 8.719058 26.770807 \n", "28 88.372155 3.454193 25.355632 \n", "29 251.803744 9.260486 25.715644 \n", "30 380.528319 13.251284 28.119729 \n", "31 100.525613 4.450572 22.479804 \n", "\n", " SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS \\\n", "0 18.798392 26.553044 \n", "1 20.662667 28.593861 \n", "2 19.737593 27.122831 \n", "3 19.740816 27.460291 \n", "4 20.447527 27.746703 \n", "5 20.556126 29.217872 \n", "6 19.394465 27.004052 \n", "7 19.385355 27.362141 \n", "8 19.543005 28.447128 \n", "9 18.897297 28.583860 \n", "10 19.784469 29.523549 \n", "11 18.487312 25.804834 \n", "12 19.625241 28.672265 \n", "13 19.554945 28.511594 \n", "14 20.900953 29.287015 \n", "15 18.070192 29.308333 \n", "16 20.284341 29.620046 \n", "17 18.814864 25.448084 \n", "18 19.921011 28.148996 \n", "19 17.681562 29.745831 \n", "20 20.167110 28.885220 \n", "21 22.509675 33.464740 \n", "22 18.654762 28.994048 \n", "23 18.565040 28.251471 \n", "24 21.213189 31.371220 \n", "25 20.315789 33.554327 \n", "26 20.757535 30.938081 \n", "27 21.349816 30.172038 \n", "28 21.409201 28.672730 \n", "29 19.312273 29.760726 \n", "30 19.995494 32.785641 \n", "31 17.936520 26.816013 \n", "\n", " SCHOOLWIDE PUPIL-TEACHER RATIO schoolyear fl_percent frl_percent \\\n", "0 0.0 20112012.0 0.0 58.983333 \n", "1 0.0 20112012.0 0.0 63.164583 \n", "2 0.0 20112012.0 0.0 58.050000 \n", "3 0.0 20112012.0 0.0 71.000000 \n", "4 0.0 20112012.0 0.0 58.671429 \n", "5 0.0 20112012.0 0.0 84.250000 \n", "6 0.0 20112012.0 0.0 79.630769 \n", "7 0.0 20112012.0 0.0 76.663636 \n", "8 0.0 20112012.0 0.0 76.800000 \n", "9 0.0 20112012.0 0.0 72.268182 \n", "10 0.0 20112012.0 0.0 66.960000 \n", "11 0.0 20112012.0 0.0 81.733333 \n", "12 0.0 20112012.0 0.0 63.861538 \n", "13 0.0 20112012.0 0.0 75.928571 \n", "14 0.0 20112012.0 0.0 70.800000 \n", "15 0.0 20112012.0 0.0 66.150000 \n", "16 0.0 20112012.0 0.0 67.635714 \n", "17 0.0 20112012.0 0.0 65.900000 \n", "18 0.0 20112012.0 0.0 71.833333 \n", "19 0.0 20112012.0 0.0 65.840000 \n", "20 0.0 20112012.0 0.0 62.227273 \n", "21 0.0 20112012.0 0.0 44.225000 \n", "22 0.0 20112012.0 0.0 70.400000 \n", "23 0.0 20112012.0 0.0 62.584615 \n", "24 0.0 20112012.0 0.0 52.950000 \n", "25 0.0 20112012.0 0.0 43.800000 \n", "26 0.0 20112012.0 0.0 61.340000 \n", "27 0.0 20112012.0 0.0 53.790909 \n", "28 0.0 20112012.0 0.0 52.362500 \n", "29 0.0 20112012.0 0.0 54.211111 \n", "30 0.0 20112012.0 0.0 38.310000 \n", "31 0.0 20112012.0 0.0 82.866667 \n", "\n", " total_enrollment ell_num ell_percent sped_num sped_percent \\\n", "0 668.500000 42.166667 10.000000 82.000000 17.083333 \n", "1 605.604167 51.625000 13.006250 70.770833 13.212500 \n", "2 661.416667 36.916667 9.025000 63.583333 14.500000 \n", "3 569.285714 21.571429 4.557143 58.571429 13.214286 \n", "4 605.857143 21.571429 4.157143 59.714286 10.428571 \n", "5 621.800000 165.700000 30.040000 80.100000 14.950000 \n", "6 481.000000 72.000000 16.661538 84.846154 17.446154 \n", "7 684.545455 72.909091 15.790909 140.545455 19.609091 \n", "8 442.800000 78.900000 19.945000 74.150000 16.655000 \n", "9 818.545455 129.090909 18.177273 99.045455 13.622727 \n", "10 562.466667 61.666667 12.133333 105.600000 17.380000 \n", "11 391.000000 105.500000 27.141667 70.666667 17.216667 \n", "12 902.000000 35.461538 8.723077 65.692308 12.084615 \n", "13 556.214286 57.785714 9.485714 97.714286 18.600000 \n", "14 562.666667 48.888889 8.844444 103.333333 17.866667 \n", "15 658.500000 18.750000 3.725000 131.750000 19.775000 \n", "16 554.285714 52.357143 11.285714 54.571429 10.621429 \n", "17 332.272727 29.545455 8.754545 53.363636 16.363636 \n", "18 492.500000 62.833333 14.941667 75.666667 15.291667 \n", "19 2462.600000 601.400000 20.800000 341.200000 15.140000 \n", "20 1160.000000 160.909091 17.163636 157.363636 14.454545 \n", "21 2142.500000 138.250000 4.400000 166.250000 6.650000 \n", "22 503.666667 8.000000 1.600000 68.666667 14.466667 \n", "23 991.000000 179.769231 21.069231 97.076923 9.607692 \n", "24 1356.750000 257.500000 19.737500 136.750000 9.975000 \n", "25 2991.600000 248.600000 7.480000 350.000000 11.960000 \n", "26 1148.200000 117.800000 6.820000 134.600000 11.950000 \n", "27 1210.636364 87.363636 4.963636 123.454545 9.827273 \n", "28 428.250000 12.625000 2.912500 56.125000 13.037500 \n", "29 1221.555556 220.555556 16.133333 125.111111 7.988889 \n", "30 1850.600000 64.200000 2.860000 301.500000 16.710000 \n", "31 420.833333 79.500000 18.533333 67.500000 15.650000 \n", "\n", " asian_num asian_per black_num black_per hispanic_num hispanic_per \\\n", "0 134.500000 17.516667 125.500000 22.333333 214.833333 40.733333 \n", "1 118.395833 14.479167 141.145833 24.733333 271.458333 49.554167 \n", "2 68.000000 6.408333 181.833333 34.000000 228.250000 44.533333 \n", "3 61.142857 6.128571 151.714286 30.028571 341.571429 61.400000 \n", "4 32.714286 7.300000 342.571429 49.000000 195.571429 36.214286 \n", "5 7.800000 0.810000 82.000000 11.470000 523.900000 86.380000 \n", "6 8.000000 1.707692 144.461538 29.253846 322.000000 67.607692 \n", "7 30.272727 2.809091 179.818182 27.809091 433.545455 66.109091 \n", "8 7.000000 1.480000 151.900000 33.110000 278.250000 64.135000 \n", "9 119.681818 6.631818 169.090909 23.027273 453.545455 63.272727 \n", "10 19.800000 3.846667 243.000000 41.346667 278.266667 50.846667 \n", "11 5.500000 1.591667 109.750000 28.075000 271.750000 69.275000 \n", "12 272.461538 10.453846 416.000000 68.476923 110.000000 16.276923 \n", "13 19.500000 3.871429 222.428571 42.228571 291.928571 49.664286 \n", "14 26.333333 4.966667 251.777778 47.000000 246.111111 41.266667 \n", "15 4.750000 1.075000 562.000000 80.150000 80.750000 16.900000 \n", "16 16.857143 3.607143 474.285714 83.071429 50.642857 10.664286 \n", "17 4.181818 1.236364 290.272727 87.327273 32.363636 9.718182 \n", "18 15.750000 3.191667 285.000000 55.066667 182.666667 39.916667 \n", "19 844.600000 32.920000 186.800000 12.620000 766.000000 32.300000 \n", "20 262.090909 17.272727 364.909091 33.409091 230.454545 23.409091 \n", "21 507.750000 17.975000 622.250000 35.100000 271.000000 12.350000 \n", "22 6.000000 1.133333 438.000000 87.133333 53.333333 10.600000 \n", "23 182.692308 17.176923 74.769231 7.800000 616.615385 63.415385 \n", "24 465.250000 40.600000 240.375000 13.062500 494.000000 29.237500 \n", "25 1259.200000 38.140000 676.400000 28.420000 604.800000 19.380000 \n", "26 263.100000 16.890000 365.700000 39.760000 394.100000 31.380000 \n", "27 403.727273 31.345455 343.727273 37.509091 283.454545 19.954545 \n", "28 20.750000 4.825000 359.875000 83.987500 39.000000 9.150000 \n", "29 281.777778 24.866667 124.222222 10.444444 607.222222 43.522222 \n", "30 177.700000 10.300000 307.300000 18.270000 418.700000 22.170000 \n", "31 6.333333 1.750000 82.000000 19.833333 326.166667 76.850000 \n", "\n", " white_num white_per male_num male_per female_num female_per \\\n", "0 186.000000 18.450000 328.166667 50.050000 340.333333 49.950000 \n", "1 68.541667 10.056250 266.500000 44.718750 339.104167 55.281250 \n", "2 172.000000 13.725000 272.166667 47.950000 389.250000 52.050000 \n", "3 9.285714 1.314286 193.285714 30.514286 376.000000 69.485714 \n", "4 31.285714 6.714286 300.000000 48.471429 305.857143 51.528571 \n", "5 5.000000 0.720000 333.600000 53.790000 288.200000 46.210000 \n", "6 3.769231 0.800000 261.769231 53.138462 219.230769 46.861538 \n", "7 36.181818 2.554545 355.000000 45.454545 329.545455 54.545455 \n", "8 3.000000 0.665000 220.700000 48.995000 222.050000 50.995000 \n", "9 71.409091 6.527273 413.181818 48.586364 405.363636 51.413636 \n", "10 16.400000 3.113333 319.866667 55.900000 242.600000 44.100000 \n", "11 3.000000 0.825000 199.916667 50.133333 191.083333 49.866667 \n", "12 96.230769 3.623077 482.769231 48.707692 419.230769 51.292308 \n", "13 18.714286 3.507143 324.714286 58.050000 231.500000 41.942857 \n", "14 36.444444 6.377778 270.666667 48.211111 292.000000 51.788889 \n", "15 4.500000 0.925000 377.750000 53.400000 280.750000 46.600000 \n", "16 9.000000 2.050000 242.857143 48.000000 311.428571 52.000000 \n", "17 3.727273 1.163636 164.909091 51.227273 167.363636 48.772727 \n", "18 5.833333 1.150000 284.500000 53.725000 208.000000 46.275000 \n", "19 658.200000 21.840000 1345.200000 44.920000 1117.400000 55.080000 \n", "20 297.454545 25.527273 616.090909 58.172727 543.909091 41.827273 \n", "21 732.000000 33.975000 1015.750000 44.300000 1126.750000 55.700000 \n", "22 3.333333 0.566667 221.333333 44.433333 282.333333 55.566667 \n", "23 113.461538 11.284615 572.769231 51.646154 418.230769 48.353846 \n", "24 148.875000 16.537500 669.500000 48.500000 687.250000 51.500000 \n", "25 432.400000 13.420000 1475.000000 49.220000 1516.600000 50.780000 \n", "26 103.900000 10.750000 610.700000 52.400000 537.500000 47.600000 \n", "27 165.636364 9.400000 590.363636 45.863636 620.272727 54.136364 \n", "28 4.125000 1.050000 219.875000 51.862500 208.375000 48.137500 \n", "29 204.444444 20.788889 604.111111 44.777778 617.444444 55.222222 \n", "30 938.700000 48.810000 948.500000 52.090000 902.100000 47.910000 \n", "31 3.833333 0.966667 217.833333 51.716667 203.000000 48.283333 \n", "\n", " rr_s rr_t rr_p N_s N_t N_p \\\n", "0 76.500000 85.333333 33.166667 525.368056 38.500000 239.166667 \n", "1 83.312500 86.416667 38.333333 495.171007 31.687500 190.000000 \n", "2 83.166667 80.833333 36.166667 519.250000 28.166667 206.416667 \n", "3 87.571429 92.714286 41.285714 504.857143 32.428571 193.142857 \n", "4 82.857143 79.285714 40.428571 442.428571 27.714286 184.857143 \n", "5 85.000000 83.500000 54.600000 482.300000 33.000000 261.500000 \n", "6 80.538462 77.153846 42.923077 376.230769 25.846154 187.615385 \n", "7 69.454545 77.636364 36.636364 459.837121 34.000000 148.545455 \n", "8 84.200000 84.650000 40.650000 349.150000 25.450000 162.300000 \n", "9 82.727273 82.136364 43.000000 648.772727 43.500000 303.590909 \n", "10 80.400000 79.933333 42.466667 400.733333 27.466667 190.800000 \n", "11 80.000000 79.750000 49.166667 277.333333 21.166667 163.083333 \n", "12 84.153846 77.615385 35.769231 759.846154 40.000000 318.384615 \n", "13 77.714286 85.500000 35.071429 395.642857 32.428571 158.214286 \n", "14 81.444444 82.888889 32.333333 433.000000 34.777778 170.000000 \n", "15 68.500000 75.250000 19.500000 458.000000 31.500000 122.000000 \n", "16 78.785714 77.714286 41.214286 426.428571 27.357143 209.857143 \n", "17 78.181818 83.363636 31.454545 222.181818 18.454545 89.181818 \n", "18 70.833333 89.166667 34.083333 311.916667 28.500000 141.333333 \n", "19 64.400000 78.200000 25.000000 1360.200000 107.000000 602.400000 \n", "20 79.272727 89.181818 34.545455 930.545455 64.454545 336.181818 \n", "21 90.250000 87.500000 48.750000 1887.250000 89.500000 1106.250000 \n", "22 84.000000 73.666667 40.666667 420.333333 25.333333 175.000000 \n", "23 83.846154 83.230769 35.692308 765.461538 47.769231 247.692308 \n", "24 87.000000 78.500000 37.625000 1028.250000 56.000000 370.875000 \n", "25 67.000000 83.400000 23.800000 1930.800000 129.800000 684.200000 \n", "26 76.900000 86.500000 40.900000 775.100000 59.200000 276.800000 \n", "27 85.090909 85.545455 40.545455 872.545455 54.727273 362.636364 \n", "28 77.625000 90.625000 44.750000 294.500000 21.375000 146.500000 \n", "29 89.000000 85.444444 42.333333 963.444444 63.111111 318.777778 \n", "30 88.900000 91.300000 43.500000 1476.600000 88.900000 593.300000 \n", "31 75.666667 72.333333 34.666667 312.833333 22.333333 137.833333 \n", "\n", " saf_p_11 com_p_11 eng_p_11 aca_p_11 saf_t_11 com_t_11 eng_t_11 \\\n", "0 8.233333 7.800000 7.683333 8.016667 7.066667 6.233333 6.700000 \n", "1 8.254167 7.495833 7.379167 7.704167 7.404167 6.520833 7.120833 \n", "2 8.316667 7.541667 7.508333 7.758333 6.675000 5.991667 6.533333 \n", "3 8.385714 7.657143 7.500000 7.928571 7.871429 7.042857 7.428571 \n", "4 8.085714 7.342857 7.357143 7.685714 6.857143 5.885714 6.242857 \n", "5 8.530000 7.990000 7.750000 8.050000 7.490000 6.610000 7.060000 \n", "6 8.423077 7.761538 7.600000 7.907692 6.969231 6.315385 6.784615 \n", "7 8.172727 7.636364 7.454545 7.718182 7.109091 6.254545 6.781818 \n", "8 8.510000 7.930000 7.755000 8.080000 6.935000 6.305000 6.905000 \n", "9 8.272727 7.768182 7.622727 7.927273 6.972727 6.154545 6.700000 \n", "10 8.053333 7.693333 7.546667 7.826667 7.026667 6.766667 7.000000 \n", "11 8.650000 8.008333 7.808333 8.166667 7.266667 6.758333 7.250000 \n", "12 8.330769 7.700000 7.692308 7.884615 7.092308 6.830769 7.069231 \n", "13 8.357143 7.864286 7.735714 7.971429 7.271429 6.650000 7.257143 \n", "14 8.077778 7.577778 7.477778 7.844444 6.566667 5.877778 6.288889 \n", "15 7.375000 6.900000 6.875000 7.075000 5.750000 5.250000 6.025000 \n", "16 7.835714 7.642857 7.550000 7.828571 6.957143 6.600000 7.192857 \n", "17 8.272727 7.718182 7.572727 7.963636 6.890909 6.400000 6.936364 \n", "18 7.591667 7.575000 7.375000 7.658333 6.516667 6.400000 6.766667 \n", "19 7.920000 7.180000 7.240000 7.460000 7.820000 7.660000 7.800000 \n", "20 7.818182 7.336364 7.263636 7.527273 7.072727 6.400000 6.963636 \n", "21 8.300000 7.325000 7.525000 7.775000 8.175000 7.400000 7.600000 \n", "22 7.900000 7.533333 7.566667 7.700000 7.033333 6.433333 6.666667 \n", "23 8.461538 7.684615 7.569231 7.869231 7.669231 6.569231 7.230769 \n", "24 8.175000 7.537500 7.412500 7.687500 7.487500 6.825000 7.200000 \n", "25 7.720000 6.980000 7.220000 7.260000 7.000000 6.580000 6.840000 \n", "26 7.830000 7.400000 7.370000 7.640000 6.930000 6.780000 7.060000 \n", "27 8.172727 7.590909 7.472727 7.781818 7.600000 7.145455 7.363636 \n", "28 7.762500 7.362500 7.325000 7.562500 7.050000 6.912500 7.162500 \n", "29 8.222222 7.444444 7.433333 7.700000 7.500000 6.655556 6.911111 \n", "30 7.800000 7.390000 7.460000 7.620000 7.210000 7.140000 7.390000 \n", "31 8.350000 8.050000 7.883333 8.216667 7.050000 6.166667 6.900000 \n", "\n", " aca_t_11 saf_s_11 com_s_11 eng_s_11 aca_s_11 saf_tot_11 com_tot_11 \\\n", "0 7.500000 6.768611 6.165787 6.736713 7.446898 7.433333 6.816667 \n", "1 7.564583 6.910660 6.204057 6.650422 7.385029 7.520833 6.720833 \n", "2 6.991667 6.716667 6.258333 6.633333 7.375000 7.233333 6.616667 \n", "3 7.885714 6.885714 6.114286 6.685714 7.571429 7.714286 6.942857 \n", "4 6.957143 6.314286 6.000000 6.428571 7.285714 7.085714 6.414286 \n", "5 7.660000 6.950000 6.150000 6.700000 7.510000 7.660000 6.920000 \n", "6 7.284615 6.800000 6.184615 6.646154 7.453846 7.392308 6.746154 \n", "7 7.390909 6.364697 5.954066 6.401843 7.143763 7.218182 6.645455 \n", "8 7.355000 6.655000 6.145000 6.705000 7.490000 7.365000 6.795000 \n", "9 7.309091 6.577273 6.040909 6.654545 7.409091 7.268182 6.645455 \n", "10 7.540000 6.186667 5.746667 6.260000 7.213333 7.086667 6.740000 \n", "11 7.816667 7.116667 6.566667 7.116667 7.783333 7.666667 7.100000 \n", "12 7.600000 6.407692 6.069231 6.576923 7.376923 7.269231 6.869231 \n", "13 7.735714 6.685714 6.207143 6.742857 7.507143 7.435714 6.914286 \n", "14 7.011111 6.177778 5.900000 6.333333 7.222222 6.933333 6.444444 \n", "15 6.275000 5.875000 5.775000 6.225000 7.100000 6.325000 5.975000 \n", "16 7.642857 6.007143 5.885714 6.400000 7.228571 6.935714 6.707143 \n", "17 7.390909 6.190909 6.018182 6.409091 7.209091 7.109091 6.718182 \n", "18 7.308333 6.150000 5.933333 6.458333 7.141667 6.758333 6.641667 \n", "19 8.280000 7.120000 6.340000 7.040000 7.640000 7.620000 7.080000 \n", "20 7.472727 6.390909 6.081818 6.500000 7.163636 7.090909 6.609091 \n", "21 8.200000 7.025000 6.050000 6.775000 7.500000 7.850000 6.900000 \n", "22 7.366667 6.233333 6.066667 6.766667 7.566667 7.066667 6.700000 \n", "23 7.723077 7.107692 6.169231 6.784615 7.500000 7.753846 6.807692 \n", "24 7.712500 6.912500 6.150000 6.712500 7.400000 7.525000 6.837500 \n", "25 7.260000 6.760000 6.060000 6.660000 7.380000 7.140000 6.540000 \n", "26 7.630000 6.390000 6.000000 6.500000 7.310000 7.010000 6.710000 \n", "27 7.972727 6.627273 6.081818 6.618182 7.454545 7.463636 6.936364 \n", "28 7.575000 6.075000 5.962500 6.487500 7.250000 6.962500 6.737500 \n", "29 7.533333 7.033333 6.166667 6.844444 7.511111 7.577778 6.744444 \n", "30 7.860000 6.530000 6.070000 6.780000 7.330000 7.200000 6.870000 \n", "31 7.483333 6.766667 6.033333 6.783333 7.483333 7.383333 6.750000 \n", "\n", " eng_tot_11 aca_tot_11 grade_span_max expgrade_span_max zip \\\n", "0 7.116667 7.683333 12.0 12.0 10003.166667 \n", "1 7.037500 7.541667 12.0 12.0 10023.770833 \n", "2 6.891667 7.391667 12.0 12.0 10023.750000 \n", "3 7.185714 7.785714 12.0 12.0 10029.857143 \n", "4 6.671429 7.314286 12.0 12.0 10030.142857 \n", "5 7.170000 7.760000 12.0 12.0 10036.200000 \n", "6 7.007692 7.553846 12.0 12.0 10452.692308 \n", "7 6.863636 7.409091 12.0 12.0 10467.000000 \n", "8 7.110000 7.665000 12.0 12.0 10456.100000 \n", "9 6.986364 7.536364 12.0 12.0 10463.181818 \n", "10 6.940000 7.526667 12.0 12.0 10467.933333 \n", "11 7.391667 7.916667 12.0 12.0 10463.166667 \n", "12 7.123077 7.607692 12.0 12.0 11207.153846 \n", "13 7.250000 7.750000 12.0 12.0 11210.785714 \n", "14 6.700000 7.366667 12.0 12.0 11214.222222 \n", "15 6.375000 6.825000 12.0 12.0 11219.000000 \n", "16 7.035714 7.571429 12.0 12.0 11220.642857 \n", "17 6.963636 7.527273 12.0 12.0 11224.000000 \n", "18 6.866667 7.350000 12.0 12.0 11207.500000 \n", "19 7.380000 7.800000 12.0 12.0 11210.200000 \n", "20 6.909091 7.390909 12.0 12.0 11221.000000 \n", "21 7.300000 7.850000 12.0 12.0 11223.000000 \n", "22 7.000000 7.566667 12.0 12.0 11219.000000 \n", "23 7.192308 7.707692 12.0 12.0 11206.153846 \n", "24 7.112500 7.612500 12.0 12.0 11361.000000 \n", "25 6.900000 7.300000 12.0 12.0 11388.600000 \n", "26 6.950000 7.510000 12.0 12.0 11556.300000 \n", "27 7.145455 7.745455 12.0 12.0 11422.000000 \n", "28 7.000000 7.475000 12.0 12.0 11413.625000 \n", "29 7.077778 7.588889 12.0 12.0 11103.000000 \n", "30 7.200000 7.610000 12.0 12.0 10307.100000 \n", "31 7.200000 7.733333 12.0 12.0 11231.666667 \n", "\n", " total_students number_programs priority08 priority09 priority10 \\\n", "0 659.500000 1.333333 0.0 0.0 0.0 \n", "1 621.395833 1.416667 0.0 0.0 0.0 \n", "2 717.916667 2.000000 0.0 0.0 0.0 \n", "3 580.857143 1.142857 0.0 0.0 0.0 \n", "4 609.857143 1.142857 0.0 0.0 0.0 \n", "5 628.900000 1.300000 0.0 0.0 0.0 \n", "6 465.846154 1.461538 0.0 0.0 0.0 \n", "7 547.636364 1.272727 0.0 0.0 0.0 \n", "8 449.700000 1.150000 0.0 0.0 0.0 \n", "9 757.863636 1.500000 0.0 0.0 0.0 \n", "10 563.666667 1.533333 0.0 0.0 0.0 \n", "11 409.000000 1.083333 0.0 0.0 0.0 \n", "12 895.153846 2.076923 0.0 0.0 0.0 \n", "13 545.357143 2.000000 0.0 0.0 0.0 \n", "14 573.111111 1.666667 0.0 0.0 0.0 \n", "15 440.250000 1.750000 0.0 0.0 0.0 \n", "16 547.071429 1.642857 0.0 0.0 0.0 \n", "17 344.000000 1.090909 0.0 0.0 0.0 \n", "18 440.416667 1.916667 0.0 0.0 0.0 \n", "19 2521.400000 3.800000 0.0 0.0 0.0 \n", "20 1098.272727 3.272727 0.0 0.0 0.0 \n", "21 2149.000000 2.250000 0.0 0.0 0.0 \n", "22 391.000000 1.333333 0.0 0.0 0.0 \n", "23 962.461538 2.230769 0.0 0.0 0.0 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-73.952288 \n", "22 40.668586 -73.912298 \n", "23 40.740621 -73.911518 \n", "24 40.745414 -73.815558 \n", "25 40.748507 -73.759176 \n", "26 40.638828 -73.807823 \n", "27 40.709344 -73.806367 \n", "28 40.685276 -73.752740 \n", "29 40.755398 -73.932306 \n", "30 40.595680 -74.125726 \n", "31 40.696295 -73.917124 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "districts = combined.groupby('school_dist').agg(numpy.mean)\n", "districts.reset_index(inplace=True)\n", "districts" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np \n", "from mpl_toolkits.basemap import Basemap\n", "m = Basemap(\n", " projection='merc', \n", " llcrnrlat=40.496044, \n", " urcrnrlat=40.915256, \n", " llcrnrlon=-74.255735, \n", " urcrnrlon=-73.700272,\n", " resolution='f'\n", ")\n", "\n", "m.drawmapboundary(fill_color='#ABD1FF')\n", "m.drawcoastlines(color='black', linewidth=.4)\n", "m.drawrivers(color='#ABD1FF', linewidth=.4)\n", "m.fillcontinents(color= '#F2F1EF',lake_color='#ABD1FF')\n", "longitudes = districts['lon'].tolist()\n", "latitudes = districts['lat'].tolist()\n", "\n", "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts['saf_tot_11'], cmap='viridis')\n", "plt.axis(aspect='equal')\n", "plt.xlabel('longitude')\n", "plt.ylabel('latitude')\n", "plt.colorbar(label='Safety Score')\n", "plt.title('New York City: Schools Safety Score')\n", "plt.savefig('new_york_schools.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From the Map, we can observe that parts of _Manhattan, Bronx and Queens_ have relatively higher safety scores. While _Brooklyn_ has mostly lower safety scores.**\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Racial differences in SAT Scores\n", "\n", "There are a few columns that indicate the percentage of each race at a given school:\n", "- `white_per`\n", "- `asian_per`\n", "- `black_per`\n", "- `hispanic_per`\n", "\n", "By plotting out the correlations between these columns and sat_score, we can determine whether there are any racial differences in SAT performance. We will investigate racial differences in SAT scores by making a bar plot of the correlations between the columns above and `sat_score`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "races_per = ['white_per', 'asian_per', 'black_per', 'hispanic_per']\n", "combined.corr()['sat_score'][races_per].plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From the bar plot above we can notice that higher percentage of White or Asian students at a school correlates with higher SAT Scores. And vice versa for Black and Hispanic Students. It can be due to various external factors, like economic background of students, funding to Schools, etc.**\n", "\n", "*We will continue exploration regarding race and SAT scores by makign a scatter plot of hispanic_per vs. sat_score.*" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "combined.plot.scatter(x='hispanic_per', y='sat_score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**There seems to be a negative correlation between hispanic students percentage and SAT Score. This may be due to a lack of funding for schools in certain areas, which are more likely to have a higher percentage of black or hispanic students.**\n", "\n", "*Filtering down on hispanic population by researching schools with a hispanic_per greater than 95%.*" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "44 MANHATTAN BRIDGES HIGH SCHOOL\n", "82 WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL\n", "89 GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...\n", "125 ACADEMY FOR LANGUAGE AND TECHNOLOGY\n", "141 INTERNATIONAL SCHOOL FOR LIBERAL ARTS\n", "176 PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE\n", "253 MULTICULTURAL HIGH SCHOOL\n", "286 PAN AMERICAN INTERNATIONAL HIGH SCHOOL\n", "Name: SCHOOL NAME, dtype: object" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[combined['hispanic_per']>95]['SCHOOL NAME']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**We can observe from our findings that the above schools are catering mostly to the immigrant community, who have recently migrated to USA from neighboring hispanic countries. And are learning English, which can be correlated to lower SAT Scores.**\n", "\n", "*Researching schools with a hispanic_per less than 10% and an average SAT score greater than 1800.*" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "37 STUYVESANT HIGH SCHOOL\n", "151 BRONX HIGH SCHOOL OF SCIENCE\n", "187 BROOKLYN TECHNICAL HIGH SCHOOL\n", "327 QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...\n", "356 STATEN ISLAND TECHNICAL HIGH SCHOOL\n", "Name: SCHOOL NAME, dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[(combined['hispanic_per'] < 10) & (combined['sat_score'] > 1800)]['SCHOOL NAME']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From our findings, we observe that the above schools are sepcialized in Science & Technology. Plus, have a criteria (clearing specific entrance exams) for admitting students, which explaining the High SAT Scores. But, there is no specific reason for low Hispanic proportion of students.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gender differences in SAT Scores\n", "\n", "There are two columns that indicate the percentage of each gender at a school:\n", "\n", "- male_per\n", "- female_per
\n", "\n", "We can plot out the correlations between each percentage and sat_score by making a bar plot." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gender_per = ['male_per', 'female_per']\n", "combined.corr()['sat_score'][gender_per].plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Here, from the bar plot we can observe that higher percentage of Female Students correlates positively with SAT Scores and vice versa for Male Students. However neither of the correlations are strong.**
\n", "\n", "*Investigating schools with high SAT scores and a high female_per by making a scatter plot of female_per vs. sat_score.*" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "combined.plot.scatter(x='female_per', y='sat_score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From the scatter plot above, there doesn't seem to be any strong correlation among the SAT Scores and Female Percentage.**\n", "\n", "*Researching schools with a female_per greater than 60% and an average SAT score greater than 1700.*" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5 BARD HIGH SCHOOL EARLY COLLEGE\n", "26 ELEANOR ROOSEVELT HIGH SCHOOL\n", "60 BEACON HIGH SCHOOL\n", "61 FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...\n", "302 TOWNSEND HARRIS HIGH SCHOOL\n", "Name: SCHOOL NAME, dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[(combined['sat_score'] > 1700) & (combined['female_per'] > 60)]['SCHOOL NAME']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**From our findings, we notice that the above schools have their major focus primarily on two things:**\n", "> - *Arts*
\n", "> - *Preparing students for college*\n", "\n", "These appear to be very selective liberal arts schools that have high academic standards." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion:\n", "\n", "There isn't enough evidence to claim that the SAT scores are unfair to certain groups. On surface, it looks like the SAT Scores are unfair. But on further investigation, we can see that there are multiple other factors (like immigration, funding, etc.) which may influence academic performance." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }