{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 12 -- Additional Data Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics Covered:\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Sort-and-Sort-Sequences\">Sort and Sort Sequences</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Drop/Keep-Columns\">Drop/Keep Columns</a> \n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Rename-Columns\">Rename Columns</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Finding-Duplicate-Values\">Find Duplicate Values</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Extract-Duplicate-Values\">Extract Duplicate Rows to a new DataFrame</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Drop-Duplicate-Rows\">Drop Duplicate Rows</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Add-a-New-DataFrame-Column\">Add a New DataFrame Column</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Cast-Strings-to-Floats\">Cast Strings to Float</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Concatenating-DataFrames-(Join)\">Concatenating DataFrames (Join)</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Crosstabs\">Crosstabs</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Sampling\">Sampling</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Binning-Continuous-Values\">Binning Continuous Values</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Save-to-Disk\">Save to Disk</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Resources\">Resources</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame, Index\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort and Sort Sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example below is used in <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2009%20--%20Panda%20Time%20Series%20and%20Date%20Handling.ipynb#Time-Series-Walk-Through\">Time Series Walk-Through</a> in Chapter 09 -- Panda Time Series and Date Handling.  It uses the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2011%20--%20Panda%20Readers.ipynb#pd.read_csv(URL)-method\"> read_csv() method</a> to construct the 'df_us' DataFrame begining its read on row 3,082."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_states = pd.read_csv(\"C:\\Data\\\\HPI_master.csv\",                 \n",
    "            skiprows=3082,\n",
    "            usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8),\n",
    "            names=('hpi_type', 'hpi_flavor', 'frequency', 'level', 'place_name', 'place_id', 'yr', 'period', 'index_nsa'),\n",
    "            header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following SAS Data Step reads the same .csv file using FIRSTOBS= to begin reading from an arbitary row position."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /********************************/\n",
    "    /* c09_read()_csv_df_states.sas */\n",
    "    /********************************/\n",
    "    data df_states;\n",
    "          infile 'C:\\Data\\HPI_master.csv' delimiter=',' missover dsd firstobs=3081; \n",
    "          informat hpi_type $12.\n",
    "                   hpi_flavor $16.\n",
    "                   frequency $9.\n",
    "                   level $28.\n",
    "                   place_name $33.\n",
    "                   place_id $8.\n",
    "                   yr $5.\n",
    "                   period $6.\n",
    "                   index_nsa 8.;\n",
    "             input hpi_type $\n",
    "                   hpi_flavor $\n",
    "                   frequency $\n",
    "                   level $\n",
    "                   place_name $\n",
    "                   place_id $\n",
    "                   yr $\n",
    "                   period $\n",
    "                   index_nsa ;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify the read using the .shape attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96244, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_states.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the first 5 rows of the 'df_states' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpi_type</th>\n",
       "      <th>hpi_flavor</th>\n",
       "      <th>frequency</th>\n",
       "      <th>level</th>\n",
       "      <th>place_name</th>\n",
       "      <th>place_id</th>\n",
       "      <th>yr</th>\n",
       "      <th>period</th>\n",
       "      <th>index_nsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>10180</td>\n",
       "      <td>1986</td>\n",
       "      <td>4</td>\n",
       "      <td>94.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>10180</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>100.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>10180</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>100.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>10180</td>\n",
       "      <td>1987</td>\n",
       "      <td>3</td>\n",
       "      <td>94.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>10180</td>\n",
       "      <td>1987</td>\n",
       "      <td>4</td>\n",
       "      <td>91.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      hpi_type        hpi_flavor  frequency level   place_name place_id    yr  \\\n",
       "0  traditional  all-transactions  quarterly   MSA  Abilene, TX    10180  1986   \n",
       "1  traditional  all-transactions  quarterly   MSA  Abilene, TX    10180  1987   \n",
       "2  traditional  all-transactions  quarterly   MSA  Abilene, TX    10180  1987   \n",
       "3  traditional  all-transactions  quarterly   MSA  Abilene, TX    10180  1987   \n",
       "4  traditional  all-transactions  quarterly   MSA  Abilene, TX    10180  1987   \n",
       "\n",
       "   period  index_nsa  \n",
       "0       4      94.95  \n",
       "1       1     100.91  \n",
       "2       2     100.76  \n",
       "3       3      94.02  \n",
       "4       4      91.43  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the first 5 rows of the SAS data set df_states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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dMV/LMwViQ+fu7Mbt3fGM456c0eFfGNn4un1M6YFe0s5Y0iuEkyJw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\nFABRRRQAUUUUAFFFFAH/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\df_states_output.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exam the default sort by supplying the the sort key as the minimum argument to the .sort_values() attribute.  The sort operation creates the new 'default_srt' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "default_srt = df_states.sort_values('index_nsa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the first 4 rows of the sorted DataFrame 'default_srt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpi_type</th>\n",
       "      <th>hpi_flavor</th>\n",
       "      <th>frequency</th>\n",
       "      <th>level</th>\n",
       "      <th>place_name</th>\n",
       "      <th>place_id</th>\n",
       "      <th>yr</th>\n",
       "      <th>period</th>\n",
       "      <th>index_nsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42692</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>San Francisco-Redwood City-South San Francisco...</td>\n",
       "      <td>41884</td>\n",
       "      <td>1975</td>\n",
       "      <td>3</td>\n",
       "      <td>18.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42856</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA</td>\n",
       "      <td>41940</td>\n",
       "      <td>1975</td>\n",
       "      <td>4</td>\n",
       "      <td>18.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48737</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>Honolulu ('Urban Honolulu'), HI</td>\n",
       "      <td>46520</td>\n",
       "      <td>1981</td>\n",
       "      <td>4</td>\n",
       "      <td>18.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42693</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>MSA</td>\n",
       "      <td>San Francisco-Redwood City-South San Francisco...</td>\n",
       "      <td>41884</td>\n",
       "      <td>1975</td>\n",
       "      <td>4</td>\n",
       "      <td>19.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hpi_type        hpi_flavor  frequency level  \\\n",
       "42692  traditional  all-transactions  quarterly   MSA   \n",
       "42856  traditional  all-transactions  quarterly   MSA   \n",
       "48737  traditional  all-transactions  quarterly   MSA   \n",
       "42693  traditional  all-transactions  quarterly   MSA   \n",
       "\n",
       "                                              place_name place_id    yr  \\\n",
       "42692  San Francisco-Redwood City-South San Francisco...    41884  1975   \n",
       "42856                 San Jose-Sunnyvale-Santa Clara, CA    41940  1975   \n",
       "48737                    Honolulu ('Urban Honolulu'), HI    46520  1981   \n",
       "42693  San Francisco-Redwood City-South San Francisco...    41884  1975   \n",
       "\n",
       "       period  index_nsa  \n",
       "42692       3      18.31  \n",
       "42856       4      18.82  \n",
       "48737       4      18.91  \n",
       "42693       4      19.26  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_srt.iloc[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By examing the first the first four rows of the sorted DataFrame, 'default_srt' we see the default sort sequence is ascending.  Of course, by reading the doc for <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html\"> pandas.DataFrame.sort_values </a> we could 'see' this as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default SAS sort syntax is:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_print_first4_rows_sorted.sas                   */\n",
    "    /******************************************************/\n",
    "84  proc sort data=df_states;\n",
    "85      by index_nsa;\n",
    "NOTE: 96244 observations were read from \"WORK.df_states\"\n",
    "NOTE: Data set \"WORK.df_states\" has 96244 observation(s) and 9 variable(s)\n",
    "86         \n",
    "87   proc print data=df_states (obs=4);\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like panda the default SAS sort sequence is ascending.  This is confirmed by the SAS doc located <a href=\"https://support.sas.com/documentation/cdl/en/proc/61895/HTML/default/viewer.htm#sort-overview.htm\"> here </a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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+PhP9xv5ipKjP/Hwn+438xV7RS/rci95NklYHjv8A5J7r/wD2D5//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DHFfm\nW5M008kRWOWdvkD7VB+ZdvJUqear9f8ANr9Ce/l+tv8AM7+4AtFRrrWJIFkkWNDIYlDOxwqjK8kn\ngDvUb2s39qQD7dcZMMh3bY8j5k4+7/nFeURXGoR6teaXfyMqWutaPIto2ry6kbZ3kO5TLKAwJCqS\nvIHUHmvYJP8AkMW//XCX/wBCjp20v5/on+or+9b+uv8AkH2Sb/oI3P8A3zH/APEVUjubaa4WCLX9\n8zO8YjWSEsWT76425yuRkds815v4s1W8XUJtV0mfU4kt9at7Q3l1rRhiZvPRHghtEykq43ZMoVuS\nQWxxJpMz2/ja3mitpbp01TWmWCEqHkP7v5QWKrk+5A96hSv91/y/zKat99vz/wAj0plC3SWravKL\niRGkSImLeyqQGYDbkgFlye2R61L9km/6CNz/AN8x/wDxFcHqu+/8d2GqXFnqelXcehagVgmvMFSr\nwgHbDK0Z++T15wpPKrihbq83h/w3axz67qeqanp41GZDr0tpCTtiDyyzBvMRR5nCRArk8p0Irpf+\nt3/8iD3S/rp/mel/ZJv+gjc/98x//EUfZJv+gjc/98x//EV5T4PlvPFLaFaaprWpSW50q8dms9Ul\nTzmjuhHG5lQqz4X+Ljd1Oc1HHf61feFtJ128v7vUYbfRYZ7uLT9Z+x3docuWujHxHOCFHEpC/I2A\nckUtErv+tWv0Eru9un/A/wAz1r7JN/0Ebn/vmP8A+Io+yTf9BG5/75j/APiK8m8X65c3Npf65olx\nq0Qsp7dPtt3rBtIo3PlMIYrWMFZSQ/zCZQTuIDEABen0yKPVrzVdU1vXL/T7m01h7NI49ReGGOMM\nqxxGLOwmRWU5Klz5o2kELh21t/XT/MV1a/8AXX/I6iK4t55IY4de8x5w5iVHhJkCHDlRt52kgHHQ\nnmrX2Sb/AKCNz/3zH/8AEV47Hfah4X8N6c+gXN5uGn61ceXLcSTqXSZArlXY52jJA9S3djnT8WXM\n/hrTL+Lw9ruo3CXPhu6vZJJr6S4aORNnlTo7MfL3b34XCnbwOKFr/Xr/AJf10pJu3n/X6/1pf0/7\nJN/0Ebn/AL5j/wDiKPsk3/QRuf8AvmP/AOIrzvVLW/07WtQ0bStfuYEutNtbjdqepy4aU3JRlWVi\nzQmRfk+ToSCoBrofAd47RalptzBqVrd2Fwontr6/F6sRZAwEc5Jd1I+bDncN3QDAoWqv/WjsTf8A\nr5XNrTrWZtLtCL64UGFCFCx4Hyjjlas/ZJv+gjc/98x//EVzPiq6ubXwDYC1uJbRbiaytri5hba8\nMMkiK7Bh907SRu/hznjFctfXU2i+KNQ0+w16/mtLXUdHTy5r6SU2wlmffGXZixDZBO4kkMB0AFEd\nbLzS/L/McnZN+V/6+49P+yTf9BG5/wC+Y/8A4ij7JN/0Ebn/AL5j/wDiK871LWZJtc8RacbnVrtp\ndZgtbOz06+EBZhZrI0RlLAxJlWZihDZHGckHH0W71K+1G20O81W7W2Hii4spFtNYmnIiWxMpi+0n\nbIwEmeuCCMZ4oj723a/5f5jkuVX/AK6/5Hq8KfafM+z6vLL5bmN9nlNtYdVOE4I9Kl+yTf8AQRuf\n++Y//iK8p8i80iJtN0N7hotT8Sz2s7Xes3MW5VjZkTz/AN48ZZgBlQGYgDPNXYoNXOq6RoWq6syW\n0uqzxPBp2tzzyxRi180RS3BCS5D/ADDPzBSoyRSj70U12T+9L/MT0bT8/wAG/wDI9Dtwt2jtaaxJ\nOqSNE5iMTBXU4ZTheCCMEdjU32Sb/oI3P/fMf/xFeVWOq3HhSGfV4552s31TV7FrYuWV5zMzwHBP\n3i0ZTPUmQCvUdFsptN0Kys7u5lu7iGFUluJXLtK+PmYk+pyacdYp+n4/1+KHLSTXm/wdv69CT7JN\n/wBBG5/75j/+Iqta2spuL3/T7hcTAEhY+f3acn5f84rTqpbf6/UP+uw/9FJSk7K4iK0KX9pHdWOs\nyXNvKN0c0JhdHHqGC4NUbrXNHsdMg1G98WW1tY3B2w3U1zbpFKeeFcrg9D0PavOfCMwtvAOk+GEO\n1vEVrA8AzjKEbboD6Im76yVZ8I2Ur6T4dbw1q1lZa7baNMkVnf2bywTQNN1UqyFSGRQSpOARlfu0\nPf8Ar5/169rDas2v68j0hZoH0v8AtJNdLWHled9qDwmLy8Z379uNuOc5xipYIzdW8c9tq000Mqh4\n5I/KZXUjIIITBBHevM31SSfSk8N2vh7N/c6zI2q2mlTrJFMsWyWd4mmaNQruyIynGC7jkjnpvhxe\nSw22peHruwudOk0q4LW9tdGMuLWUl4uY2ZSB86DBPCeuaa1/r0/z/B6E7f16/wCX4o6FLWb+1Jx9\nuuMiGM7tseT8z8fd/wA5qpJrWkRa0NHl8V26amxAFi1zbiYkjIHl7d3I56dK1I/+Qxcf9cIv/QpK\n8y1K3ntPB+r3Om3Gk+IfChuru5v7C9jltruM72d0WZSf3iy/dDIp+6N3Q0uupXQ9N+yTf9BG5/75\nj/8AiKPsk3/QRuf++Y//AIivKfG+r3bLqmr6NNqsH9nXUERu7rWmtYYpMxkQxWqZExO47hMoJ3EB\nmGALd3JexDVNcXVtT+1WfimKzhj+2yeQsDTRI0ZizsYESNyQSMjBGBhx1kl3/wA0v1Jv/X3/AOR6\nX9km/wCgjc/98x//ABFRW6fa4FntdXlnif7skflMrduCEqWfUrS21C1sZ5glxdhzAhB+fYAW56ZA\nOcfX0NeZ+Fx/bX9k6RfaheWemrpL3tuLK9e1Nw5ndWbfGyswRdhxnb+8BIPGJv7yX9aaldL/ANb2\nPTPsk3/QRuf++Y//AIij7JN/0Ebn/vmP/wCIryVtT1i70WHXrzUbvU7O0sFkl/s3Vvsd1aosk2Ls\nwcRTiRUQ4kO35W2qQSKtXd54j1zUNe1GyubeyOm3KpbTXuvT2aWsflo6NLbLEY3VtxJLkkgkArgY\npauwunqenSwPDE8s2qTxxopZ3cRAKB1JOzgVQ0nVtM19ZW0LxRDqYhIEhs57ebZnpnapxnB/Ksjx\nZeJqnhvU9P121v8AQLPdGiarMIJYS/mqEbakjNsLbSd6qNuckVzvie41zTbnUW1yLTp9SbRL/wDs\nrVtHM1vPGixKzLJCWbA34IcOcELwC1S3ZNvYqK5mkelfZJv+gjc/98x//EUfZJv+gjc/98x//EV5\nl4y169iicadq88T/APCKPdAwXByHMsQWXGev3sN9fek8SvfaBca/pul6xqiRHT7C5SSW9kmkile6\naN2RpCxUFQPl+77cmras7ev4N/5ERd1f0/G3+aPTvsk3/QRuf++Y/wD4ij7JN/0Ebn/vmP8A+Irz\njUTe+H9a1yLTtT1FrLRU0/VTHc3stwfLLyrcKWdixUxoWCkkBgCAKmn1yfR7Ow8b32pXB0m6v5jc\nI07GFLSUbIHCZ2j/AFcTf9tX9TU3S3/rb+vkVZ3t/Xf/AC+89B+yTf8AQRuf++Y//iKhUK949ous\nSNcxosjwgxb1ViQGK7cgEqcH2PpXnF3LfXOm21vdS6rNqX9nNql3nXX060sVmd33PLH+8bbtKKuG\nQBeQvWsG31/WXsbe/juN95qGiaLFdXUlwYMLLNMrsZVVimc7d4UkbsjBwaet7edvz/VB0v8AP8v0\nZ7X9km/6CNz/AN8x/wDxFZmqa1pGiXEUGteK7fTpphmKO7ubeJnGcZAZRnn0rC0GTXvD82pQXNk2\npoDC0Wm2Wt/b7m2LB9zvJd+UQjbVwpLHO7HHAhlt9Tutd1288J3umtdzC2XVdF1y2fMZEY+USxsd\nqmM9lkTcGwfvUnpsJHbfZJv+gjc/98xf/EUfZJv+gjc/98x//EV5LcalBqXha3j0C31Oyi0/REnM\nf/CQPY2enRkSBW81P3kzHyvl3KyFQDlcnIbvUtX8P65ql1rOqJc6f4Zs7+3+z3skKLcGCR2cohCt\nkqMqwKn0ptpJvt/wf8hpXkorr/wP8z1VE82eWGPV5Xlhx5sa+UWTIyMjZxketS/ZJv8AoI3P/fMf\n/wARXm7Pb2V74l1GefWBNfHToSlhekSSPMFGxPMcJHknG4FSoJ2lTitL4d3GoR+J/Eml38jKlqtr\nIto2ry6kbZ3RtymWUBgSFUleQOoPNCT1T6E8ysn3Oj1TWNK0OaKLWvFUGnSTjMSXdxbxGTt8oZRn\nr2q6ArXjWi6xIblYxK0IMW8ISQGK7c4JBGfY1zvhL7J9p8V/2t5H2/8AtGb7d52M/ZsfuN2f+Wfl\ndO33/euWe/0Wy1S6fQ5r9NJk0CwtrI6c+2Zle7ljRY2mI2g5ADEjCnKkcGkne3n/AJNl238v80j1\nH7JN/wBBG5/75j/+Io+yTf8AQRuf++Y//iK8eN/rFpqGs6Q97d2KLeaMDbx65NfS23n3BSRDM4Do\nzIFygJAzkHmrHj26m03Tdfn0C61hToMSxm9vPEEkMNvIER0RIwWa5dt4J84YOdofoBSV/wCvT/NC\ns72PWfsk3/QRuf8AvmP/AOIrM1TWNK0OaKLWvFUGnSTjMSXdxbxGTt8oZRnr2rbjJaJSepAJrj/C\nX2T7T4r/ALW8j7f/AGjN9u87Gfs2P3G7P/LPyunb7/vU9beV/wAv8xJ3in3N++tZRbITf3DDzouC\nsf8Az0XnhfxqwbWYAk6jcADqSsX/AMRWZpZ0o+ENO/4R0k6UGgFp9/HliVQMb+dvp2xjHGKo+OtT\nEMFvpc1vqLWl6Ha7nsrCe5AiXGYj5SMVLkgZOPlD85xRJ8t7DjqbIntzpf8AaQ10mw8rzvtW+Hyt\nmM79+3G3HOc4qHU9S0/RI4n1nxLHp6Tttia7mgiEh9F3KMn6V5pdafY65+zHb3M4uyLDQWMcLGWG\nNpFjGGZDgSAFcqSCvcdjXY6xbXFl4sXWbB9HuJf7J8m4t9Ru2ha3hV9xlQhHypzhgQAdqfMMVU1y\nv+v6/wAtxdF/Xb/P9DpLkCytXubzWJLeCMbnllMSqo9SSuBVW91LTtNvbaz1HxLHaXV2222gnmgR\n5jkDCKVyxyQOPWvIdc1GST4RWumarBqZt7XSEuEkTS7horiRj+6y6qVRUUA4ZvvFP7vPV3c9jdWf\nxDn1ASKJbGF0E8ZjkFubXMeFYBl/eebgEAhs96mTsm+39feOK5ml3PQvsk3/AEEbn/vmP/4ij7JN\n/wBBG5/75j/+Io0vz/7Hs/tmftHkJ5ueu/aM/rVqqas7ERd0mZl/azLbqTfXDfvohgrH/wA9F54W\npLhPsdtJcXerywQRKWkll8pVQDqSSmAKl1H/AI9U/wCu8P8A6MWuR+LWjWuo+AdRurszObK3d4oh\nO6xl8rhmQHDkY43A4PI55pdSjf1PUtP0XyP7Y8Sx6f8AaG2w/apoIvNPou5Rk8jpVXUvEmgaNeG0\n1jxlZ2FyFDGG6vLaJwD0O1gDis3XLeew8WXGs2b6NcbtIEV5FqV00TWkCuzGVcI+5Dubcp2glF+a\nsezS51DwD4K8K3jyFtWtka+3ZRzaRRhnUjqN2Y0I64c0t/69f8r/APDB/X4X/wCB/wAOdVeeJNA0\n57dNQ8ZWdq1zGssCz3lshlRvusuR8wPYjg1oXVrKLiy/0+4bdMcErHx+7fkfL/nNchrhk0688T63\noviZra40uCHfpn2OMRZSPckbllLsr7sAxlcFiOSK7SR3lOmSSxmN2l3Mh/hJifIp6P8Ar7g16iXI\nFlayXN5rElvBGNzyymJVQepJXAqX7JN/0Ebn/vmL/wCIrzf4wamk+j6lpt3bakLS1s/PVotOuJIp\n5yfkzKiFAqAEncfvMv8Adr0y0nS6soZ4hIqSxq6iWNo2AIzyrAMp9iARQtVcHo7Ef2Sb/oI3P/fM\nf/xFH2Sb/oI3P/fMf/xFWqKAKv2Sb/oI3P8A3zH/APEVW1G1mXS7sm+uGAhclSseD8p44WtOquqf\n8ge8/wCuD/8AoJoAPsk3/QRuf++Y/wD4ij7JN/0Ebn/vmP8A+Iq1RQBV+yTf9BG5/wC+Y/8A4ij7\nJN/0Ebn/AL5j/wDiKtUUAVfsk3/QRuf++Y//AIiqct3aQWE99P4gEdpbsyzXDyQCOIqdrBmK4BBG\nDnoeKvahZJqNjLaTSzxJKAGe3maKTGc4DqQy56ZBBryixtILXwBpul2Ua24/4S2aGzduYrd0upWj\nLqfvr8gG3KliQAynkLrb0/Fpfr/ww/s3/rZv9D0zT5oNWsY73StdN7ayZ2T2zwyI2Dg4ZVIPII/C\noItT06fWZNIh8TRSanEu6SySeAzIMA5KbdwHI7dxWZ4IZ49R8S2t00c95HqQe5ubcbYZXaGP7icm\nMgBQVLMc87juwOR0gvFHomoF4n0ybxJcG3sFH+lxTySTIzNL0cDc7GMIrKvV2CHc92kutvxt/n/w\nwujfa/4X/wAj0G31PTrzVptLtPE0U+oW4Jms4p4GmjAwCWQLkdR1HcVoWEbRapdB5nmPkxHc4UEf\nNJxwBXH6TZWmq6xpUXhu1+zeH/D08rpebiTdTlXjaOMtksgLsXkJ+ZgAM/MR2lt/yGLn/rhF/wCh\nSUn8Nx9bF6iiioKMnVpooNStGnkSNTFKAXYAZzH61B/aNl/z+W//AH9X/Gt2irjNpWIcLu5hf2jZ\nf8/lv/39X/Gj+0bL/n8t/wDv6v8AjW7RVe0YvZo5qybRtNt/I042NpDuZ/LgKIu4nJOBxknkmnId\nF1Hw7BYaubC6t3gjEttdbHRiADhlbjgjv3FdHRUuVxqNjn5YfDc9jcWU8elSWl02+e3dYzHK3HLK\neGPA5PoKhi03wlBZzWcFlosVtPEIZYEiiCSRjJCMoGCo3NweOT6101FK5VjnLWx8K2CRrY2uj2yx\nSLLGIY4kCOq7Awx0IX5QfTjpTrS38M2E3m2MOk20mZG3wrGhy5Bc5H94qpPrgZ6V0NFHMKxzNppn\nhGwsp7OxstFtrW5YNPBDFEiSkdCygYJ+tWLuPw7fi4F8ml3P2qIQz+cI381ASQjZ+8oJJAPHJreo\nouOxzNtpvhKyRFs7LRbdY9mwRRRKF2MWTGBxtZmI9CSR1q2+pWJ1SBxeW+0QyAt5q4BLJgdfY/lW\n3RRzMXKcxPpXhC51Ca+ubDRJry4ULNcSQwtJIBjAZiMkfKvX0HpVqP8AsCG4WeL+zUmV3kEi+WGD\nP99s9ctgZPfHNbtFFx2MK7/4R/UJoJr/APs26ltyxheby3aIsMNtJ6ZHBx1FQXun+E9StLa11G00\na7t7TH2eGeKJ0hwMDYCMLwMcV0lFHMFjBtk8PWcwls10y3kAcB4hGpw7bn5H95vmPqeTVW40jwdd\nC0F1p+hzCxG21EkELfZxnOEyPl5A6YrqKKOYVjmLvS/CF/fy3t9Y6Jc3c0flSXE0MLyOmMbSxGSM\ncYqSWz8LT6xFq01to8mpQgLHePHEZkAyAA/3h1PfvXR0Ucw7HPWtv4Zsrprmzh0m3nZ5JGliWJWL\nvjexI5y21cnvtGelRW2meEbK0u7WzstFt7e9/wCPqGKKJUn/AN8AYbqeuetdNRRzBYwbpfD195v2\n1dMufOh8iXzhG/mR5zsOeq55weKXTR4f0azFpo40ywtgxYQ2vlxICep2rgZrdoo5hcpgWt1pc+gw\n2l7PaSRyWyxzQzOpVgVwVZT1HYg1jad4Y8Ladfar5I0gaZqUEMLaYsESwqIy5OV+6dxfOMDp3ruK\nKLjscu2k+D20xtOaw0M2LlS1qYYfKYrwuUxjjAxxxU1taeF7Joms7fSLdoWDxmJIlKME8sEY6EJ8\nmf7vHSuioo5hWObnsfCt0t6t1a6PMNQKm8EkcTfaSv3fMz9/GBjOcVJaW/hqwgtYbCLSbaKzZmto\n4VjRYC2dxQD7pOTnHXJroKKOYdjk9QsdKvZrOOK90230+K7+23Fuka7p5g29W3BgB8/zH5SWOORz\nna/tTT/+f62/7/L/AI1pUUc1lYLa3M3+1NP/AOf62/7/AC/41WtdRsVuL0teW4DTArmVeR5aD19Q\na26KOYLGFD/wj9sLYW/9mxC0QpbBPLXyVPBCY+6DgcCq15pvhLUNOh0+/s9FurKBt0VtPFE8cZ55\nVSMA8np6mumoouFjBtU8O2LQtZLpls0EP2eEwiNPLiznYuOi5AOBxUwuNGF816JrEXTRiIz7k3lA\nSQu7rjJJx05rYoo5hcpiJqViNUnc3lvtMMYDeauCQz5HX3H51Ul0zwjPrK6vPY6JJqasrC9eGIzA\ngYB3kbsgAAc101FHMOxzF3pfhC/v5b2+sdEubuaPypLiaGF5HTGNpYjJGOMVaI8PtC8TDTDHJMLh\n0Pl4aUEEOR3YFQc9cgelbtFHMFjA1SLw5rlqLbWk0vUbdXDiK7EcqBgCAcNkZ5PPvTNRtPC+r2cN\npq1vpF9bQEGKC5SKRIyBgFVbIHHHFdFRRzBY5u+sPCmp3Vvc6la6NeXFrj7PLcRxSPDg5G0nleee\nKW6svCt/qkOp31to9zf2+PJu5o4nljwcja55GCSeDXR0UcwrGXLf6ZNE8U13aSRupV0eRSGB6gju\nKztK0/wnoQmGh2mjaaJwBKLOKKHzMZxu2gZxk9fU10tFFx2OWttH8G2VvJBZ6doVvDKGWSOKCFVc\nNt3AgDBztXPrtHoKuXA8P3cjyXQ02Z5EWN2k8tiyq25VJPUBiSB2PNbtFHMFjmNahsNRsbyOx1HT\nrK6vohb3F2USR2i5G37w5AZtpOQpOdp5BsyL4fn0gaXcnT7iwEaxfZpijxlVxgFTwQMD8q3qKOYL\nHO39r4Y1S5trnU4NJvJ7Q7raW4SKRoTkHKE5K8gHj0FEdp4XitpLeK30hIJYBbSRKkQV4hnEZHQr\n8zfL0+Y+tdFRRzBYwNLj8OaJam10VNL063LFzDaCOJCx6nC4GeBzUGq6b4R12aOXXLLRdSkiXbG9\n5FFMUHXALA4FdNRRzBY529tPC+pXtveajb6Rd3Vr/qJ50id4uc/Kx5Xn0pYrfwzBay20EOkxwTQL\nbyRIsYV4gCBGQOCoBIC9ME10NFF9LBY5y5svCt5JK93baPO80AtpGljiYvCDkRknquedvSn6db+G\ntI/5BMWlWP7sR/6MscfyAkhflxwCzHHqx9a6CijmFynNapp3hLW7iKfWrPRdRmhGIpLuKKVkGc4B\nYHHPpU90nh6+8/7aumXH2iEQTeaI382MEkI2eq5JODxya3qKL9B2OYttL8IWcaR2djoluibNixQw\nqF2OXXAA4w5LD0JJ60t5pvhHUdQN9qFlot1eGMwm4niieQoQQV3EZ2kEjHTBNdNRRzBYwbIaNYXd\n3cW13ao92yNIFkRR8qBAOMdlHXJ7ZwABBqmneEtbuIp9as9F1GaEYiku4opWQZzgFgcc+ldLRRzB\nYxL7UbFrZAl5bkiaI4Eq9BIpPf0qwdT08gg3tqQeoMq/41p0UcwWMPOgHS/7MP8AZv2DyvJ+yfu/\nK2Yxs2dNuOMYxUOp2nhfW1hGs2+kagIDuhF2kUvln1XdnHQdK6KijmCxkXN1o97avbXk9jcQSDa8\nUroysPQg8Gqt7beGNSvba81GDSbu6tG3W086RO8JyDlGPKnIB49K6GijmFYzf7U0/wD5/rb/AL/L\n/jR/amn/APP9bf8Af5f8a0qKOYdjEv8AUrF7dQl5bsfOiOBKp4Eiknr6VJc3WkXtrJbXk9jcQSDa\n8UroyuPQg8GteijmCxzup2nhfWmgbWbfSNQa2JaA3SRS+UTjJXdnHQdPQVaa50d7uO6eexa4iRkj\nmLoXRWwWAbqAdoyO+B6VsUUcwWOdu7XwxqGpW+oX8GkXV7a48i5mSJ5IsHI2seV554qe61Kxa4sy\nt5bkLMSxEq8Dy3GevqRW3RRzBYyLm60i9tZLa8nsbiCQbXildGVx6EHg1L/amn/8/wBbf9/l/wAa\n0qKOYVjN/tTT/wDn+tv+/wAv+NH9qaf/AM/1t/3+X/GtKijmHYzf7U0//n+tv+/y/wCNVtR1KxfS\n7tEvLdmaFwFEqkk7Tx1rboo5gsZv9qaf/wA/1t/3+X/Gj+1NP/5/rb/v8v8AjWlRRzBYzf7U0/8A\n5/rb/v8AL/jR/amn/wDP9bf9/l/xrSoo5gsZv9qaf/z/AFt/3+X/ABqnJH4dl0+awlTS3s7hmaa2\nYRmOQsdzFl6Ekkk56nmt6ii4WOetbbwzY2dvaWUOk29tbSebBDEkSJE/PzKo4U8nkc8mmpZeFo9Y\nk1aO20dNSlG2S9WOITOMYwX+8eAB17V0dFHMKxyNl4d8C6bfR3unaP4etLqI5jngtYEdD0yGAyOt\nblhcQ3GqXT28qSqIYgWRgwzuk44rSopX0sO2twooopDP/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\default_first_four.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast to the panda default sort the default SAS sort sequence places its missing values first in the data set as illustrated above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the last 4 rows in the 'default_srt' DataFrame finds the 2 NaN's sorted last since this is the default location panda places its missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpi_type</th>\n",
       "      <th>hpi_flavor</th>\n",
       "      <th>frequency</th>\n",
       "      <th>level</th>\n",
       "      <th>place_name</th>\n",
       "      <th>place_id</th>\n",
       "      <th>yr</th>\n",
       "      <th>period</th>\n",
       "      <th>index_nsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54262</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>780.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54263</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>791.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60576</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61098</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hpi_type        hpi_flavor  frequency  level            place_name  \\\n",
       "54262  traditional  all-transactions  quarterly  State  District of Columbia   \n",
       "54263  traditional  all-transactions  quarterly  State  District of Columbia   \n",
       "60576  traditional  all-transactions  quarterly  State               Vermont   \n",
       "61098  traditional  all-transactions  quarterly  State         West Virginia   \n",
       "\n",
       "      place_id    yr  period  index_nsa  \n",
       "54262       DC  2016       1     780.98  \n",
       "54263       DC  2016       2     791.71  \n",
       "60576       VT  1976       1        NaN  \n",
       "61098       WV  1982       1        NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_srt.tail(4)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "panda's default sort sequence places NaN's last in the sort sequence by default and can be used as an alternative to <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2005%20--%20Understanding%20Indexes.ipynb#Mixing-.loc-Indexer-with-Boolean-Operators\">boolean operators and the .loc() method</a> to detect missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS program to access the last four observations in the data set is:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_print_last4_rows_sorted.sas                    */\n",
    "    /******************************************************/\n",
    "    79         data last4;  \n",
    "    80           set df_states (firstobs=96241);\n",
    "    81           by index_nsa;\n",
    "\n",
    "    NOTE: 4 observations were read from \"WORK.last4\"\n",
    "````    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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YM/8Ax7yf7h/lWL4o8XWXhOOza+try5a9mMEMdnD5js+0kDbkE5xg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\nYWRniC9lu7Z54oI0hkLkpMzE/Iy4wVH971rQoopDP//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\sort_last_four.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alter the organization of the 'states_desc' DataFrame by supplying arguments and values to the sort_values() attribute.  The example below sorts descending placeing missing values at the beginning of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "states_desc = df_states.sort_values('index_nsa', ascending=False, na_position='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the na_position= argument to 'first' places NaN's at the beginning of the sort sequence.  This is the same beavhior for SAS' sort sequence.  Details describing SAS sort order for missing values is descried <a href=\"http://support.sas.com/documentation/cdl/en/lrcon/68089/HTML/default/viewer.htm#p0tmoipp0ci6wkn0z27ev47sv0ov.htm\"> here </a>.\n",
    "\n",
    "The first two rows in the DataFrame 'states_desc' contain the NaN's values for the 'index_nsa' column, while the next 2 rows contain the highest values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpi_type</th>\n",
       "      <th>hpi_flavor</th>\n",
       "      <th>frequency</th>\n",
       "      <th>level</th>\n",
       "      <th>place_name</th>\n",
       "      <th>place_id</th>\n",
       "      <th>yr</th>\n",
       "      <th>period</th>\n",
       "      <th>index_nsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60576</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61098</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54263</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>791.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54262</th>\n",
       "      <td>traditional</td>\n",
       "      <td>all-transactions</td>\n",
       "      <td>quarterly</td>\n",
       "      <td>State</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>780.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hpi_type        hpi_flavor  frequency  level            place_name  \\\n",
       "60576  traditional  all-transactions  quarterly  State               Vermont   \n",
       "61098  traditional  all-transactions  quarterly  State         West Virginia   \n",
       "54263  traditional  all-transactions  quarterly  State  District of Columbia   \n",
       "54262  traditional  all-transactions  quarterly  State  District of Columbia   \n",
       "\n",
       "      place_id    yr  period  index_nsa  \n",
       "60576       VT  1976       1        NaN  \n",
       "61098       WV  1982       1        NaN  \n",
       "54263       DC  2016       2     791.71  \n",
       "54262       DC  2016       1     780.98  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_desc.iloc[0:4,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SAS has missing 28 missing value indicators for numerics described <a href=\"http://support.sas.com/documentation/cdl/en/lrcon/68089/HTML/default/viewer.htm#p175x77t7k6kggn1io94yedqagl3.htm\"> here </a>.  However, if you want missing values to be 'first' in a data set using an ascending sort sequence, then the missing value indicator must be an actual numeric value larger than the largest non-missing value.  In other words, by default, the SAS missing value indicator for numerics is always the smallest numeric value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS example below sorts the df_states data set in descending order by the 'index_nsa' variable.  Like the panda example above, the \n",
    "\n",
    "    out=states_srt2\n",
    "    \n",
    "syntax creates the new SAS data set 'states_srt2'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_print_last4_rows_sorted_descending.sas        */\n",
    "    /*****************************************************/\n",
    "    44        proc sort data=df_states\n",
    "    45           out=states_srt2;\n",
    "    46           by descending index_nsa;\n",
    "    NOTE: 96244 observations were read from \"WORK.df_states\"\n",
    "    NOTE: Data set \"WORK.states_srt2\" has 96244 observation(s) and 9 variable(s)\n",
    "    47        \n",
    "    48         data first4;\n",
    "    49           set states_srt2 (obs=4);\n",
    "    50           by descending index_nsa;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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495P9w/ypujFISqSub3/CUTf8+Cf+BB/+Jo/4Sib/AJ8E/wDAg/8A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\netaFFF2FgooopDP/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\sort_last4_descending.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The section below examines a number of common data management patterns for analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####    1. Read the Lending Club Loan Status file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42633, 51)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\",\n",
    "                 low_memory=False)     \n",
    "lc.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Missing-Value-Identification\">missing values</a>.  Clearly, some columns are not useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                 2\n",
       "Member_ID                          3\n",
       "Loan_Amnt                          3\n",
       "Term                               3\n",
       "Int_Rate                           3\n",
       "Installment                        3\n",
       "Grade                              3\n",
       "Sub_Grade                          3\n",
       "Emp_Length                         3\n",
       "Home_Ownership                     3\n",
       "Annual_Inc                         7\n",
       "Verification_Status                3\n",
       "Loan_Status                        3\n",
       "Purpose                            3\n",
       "Zip_Code                           3\n",
       "Addr_State                         3\n",
       "DTI                                3\n",
       "Delinq_2yrs                       32\n",
       "Earliest_Cr_Line                  32\n",
       "Inq_Last_6mths                    32\n",
       "Mths_Since_Last_Delinq         26998\n",
       "Mths_Since_Last_Record         38978\n",
       "Open_Acc                          32\n",
       "Pub_Rec                           32\n",
       "Revol_Bal                          3\n",
       "Revol_Util                        93\n",
       "Total_Acc                         32\n",
       "Initial_List_Status                3\n",
       "Out_Prncp                          3\n",
       "Out_Prncp_Inv                      3\n",
       "Total_Pymnt                        3\n",
       "Total_Pymnt_Inv                    3\n",
       "Total_Rec_Prncp                    3\n",
       "Total_Rec_Int                      3\n",
       "Total_Rec_Late_Fee                 3\n",
       "Recoveries                         3\n",
       "Collection_Recovery_Fee            3\n",
       "Last_Pymnt_D                      85\n",
       "Last_Pymnt_Amnt                    3\n",
       "Next_Pymnt_D                   39412\n",
       "Last_Credit_Pull_D                 7\n",
       "Collections_12_Mths_Ex_Med       148\n",
       "Mths_Since_Last_Major_Derog    42633\n",
       "Policy_Code                        3\n",
       "Application_Type                   3\n",
       "Annual_Inc_Joint               42633\n",
       "Acc_Now_Delinq                    32\n",
       "Chargeoff_Within_12_Mths         148\n",
       "Delinq_Amnt                       32\n",
       "Pub_Rec_Bankruptcies            1368\n",
       "Tax_Liens                        108\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop/Keep Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep some of the columns in the 'lc' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc = lc[['ID', 'Member_ID', 'Loan_Amnt', 'Term', 'Int_Rate', 'Installment', 'Grade', 'Sub_Grade', 'Emp_Length',\n",
    "                     'Home_Ownership', 'Annual_Inc', 'Verification_Status', 'Loan_Status', 'Purpose', 'Zip_Code',\n",
    "                     'Addr_State', 'DTI', 'Delinq_2yrs', 'Earliest_Cr_Line', 'Inq_Last_6mths', 'Open_Acc', 'Revol_Bal',                          \n",
    "                     'Revol_Util']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lc = lc.rename(columns = {\n",
    "           'Member_ID'              :'mem_id',\n",
    "           'Loan_Amtn'              :'ln_amt',\n",
    "           'Int_Rate'               :'rate',\n",
    "           'Home_Ownership'         :'own_rnt',\n",
    "           'Verification_Status'    :'vrfy_stat',\n",
    "           'Loan_Status'            :'ln_stat',\n",
    "           'Addr_State'             :'state',\n",
    "           'Earliest_Cr_Line'       :'earliest_ln'\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower-case all the 'lc' DataFrame column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc.columns = map(str.lower, lc.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An investigation of the LC_Loan_Stats.csv file reveals it has 2 parts.  The first part, rows 0 to 39786 contain data for those loans meeting their credit policies.  Rows 39790 to the end contain data for those loans that are outside their normal lending policies.  \n",
    "\n",
    "Create the 'lc1' DataFrame by reading a sub-set of columns and rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39786, 21)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1 = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\", \n",
    "          low_memory=False,            \n",
    "          usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 22, 24, 25),\n",
    "          names=('id','mem_id', 'ln_amt', 'term','rate', 'm_pay', 'grade', 'sub_grd', 'emp_len',\n",
    "                 'own_rnt', 'income', 'ln_stat', 'purpose', 'state', 'dti', 'delinq_2yrs', 'ln_fst',\n",
    "                'inq_6mnth', 'open_acc', 'revol_bal', 'revol_util' ),\n",
    "          skiprows=1,\n",
    "          nrows=39786,\n",
    "          header=None)\n",
    "lc1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program for reading sub-sets of rows and columns from the 'LC_Loan_Stats.csv' file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_read_1st_half_loan_cvs.sas                     */\n",
    "    /******************************************************/\n",
    "    4         options obs=39787;\n",
    "    5         proc import datafile=\"c:\\data\\LC_Loan_Stats.csv\"\n",
    "    6              dbms=csv\n",
    "    7              out=loans\n",
    "    8              replace;\n",
    "    9\n",
    "    10          data lc1(rename=(Member_ID     = mem_id\n",
    "    12                        Loan_Amnt        = ln_amt\n",
    "    13                        Int_Rate         = rate\n",
    "    14                        Installment      = m_pay\n",
    "    15                        Sub_Grade        = sub_grd\n",
    "    16                        Emp_Length       = emp_len\n",
    "    17                        Home_Ownership   = own_rnt\n",
    "    18                        Annual_Inc       = income\n",
    "    19                        Loan_Status      = ln_stat\n",
    "    20                        Addr_State       = state\n",
    "    21                        Earliest_Cr_Line = ln_fst\n",
    "    22                        Inq_Last_6mths   = inq_6mnth));\n",
    "    23       \n",
    "    24         set loans(keep =  ID\n",
    "    25                           Member_ID\n",
    "    26                           Loan_Amnt\n",
    "    27                           Term\n",
    "    28                           Int_Rate\n",
    "    29                           Installment\n",
    "    30                           Grade\n",
    "    31                           Sub_Grade\n",
    "    32                           Emp_Length\n",
    "    33                           Home_Ownership\n",
    "    34                           Annual_Inc\n",
    "    35                           Loan_Status\n",
    "    36                           Purpose\n",
    "    37                           Addr_State\n",
    "    38                           DTI\n",
    "    39                           Delinq_2yrs\n",
    "    40                           Earliest_Cr_Line\n",
    "    41                           Inq_Last_6mths\n",
    "    42                           Open_Acc\n",
    "    43                           Revol_Bal\n",
    "    44                           Revol_Util);\n",
    "    45\n",
    "    46        ln_plcy = 'True';\n",
    "\n",
    "    NOTE: 39786 observations were read from \"WORK.loans\"\n",
    "    NOTE: Data set \"WORK.lc1\" has 39786 observation(s) and 22 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Duplicate Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the 'lc1' DataFrame we expect to find no duplicate id values.  Setting the .duplicated attribute to False returns all of the duplicate values, in our case for the lc1['id'] column.  The .duplicated() attribute for DataFrames is documented <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html\"> here</a>.\n",
    "\n",
    "False marks all duplicate values as True.  Create the new 'lc1_dups' DataFrame containing the duplicates for the lc1['id'] column.  If its length is zero then there are no duplicates.  Locating the duplicate rows is not the same as removing then which is illustrated below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by setting the index to the lc1['id'] column.  Next, \n",
    "create a <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2005%20--%20Understanding%20Indexes.ipynb#Mixing-.loc-Indexer-with-Boolean-Operators\"> Boolean mask</a> locating the duplicate values for the lc1['id'] column.  The keep='first' argument behaves similiar to SAS FIRST.variable in BY-Group processing.  We want to ignore the first occurance of the **duplicate** 'index' column value and extract the remaining ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc1.set_index('id', inplace=True, drop=False)\n",
    "dup_mask = lc1.duplicated('id', keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Duplicate Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Apply the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2005%20--%20Understanding%20Indexes.ipynb#Mixing-.loc-Indexer-with-Boolean-Operators\"> Boolean mask using the .loc attribute</a> to create the new 'lc1_dups' DataFrame.  The .shape attribute shows there are 35 rows with duplicate 'id' values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 21)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1_dups = lc1.loc[dup_mask]\n",
    "lc1_dups.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS SORT option <a href=\"https://support.sas.com/documentation/cdl/en/proc/61895/HTML/default/viewer.htm#a000146878.htm\"> NODUPKEY</a> checks for and eliminates observations with duplicate BY values.  The DUPOUT= option names the target SAS data set to write the duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_sort_nodupkey.sas                              */\n",
    "    /******************************************************/\n",
    "    50       proc sort data = lc1 dupout=lc1_dups nodupkey;\n",
    "    51          by id;\n",
    "    NOTE: 39786 observations were read from \"WORK.lc1\"\n",
    "    NOTE: 35 observations were found and deleted due to having duplicate sort keys\n",
    "    NOTE: Data set \"WORK.lc1\" has 39751 observation(s) and 21 variable(s)\n",
    "    NOTE: Data set \"WORK.lc1_dups\" has 35 observation(s) and 21 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop Duplicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39786, 21)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html\">.drop_duplicates attribute</a> to drop duplicate values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc1.drop_duplicates(['id'], keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39751, 21)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add a New DataFrame Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the new column lc1['ln_plcy'] as a flag to indicate all rows read from row 2 to 39786 are loans granted within standard lending guidelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39751, 22)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1['ln_plcy'] = lc1['id'].map(lambda x: True)\n",
    "lc1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cast Strings to Floats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grd</th>\n",
       "      <th>emp_len</th>\n",
       "      <th>own_rnt</th>\n",
       "      <th>ln_stat</th>\n",
       "      <th>purpose</th>\n",
       "      <th>state</th>\n",
       "      <th>ln_fst</th>\n",
       "      <th>revol_util</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39751</td>\n",
       "      <td>39701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>371</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>529</td>\n",
       "      <td>1092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>36 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>Nov-98</td>\n",
       "      <td>0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>29070</td>\n",
       "      <td>958</td>\n",
       "      <td>12021</td>\n",
       "      <td>2918</td>\n",
       "      <td>8891</td>\n",
       "      <td>18893</td>\n",
       "      <td>33639</td>\n",
       "      <td>18661</td>\n",
       "      <td>7095</td>\n",
       "      <td>371</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              term    rate  grade sub_grd    emp_len own_rnt     ln_stat  \\\n",
       "count        39751   39751  39751   39751      39751   39751       39751   \n",
       "unique           2     371      7      35         12       5           7   \n",
       "top      36 months  10.99%      B      B3  10+ years    RENT  Fully Paid   \n",
       "freq         29070     958  12021    2918       8891   18893       33639   \n",
       "\n",
       "                   purpose  state  ln_fst revol_util  \n",
       "count                39751  39751   39751      39701  \n",
       "unique                  14     50     529       1092  \n",
       "top     debt_consolidation     CA  Nov-98         0%  \n",
       "freq                 18661   7095     371        980  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the lc1['rate'] and lc1['revol_util'] column values are formatted with Excel's percent format (%), making them string values.  The <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html\"> .replace() method</a> replaces the '%' with a white-space.  The <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html\"> .astype attribute</a> converts the string into a float.  The resulting float is divided by 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc1['revol_util'] = lc1.revol_util.replace('%','',regex=True).astype('float')/100\n",
    "lc1['rate'] = lc1.rate.replace('%','',regex=True).astype('float')/100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the lc1['revol_util'] column values cast to floats use the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Missing-Value-Replacement\"> .fillna() attribute</a> to replace missing values with the calculated mean value from the lc1['revol_util'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc1[\"revol_util\"] = lc1[[\"revol_util\"]].fillna(lc1.revol_util.mean())\n",
    "lc1.revol_util.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program uses PROC SQL to insert the mean value for 'revol_util' into the SAS Macro variable 'mean_revol'.  A WHERE statement is used to locate the NULL values for the variable 'revol_util' and if found then the UPDATE statement inserts the value from the SAS MACRO variable &mean_revol into the 'revol_util' column.\n",
    "\n",
    "The CREATE TABLE statement starting at line 13 tests if there are missing values found for the column 'revol_util'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_update_missing_revol_until.sas                 */\n",
    "    /******************************************************/\n",
    "    6       proc sql;\n",
    "    7          select mean(revol_util) format 6.2 into :mean_revol\n",
    "    8          from lc1;\n",
    "    9       update lc1\n",
    "    10            set revol_util = &mean_revol\n",
    "    11            where revol_util is null;\n",
    "    NOTE: 50 record(s) updated in table WORK.lc1\n",
    "    12       \n",
    "    13       create table miss as\n",
    "    14          select revol_util\n",
    "    15          from lc1\n",
    "    16          where revol_util is null;\n",
    "    NOTE: Data set \"WORK.miss\" has 0 observation(s) and 1 variable(s)\n",
    "    17       quit;\n",
    "\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ####    2. Read the second portion of the LC_Loan_Stats.csv file beginning with row 39790."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the 'lc0' DataFrame by reading the loan status file, supplying a <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2002%20--%20Data%20Structures.ipynb#tuple\"> tuple</a> of integers for the usecols= argument and a tuple of names for the column labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2844, 21)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc0 = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\", \n",
    "          low_memory=False,            \n",
    "          usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 22, 24, 25),\n",
    "          names=('id','mem_id', 'ln_amt', 'term','rate', 'm_pay', 'grade', 'sub_grd', 'emp_len',\n",
    "                 'own_rnt', 'income', 'ln_stat', 'purpose', 'state', 'dti', 'delinq_2yrs', 'ln_fst',\n",
    "                'inq_6mnth', 'open_acc', 'revol_bal', 'revol_util' ),\n",
    "          skiprows=39790,\n",
    "          header=None)\n",
    "lc0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the column lc0['ln_plcy'] with a value of False to indicate rows 39790 to the end of the file are loans made outside the standard credit lending policy. The assignment below uses the .map() attribute and calls the anonymous lambda function to create the new column lc0[''ln_plcy']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lc0['ln_plcy'] = lc0['id'].map(lambda x: False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similiar to above, the lc0['rate'] and lc0['revol_util'] column values are formatted with Excel's percent format (%), making them string values. The .replace() method replaces the '%' with a white-space. The .astype() attribute converts the string into a float. The resulting float is divided by 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lc0['revol_util'] = lc0.revol_util.replace('%','',regex=True).astype('float')/100\n",
    "lc0['rate'] = lc0.rate.replace('%','',regex=True).astype('float')/100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the mean value for 'revol_util' and use the .fillna method to replace the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc0['revol_util'] = lc0[[\"revol_util\"]].fillna(lc0.revol_util.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the number of missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lc0.revol_util.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenating DataFrames (Join)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb\"> Chapter 7 -- Pandas, Part 2</a> covers joining operations by illustrating the pd.merge() method which follows SQL's relational algebraic statements.  An alternative is the concat function described <a href=\"http://pandas.pydata.org/pandas-docs/stable/merging.html#concatenating-objects\"> here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2844, 22)\n",
      "(39751, 22)\n"
     ]
    }
   ],
   "source": [
    "print(lc0.shape)\n",
    "print(lc1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the pd.concat() method to join the 'lc1' and 'lc0' DataFrames.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42595, 22)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [lc1, lc0]\n",
    "df = pd.concat(frames)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program uses the SET statement to join the data sets 'lc0' and 'lc1' together.  The variable names are identical in both data sets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c12_concatenate_lc0_lc1.sas                        */\n",
    "    /******************************************************/\n",
    "    7       data df;\n",
    "    8          set lc0\n",
    "    9              lc1;\n",
    "    10       run;\n",
    "\n",
    "    NOTE: 2844 observations were read from \"WORK.lc0\"\n",
    "    NOTE: 39751 observations were read from \"WORK.lc1\"\n",
    "    NOTE: Data set \"WORK.df\" has 42595 observation(s) and 22 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crosstabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>own_rnt</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <th>NONE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>OWN</th>\n",
       "      <th>RENT</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5227</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>872</td>\n",
       "      <td>4073</td>\n",
       "      <td>10202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5427</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>957</td>\n",
       "      <td>5986</td>\n",
       "      <td>12408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3587</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>642</td>\n",
       "      <td>4494</td>\n",
       "      <td>8747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>2368</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>433</td>\n",
       "      <td>3198</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1499</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>242</td>\n",
       "      <td>1646</td>\n",
       "      <td>3401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>620</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>600</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>253</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>216</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>18981</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>3257</td>\n",
       "      <td>20213</td>\n",
       "      <td>42595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "own_rnt  MORTGAGE  NONE  OTHER   OWN   RENT    All\n",
       "grade                                             \n",
       "A            5227     5     25   872   4073  10202\n",
       "B            5427     2     36   957   5986  12408\n",
       "C            3587     0     24   642   4494   8747\n",
       "D            2368     0     26   433   3198   6025\n",
       "E            1499     0     14   242   1646   3401\n",
       "F             620     1      4    75    600   1300\n",
       "G             253     0      7    36    216    512\n",
       "All         18981     8    136  3257  20213  42595"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.grade, df.own_rnt , margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purpose</th>\n",
       "      <th>car</th>\n",
       "      <th>credit_card</th>\n",
       "      <th>debt_consolidation</th>\n",
       "      <th>educational</th>\n",
       "      <th>home_improvement</th>\n",
       "      <th>house</th>\n",
       "      <th>major_purchase</th>\n",
       "      <th>medical</th>\n",
       "      <th>moving</th>\n",
       "      <th>other</th>\n",
       "      <th>renewable_energy</th>\n",
       "      <th>small_business</th>\n",
       "      <th>vacation</th>\n",
       "      <th>wedding</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th>own_rnt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>359</td>\n",
       "      <td>685</td>\n",
       "      <td>1813</td>\n",
       "      <td>36</td>\n",
       "      <td>806</td>\n",
       "      <td>42</td>\n",
       "      <td>458</td>\n",
       "      <td>100</td>\n",
       "      <td>49</td>\n",
       "      <td>540</td>\n",
       "      <td>20</td>\n",
       "      <td>187</td>\n",
       "      <td>60</td>\n",
       "      <td>72</td>\n",
       "      <td>5227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>62</td>\n",
       "      <td>90</td>\n",
       "      <td>315</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>195</td>\n",
       "      <td>638</td>\n",
       "      <td>1761</td>\n",
       "      <td>47</td>\n",
       "      <td>66</td>\n",
       "      <td>53</td>\n",
       "      <td>287</td>\n",
       "      <td>89</td>\n",
       "      <td>118</td>\n",
       "      <td>460</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>61</td>\n",
       "      <td>154</td>\n",
       "      <td>4073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">B</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>252</td>\n",
       "      <td>732</td>\n",
       "      <td>2400</td>\n",
       "      <td>28</td>\n",
       "      <td>742</td>\n",
       "      <td>40</td>\n",
       "      <td>237</td>\n",
       "      <td>90</td>\n",
       "      <td>33</td>\n",
       "      <td>450</td>\n",
       "      <td>20</td>\n",
       "      <td>270</td>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "      <td>5427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>44</td>\n",
       "      <td>96</td>\n",
       "      <td>409</td>\n",
       "      <td>10</td>\n",
       "      <td>104</td>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>122</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>208</td>\n",
       "      <td>871</td>\n",
       "      <td>2948</td>\n",
       "      <td>72</td>\n",
       "      <td>87</td>\n",
       "      <td>67</td>\n",
       "      <td>312</td>\n",
       "      <td>100</td>\n",
       "      <td>147</td>\n",
       "      <td>709</td>\n",
       "      <td>14</td>\n",
       "      <td>193</td>\n",
       "      <td>77</td>\n",
       "      <td>181</td>\n",
       "      <td>5986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">C</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>98</td>\n",
       "      <td>487</td>\n",
       "      <td>1671</td>\n",
       "      <td>30</td>\n",
       "      <td>467</td>\n",
       "      <td>31</td>\n",
       "      <td>139</td>\n",
       "      <td>63</td>\n",
       "      <td>18</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>194</td>\n",
       "      <td>16</td>\n",
       "      <td>67</td>\n",
       "      <td>3587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>278</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>132</td>\n",
       "      <td>605</td>\n",
       "      <td>2238</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>44</td>\n",
       "      <td>221</td>\n",
       "      <td>74</td>\n",
       "      <td>102</td>\n",
       "      <td>576</td>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>54</td>\n",
       "      <td>137</td>\n",
       "      <td>4494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">D</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>49</td>\n",
       "      <td>277</td>\n",
       "      <td>1172</td>\n",
       "      <td>12</td>\n",
       "      <td>268</td>\n",
       "      <td>21</td>\n",
       "      <td>88</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>177</td>\n",
       "      <td>5</td>\n",
       "      <td>180</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>205</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>80</td>\n",
       "      <td>361</td>\n",
       "      <td>1743</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>371</td>\n",
       "      <td>4</td>\n",
       "      <td>145</td>\n",
       "      <td>19</td>\n",
       "      <td>89</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">E</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>16</td>\n",
       "      <td>170</td>\n",
       "      <td>790</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>14</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>1499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>113</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>42</td>\n",
       "      <td>164</td>\n",
       "      <td>918</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>1646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>9</td>\n",
       "      <td>53</td>\n",
       "      <td>344</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">G</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>1614</td>\n",
       "      <td>5495</td>\n",
       "      <td>19813</td>\n",
       "      <td>422</td>\n",
       "      <td>3203</td>\n",
       "      <td>427</td>\n",
       "      <td>2310</td>\n",
       "      <td>753</td>\n",
       "      <td>629</td>\n",
       "      <td>4427</td>\n",
       "      <td>106</td>\n",
       "      <td>1993</td>\n",
       "      <td>400</td>\n",
       "      <td>1003</td>\n",
       "      <td>42595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "purpose          car  credit_card  debt_consolidation  educational  \\\n",
       "grade own_rnt                                                        \n",
       "A     MORTGAGE   359          685                1813           36   \n",
       "      NONE         0            0                   1            0   \n",
       "      OTHER        1            4                   7            2   \n",
       "      OWN         62           90                 315            3   \n",
       "      RENT       195          638                1761           47   \n",
       "B     MORTGAGE   252          732                2400           28   \n",
       "      NONE         0            0                   0            0   \n",
       "      OTHER        1            3                  17            1   \n",
       "      OWN         44           96                 409           10   \n",
       "      RENT       208          871                2948           72   \n",
       "C     MORTGAGE    98          487                1671           30   \n",
       "      OTHER        1            2                   8            0   \n",
       "      OWN         27           57                 278           10   \n",
       "      RENT       132          605                2238           75   \n",
       "D     MORTGAGE    49          277                1172           12   \n",
       "      OTHER        0            4                  12            0   \n",
       "      OWN         20           43                 205            4   \n",
       "      RENT        80          361                1743           36   \n",
       "E     MORTGAGE    16          170                 790            3   \n",
       "      OTHER        0            3                   5            0   \n",
       "      OWN          8           23                 113            6   \n",
       "      RENT        42          164                 918           28   \n",
       "F     MORTGAGE     5           71                 345            3   \n",
       "      NONE         0            0                   0            0   \n",
       "      OTHER        0            0                   1            0   \n",
       "      OWN          2            4                  33            0   \n",
       "      RENT         9           53                 344            8   \n",
       "G     MORTGAGE     1           29                 129            1   \n",
       "      OTHER        0            0                   2            2   \n",
       "      OWN          0            2                  16            1   \n",
       "      RENT         2           21                 119            4   \n",
       "All             1614         5495               19813          422   \n",
       "\n",
       "purpose         home_improvement  house  major_purchase  medical  moving  \\\n",
       "grade own_rnt                                                              \n",
       "A     MORTGAGE               806     42             458      100      49   \n",
       "      NONE                     0      0               0        0       0   \n",
       "      OTHER                    2      1               3        1       0   \n",
       "      OWN                     99      6              90       23       6   \n",
       "      RENT                    66     53             287       89     118   \n",
       "B     MORTGAGE               742     40             237       90      33   \n",
       "      NONE                     0      0               0        0       0   \n",
       "      OTHER                    3      1               4        0       0   \n",
       "      OWN                    104      8              71       19      11   \n",
       "      RENT                    87     67             312      100     147   \n",
       "C     MORTGAGE               467     31             139       63      18   \n",
       "      OTHER                    1      0               1        1       1   \n",
       "      OWN                     69     10              48       15       7   \n",
       "      RENT                    68     44             221       74     102   \n",
       "D     MORTGAGE               268     21              88       43       8   \n",
       "      OTHER                    0      0               0        2       1   \n",
       "      OWN                     47      3              24        6       1   \n",
       "      RENT                    40     35             162       48      65   \n",
       "E     MORTGAGE               172     14              42       26       6   \n",
       "      OTHER                    0      0               0        0       1   \n",
       "      OWN                     25      5              11        8       2   \n",
       "      RENT                    31     19              71       22      33   \n",
       "F     MORTGAGE                63      5              13        7       2   \n",
       "      NONE                     0      0               0        0       0   \n",
       "      OTHER                    1      0               0        1       0   \n",
       "      OWN                      8      1               5        0       1   \n",
       "      RENT                     5     13              13        9      11   \n",
       "G     MORTGAGE                25      6               1        3       1   \n",
       "      OTHER                    0      1               0        0       0   \n",
       "      OWN                      2      1               4        1       0   \n",
       "      RENT                     2      0               5        2       5   \n",
       "All                         3203    427            2310      753     629   \n",
       "\n",
       "purpose         other  renewable_energy  small_business  vacation  wedding  \\\n",
       "grade own_rnt                                                                \n",
       "A     MORTGAGE    540                20             187        60       72   \n",
       "      NONE          4                 0               0         0        0   \n",
       "      OTHER         2                 0               2         0        0   \n",
       "      OWN         116                 2              26        14       20   \n",
       "      RENT        460                 8             136        61      154   \n",
       "B     MORTGAGE    450                20             270        43       90   \n",
       "      NONE          2                 0               0         0        0   \n",
       "      OTHER         2                 0               3         0        1   \n",
       "      OWN         122                 3              33        11       16   \n",
       "      RENT        709                14             193        77      181   \n",
       "C     MORTGAGE    297                 9             194        16       67   \n",
       "      OTHER         7                 0               2         0        0   \n",
       "      OWN          77                 3              28         2       11   \n",
       "      RENT        576                 6             162        54      137   \n",
       "D     MORTGAGE    177                 5             180        14       54   \n",
       "      OTHER         3                 1               3         0        0   \n",
       "      OWN          53                 1              14         3        9   \n",
       "      RENT        371                 4             145        19       89   \n",
       "E     MORTGAGE    126                 2             105         8       19   \n",
       "      OTHER         4                 0               1         0        0   \n",
       "      OWN          27                 1               9         2        2   \n",
       "      RENT        147                 2             109        12       48   \n",
       "F     MORTGAGE     40                 1              56         1        8   \n",
       "      NONE          1                 0               0         0        0   \n",
       "      OTHER         0                 0               1         0        0   \n",
       "      OWN          10                 0              11         0        0   \n",
       "      RENT         61                 2              55         2       15   \n",
       "G     MORTGAGE     16                 0              36         0        5   \n",
       "      OTHER         1                 0               1         0        0   \n",
       "      OWN           3                 0               6         0        0   \n",
       "      RENT         23                 2              25         1        5   \n",
       "All              4427               106            1993       400     1003   \n",
       "\n",
       "purpose           All  \n",
       "grade own_rnt          \n",
       "A     MORTGAGE   5227  \n",
       "      NONE          5  \n",
       "      OTHER        25  \n",
       "      OWN         872  \n",
       "      RENT       4073  \n",
       "B     MORTGAGE   5427  \n",
       "      NONE          2  \n",
       "      OTHER        36  \n",
       "      OWN         957  \n",
       "      RENT       5986  \n",
       "C     MORTGAGE   3587  \n",
       "      OTHER        24  \n",
       "      OWN         642  \n",
       "      RENT       4494  \n",
       "D     MORTGAGE   2368  \n",
       "      OTHER        26  \n",
       "      OWN         433  \n",
       "      RENT       3198  \n",
       "E     MORTGAGE   1499  \n",
       "      OTHER        14  \n",
       "      OWN         242  \n",
       "      RENT       1646  \n",
       "F     MORTGAGE    620  \n",
       "      NONE          1  \n",
       "      OTHER         4  \n",
       "      OWN          75  \n",
       "      RENT        600  \n",
       "G     MORTGAGE    253  \n",
       "      OTHER         7  \n",
       "      OWN          36  \n",
       "      RENT        216  \n",
       "All             42595  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([df.grade, df.own_rnt], df.purpose, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samp = df.sample(n=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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       "\n",
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       "\n",
       "  function load_libs(js_urls, callback) {\n",
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       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
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       "        console.warn(\"failed to load library \" + url);\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "  }\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.2.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      Bokeh.$(\"#65c95fc6-ef99-40da-99da-9f8cc49d47a2\").text(\"BokehJS is loading...\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === \"1\") {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (!force) {\n",
       "      var cell = $(\"#65c95fc6-ef99-40da-99da-9f8cc49d47a2\").parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bokeh.charts\n",
    "import bokeh.charts.utils\n",
    "import bokeh.io\n",
    "import bokeh.models\n",
    "import bokeh.palettes\n",
    "import bokeh.plotting\n",
    "from bokeh.charts import Bar, Scatter, BoxPlot, Histogram\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"plotdiv\" id=\"fe50e06f-061a-403f-8130-e3e9f7bf1b99\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = \"\";\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        Bokeh.$(\"#fe50e06f-061a-403f-8130-e3e9f7bf1b99\").text(\"BokehJS successfully loaded.\");\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"fe50e06f-061a-403f-8130-e3e9f7bf1b99\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'fe50e06f-061a-403f-8130-e3e9f7bf1b99' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === \"1\") {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (!force) {\n",
       "        var cell = $(\"#fe50e06f-061a-403f-8130-e3e9f7bf1b99\").parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = BoxPlot(samp, values='income', label='grade', color='firebrick',\n",
    "                      title=\"Average Interest Rate by Credit Grade\")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning Continuous Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the minimum and maximum values.  Scalars are returned.  Returning a row using a minimum value is illustrated <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2009%20--%20Panda%20Time%20Series%20and%20Date%20Handling.ipynb#Return-a-Row-using-a-Minimum-Value\"> here</a>  and for maximum value <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2009%20--%20Panda%20Time%20Series%20and%20Date%20Handling.ipynb#Return-a-Row-using-a-Maximum-Value\"> here</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "29.99\n"
     ]
    }
   ],
   "source": [
    "print(df.dti.min())\n",
    "print(df.dti.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2002%20--%20Data%20Structures.ipynb#list\"> list</a> 'bins' containing integer values used to define bin levels.  Construct the list 'names' to define the labels for the bins. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = [0, 5, 10, 15, 20, 25, 30]\n",
    "names=['0-5%', '5-10%', '10-15%', '15-20%', '20-25%', '25-30%']\n",
    "df['dti_cat'] = pd.cut(df['dti'], bins, labels=names, include_lowest=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call pd.value_counts() method to return a count of values for each bin level. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-15%    10502\n",
       "15-20%     9775\n",
       "5-10%      8563\n",
       "20-25%     7376\n",
       "0-5%       5552\n",
       "25-30%      827\n",
       "Name: dti_cat, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['dti_cat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the count of missing values for the df['income'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the .fillna() method to replace missing values for the df['income'] column with the calcuated column mean.  Additional examples for detecting and replacing missing values are described <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Handling-Missing-Data\"> here</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['income'] = df[[\"income\"]].fillna(df.income.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000000.0\n",
      "1896.0\n"
     ]
    }
   ],
   "source": [
    "print(df.income.max())\n",
    "print(df.income.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the pd.qcut() method for creating deciles for the df['income'] column and copy them into the new df['inc_cat'] column.  A more detailed example of creating deciles is described in the section <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Understanding-Binning\"> Understanding Binning</a> in Chapter 10--GroupBy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['inc_cat'] = pd.qcut(df['income'].values, 10).codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the count of values from the income decile values in the df['inc_cat'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5088\n",
       "7    4436\n",
       "4    4309\n",
       "3    4306\n",
       "2    4265\n",
       "9    4260\n",
       "6    4253\n",
       "5    4163\n",
       "8    4082\n",
       "1    3433\n",
       "Name: inc_cat, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['inc_cat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The df['inc_cat'] column containing income deciles used as the rows and values for df['grade'] nested inside values from the df['ln_plcy'] column as the header in a Crosstab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>ln_plcy</th>\n",
       "      <th colspan=\"7\" halign=\"left\">False</th>\n",
       "      <th colspan=\"7\" halign=\"left\">True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>105</td>\n",
       "      <td>130</td>\n",
       "      <td>92</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>1219</td>\n",
       "      <td>1468</td>\n",
       "      <td>1025</td>\n",
       "      <td>625</td>\n",
       "      <td>213</td>\n",
       "      <td>51</td>\n",
       "      <td>15</td>\n",
       "      <td>5088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>824</td>\n",
       "      <td>999</td>\n",
       "      <td>711</td>\n",
       "      <td>427</td>\n",
       "      <td>183</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>3433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>71</td>\n",
       "      <td>85</td>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>1025</td>\n",
       "      <td>1264</td>\n",
       "      <td>849</td>\n",
       "      <td>522</td>\n",
       "      <td>242</td>\n",
       "      <td>62</td>\n",
       "      <td>20</td>\n",
       "      <td>4265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>58</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>1034</td>\n",
       "      <td>1196</td>\n",
       "      <td>887</td>\n",
       "      <td>565</td>\n",
       "      <td>263</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>4306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>1070</td>\n",
       "      <td>1221</td>\n",
       "      <td>805</td>\n",
       "      <td>531</td>\n",
       "      <td>309</td>\n",
       "      <td>91</td>\n",
       "      <td>21</td>\n",
       "      <td>4309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>1023</td>\n",
       "      <td>1177</td>\n",
       "      <td>789</td>\n",
       "      <td>524</td>\n",
       "      <td>281</td>\n",
       "      <td>110</td>\n",
       "      <td>29</td>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>1037</td>\n",
       "      <td>1224</td>\n",
       "      <td>753</td>\n",
       "      <td>537</td>\n",
       "      <td>303</td>\n",
       "      <td>122</td>\n",
       "      <td>33</td>\n",
       "      <td>4253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>1033</td>\n",
       "      <td>1263</td>\n",
       "      <td>791</td>\n",
       "      <td>559</td>\n",
       "      <td>318</td>\n",
       "      <td>140</td>\n",
       "      <td>40</td>\n",
       "      <td>4436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>940</td>\n",
       "      <td>1094</td>\n",
       "      <td>763</td>\n",
       "      <td>499</td>\n",
       "      <td>316</td>\n",
       "      <td>160</td>\n",
       "      <td>56</td>\n",
       "      <td>4082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "      <td>49</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>874</td>\n",
       "      <td>1115</td>\n",
       "      <td>727</td>\n",
       "      <td>534</td>\n",
       "      <td>429</td>\n",
       "      <td>185</td>\n",
       "      <td>76</td>\n",
       "      <td>4260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>123</td>\n",
       "      <td>387</td>\n",
       "      <td>647</td>\n",
       "      <td>702</td>\n",
       "      <td>544</td>\n",
       "      <td>247</td>\n",
       "      <td>194</td>\n",
       "      <td>10079</td>\n",
       "      <td>12021</td>\n",
       "      <td>8100</td>\n",
       "      <td>5323</td>\n",
       "      <td>2857</td>\n",
       "      <td>1053</td>\n",
       "      <td>318</td>\n",
       "      <td>42595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ln_plcy False                                 True                           \\\n",
       "grade       A    B    C    D    E    F    G      A      B     C     D     E   \n",
       "inc_cat                                                                       \n",
       "0          23   73  105  130   92   31   18   1219   1468  1025   625   213   \n",
       "1           6   29   46   54   57   19    9    824    999   711   427   183   \n",
       "2          10   25   71   85   53   20   17   1025   1264   849   522   242   \n",
       "3          15   39   57   62   58   23   16   1034   1196   887   565   263   \n",
       "4           8   26   65   66   55   25   16   1070   1221   805   531   309   \n",
       "5          10   29   54   54   40   20   23   1023   1177   789   524   281   \n",
       "6          12   37   50   62   45   25   13   1037   1224   753   537   303   \n",
       "7          16   41   67   55   57   33   23   1033   1263   791   559   318   \n",
       "8           9   43   66   57   38   23   18    940   1094   763   499   316   \n",
       "9          14   45   66   77   49   28   41    874   1115   727   534   429   \n",
       "All       123  387  647  702  544  247  194  10079  12021  8100  5323  2857   \n",
       "\n",
       "ln_plcy               All  \n",
       "grade       F    G         \n",
       "inc_cat                    \n",
       "0          51   15   5088  \n",
       "1          57   12   3433  \n",
       "2          62   20   4265  \n",
       "3          75   16   4306  \n",
       "4          91   21   4309  \n",
       "5         110   29   4163  \n",
       "6         122   33   4253  \n",
       "7         140   40   4436  \n",
       "8         160   56   4082  \n",
       "9         185   76   4260  \n",
       "All      1053  318  42595  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.inc_cat, [df.ln_plcy, df.grade] , margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save to Disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .to_pickle() method serializes the 'df' DataFrame in order to write to disk.  The .to_pickle() method is documented <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_pickle.html\"> here</a>.  The DataFrame is read in <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb\"> Chapter 10-- GroupBy</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_pickle('lending_club.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also write the 'lc0' and 'lc1' DataFrames out as a .csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc0.to_csv(\"C:\\Data\\\\Loans_lc0.csv\")\n",
    "lc1.to_csv(\"C:\\Data\\\\Loans_lc1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "Delete Duplicates in Pandas, by Chris Albon located <a href=\"http://chrisalbon.com/python/pandas_delete_duplicates.html\"> here</a>.\n",
    "\n",
    "pandas.cut API doc located <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html\"> here</a>."
   ]
  },
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    "## Navigation\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/tree/master/\"> Return to Chapter List </a>    "
   ]
  }
 ],
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