{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 10 -- Groupby" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Topics Covered:\n", "\n", "Setting Display Options\n", "\n", "Read 'pickled' DataFrame\n", "\n", "Create GroupBy Object\n", "\n", "GroupBy with Aggregations\n", "\n", "Understanding Binning\n", "\n", "Defining Functions\n", "\n", "Applying Functions to Groups\n", "\n", "Applying Transformations to Groups\n", "\n", "Top/Bottom N processing\n", "\n", "Resources" ] }, { "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 Chapter 12, Additional Data Handling. The pd.read_pickle() method is documented here. " ] }, { "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": [ "\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 here." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "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": [ "
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meanstdcount
A66,711.8854,049.4810202
B67,918.6960,705.9412408
C68,199.9686,568.698747
D68,277.0249,031.336025
E75,889.1655,312.083401
F83,095.5363,771.871300
G93,055.8273,522.11512
\n", "
" ], "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": { "image/jpeg": 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8GeKtO8J2LWukl5LLXl1C10V9RVzbwAEeWJm+Xrz34OeTmrGoaJ47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\nuzQtbef/AAP81+oPRNvp/X6HZfa5v+gdc/8AfUf/AMXR9rm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"text/plain": [ "" ] }, "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 here ." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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incomedti
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
A10,202.0066,711.8854,049.483,300.0040,000.0057,632.0080,000.001,900,000.0010,202.0012.047.010.006.4611.5017.1029.99
B12,408.0067,918.6960,705.942,000.0040,000.0057,996.0080,497.003,900,000.0012,408.0013.396.660.008.3113.5118.6229.95
C8,747.0068,199.9686,568.694,000.0040,000.0056,000.0080,000.006,000,000.008,747.0013.856.500.008.9714.0518.9929.78
D6,025.0068,277.0249,031.334,000.0040,000.0058,000.0082,000.001,200,000.006,025.0013.986.430.009.1614.3119.2529.63
E3,401.0075,889.1655,312.084,200.0045,000.0062,000.0090,000.00750,000.003,401.0014.186.530.009.3614.6219.6029.70
F1,300.0083,095.5363,771.877,280.0050,000.0070,679.00100,000.001,440,000.001,300.0014.656.550.009.8115.1619.7629.95
G512.0093,055.8273,522.111,896.0052,000.0075,000.00110,000.00725,000.00512.0015.697.290.009.8915.9621.3329.96
\n", "
" ], "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": { 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C2X/XvH/6CKraRpsOj6LZabarthtIEhQeyqB/SrOk/wDIFsv+veP/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\n", "text/plain": [ "" ] }, "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": [ "" ] }, "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 Sort and Sort Sequences 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 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V/Zn9laB/Z4l877J9nh8rzMY37MY3Y4zjNa+p6pHpOjTajcwyssSB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\noooAKKKKACiiigAooooAKKKKAP/Z\n", "text/plain": [ "" ] }, "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 here. 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": [ "
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countmeanstd
dti_cat
Low14,067.0077,863.7397,182.50
Medium20,295.0067,121.0038,976.54
High8,233.0059,210.7133,334.58
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" ], "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": [ { "data": { "image/jpeg": 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segUV544tY5I45BCrykiNWwC5AyQB34BNaGjRomu2ZRFU7m5Ax/A1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\nFf3caHdxnqWAXJAHcd+Krx+O7KO2u5d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"text/plain": [ "" ] }, "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": [ "
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dti_catLowMediumHigh
A1,896,700.00377,000.00234,000.00
B3,898,000.00483,400.00241,912.00
C5,996,000.00335,200.001,245,200.00
D1,195,200.00524,000.00519,200.00
E745,800.00351,600.00308,600.00
F1,425,600.00342,720.00218,724.00
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" ], "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 here. 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": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "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 here." ] }, { "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": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "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": [ "
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countmeanstd
inc_cat_dec
05,088.0023,505.595,733.08
13,433.0034,240.731,863.40
24,265.0040,730.021,888.30
34,306.0047,674.111,948.03
44,309.0054,526.422,339.30
54,163.0062,090.002,108.14
64,253.0071,171.322,872.85
74,436.0083,137.044,291.91
84,082.00101,756.616,990.66
94,260.00175,721.94152,667.92
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" ], "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 crosstabs section of Chapter, 12--Additional Data Handling." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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Income Deciles0123456789All
Debt/Income Ratio
Low1,882.00963.001,210.001,281.001,181.001,266.001,313.001,476.001,493.002,002.0014,067.00
Medium2,179.001,602.002,034.002,058.002,165.002,074.002,129.002,217.001,988.001,849.0020,295.00
High1,027.00868.001,021.00967.00963.00823.00811.00743.00601.00409.008,233.00
All5,088.003,433.004,265.004,306.004,309.004,163.004,253.004,436.004,082.004,260.0042,595.00
\n", "
" ], "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": [ { "data": { "image/jpeg": 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D/wA9U/76FHnw/wDPVP8AvoUXQWZJRUfnw/8APVP++hR58P8Az1T/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"text/plain": [ "" ] }, "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": [ "
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zscore w/ overall meanzscore with group mean
id
51354292.5838.15
51995459.8024.39
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" ], "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": [ { "data": { "image/jpeg": 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j1q0tdHi14Wq2sk08mjQWrMW3KFBa5GzpuO1fmORxjNUmJnUeZqH/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\n5wG4r0P7XN/0Drn/AL6j/wDi6Ptc3/QOuf8AvqP/AOLo7+d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"text/plain": [ "" ] }, "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 here.\n", "\n", "Apply Operations to Groups in Pandas, by Chris Albon, located here.\n", "\n", "GroupBy-fu: improvements in grouping and aggregating data in pandas, by Wes McKinney, located here.\n", "\n", "MERGING vs. JOINING: Comparing the DATA Step with SQL, by Malachy J. Foley, University of North Carolina at Chapel Hill, located here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Navigation\n", "\n", "Return to Chapter List" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }