{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 10 -- Groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics Covered:\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Setting-Display-Options\">Setting Display Options</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Read-'pickled'-DataFrame\">Read 'pickled' DataFrame</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Create-GroupBy-Object\">Create GroupBy Object</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#GroupBy-with-Aggregations\">GroupBy with Aggregations</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Understanding-Binning\">Understanding Binning</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Defining-Functions\">Defining Functions</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Applying-Functions-to-Groups\">Applying Functions to Groups</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Applying-Transformations-to-Groups\">Applying Transformations to Groups</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Top/Bottom-N-processing\">Top/Bottom N processing</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb#Resources\">Resources</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "## Setting Display Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this notebook, display floats with a field width of 20 and two places left of the decimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:20,.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read 'pickled' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pd.read_pickle() function loads the Lending Club Data Frame created in <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Save-to-Disk\"> Chapter 12, Additional Data Handling</a>.  The pd.read_pickle() method is documented <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_pickle.html\">here</a>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42595, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans = pd.read_pickle('lending_club.pkl')\n",
    "loans.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display attribute information for the 'loans' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42595 entries, 872482 to 1064908\n",
      "Data columns (total 23 columns):\n",
      "mem_id         42595 non-null int64\n",
      "ln_amt         42595 non-null int64\n",
      "term           42595 non-null object\n",
      "rate           42595 non-null float64\n",
      "m_pay          42595 non-null float64\n",
      "grade          42595 non-null object\n",
      "sub_grd        42595 non-null object\n",
      "emp_len        42595 non-null object\n",
      "own_rnt        42595 non-null object\n",
      "income         42595 non-null float64\n",
      "ln_stat        42595 non-null object\n",
      "purpose        42595 non-null object\n",
      "state          42595 non-null object\n",
      "dti            42595 non-null float64\n",
      "delinq_2yrs    42566 non-null float64\n",
      "ln_fst         42566 non-null object\n",
      "inq_6mnth      42566 non-null float64\n",
      "open_acc       42566 non-null float64\n",
      "revol_bal      42595 non-null int64\n",
      "revol_util     42595 non-null float64\n",
      "ln_plcy        42595 non-null bool\n",
      "dti_cat        42595 non-null category\n",
      "inc_cat        42595 non-null int8\n",
      "dtypes: bool(1), category(1), float64(8), int64(3), int8(1), object(9)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create GroupBy Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the GroupBy object 'grp_grd' using the key column 'grade'. It does not compute anything until an operation is applied to the resulting groups.  One of the simplest aggregation method applied is the len() function used to return the number of groups. The GroupBy: split-apply-combine doc for panda is located <a href=\"http://pandas.pydata.org/pandas-docs/stable/groupby.html\"> here</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.DataFrameGroupBy'>\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "grp_grd = loans.groupby('grade')\n",
    "print(type(grp_grd))\n",
    "print(len(grp_grd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GroupBy object has a number of aggregation methods which can be applied to individual group levels, for example .mean()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "A              66,711.88\n",
       "B              67,918.69\n",
       "C              68,199.96\n",
       "D              68,277.02\n",
       "E              75,889.16\n",
       "F              83,095.53\n",
       "G              93,055.82\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd['income'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if we do not use the GroupBy object created above, we can still render the average income for each level of the column 'grade' by passing the DataFrame column name.  In this case, 'income' grouped by grade to calculate the group mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "A              66,711.88\n",
       "B              67,918.69\n",
       "C              68,199.96\n",
       "D              68,277.02\n",
       "E              75,889.16\n",
       "F              83,095.53\n",
       "G              93,055.82\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('grade')['income'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, the pandas GroupBy logic is analogous to SQL's group by syntax.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_grade.sas                              */\n",
    "    /******************************************************/\n",
    "    6       proc sql;\n",
    "    7       select grade label = 'Grade',\n",
    "    8              mean(income) label = 'Mean Income'\n",
    "    9                           \n",
    "    10       from df\n",
    "    11       group by grade\n",
    "    12       order by mean;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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5/wC+Y/8A4ij7JN/0Ebn/AL5j/wDiKp+Kr2403wdrN9Y8XNtYzTQn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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  Image(filename='Anaconda3\\\\output\\\\income_groupby_grade.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy with Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the .aggregate() attribute to apply multiple methods to the group levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>66,711.88</td>\n",
       "      <td>54,049.48</td>\n",
       "      <td>10202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>67,918.69</td>\n",
       "      <td>60,705.94</td>\n",
       "      <td>12408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>68,199.96</td>\n",
       "      <td>86,568.69</td>\n",
       "      <td>8747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>68,277.02</td>\n",
       "      <td>49,031.33</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>75,889.16</td>\n",
       "      <td>55,312.08</td>\n",
       "      <td>3401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>83,095.53</td>\n",
       "      <td>63,771.87</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>93,055.82</td>\n",
       "      <td>73,522.11</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                  std  count\n",
       "grade                                                 \n",
       "A                66,711.88            54,049.48  10202\n",
       "B                67,918.69            60,705.94  12408\n",
       "C                68,199.96            86,568.69   8747\n",
       "D                68,277.02            49,031.33   6025\n",
       "E                75,889.16            55,312.08   3401\n",
       "F                83,095.53            63,771.87   1300\n",
       "G                93,055.82            73,522.11    512"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd['income'].aggregate(['mean', 'std', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program using the MEAN, STD, and COUNT function to produce the same results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_grade_stats.sas                        */\n",
    "    /******************************************************/\n",
    "    3       proc sql;\n",
    "    4       select grade label = 'Grade'\n",
    "    5              , mean(income) label = 'Mean Income' as mean\n",
    "    6              , std(income) label = 'Standard Deviation'\n",
    "    7              , count(income) label = 'Count'\n",
    "    8       \n",
    "   9       from df\n",
    "   10       group by grade\n",
    "   11       order by mean;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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8GeKtO8J2LWukl5LLXl1C10V9RVzbwAEeWJm+Xrz34OeTmrGoaJ47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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  Image(filename='Anaconda3\\\\output\\\\groupby_income_stats.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can requests multiple columns as part of the GroupBy operation.  In this case, loans['income'] and loans['dti'] (debt-to-income ratio).  The .describe() attribute is applied to each of the group levels.  The .stack() .and unstack() attributes are discussed <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2006%20--%20Hierarchical%20Indexing.ipynb#stack()-and-unstack()-methods\"> here </a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">income</th>\n",
       "      <th colspan=\"8\" halign=\"left\">dti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</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",
       "      <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>A</th>\n",
       "      <td>10,202.00</td>\n",
       "      <td>66,711.88</td>\n",
       "      <td>54,049.48</td>\n",
       "      <td>3,300.00</td>\n",
       "      <td>40,000.00</td>\n",
       "      <td>57,632.00</td>\n",
       "      <td>80,000.00</td>\n",
       "      <td>1,900,000.00</td>\n",
       "      <td>10,202.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>7.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.46</td>\n",
       "      <td>11.50</td>\n",
       "      <td>17.10</td>\n",
       "      <td>29.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>12,408.00</td>\n",
       "      <td>67,918.69</td>\n",
       "      <td>60,705.94</td>\n",
       "      <td>2,000.00</td>\n",
       "      <td>40,000.00</td>\n",
       "      <td>57,996.00</td>\n",
       "      <td>80,497.00</td>\n",
       "      <td>3,900,000.00</td>\n",
       "      <td>12,408.00</td>\n",
       "      <td>13.39</td>\n",
       "      <td>6.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.31</td>\n",
       "      <td>13.51</td>\n",
       "      <td>18.62</td>\n",
       "      <td>29.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>8,747.00</td>\n",
       "      <td>68,199.96</td>\n",
       "      <td>86,568.69</td>\n",
       "      <td>4,000.00</td>\n",
       "      <td>40,000.00</td>\n",
       "      <td>56,000.00</td>\n",
       "      <td>80,000.00</td>\n",
       "      <td>6,000,000.00</td>\n",
       "      <td>8,747.00</td>\n",
       "      <td>13.85</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.97</td>\n",
       "      <td>14.05</td>\n",
       "      <td>18.99</td>\n",
       "      <td>29.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>6,025.00</td>\n",
       "      <td>68,277.02</td>\n",
       "      <td>49,031.33</td>\n",
       "      <td>4,000.00</td>\n",
       "      <td>40,000.00</td>\n",
       "      <td>58,000.00</td>\n",
       "      <td>82,000.00</td>\n",
       "      <td>1,200,000.00</td>\n",
       "      <td>6,025.00</td>\n",
       "      <td>13.98</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.16</td>\n",
       "      <td>14.31</td>\n",
       "      <td>19.25</td>\n",
       "      <td>29.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3,401.00</td>\n",
       "      <td>75,889.16</td>\n",
       "      <td>55,312.08</td>\n",
       "      <td>4,200.00</td>\n",
       "      <td>45,000.00</td>\n",
       "      <td>62,000.00</td>\n",
       "      <td>90,000.00</td>\n",
       "      <td>750,000.00</td>\n",
       "      <td>3,401.00</td>\n",
       "      <td>14.18</td>\n",
       "      <td>6.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.36</td>\n",
       "      <td>14.62</td>\n",
       "      <td>19.60</td>\n",
       "      <td>29.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1,300.00</td>\n",
       "      <td>83,095.53</td>\n",
       "      <td>63,771.87</td>\n",
       "      <td>7,280.00</td>\n",
       "      <td>50,000.00</td>\n",
       "      <td>70,679.00</td>\n",
       "      <td>100,000.00</td>\n",
       "      <td>1,440,000.00</td>\n",
       "      <td>1,300.00</td>\n",
       "      <td>14.65</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.81</td>\n",
       "      <td>15.16</td>\n",
       "      <td>19.76</td>\n",
       "      <td>29.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>512.00</td>\n",
       "      <td>93,055.82</td>\n",
       "      <td>73,522.11</td>\n",
       "      <td>1,896.00</td>\n",
       "      <td>52,000.00</td>\n",
       "      <td>75,000.00</td>\n",
       "      <td>110,000.00</td>\n",
       "      <td>725,000.00</td>\n",
       "      <td>512.00</td>\n",
       "      <td>15.69</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.89</td>\n",
       "      <td>15.96</td>\n",
       "      <td>21.33</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    income                                            \\\n",
       "                     count                 mean                  std   \n",
       "grade                                                                  \n",
       "A                10,202.00            66,711.88            54,049.48   \n",
       "B                12,408.00            67,918.69            60,705.94   \n",
       "C                 8,747.00            68,199.96            86,568.69   \n",
       "D                 6,025.00            68,277.02            49,031.33   \n",
       "E                 3,401.00            75,889.16            55,312.08   \n",
       "F                 1,300.00            83,095.53            63,771.87   \n",
       "G                   512.00            93,055.82            73,522.11   \n",
       "\n",
       "                                                                      \\\n",
       "                       min                  25%                  50%   \n",
       "grade                                                                  \n",
       "A                 3,300.00            40,000.00            57,632.00   \n",
       "B                 2,000.00            40,000.00            57,996.00   \n",
       "C                 4,000.00            40,000.00            56,000.00   \n",
       "D                 4,000.00            40,000.00            58,000.00   \n",
       "E                 4,200.00            45,000.00            62,000.00   \n",
       "F                 7,280.00            50,000.00            70,679.00   \n",
       "G                 1,896.00            52,000.00            75,000.00   \n",
       "\n",
       "                                                                 dti  \\\n",
       "                       75%                  max                count   \n",
       "grade                                                                  \n",
       "A                80,000.00         1,900,000.00            10,202.00   \n",
       "B                80,497.00         3,900,000.00            12,408.00   \n",
       "C                80,000.00         6,000,000.00             8,747.00   \n",
       "D                82,000.00         1,200,000.00             6,025.00   \n",
       "E                90,000.00           750,000.00             3,401.00   \n",
       "F               100,000.00         1,440,000.00             1,300.00   \n",
       "G               110,000.00           725,000.00               512.00   \n",
       "\n",
       "                                                                      \\\n",
       "                      mean                  std                  min   \n",
       "grade                                                                  \n",
       "A                    12.04                 7.01                 0.00   \n",
       "B                    13.39                 6.66                 0.00   \n",
       "C                    13.85                 6.50                 0.00   \n",
       "D                    13.98                 6.43                 0.00   \n",
       "E                    14.18                 6.53                 0.00   \n",
       "F                    14.65                 6.55                 0.00   \n",
       "G                    15.69                 7.29                 0.00   \n",
       "\n",
       "                                                                      \\\n",
       "                       25%                  50%                  75%   \n",
       "grade                                                                  \n",
       "A                     6.46                11.50                17.10   \n",
       "B                     8.31                13.51                18.62   \n",
       "C                     8.97                14.05                18.99   \n",
       "D                     9.16                14.31                19.25   \n",
       "E                     9.36                14.62                19.60   \n",
       "F                     9.81                15.16                19.76   \n",
       "G                     9.89                15.96                21.33   \n",
       "\n",
       "                            \n",
       "                       max  \n",
       "grade                       \n",
       "A                    29.99  \n",
       "B                    29.95  \n",
       "C                    29.78  \n",
       "D                    29.63  \n",
       "E                    29.70  \n",
       "F                    29.95  \n",
       "G                    29.96  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd['income', 'dti'].describe().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GroupBy .size() attribute returns a count of the number of values for each level.  Of course, this same information is available as the loans['count'] column from the operation executed in the cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "A    10202\n",
       "B    12408\n",
       "C     8747\n",
       "D     6025\n",
       "E     3401\n",
       "F     1300\n",
       "G      512\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_grade_count.sas                        */\n",
    "    /******************************************************/\n",
    "    21       proc sql;\n",
    "    22       select grade\n",
    "    23              ,count(grade) label='N'\n",
    "    24       from df\n",
    "    25       group by grade;\n",
    "    26       quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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C2X/XvH/6CKraRpsOj6LZabarthtIEhQeyqB/SrOk/wDIFsv+veP/\nANBFKdr2QR1Vy3RRRUFhRRRQAVlztcLrU32aKOT/AEeLd5khTHzSeimtSqB/5DVx/wBe8X/oUlNb\niewzzNQ/59rb/wACW/8AiKPM1D/n2tv/AAJb/wCIrjdH8PJr0WsXlxq2tQ3i6ndRwyw6tcqkISQh\nMRb/ACyBgfKVIPcGuk8Japca14P0rUr3Z9oubVJJTGMKWI5IHoTyPaqi7pPyT+8TVm12uvuL3mah\n/wA+1t/4Et/8RR5mof8APtbf+BLf/EVaopiKvmah/wA+1t/4Et/8RVawe+Fu2y3tyPOl6zsOfMbP\n8HrWnVXTv+PV/wDrvN/6MagA8zUP+fa2/wDAlv8A4ijzNQ/59rb/AMCW/wDiKyvHOq3OjeC9QvbB\n2juQqxxyLGZGjLuqbwoBLEbsgYOSMVxd1qJ0SxudOsp/EWl6jd/ZIVTWb37SWjkuEha4ifzJNrAO\nRt3LglSU6Ulq+Vb/ANf1/THbS/8AX9f12PSfM1D/AJ9rb/wJb/4ijzNQ/wCfa2/8CW/+Irjhpl3F\n4h1PwxYa5fW1i9pbXvmzXUlxPEpkdZUWWRi6h1QANu+U7iuDWfAur6noWqReEnv9Q0mbULdLKW41\nSVHeMEeey3LEy+USMBgWbliuRtovfb+tbfdf8hbb/wBaX+89B8zUP+fa2/8AAlv/AIip9J/5Atl/\n17x/+giuc8FyiOHUNMntrq1vrK4AuI59Tmv1O5QytHNKdxUr2IXBDcdz0ek/8gWy/wCveP8A9BFK\nQ4luiiipKCiiigAqgf8AkNXH/XvF/wChSVfrLnt0uNam8xpBtt4seXKyfxSf3SM01uJ7GGfBLCS8\nWDxLrVvZXs8k81lCbdUJkbLqH8nzVByeQ4I7EV0VpaW9hZQ2dlCkFvbxrHFEgwqKBgAD0AFc7d+K\nfBdhdy2t94s062uIWKSQza0EdGHUFTJkGtxLG2kRXjluGVhlWW7kII9fvVa202/qxL31LlFVf7Oh\n/v3P/gVJ/wDFUf2dD/fuf/AqT/4qgC1VXTv+PV/+u83/AKMaj+zof79z/wCBUn/xVVrCwha3Yl7j\n/XSji5kHSRh2agC1qWnW2rabPYX0Zkt50KOoYqceoI5BHUEcg81gHwHZXK3R1rUdQ1e4uIBbrc3b\nxq8CBg48vykRQQ4VtxBbKrzwBW6dPgAJMlwAOpN1J/8AFVS0u60TXLd59E1ZNRhjfY8lpqTSqrYz\nglXODgjijqBlXvw+t9R0m/tL/XNWnn1ExLc3rGASyRRklYdoiEfl5LZXZ825s5Bqy/g43GnrbX+v\n6pdvBMk9ncultHJZyICA0flwqp4JBDqwI4xitBjpSR3bvqBVLLP2pjfuBb4UMd53fL8pB5xwc0mm\nyaRrNmLvR9SF/bFiomtdQeVCR1G5XIzQA7RdEj0WGf8A0q4vrm6l824u7oqZJmwAM7FVQAoAAVQM\nD1JJ0NJ/5Atl/wBe8f8A6CKp29vZXcImtbmWeIkgPHeyMpIOCMhuxBH4Vc0n/kC2X/XvH/6CKmQ4\nluiiipKCiiigAqgf+Q1cf9e8X/oUlX6y57q3ttam+0zxxbreLb5jhc/NJ601uJ7HHeG7XxNL/a7a\nTq+k2tr/AGxd7YrnSpJ3B805+dbhAf8Avn8670dOetcvd+GfAV/dy3V9onhy5uJmLyTTWkDu7HqS\nxGSa24b7S7eFIYLqziijUIiJIoVVAwAAOgFVHSKXZL8iZayb73/Mu0VV/tTT/wDn+tv+/wAv+NH9\nqaf/AM/1t/3+X/GmBaqrp3/Hq/8A13m/9GNR/amn/wDP9bf9/l/xqtYalYpbsHvLdT50pwZVHBkY\ng9fSgA8S/wBlf8IzqA8Q5/sswMLvG/8A1Z+9nZ82Mdcds9q4q81K58NeMNYnSL+1Lu50uD7Mum2c\njLbRLMyRCSJN7N/rWYuOqxsFQY571tS051Kte2rKRggyrgj86o6Vb+GdBgeHQ4dJ02KRt7x2axQq\nzYxkhcZOO9LqHS39dDyvTZrIW2v20f8AaDWo8TadLeS3llNAZFYW+9pPMVcZcZZT2OcbSK7mPS4N\nZ8ceKrF3uEsZbex+0/ZZ2hLTjzCwLoQwJj8kHkZXaOldAzaC8d2jnTmS9z9qUmMi4yoU7x/F8oA5\nzwMUmmjw/o1mLTRxplhbBiwhtfLiQE9TtXAzTVrW/rZL9Ad73X9a3Mj4YRJB8OdNihRUjjaZVVRg\nKBM4AFdTpP8AyBbL/r3j/wDQRVO2utHsrdYLOext4VyVjidFUZOTgDjqSauaT/yBbL/r3j/9BFSx\nxVlYt0UUVJQUUUUAFUD/AMhq4/694v8A0KSr9UD/AMhq4/694v8A0KSmtxPYwE8Y3NzcXSab4U1q\n+itriS2a4hks1RnRtrYD3CtjI7gV0w5HpXFeG/CcD3Oo398dXt7g6vcypGupXUETL5pKt5SyBCpH\nP3cN3zXa1cdYJvey/ImXxNLu/wAwooopgFVdO/49X/67zf8AoxqtVV07/j1f/rvN/wCjGpATzTRW\n8Ek1xIkUUal3kdgqqoGSST0Arm7T4g6BfaLqurWk8s1jplyLVpUjyJ3KoV8oDlwxkUKehJ44wT0r\noksbJIqujDDKwyCPQiuN0ttS0WbxlfxaTPdM2sLNFDgo08X2e3VmjyDvIw+AOpXbkGjq79v1X+Yd\nF6/5l8eOLKOwv5dQsb6wu7F40k0+cRtO7SnEQTY7I29jtHzdc5xg0HxvaR2dw13puoWt/BPHbf2Z\nIsZuJJJOYwpVzGQwyd2/A2tuI2nHHzafdT3Wo6noOnalJpMN3Y3zR6jBMLm4lilLTGNZh5rARbNq\nnjK4Qdan1C3u9S8VHxhaabfGws7uzHlS2MkdxNHGlwskiwsBIdpuRgbcny22g8ZFqtdNfw01/P8A\nMO/p977f16Hd6JrkWtw3GLW4srm1mMNzaXQXzIXwGAOxmU5VlYFWIwfXIGhpP/IFsv8Ar3j/APQR\nXOeFVlu9X13WjbT21tqE8S2y3Nu0MrpHGFLsjgMuW3ABgDhQehFdHpP/ACBbL/r3j/8AQRSl0HEt\n0UUVBQUUUUAFZc8zxa1N5dvJNm3iz5ZUY+aT+8RWpVA/8hq4/wCveL/0KSmtxPYZ9rm/6B1z/wB9\nR/8AxdH2ub/oHXP/AH1H/wDF1z8WpeI/EE15N4euNL0+wtbiS1ie9tJLl7l42Ku2FljEahwyj7xO\nM8cCt/S57250uCXVbNbK8Zf30CSiRUYHHDDqD1HAODyAeKvoS9HYX7XN/wBA65/76j/+Lo+1zf8A\nQOuf++o//i6tUUAVftc3/QOuf++o/wD4uq1hdTLbsBY3DfvpTkNH/wA9G45atOqunf8AHq//AF3m\n/wDRjUAH2ub/AKB1z/31H/8AF0fa5v8AoHXP/fUf/wAXVqsjxXrJ8P8AhXUNUTyxJbxZj81Sy7yQ\nq5A5IyRwOTQ9FcC79rm/6B1z/wB9R/8AxdH2ub/oHXP/AH1H/wDF1zug+JLy50/UJri4XWbm1KAW\nNnpEunXC7um6O5lJweoY7V4bk44h0bxH4l17wml1a6ZZwanJqU9pIrv5kNmkcroXb5lMpAQDCldx\nP8Izg8g6XOo+1zf9A65/76j/APi6n0n/AJAtl/17x/8AoIrF8L6ve6j/AGna6obeW5028Nq9zaRN\nHFP8ivlVZmKkb9pG5uVPPOBtaT/yBbL/AK94/wD0EVMug0W6KKKkoKKKKACqB/5DVx/17xf+hSVf\nqlPZ3DXrXFtPHHvjVGWSIv8AdLHPDD+9TW4mcheeGtUtob/TbTSdD1/Rry5e7W01aZovId23suBD\nKsi7yWGQpXOOeDWt4L8ON4U8KwaS8sErRyTSk20HkxKZJWk2omThV3bRz0FbH2bUP+fu2/8AAZv/\nAIuj7NqH/P3bf+Azf/F1SkkrCabJqKh+zah/z923/gM3/wAXR9m1D/n7tv8AwGb/AOLo5kFmTVV0\n7/j1f/rvN/6MapPs2of8/dt/4DN/8XUcNhfQRlEvLcguz82zdWYsf4/U0XQWLVUdag1G40mVNEu0\ntL4FWikkQOhIYEowwflYAqSOQDkcirH2bUP+fu2/8Bm/+Lo+zah/z923/gM3/wAXRdBYxdE0vUzr\n95rmvRWdtdT28dpHb2U7TIkaMzZMjIhLFnPG0AADrk1kP4d8U6b4PuNN0C4sUvbnVLi4kma4eLZb\nyzvIQj+U+JNrAZKkDJIzgV2P2bUP+fu2/wDAZv8A4uj7NqH/AD923/gM3/xdF1t/W9x6/wBeljL8\nK2FzpWipYXGlWGlxwHbDDZXr3IYHkszvGhLEkkk5JJyTk1r6T/yBbL/r3j/9BFM+zah/z923/gM3\n/wAXVm0g+y2UFvu3eVGqbsYzgYzSk7iSsS0UUVJQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU\nAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  Image(filename='Anaconda3\\\\output\\\\groupby_grade_count.JPG')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .get_group attribute returns information about a particular group level.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd.get_group('G').mem_id.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_grade_where.sas                        */\n",
    "    /******************************************************/\n",
    "    32       proc sql;\n",
    "    33       select count(grade) label='N'\n",
    "    34       from df\n",
    "    35       where grade = 'G'\n",
    "    36       group by grade;\n",
    "    37       \n",
    "    38       quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  Image(filename='Anaconda3\\\\output\\\\groupby_grade_G.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can chain attributes together to produce the desired results.  Details for sorting values in DataFrames are discussed in <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> in Chapter 12--Additional Data Handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "G              93,055.82\n",
       "F              83,095.53\n",
       "E              75,889.16\n",
       "D              68,277.02\n",
       "C              68,199.96\n",
       "B              67,918.69\n",
       "A              66,711.88\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd.income.mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS PROC SQL example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_grade_orderby.sas                      */\n",
    "    /******************************************************/\n",
    "    44       proc sql;\n",
    "    45       select grade\n",
    "    46              ,mean(income) label='Mean Income'\n",
    "    47       from df\n",
    "    48       group by grade\n",
    "    49       order by 2 descending;\n",
    "    50       \n",
    "    51       quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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V/Zn9laB/Z4l877J9nh8rzMY37MY3Y4zjNa+p6pHpOjTajcwyssSB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\noooAKKKKACiiigAooooAKKKKAP/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  Image(filename='Anaconda3\\\\output\\\\groupby_grade_income_descend.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we want to create output displaying the statistics, N, mean, and standard deviation for income by binning the values for loan to debt ratio (loans['dti']) into the categories, 'low', 'medium', 'high'. \n",
    "\n",
    "Start by displaying the min and max values for the loans['dti'] column to determine 'bucket' sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "29.99\n"
     ]
    }
   ],
   "source": [
    "print(loans.dti.min())\n",
    "print(loans.dti.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the count of continous values in the column loans['dti']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42595"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.dti.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the pd.cut() method to bin continuous vales into discreet values, or categories.  Additional examples for pd.cut() are found in the section \"Binning Continuous Values\" in Chapter 12, located <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Binning-Continuous-Values\"> here</a>. pd.cut is analogous to user defined SAS formats. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = [0.0, 10.0, 20.0, 30.0]\n",
    "names=['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the new column loans['dti_cat'] in the loans DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans['dti_cat'] = pd.cut(loans['dti'], bins, labels=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect the number of values for both the value count for loans['dti'] and categorical values in the  loans['dti_cat'] column to be the same.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.dti.count() == loans.dti_cat.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the pd.cut() method sets the right= argument to True.  From the doc, \"Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4]\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans['dti_cat'] = pd.cut(loans['dti'], bins, right=False, labels=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.dti.count() == loans.dti_cat.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Functions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the function 'stats' which returns the desired statistics, mean, std (standard deviation), and count (N).  The type() method returns the object's type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def stats(grp):\n",
    "    return {'mean': grp.mean(), 'std': grp.std(), 'count':grp.count()}\n",
    "type(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Functions to Groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting these together, the 'income' column is grouped-by the loans['dti_cat'] column displaying the rows labeled, 'Low', 'Medium', and 'High'.  The .apply() attribute applies the 'stats' function to create the columns, 'count', 'mean', 'std'.  The .unstack() attribute places the output in a 'tall and skinny' format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>14,067.00</td>\n",
       "      <td>77,863.73</td>\n",
       "      <td>97,182.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>20,295.00</td>\n",
       "      <td>67,121.00</td>\n",
       "      <td>38,976.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>8,233.00</td>\n",
       "      <td>59,210.71</td>\n",
       "      <td>33,334.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count                 mean                  std\n",
       "dti_cat                                                               \n",
       "Low                14,067.00            77,863.73            97,182.50\n",
       "Medium             20,295.00            67,121.00            38,976.54\n",
       "High                8,233.00            59,210.71            33,334.58"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['income'].groupby(loans['dti_cat']).apply(stats).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program uses PROC SQL to find min and man for the 'dti' column, performs the aggregation funtions, and uses the CASE statement to define 'bins' for the new column 'dti_cat'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_groupby_dti_cat.sas                            */\n",
    "    /******************************************************/\n",
    "    29      proc sql;\n",
    "    30      select min(dti) as dit_min\n",
    "    31            ,max(dti) as dti_max\n",
    "    32      from df;\n",
    "    33      \n",
    "    34      select count(income) as count\n",
    "    35            ,mean(income) as mean\n",
    "    36            ,std(income) as std\n",
    "    37            ,\n",
    "    38      case\n",
    "    39         when dti < 10 then       'Low'\n",
    "    40         when 10 <= dti < 20 then 'Medium'\n",
    "    41         else                     'High'\n",
    "    42      end as dti_cat\n",
    "    43      from df\n",
    "    44      group by calculated dti_cat;\n",
    "    45      quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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segUV544tY5I45BCrykiNWwC5AyQB34BNaGjRomu2ZRFU7m5Ax/A1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\n7b/wGb/4uhbK6a6gkuLmF1hcuFSAqSdpXqWP970q9RRdhYKKKKQz/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\groupby_dti_cat_income.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For another example define the 'max_min' function for calculating a range and apply it income values grouped by loans['dti_cat'] levels nested inside loans['grade'] levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>dti_cat</th>\n",
       "      <th>Low</th>\n",
       "      <th>Medium</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1,896,700.00</td>\n",
       "      <td>377,000.00</td>\n",
       "      <td>234,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>3,898,000.00</td>\n",
       "      <td>483,400.00</td>\n",
       "      <td>241,912.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>5,996,000.00</td>\n",
       "      <td>335,200.00</td>\n",
       "      <td>1,245,200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1,195,200.00</td>\n",
       "      <td>524,000.00</td>\n",
       "      <td>519,200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>745,800.00</td>\n",
       "      <td>351,600.00</td>\n",
       "      <td>308,600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1,425,600.00</td>\n",
       "      <td>342,720.00</td>\n",
       "      <td>218,724.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>715,400.00</td>\n",
       "      <td>598,104.00</td>\n",
       "      <td>235,800.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dti_cat                  Low               Medium                 High\n",
       "grade                                                                 \n",
       "A               1,896,700.00           377,000.00           234,000.00\n",
       "B               3,898,000.00           483,400.00           241,912.00\n",
       "C               5,996,000.00           335,200.00         1,245,200.00\n",
       "D               1,195,200.00           524,000.00           519,200.00\n",
       "E                 745,800.00           351,600.00           308,600.00\n",
       "F               1,425,600.00           342,720.00           218,724.00\n",
       "G                 715,400.00           598,104.00           235,800.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " def max_min(x):\n",
    "        return x.max() - x.min()\n",
    "dti_grd_grp = loans.groupby(['grade', 'dti_cat'])\n",
    "dti_grd_grp.income.agg(max_min).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The range for the income values is large with extremes in both directions.  Rather than 'bucketing' these values into arbitrary sizes, another approach is to place values into deciles.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1896.0\n",
      "6000000.0\n"
     ]
    }
   ],
   "source": [
    "print(loans.income.min())\n",
    "print(loans.income.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the pd.cut() method is the pd.qcut() method for creating deciles which is documented <a href=\"http://pandas.pydata.org/pandas-docs/version/0.15.0/generated/pandas.qcut.html\"> here</a>.  The operation below creates the new column loans['inc_cat_dec'] for the 'loans' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loans['inc_cat_dec'] = pd.qcut(loans['income'], q=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the income deciles, return a count for each level in descending sorted order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1896, 30000]          5088\n",
       "(75600, 90000]         4436\n",
       "(50004, 59000]         4309\n",
       "(44500, 50004]         4306\n",
       "(37000, 44500]         4265\n",
       "(116690.4, 6000000]    4260\n",
       "(65600, 75600]         4253\n",
       "(59000, 65600]         4163\n",
       "(90000, 116690.4]      4082\n",
       "(30000, 37000]         3433\n",
       "Name: inc_cat_dec, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(loans['inc_cat_dec'].sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bin value ranges are a bit unwieldy.  An alternative is to map the bin value ranges into category codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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_dec, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['inc_cat_dec'] = pd.qcut(loans['income'].values, 10).codes\n",
    "pd.value_counts(loans['inc_cat_dec'].sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.income.count() == loans.inc_cat_dec.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With SAS, the traditional method for creating deciles is through PROC RANK as illustrated below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_default_deciles.sas                            */\n",
    "    /******************************************************/\n",
    "    98      proc rank data=df groups=10 out=r_df;\n",
    "    99           var income;\n",
    "    100           ranks r_income;\n",
    "    NOTE: Data set \"WORK.r_df\" has 42595 observation(s) and 23 variable(s)\n",
    "    101      \n",
    "    102      proc sql;\n",
    "    103         select count(r_income) label='Income Deciles' as count\n",
    "    104      from r_df\n",
    "    105      group by r_income\n",
    "    106      order by count descending;\n",
    "    107      quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the **default** results between pd.qcut() method and PROC RANK are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\default_income_deciles.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference are attributable to the method by which PROC RANK handles 'tied' values.  PROC RANK provides the TIES= option and when set to LOW, the results are the same as the pd.qcut() method.  You can read more about how PROC RANK treats tied values <a href=\"https://support.sas.com/documentation/cdl/en/proc/65145/HTML/default/viewer.htm#n1pe8iekjrnnfin1hdw34ik02tcw.htm#p1lkzb8a7dpbkxn1k6bspo5f9680\"> here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_deciles_ties_low.sas                           */\n",
    "    /******************************************************/\n",
    "57      proc rank data=df groups=10 ties=low out=r_df;\n",
    "58           var income;\n",
    "59           ranks r_income;\n",
    "NOTE: Data set \"WORK.r_df\" has 42595 observation(s) and 23 variable(s)\n",
    "60      \n",
    "61      proc sql;\n",
    "62         select count(r_income) label='Income Deciles' as count\n",
    "63      from r_df\n",
    "64      group by r_income\n",
    "65      order by count descending;\n",
    "66      quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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CB0+0TYLzFSf3jdtp/nWzWdYf8e8n/XxN/wCjWqo7iYnmah/z7W3/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\ngAooooAKKKKACiiigAooooAKKKKAP//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\deciles_ties_low.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the created column loans['inc_cat_dec'] for income deciles to display statistics provided by the 'stats' function created above to return count, mean, and standard deviation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc_cat_dec</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5,088.00</td>\n",
       "      <td>23,505.59</td>\n",
       "      <td>5,733.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3,433.00</td>\n",
       "      <td>34,240.73</td>\n",
       "      <td>1,863.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4,265.00</td>\n",
       "      <td>40,730.02</td>\n",
       "      <td>1,888.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4,306.00</td>\n",
       "      <td>47,674.11</td>\n",
       "      <td>1,948.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4,309.00</td>\n",
       "      <td>54,526.42</td>\n",
       "      <td>2,339.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4,163.00</td>\n",
       "      <td>62,090.00</td>\n",
       "      <td>2,108.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4,253.00</td>\n",
       "      <td>71,171.32</td>\n",
       "      <td>2,872.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4,436.00</td>\n",
       "      <td>83,137.04</td>\n",
       "      <td>4,291.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4,082.00</td>\n",
       "      <td>101,756.61</td>\n",
       "      <td>6,990.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4,260.00</td>\n",
       "      <td>175,721.94</td>\n",
       "      <td>152,667.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count                 mean                  std\n",
       "inc_cat_dec                                                               \n",
       "0                       5,088.00            23,505.59             5,733.08\n",
       "1                       3,433.00            34,240.73             1,863.40\n",
       "2                       4,265.00            40,730.02             1,888.30\n",
       "3                       4,306.00            47,674.11             1,948.03\n",
       "4                       4,309.00            54,526.42             2,339.30\n",
       "5                       4,163.00            62,090.00             2,108.14\n",
       "6                       4,253.00            71,171.32             2,872.85\n",
       "7                       4,436.00            83,137.04             4,291.91\n",
       "8                       4,082.00           101,756.61             6,990.66\n",
       "9                       4,260.00           175,721.94           152,667.92"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " loans['income'].groupby(loans['inc_cat_dec']).apply(stats).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas crosstabs is another method for accessing GroupBy processing using two factors, or categorical columns.  In the example below, the values= argument is monthly payments loans['income'] column using the aggregation function count.  Additional crosstab examples are found in the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Crosstabs\"> crosstabs</a> section of Chapter, 12--Additional Data Handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Income Deciles</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debt/Income Ratio</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>Low</th>\n",
       "      <td>1,882.00</td>\n",
       "      <td>963.00</td>\n",
       "      <td>1,210.00</td>\n",
       "      <td>1,281.00</td>\n",
       "      <td>1,181.00</td>\n",
       "      <td>1,266.00</td>\n",
       "      <td>1,313.00</td>\n",
       "      <td>1,476.00</td>\n",
       "      <td>1,493.00</td>\n",
       "      <td>2,002.00</td>\n",
       "      <td>14,067.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>2,179.00</td>\n",
       "      <td>1,602.00</td>\n",
       "      <td>2,034.00</td>\n",
       "      <td>2,058.00</td>\n",
       "      <td>2,165.00</td>\n",
       "      <td>2,074.00</td>\n",
       "      <td>2,129.00</td>\n",
       "      <td>2,217.00</td>\n",
       "      <td>1,988.00</td>\n",
       "      <td>1,849.00</td>\n",
       "      <td>20,295.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>1,027.00</td>\n",
       "      <td>868.00</td>\n",
       "      <td>1,021.00</td>\n",
       "      <td>967.00</td>\n",
       "      <td>963.00</td>\n",
       "      <td>823.00</td>\n",
       "      <td>811.00</td>\n",
       "      <td>743.00</td>\n",
       "      <td>601.00</td>\n",
       "      <td>409.00</td>\n",
       "      <td>8,233.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>5,088.00</td>\n",
       "      <td>3,433.00</td>\n",
       "      <td>4,265.00</td>\n",
       "      <td>4,306.00</td>\n",
       "      <td>4,309.00</td>\n",
       "      <td>4,163.00</td>\n",
       "      <td>4,253.00</td>\n",
       "      <td>4,436.00</td>\n",
       "      <td>4,082.00</td>\n",
       "      <td>4,260.00</td>\n",
       "      <td>42,595.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Income Deciles                       0                    1  \\\n",
       "Debt/Income Ratio                                             \n",
       "Low                           1,882.00               963.00   \n",
       "Medium                        2,179.00             1,602.00   \n",
       "High                          1,027.00               868.00   \n",
       "All                           5,088.00             3,433.00   \n",
       "\n",
       "Income Deciles                       2                    3  \\\n",
       "Debt/Income Ratio                                             \n",
       "Low                           1,210.00             1,281.00   \n",
       "Medium                        2,034.00             2,058.00   \n",
       "High                          1,021.00               967.00   \n",
       "All                           4,265.00             4,306.00   \n",
       "\n",
       "Income Deciles                       4                    5  \\\n",
       "Debt/Income Ratio                                             \n",
       "Low                           1,181.00             1,266.00   \n",
       "Medium                        2,165.00             2,074.00   \n",
       "High                            963.00               823.00   \n",
       "All                           4,309.00             4,163.00   \n",
       "\n",
       "Income Deciles                       6                    7  \\\n",
       "Debt/Income Ratio                                             \n",
       "Low                           1,313.00             1,476.00   \n",
       "Medium                        2,129.00             2,217.00   \n",
       "High                            811.00               743.00   \n",
       "All                           4,253.00             4,436.00   \n",
       "\n",
       "Income Deciles                       8                    9  \\\n",
       "Debt/Income Ratio                                             \n",
       "Low                           1,493.00             2,002.00   \n",
       "Medium                        1,988.00             1,849.00   \n",
       "High                            601.00               409.00   \n",
       "All                           4,082.00             4,260.00   \n",
       "\n",
       "Income Deciles                     All  \n",
       "Debt/Income Ratio                       \n",
       "Low                          14,067.00  \n",
       "Medium                       20,295.00  \n",
       "High                          8,233.00  \n",
       "All                          42,595.00  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.crosstab([loans.dti_cat], [loans.inc_cat_dec], \\\n",
    "             values=loans.income, aggfunc='count', margins=True, colnames=['Income Deciles'], rownames=['Debt/Income Ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program uses PROC FREQ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````    \n",
    "    /******************************************************/\n",
    "    /* c10_pd.crosstabs.sas                               */\n",
    "    /******************************************************/\n",
    "    5      proc rank data=df groups=10 ties=low out=r_df;\n",
    "    6          var income;\n",
    "    7           ranks r_income;\n",
    "    8\n",
    "    9      data tables;\n",
    "    10         set r_df (keep = r_income dti);\n",
    "    11      length dti_cat $ 6;\n",
    "    12      if dti < 10 then dti_cat = 'Low';\n",
    "    13          else if dti < 20 then dti_cat = 'Medium';\n",
    "    14         else dti_cat = 'High';\n",
    "    15      \n",
    "    16      proc freq data=tables order=formatted;\n",
    "    17      tables dti_cat * r_income /nocol nocum norow nopercent;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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D/wA9U/76FHnw/wDPVP8AvoUXQWZJRUfnw/8APVP++hR58P8Az1T/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KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC\niiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\pd_crosstab.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Transformations to Groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fairly common pattern is standardization and transformation of values.  In the cell below, the new column loans['iz_all'] is added to the 'loans' DataFrame.  It computes a zscore by subtracting the mean value for income from income and dividing by the standard deviation.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loans['iz_all'] = (loans.income - loans.income.mean()) / loans.income.std() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['iz_all'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we want to calculate zscores by deciles rather than the overall mean for income.  Start by creating the grouper for the loans['inc_cat'] column which is the deciles created with the pd.qcut() method above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grp_inc_cat = loans.groupby('inc_cat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the zcore function using lambda as an anonymous function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore = lambda x: (x - x.mean()) / x.std()\n",
    "type(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the 'grp_inc_cat' grouper created above, call the .transform() attribute to apply the zscore function and assign the results to a new DataFrame called 't_loans'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t_loans = grp_inc_cat.transform(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The zscore function is applied to all of the numeric columns in the 'loans' DataFrame, so extract the transformed t_loans['income'] column from the 't_loans' DataFrame and assign it as the column loans['iz_grp'] in the 'loans' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans['iz_grp'] = t_loans['income']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the transformed income values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id\n",
      "513542                  92.58\n",
      "519954                  59.80\n",
      "269818                  30.76\n",
      "611872                  28.58\n",
      "884755                  26.74\n",
      "502114                  21.40\n",
      "458760                  21.40\n",
      "453667                  20.18\n",
      "830027                  18.43\n",
      "603818                  17.65\n",
      "Name: iz_all, dtype: float64 id\n",
      "513542                  38.15\n",
      "519954                  24.39\n",
      "269818                  12.21\n",
      "611872                  11.29\n",
      "884755                  10.52\n",
      "502114                   8.28\n",
      "458760                   8.28\n",
      "453667                   7.77\n",
      "830027                   7.04\n",
      "468400                   6.71\n",
      "Name: iz_grp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(loans['iz_all'].sort_values(ascending=False).head(10), \n",
    "      loans['iz_grp'].sort_values(ascending=False).head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We would like to display the transformed income values side-by-side.  Create the new DataFrame 'prt' by extracting the loans['iz_all'] column (income zscores computed with column mean) and the loans['iz_grp'] (income zscores computed with the group mean). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prt = loans[['iz_all', 'iz_grp']]\n",
    "type(prt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide descriptive column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prt.columns = ['zscore w/ overall mean','zscore with group mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the transformed income values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>zscore w/ overall mean</th>\n",
       "      <th>zscore with group mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>513542</th>\n",
       "      <td>92.58</td>\n",
       "      <td>38.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519954</th>\n",
       "      <td>59.80</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269818</th>\n",
       "      <td>30.76</td>\n",
       "      <td>12.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611872</th>\n",
       "      <td>28.58</td>\n",
       "      <td>11.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884755</th>\n",
       "      <td>26.74</td>\n",
       "      <td>10.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502114</th>\n",
       "      <td>21.40</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458760</th>\n",
       "      <td>21.40</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453667</th>\n",
       "      <td>20.18</td>\n",
       "      <td>7.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830027</th>\n",
       "      <td>18.43</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468400</th>\n",
       "      <td>17.65</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        zscore w/ overall mean  zscore with group mean\n",
       "id                                                    \n",
       "513542                   92.58                   38.15\n",
       "519954                   59.80                   24.39\n",
       "269818                   30.76                   12.21\n",
       "611872                   28.58                   11.29\n",
       "884755                   26.74                   10.52\n",
       "502114                   21.40                    8.28\n",
       "458760                   21.40                    8.28\n",
       "453667                   20.18                    7.77\n",
       "830027                   18.43                    7.04\n",
       "468400                   17.65                    6.71"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prt.sort_values('zscore w/ overall mean', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analog SAS program combines the creating of income deciles using PROC RANK and PROC SQL to calculate income zscores based on overall mean and income decile group mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c10_zscore_all_bygroup.sas                         */\n",
    "    /******************************************************/\n",
    "    48      proc rank data=df groups=10 ties=low out=r_df;\n",
    "    49           var income;\n",
    "    50           ranks r_income;\n",
    "    51      \n",
    "    52      proc sql ;\n",
    "    53      create table all_mean as\n",
    "    54      select id\n",
    "    55            ,(income - mean) / std as iz_all format=6.2\n",
    "    56         from\n",
    "    57            (select id\n",
    "    58                    ,income\n",
    "    59                    ,mean(income) as mean\n",
    "    60                    ,std(income) as std\n",
    "    61         from r_df)\n",
    "    62      order by iz_all desc;\n",
    "    63      \n",
    "    64      create table grp_mean as\n",
    "    65      select id\n",
    "    66            ,(income - mean) / std as iz_grp format=6.2\n",
    "    67         from\n",
    "    68            (select id\n",
    "    69                    ,income\n",
    "    70                    ,mean(income) as mean\n",
    "    71                    ,std(income) as std\n",
    "    72             from r_df\n",
    "    73             group by r_income)\n",
    "    74      order by iz_grp descending;\n",
    "    75      \n",
    "    76      create table all(drop=old_id) as\n",
    "    77      select coalesce(all_mean.old_id, grp_mean.old_id) as id\n",
    "    78            ,*\n",
    "    79         from all_mean (rename=(id=old_id))\n",
    "    80      full join grp_mean (rename=(id=old_id))\n",
    "    81          on all_mean.old_id = grp_mean.old_id\n",
    "    82      order by iz_all desc;\n",
    "    83      \n",
    "    84      select *\n",
    "    85         from all(obs=10);\n",
    "    86      quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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j1q0tdHi14Wq2sk08mjQWrMW3KFBa5GzpuO1fmORxjNUmJnUeZqH/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\nKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\income_zcore_table.JPG') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    " ## Top/Bottom N processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Start by defining a function with 3 argument values.  First one is positional (df) followed by two named arguments (n=, and sort_col=).  The function returns a Series of n= values in descending sort order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def topn(df, n=3, sort_col='income'):\n",
    "    return pd.Series(df[sort_col]).sort_values(ascending=False).head(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the 'topn' function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "793919                  29.99\n",
       "234391                  29.96\n",
       "289144                  29.95\n",
       "754868                  29.95\n",
       "831945                  29.93\n",
       "Name: dti, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topn(loans, sort_col='dti', n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function can be applied to levels of a GroupBy object using the .apply() attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade  id    \n",
       "A      611872           1,900,000.00\n",
       "       458760           1,440,000.00\n",
       "       453667           1,362,000.00\n",
       "B      519954           3,900,000.00\n",
       "       624215             984,000.00\n",
       "       643926             948,000.00\n",
       "C      513542           6,000,000.00\n",
       "       269818           2,039,784.00\n",
       "       884755           1,782,000.00\n",
       "D      603818           1,200,000.00\n",
       "       514680             840,000.00\n",
       "       752994             648,000.00\n",
       "E      792270             750,000.00\n",
       "       565565             700,053.85\n",
       "       508436             660,000.00\n",
       "F      502114           1,440,000.00\n",
       "       473872             600,000.00\n",
       "       843071             350,000.00\n",
       "G      989796             725,000.00\n",
       "       391263             600,000.00\n",
       "       115363             500,000.00\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('grade').apply(topn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, we did not need to create this function since the .nlargest() and .nsmallest() attributes performs the same operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade  id    \n",
       "A      611872           1,900,000.00\n",
       "       458760           1,440,000.00\n",
       "       453667           1,362,000.00\n",
       "B      519954           3,900,000.00\n",
       "       624215             984,000.00\n",
       "       643926             948,000.00\n",
       "C      513542           6,000,000.00\n",
       "       269818           2,039,784.00\n",
       "       884755           1,782,000.00\n",
       "D      603818           1,200,000.00\n",
       "       514680             840,000.00\n",
       "       752994             648,000.00\n",
       "E      792270             750,000.00\n",
       "       565565             700,053.85\n",
       "       508436             660,000.00\n",
       "F      502114           1,440,000.00\n",
       "       473872             600,000.00\n",
       "       843071             350,000.00\n",
       "G      989796             725,000.00\n",
       "       391263             600,000.00\n",
       "       115363             500,000.00\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd['income'].nlargest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the 3 smallest income values for each debt-to-income loans['dti_cat'] column levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade  id     \n",
       "A      288342                3,300.00\n",
       "       228954                3,500.00\n",
       "       398765                5,500.00\n",
       "B      139940                2,000.00\n",
       "       267670                3,600.00\n",
       "       524201                4,080.00\n",
       "C      434740                4,000.00\n",
       "       99987                 4,000.00\n",
       "       503299                4,200.00\n",
       "D      91126                 4,000.00\n",
       "       565967                4,800.00\n",
       "       521396                6,000.00\n",
       "E      367694                4,200.00\n",
       "       403941                4,800.00\n",
       "       388623                5,843.00\n",
       "F      119948                7,280.00\n",
       "       1000862               9,960.00\n",
       "       708331               12,000.00\n",
       "G      123688                1,896.00\n",
       "       108473                9,600.00\n",
       "       387462               10,000.00\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_grd['income'].nsmallest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "The GroupBy: split-apply-combine for panda is located <a href=\"http://pandas.pydata.org/pandas-docs/stable/groupby.html\"> here</a>.\n",
    "\n",
    "Apply Operations to Groups in Pandas, by Chris Albon, located <a href=\"http://chrisalbon.com/python/pandas_apply_operations_to_groups.html\"> here.</a>\n",
    "\n",
    "GroupBy-fu: improvements in grouping and aggregating data in pandas, by Wes McKinney, located <a href=\"http://wesmckinney.com/blog/groupby-fu-improvements-in-grouping-and-aggregating-data-in-pandas/\"> here</a>.\n",
    "\n",
    "MERGING vs. JOINING: Comparing the DATA Step with SQL, by Malachy J. Foley, University of North Carolina at Chapel Hill, located <a href=\"http://www2.sas.com/proceedings/sugi30/249-30.pdf\"> here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Navigation\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/tree/master/\">Return to Chapter List</a>"
   ]
  }
 ],
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