{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 12 -- Additional Data Handling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Topics Covered:\n", "\n", "Sort and Sort Sequences\n", "\n", "Drop/Keep Columns \n", "\n", "Rename Columns\n", "\n", "Find Duplicate Values\n", "\n", "Extract Duplicate Rows to a new DataFrame\n", "\n", "Drop Duplicate Rows\n", "\n", "Add a New DataFrame Column\n", "\n", "Cast Strings to Float\n", "\n", "Concatenating DataFrames (Join)\n", "\n", "Crosstabs\n", "\n", "Sampling\n", "\n", "Binning Continuous Values\n", "\n", "Save to Disk\n", "\n", "Resources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pandas import Series, DataFrame, Index\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sort and Sort Sequences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example below is used in Time Series Walk-Through in Chapter 09 -- Panda Time Series and Date Handling. It uses the read_csv() method to construct the 'df_us' DataFrame begining its read on row 3,082." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_states = pd.read_csv(\"C:\\Data\\\\HPI_master.csv\", \n", " skiprows=3082,\n", " usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8),\n", " names=('hpi_type', 'hpi_flavor', 'frequency', 'level', 'place_name', 'place_id', 'yr', 'period', 'index_nsa'),\n", " header=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following SAS Data Step reads the same .csv file using FIRSTOBS= to begin reading from an arbitary row position." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /********************************/\n", " /* c09_read()_csv_df_states.sas */\n", " /********************************/\n", " data df_states;\n", " infile 'C:\\Data\\HPI_master.csv' delimiter=',' missover dsd firstobs=3081; \n", " informat hpi_type $12.\n", " hpi_flavor$16.\n", " frequency $9.\n", " level$28.\n", " place_name $33.\n", " place_id$8.\n", " yr $5.\n", " period$6.\n", " index_nsa 8.;\n", " input hpi_type $\n", " hpi_flavor$\n", " frequency $\n", " level$\n", " place_name $\n", " place_id$\n", " yr $\n", " period$\n", " index_nsa ;\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify the read using the .shape attribute." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(96244, 9)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_states.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the first 5 rows of the 'df_states' DataFrame." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " hpi_type hpi_flavor frequency level \\\n", "42692 traditional all-transactions quarterly MSA \n", "42856 traditional all-transactions quarterly MSA \n", "48737 traditional all-transactions quarterly MSA \n", "42693 traditional all-transactions quarterly MSA \n", "\n", " place_name place_id yr \\\n", "42692 San Francisco-Redwood City-South San Francisco... 41884 1975 \n", "42856 San Jose-Sunnyvale-Santa Clara, CA 41940 1975 \n", "48737 Honolulu ('Urban Honolulu'), HI 46520 1981 \n", "42693 San Francisco-Redwood City-South San Francisco... 41884 1975 \n", "\n", " period index_nsa \n", "42692 3 18.31 \n", "42856 4 18.82 \n", "48737 4 18.91 \n", "42693 4 19.26 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "default_srt.iloc[0:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By examing the first the first four rows of the sorted DataFrame, 'default_srt' we see the default sort sequence is ascending. Of course, by reading the doc for pandas.DataFrame.sort_values we could 'see' this as well. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default SAS sort syntax is:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_print_first4_rows_sorted.sas */\n", " /******************************************************/\n", "84 proc sort data=df_states;\n", "85 by index_nsa;\n", "NOTE: 96244 observations were read from \"WORK.df_states\"\n", "NOTE: Data set \"WORK.df_states\" has 96244 observation(s) and 9 variable(s)\n", "86 \n", "87 proc print data=df_states (obs=4);\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like panda the default SAS sort sequence is ascending. This is confirmed by the SAS doc located here ." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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+PhP9xv5ipKjP/Hwn+438xV7RS/rci95NklYHjv8A5J7r/wD2D5//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DHFfm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\nN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"text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Anaconda3\\\\output\\\\default_first_four.JPG') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In contrast to the panda default sort the default SAS sort sequence places its missing values first in the data set as illustrated above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Displaying the last 4 rows in the 'default_srt' DataFrame finds the 2 NaN's sorted last since this is the default location panda places its missing values." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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hpi_typehpi_flavorfrequencylevelplace_nameplace_idyrperiodindex_nsa
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" ], "text/plain": [ " hpi_type hpi_flavor frequency level place_name \\\n", "54262 traditional all-transactions quarterly State District of Columbia \n", "54263 traditional all-transactions quarterly State District of Columbia \n", "60576 traditional all-transactions quarterly State Vermont \n", "61098 traditional all-transactions quarterly State West Virginia \n", "\n", " place_id yr period index_nsa \n", "54262 DC 2016 1 780.98 \n", "54263 DC 2016 2 791.71 \n", "60576 VT 1976 1 NaN \n", "61098 WV 1982 1 NaN " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "default_srt.tail(4) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "panda's default sort sequence places NaN's last in the sort sequence by default and can be used as an alternative to boolean operators and the .loc() method to detect missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SAS program to access the last four observations in the data set is:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_print_last4_rows_sorted.sas */\n", " /******************************************************/\n", " 79 data last4; \n", " 80 set df_states (firstobs=96241);\n", " 81 by index_nsa;\n", "\n", " NOTE: 4 observations were read from \"WORK.last4\"\n", " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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YM/8Ax7yf7h/lWL4o8XWXhOOza+try5a9mMEMdnD5js+0kDbkE5xg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\nS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"text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Anaconda3\\\\output\\\\sort_last_four.JPG') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alter the organization of the 'states_desc' DataFrame by supplying arguments and values to the sort_values() attribute. The example below sorts descending placeing missing values at the beginning of the DataFrame." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "states_desc = df_states.sort_values('index_nsa', ascending=False, na_position='first')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the na_position= argument to 'first' places NaN's at the beginning of the sort sequence. This is the same beavhior for SAS' sort sequence. Details describing SAS sort order for missing values is descried here .\n", "\n", "The first two rows in the DataFrame 'states_desc' contain the NaN's values for the 'index_nsa' column, while the next 2 rows contain the highest values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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hpi_typehpi_flavorfrequencylevelplace_nameplace_idyrperiodindex_nsa
\n", "
" ], "text/plain": [ " hpi_type hpi_flavor frequency level place_name \\\n", "60576 traditional all-transactions quarterly State Vermont \n", "61098 traditional all-transactions quarterly State West Virginia \n", "54263 traditional all-transactions quarterly State District of Columbia \n", "54262 traditional all-transactions quarterly State District of Columbia \n", "\n", " place_id yr period index_nsa \n", "60576 VT 1976 1 NaN \n", "61098 WV 1982 1 NaN \n", "54263 DC 2016 2 791.71 \n", "54262 DC 2016 1 780.98 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "states_desc.iloc[0:4,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SAS has missing 28 missing value indicators for numerics described here . However, if you want missing values to be 'first' in a data set using an ascending sort sequence, then the missing value indicator must be an actual numeric value larger than the largest non-missing value. In other words, by default, the SAS missing value indicator for numerics is always the smallest numeric value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SAS example below sorts the df_states data set in descending order by the 'index_nsa' variable. Like the panda example above, the \n", "\n", " out=states_srt2\n", " \n", "syntax creates the new SAS data set 'states_srt2'. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_print_last4_rows_sorted_descending.sas */\n", " /*****************************************************/\n", " 44 proc sort data=df_states\n", " 45 out=states_srt2;\n", " 46 by descending index_nsa;\n", " NOTE: 96244 observations were read from \"WORK.df_states\"\n", " NOTE: Data set \"WORK.states_srt2\" has 96244 observation(s) and 9 variable(s)\n", " 47 \n", " 48 data first4;\n", " 49 set states_srt2 (obs=4);\n", " 50 by descending index_nsa;\n", "" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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495P9w/ypujFISqSub3/CUTf8+Cf+BB/+Jo/4Sib/AJ8E/wDAg/8A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\nD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"text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Anaconda3\\\\output\\\\sort_last4_descending.JPG') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The section below examines a number of common data management patterns for analysis. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Read the Lending Club Loan Status file." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(42633, 51)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\",\n", " low_memory=False) \n", "lc.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check for missing values. Clearly, some columns are not useful." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ID 2\n", "Member_ID 3\n", "Loan_Amnt 3\n", "Term 3\n", "Int_Rate 3\n", "Installment 3\n", "Grade 3\n", "Sub_Grade 3\n", "Emp_Length 3\n", "Home_Ownership 3\n", "Annual_Inc 7\n", "Verification_Status 3\n", "Loan_Status 3\n", "Purpose 3\n", "Zip_Code 3\n", "Addr_State 3\n", "DTI 3\n", "Delinq_2yrs 32\n", "Earliest_Cr_Line 32\n", "Inq_Last_6mths 32\n", "Mths_Since_Last_Delinq 26998\n", "Mths_Since_Last_Record 38978\n", "Open_Acc 32\n", "Pub_Rec 32\n", "Revol_Bal 3\n", "Revol_Util 93\n", "Total_Acc 32\n", "Initial_List_Status 3\n", "Out_Prncp 3\n", "Out_Prncp_Inv 3\n", "Total_Pymnt 3\n", "Total_Pymnt_Inv 3\n", "Total_Rec_Prncp 3\n", "Total_Rec_Int 3\n", "Total_Rec_Late_Fee 3\n", "Recoveries 3\n", "Collection_Recovery_Fee 3\n", "Last_Pymnt_D 85\n", "Last_Pymnt_Amnt 3\n", "Next_Pymnt_D 39412\n", "Last_Credit_Pull_D 7\n", "Collections_12_Mths_Ex_Med 148\n", "Mths_Since_Last_Major_Derog 42633\n", "Policy_Code 3\n", "Application_Type 3\n", "Annual_Inc_Joint 42633\n", "Acc_Now_Delinq 32\n", "Chargeoff_Within_12_Mths 148\n", "Delinq_Amnt 32\n", "Pub_Rec_Bankruptcies 1368\n", "Tax_Liens 108\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drop/Keep Columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keep some of the columns in the 'lc' DataFrame." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc = lc[['ID', 'Member_ID', 'Loan_Amnt', 'Term', 'Int_Rate', 'Installment', 'Grade', 'Sub_Grade', 'Emp_Length',\n", " 'Home_Ownership', 'Annual_Inc', 'Verification_Status', 'Loan_Status', 'Purpose', 'Zip_Code',\n", " 'Addr_State', 'DTI', 'Delinq_2yrs', 'Earliest_Cr_Line', 'Inq_Last_6mths', 'Open_Acc', 'Revol_Bal', \n", " 'Revol_Util']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rename Columns" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lc = lc.rename(columns = {\n", " 'Member_ID' :'mem_id',\n", " 'Loan_Amtn' :'ln_amt',\n", " 'Int_Rate' :'rate',\n", " 'Home_Ownership' :'own_rnt',\n", " 'Verification_Status' :'vrfy_stat',\n", " 'Loan_Status' :'ln_stat',\n", " 'Addr_State' :'state',\n", " 'Earliest_Cr_Line' :'earliest_ln'\n", " })" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lower-case all the 'lc' DataFrame column names." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc.columns = map(str.lower, lc.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An investigation of the LC_Loan_Stats.csv file reveals it has 2 parts. The first part, rows 0 to 39786 contain data for those loans meeting their credit policies. Rows 39790 to the end contain data for those loans that are outside their normal lending policies. \n", "\n", "Create the 'lc1' DataFrame by reading a sub-set of columns and rows. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(39786, 21)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1 = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\", \n", " low_memory=False, \n", " usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 22, 24, 25),\n", " names=('id','mem_id', 'ln_amt', 'term','rate', 'm_pay', 'grade', 'sub_grd', 'emp_len',\n", " 'own_rnt', 'income', 'ln_stat', 'purpose', 'state', 'dti', 'delinq_2yrs', 'ln_fst',\n", " 'inq_6mnth', 'open_acc', 'revol_bal', 'revol_util' ),\n", " skiprows=1,\n", " nrows=39786,\n", " header=None)\n", "lc1.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analog SAS program for reading sub-sets of rows and columns from the 'LC_Loan_Stats.csv' file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_read_1st_half_loan_cvs.sas */\n", " /******************************************************/\n", " 4 options obs=39787;\n", " 5 proc import datafile=\"c:\\data\\LC_Loan_Stats.csv\"\n", " 6 dbms=csv\n", " 7 out=loans\n", " 8 replace;\n", " 9\n", " 10 data lc1(rename=(Member_ID = mem_id\n", " 12 Loan_Amnt = ln_amt\n", " 13 Int_Rate = rate\n", " 14 Installment = m_pay\n", " 15 Sub_Grade = sub_grd\n", " 16 Emp_Length = emp_len\n", " 17 Home_Ownership = own_rnt\n", " 18 Annual_Inc = income\n", " 19 Loan_Status = ln_stat\n", " 20 Addr_State = state\n", " 21 Earliest_Cr_Line = ln_fst\n", " 22 Inq_Last_6mths = inq_6mnth));\n", " 23 \n", " 24 set loans(keep = ID\n", " 25 Member_ID\n", " 26 Loan_Amnt\n", " 27 Term\n", " 28 Int_Rate\n", " 29 Installment\n", " 30 Grade\n", " 31 Sub_Grade\n", " 32 Emp_Length\n", " 33 Home_Ownership\n", " 34 Annual_Inc\n", " 35 Loan_Status\n", " 36 Purpose\n", " 37 Addr_State\n", " 38 DTI\n", " 39 Delinq_2yrs\n", " 40 Earliest_Cr_Line\n", " 41 Inq_Last_6mths\n", " 42 Open_Acc\n", " 43 Revol_Bal\n", " 44 Revol_Util);\n", " 45\n", " 46 ln_plcy = 'True';\n", "\n", " NOTE: 39786 observations were read from \"WORK.loans\"\n", " NOTE: Data set \"WORK.lc1\" has 39786 observation(s) and 22 variable(s)\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find Duplicate Values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the 'lc1' DataFrame we expect to find no duplicate id values. Setting the .duplicated attribute to False returns all of the duplicate values, in our case for the lc1['id'] column. The .duplicated() attribute for DataFrames is documented here.\n", "\n", "False marks all duplicate values as True. Create the new 'lc1_dups' DataFrame containing the duplicates for the lc1['id'] column. If its length is zero then there are no duplicates. Locating the duplicate rows is not the same as removing then which is illustrated below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Begin by setting the index to the lc1['id'] column. Next, \n", "create a Boolean mask locating the duplicate values for the lc1['id'] column. The keep='first' argument behaves similiar to SAS FIRST.variable in BY-Group processing. We want to ignore the first occurance of the **duplicate** 'index' column value and extract the remaining ones." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc1.set_index('id', inplace=True, drop=False)\n", "dup_mask = lc1.duplicated('id', keep='first')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract Duplicate Values" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Apply the Boolean mask using the .loc attribute to create the new 'lc1_dups' DataFrame. The .shape attribute shows there are 35 rows with duplicate 'id' values." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(35, 21)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1_dups = lc1.loc[dup_mask]\n", "lc1_dups.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SAS SORT option NODUPKEY checks for and eliminates observations with duplicate BY values. The DUPOUT= option names the target SAS data set to write the duplicates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_sort_nodupkey.sas */\n", " /******************************************************/\n", " 50 proc sort data = lc1 dupout=lc1_dups nodupkey;\n", " 51 by id;\n", " NOTE: 39786 observations were read from \"WORK.lc1\"\n", " NOTE: 35 observations were found and deleted due to having duplicate sort keys\n", " NOTE: Data set \"WORK.lc1\" has 39751 observation(s) and 21 variable(s)\n", " NOTE: Data set \"WORK.lc1_dups\" has 35 observation(s) and 21 variable(s)\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drop Duplicate Rows" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(39786, 21)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the .drop_duplicates attribute to drop duplicate values. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc1.drop_duplicates(['id'], keep='first', inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(39751, 21)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1.shape " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add a New DataFrame Column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the new column lc1['ln_plcy'] as a flag to indicate all rows read from row 2 to 39786 are loans granted within standard lending guidelines." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(39751, 22)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1['ln_plcy'] = lc1['id'].map(lambda x: True)\n", "lc1.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cast Strings to Floats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " term rate grade sub_grd emp_len own_rnt ln_stat \\\n", "count 39751 39751 39751 39751 39751 39751 39751 \n", "unique 2 371 7 35 12 5 7 \n", "top 36 months 10.99% B B3 10+ years RENT Fully Paid \n", "freq 29070 958 12021 2918 8891 18893 33639 \n", "\n", " purpose state ln_fst revol_util \n", "count 39751 39751 39751 39701 \n", "unique 14 50 529 1092 \n", "top debt_consolidation CA Nov-98 0% \n", "freq 18661 7095 371 980 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1.describe(include=['O'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both the lc1['rate'] and lc1['revol_util'] column values are formatted with Excel's percent format (%), making them string values. The .replace() method replaces the '%' with a white-space. The .astype attribute converts the string into a float. The resulting float is divided by 100." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc1['revol_util'] = lc1.revol_util.replace('%','',regex=True).astype('float')/100\n", "lc1['rate'] = lc1.rate.replace('%','',regex=True).astype('float')/100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the lc1['revol_util'] column values cast to floats use the .fillna() attribute to replace missing values with the calculated mean value from the lc1['revol_util'] column." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc1[\"revol_util\"] = lc1[[\"revol_util\"]].fillna(lc1.revol_util.mean())\n", "lc1.revol_util.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analog SAS program uses PROC SQL to insert the mean value for 'revol_util' into the SAS Macro variable 'mean_revol'. A WHERE statement is used to locate the NULL values for the variable 'revol_util' and if found then the UPDATE statement inserts the value from the SAS MACRO variable &mean_revol into the 'revol_util' column.\n", "\n", "The CREATE TABLE statement starting at line 13 tests if there are missing values found for the column 'revol_util'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_update_missing_revol_until.sas */\n", " /******************************************************/\n", " 6 proc sql;\n", " 7 select mean(revol_util) format 6.2 into :mean_revol\n", " 8 from lc1;\n", " 9 update lc1\n", " 10 set revol_util = &mean_revol\n", " 11 where revol_util is null;\n", " NOTE: 50 record(s) updated in table WORK.lc1\n", " 12 \n", " 13 create table miss as\n", " 14 select revol_util\n", " 15 from lc1\n", " 16 where revol_util is null;\n", " NOTE: Data set \"WORK.miss\" has 0 observation(s) and 1 variable(s)\n", " 17 quit;\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " #### 2. Read the second portion of the LC_Loan_Stats.csv file beginning with row 39790." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the 'lc0' DataFrame by reading the loan status file, supplying a tuple of integers for the usecols= argument and a tuple of names for the column labels." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2844, 21)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc0 = pd.read_csv(\"C:\\\\Data\\\\LC_Loan_Stats.csv\", \n", " low_memory=False, \n", " usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 19, 22, 24, 25),\n", " names=('id','mem_id', 'ln_amt', 'term','rate', 'm_pay', 'grade', 'sub_grd', 'emp_len',\n", " 'own_rnt', 'income', 'ln_stat', 'purpose', 'state', 'dti', 'delinq_2yrs', 'ln_fst',\n", " 'inq_6mnth', 'open_acc', 'revol_bal', 'revol_util' ),\n", " skiprows=39790,\n", " header=None)\n", "lc0.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the column lc0['ln_plcy'] with a value of False to indicate rows 39790 to the end of the file are loans made outside the standard credit lending policy. The assignment below uses the .map() attribute and calls the anonymous lambda function to create the new column lc0[''ln_plcy']." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lc0['ln_plcy'] = lc0['id'].map(lambda x: False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similiar to above, the lc0['rate'] and lc0['revol_util'] column values are formatted with Excel's percent format (%), making them string values. The .replace() method replaces the '%' with a white-space. The .astype() attribute converts the string into a float. The resulting float is divided by 100." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lc0['revol_util'] = lc0.revol_util.replace('%','',regex=True).astype('float')/100\n", "lc0['rate'] = lc0.rate.replace('%','',regex=True).astype('float')/100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the mean value for 'revol_util' and use the .fillna method to replace the missing values." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lc0['revol_util'] = lc0[[\"revol_util\"]].fillna(lc0.revol_util.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Return the number of missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "lc0.revol_util.isnull().sum() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concatenating DataFrames (Join)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ " Chapter 7 -- Pandas, Part 2 covers joining operations by illustrating the pd.merge() method which follows SQL's relational algebraic statements. An alternative is the concat function described here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2844, 22)\n", "(39751, 22)\n" ] } ], "source": [ "print(lc0.shape)\n", "print(lc1.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the pd.concat() method to join the 'lc1' and 'lc0' DataFrames. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(42595, 22)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frames = [lc1, lc0]\n", "df = pd.concat(frames)\n", "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analog SAS program uses the SET statement to join the data sets 'lc0' and 'lc1' together. The variable names are identical in both data sets. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c12_concatenate_lc0_lc1.sas */\n", " /******************************************************/\n", " 7 data df;\n", " 8 set lc0\n", " 9 lc1;\n", " 10 run;\n", "\n", " NOTE: 2844 observations were read from \"WORK.lc0\"\n", " NOTE: 39751 observations were read from \"WORK.lc1\"\n", " NOTE: Data set \"WORK.df\" has 42595 observation(s) and 22 variable(s)\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Crosstabs" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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own_rntMORTGAGENONEOTHEROWNRENTAll
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" ], "text/plain": [ "own_rnt MORTGAGE NONE OTHER OWN RENT All\n", "grade \n", "A 5227 5 25 872 4073 10202\n", "B 5427 2 36 957 5986 12408\n", "C 3587 0 24 642 4494 8747\n", "D 2368 0 26 433 3198 6025\n", "E 1499 0 14 242 1646 3401\n", "F 620 1 4 75 600 1300\n", "G 253 0 7 36 216 512\n", "All 18981 8 136 3257 20213 42595" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(df.grade, df.own_rnt , margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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