{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 04 -- Pandas, Part 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Topics covered:\n", "\n", "Importing Packages\n", "\n", "Series\n", "\n", " DataFrames \n", "\n", " Read .csv files \n", "\n", " Inspection \n", "\n", " Handling Missing Data \n", "\n", " Missing Data Detection\n", "\n", " Missing Value Replacement \n", "\n", " Resources \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This chapter introduces the Pandas library (or package). panda is a package built using NumPy (pronounced 'numb pie'). \n", "\n", "Up until this point, the examples seen in this notebook utilize Python's built-in types and functions. NumPy has its ndarray object for array arithmetic. NumPy is a package built to support scientific computing in Python. We will illustrate a few useful NumPy objects as a way of illustrating pandas\n", "\n", "panda was developed to support data analysis with added flexibility than offered by the ndarray object in NumPy. For data analysis tasks we often need to group dis-similar data types together. An examples are categorical data using strings, frequencies and counts using ints and floats for continuous values. In addition, we would like to be able to attach labels to columns, pivot data, and so on.\n", "\n", "We begin by introducing the Series object as a component of the DataFrame objects. A Series can be thought of as an indexed, one-dimensional array, similar to a column of values. DataFrames can be thought of as a two-dimensional array indexed by both rows and columns. A good analogy is an Excel cell addressable by row and column location. \n", "\n", "In other words, a DataFrame looks a great deal like a SAS data set (or relational table). The table below compares panda components to those found in SAS.\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 Pandas SAS DataFrame SAS data set row observation column variable groupby BY-Group NaN . slice sub-set axis 0 observation axis 1 column
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Panda Pre-requisites\n", "\n", "panda have three main data structures:\n", "\n", " 1. Series\n", " 2. DataFrame\n", " 3. Index\n", " \n", "Indexes are covered in detail in Chapter 6, Understanding Indexes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Packages\n", "\n", "To begin utilizing panda objects, or other objects, begin by importing libraies by name into our namespace. To avoid having to re-typing package name repeatedly, use the standard aliases of np for NumPy and pd for panda." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from numpy.random import randn\n", "from pandas import Series, DataFrame, Index\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Series\n", "A Series can be thought of as a one-dimensional array with labels. This structure includes an index of labels used as keys to locate values. Data in a Series can be any data type. panda data types are covered in detail here . In the SAS examples, we use Data Step ARRAYs as an analog to the Series." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by creating a Series of random values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1.470961\n", "1 0.724744\n", "2 -1.601498\n", "3 0.201619\n", "4 -1.106859\n", "dtype: float64\n" ] } ], "source": [ "s1 = Series(randn(10))\n", "print(s1.head(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the index start position begins with 0. Most SAS automatic variables like \\_n\\_ use 1 as the index start position. Iteration of the SAS DO loop 0 to 9 in conjunction with an ARRAY produces an array subscript out of range error.\n", "\n", "In the SAS example below the DO loop is used to iterate over the array elements locating the target elements. \n", "\n", "Arrays in SAS are used primarily for iteratively processing like variables together. SAS/IML is a closer analog to NumPy arrays. SAS/IML is outside the scope of these examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_array_random_values.sas */\n", " /******************************************************/\n", " 4 data _null_;\n", " 5 \n", " 6 call streaminit(54321);\n", " 7 \n", " 8 array s2 {10} ;\n", " 9 do i = 1 to 10;\n", " 10 s2{i} = rand(\"Uniform\");\n", " 11 \n", " 12 if i <= 5 then put\n", " 13 s2{i};\n", " 14 end;\n", "\n", " 0.4322317772\n", " 0.5977982976\n", " 0.7785986473\n", " 0.1748250183\n", " 0.3941470125\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Series can have a list of index labels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.057676\n", "b 0.154904\n", "c -1.358674\n", "d -1.661059\n", "e 2.111150\n", "dtype: float64\n" ] } ], "source": [ "s2 = Series(randn(10), index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])\n", "print(s2.head(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Series is indexed by integer value with the start position at 0. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.05767568654\n" ] } ], "source": [ "print(s2[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SAS example uses a DO loop as the index subscript into the array." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_return_1st_array_element.sas */\n", " /******************************************************/\n", " 4 data _null_;\n", " 5 \n", " 6 call streaminit(54321);\n", " 7 \n", " 8 array s2 {10} ;\n", " 9 do i = 1 to 10;\n", " 10 s2{i} = rand(\"Uniform\");\n", " 11 \n", " 12 if i = 1 then put\n", " 13 s2{i};\n", " 14 end;\n", "\n", " 0.4322317772\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Return the first 3 elements in the Series." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.057676\n", "b 0.154904\n", "c -1.358674\n", "dtype: float64\n" ] } ], "source": [ "print(s2[:3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_return_first_3_array_elements.sas */\n", " /******************************************************/\n", " 20 data _null_;\n", " 21 \n", " 22 call streaminit(54321);\n", " 23 \n", " 24 array s2 {10} ;\n", " 25 do i = 1 to 10;\n", " 26 s2{i} = rand(\"Uniform\");\n", " 27 \n", " 28 if i <= 3 then put\n", " 29 s2{i};\n", " 30 end;\n", "\n", " 0.4322317772\n", " 0.5977982976\n", " 0.7785986473\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example has two operations. The s2.mean() method calculates mean followed by a boolen test less than this calculated mean." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "c -1.358674\n", "d -1.661059\n", "g -0.426155\n", "h -0.841126\n", "dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2[s2 < s2.mean()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series and other objects have attributes using a dot (.) chaining-style syntax. .name is one a number of attributes for the Series object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.057676\n", "b 0.154904\n", "c -1.358674\n", "d -1.661059\n", "e 2.111150\n", "Name: Arbitrary Name, dtype: float64\n" ] } ], "source": [ "s2.name='Arbitrary Name'\n", "print(s2.head(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataFrames\n", "\n", "DataFrames are relational-like structures with labels. Alternatively, a DataFrame with a single column is a Series.\n", "\n", "Like SAS, DataFrames have different methods for creation. DataFrames can be created by loading values from other Python objects. Data values can also be loaded from a range of non-Python input sources, including .csv files, DBMS tables, Web API's, and even SAS data sets (.sas7bdat), etc. Details are discussed in Chapter 11 -- Panda Readers \n", "\n", "Start by reading the UK_Accidents .csv file. It contains vehicular accident data in the U.K from January 1, 2015 to December 31, 2015. The .csv file is located here.\n", "\n", "There are multiple reports for each day of the year. The values are mostly integer values using the Road-Accident_Safety-Data_Guide.xls file found here to map values to descriptive labels. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pd.read_csv()\n", "\n", "The default values are used in the example below. Pandas provide a number of readers having parameters for controling missing values, date parsing, line skipping, data type mapping, etc. These parameters are analogous to SAS' INFILE/INPUT processing.\n", "\n", "Additional examples of reading various data inputs into a DataFrame are covered in Chapter 11 -- Panda Readers\n", "\n", "Notice the backslash (\\\\) to normalize the Window's path name." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "file_loc2 = \"C:\\Data\\\\uk_accidents.csv\"\n", "df = pd.read_csv(file_loc2, low_memory=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PROC IMPORT is used to read the same .csv file. This is one of several methods for SAS to read a .csv file. Here we have taken the defaults." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_read_csv_proc_import.sas */\n", " /******************************************************/\n", " 5 proc import datafile='c:\\data\\uk_accidents.csv' out=uk_accidents;\n", " \n", " NOTE: The file 'c:\\data\\uk_accidents.csv' is:\n", " File Name 'c:\\data\\uk_accidents.csv',\n", " Lrecl=32760, Recfm=V\n", "\n", " NOTE: 266776 records were read from file 'c:\\data\\uk_accidents.csv'\n", " The minimum record length was 65\n", " The maximum record length was 77\n", " NOTE: Data set \"WORK.uk_accidents\" has 266776 observation(s) and 27 variable(s)\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike SAS, the Python interpreter is mainly silent upon normal execution. When debugging it is helpful to invoke methods and functions to return information about these objects. This is somewhat analogous to use PUT statements in the SAS log to examine variable values.\n", "\n", "The size, shape, and ndim attributes report respectively, number of cells, rows/columns, and number of dimensions are shown below. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7202952 (266776, 27) 2\n" ] } ], "source": [ "print(df.size, df.shape, df.ndim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Read Verification\n", "\n", "After reading a file, you often want to understand its content and structure. The DataFrame .info() method returns descriptions of the DataFrame's attributes. In SAS, this same information is generally found in the output from PROC CONTENTS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 266776 entries, 0 to 266775\n", "Data columns (total 27 columns):\n", "Accident_Severity 266776 non-null int64\n", "Number_of_Vehicles 266776 non-null int64\n", "Number_of_Casualties 266776 non-null int64\n", "Day_of_Week 266776 non-null int64\n", "Time 266752 non-null object\n", "Road_Type 266776 non-null int64\n", "Speed_limit 266776 non-null int64\n", "Junction_Detail 266776 non-null int64\n", "Light_Conditions 266776 non-null int64\n", "Weather_Conditions 266776 non-null int64\n", "Road_Surface_Conditions 266776 non-null int64\n", "Urban_or_Rural_Area 266776 non-null int64\n", "Vehicle_Reference 266776 non-null int64\n", "Vehicle_Type 266776 non-null int64\n", "Skidding_and_Overturning 266776 non-null int64\n", "Was_Vehicle_Left_Hand_Drive_ 266776 non-null int64\n", "Sex_of_Driver 266776 non-null int64\n", "Age_of_Driver 266776 non-null int64\n", "Engine_Capacity__CC_ 266776 non-null int64\n", "Propulsion_Code 266776 non-null int64\n", "Age_of_Vehicle 266776 non-null int64\n", "Casualty_Class 266776 non-null int64\n", "Sex_of_Casualty 266776 non-null int64\n", "Age_of_Casualty 266776 non-null int64\n", "Casualty_Severity 266776 non-null int64\n", "Car_Passenger 266776 non-null int64\n", "Date 266776 non-null object\n", "dtypes: int64(25), object(2)\n", "memory usage: 55.0+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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aiYmtpEjuZPs8rRW5cKUMkgUpGrB1wzEA8jPBxTjZ2Epcyuje/tS2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\n8P2htzajT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RcLElFR5m/uJ/32f8KMzf3E/77P8AhRcLElFR5m/uJ/32f8KMzf3E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\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Grqnjr+x7WW6vvDWuJa2sSy3twIodlqDyc/vcybRyxi3gdM5BFXvD2t3Gr6lr8E6RLHpu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\nP3hM8m2XZ3GxNwyRyApXW39f1/TDobv2ub/oHXP/AH1H/wDF1jajoNt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"text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Anaconda3\\\\output\\\\contents2.JPG')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspection\n", "\n", "panda have methods which used to inspect to data values. The DataFrame .head() method displays the first 5 rows by default. The .tail() method displays the last 5 rows by default. The row count value can be an arbitrary integer value such as:\n", "\n", " # display the last 20 rows of the DataFrame\n", " df.tail(20)\n", " \n", "SAS uses the FIRSTOBS and OBS options with procedures to determine input observations. The SAS code to print the last 20 observations of the uk_accidents data set is:\n", "\n", " proc print data=uk_accidents (firstobs = 266756);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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0321619:00340141...30-1-1-11154301/9/2015
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5 rows Ã— 27 columns

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" ], "text/plain": [ " Accident_Severity Number_of_Vehicles Number_of_Casualties Day_of_Week \\\n", "0 3 2 1 6 \n", "1 3 2 1 6 \n", "2 3 3 1 2 \n", "3 3 3 1 2 \n", "4 3 3 1 2 \n", "\n", " Time Road_Type Speed_limit Junction_Detail Light_Conditions \\\n", "0 19:00 3 40 1 4 \n", "1 19:00 3 40 1 4 \n", "2 18:30 3 40 1 4 \n", "3 18:30 3 40 1 4 \n", "4 18:30 3 40 1 4 \n", "\n", " Weather_Conditions ... Age_of_Driver Engine_Capacity__CC_ \\\n", "0 1 ... 30 -1 \n", "1 1 ... 54 1499 \n", "2 2 ... 30 -1 \n", "3 2 ... 20 1199 \n", "4 2 ... 30 -1 \n", "\n", " Propulsion_Code Age_of_Vehicle Casualty_Class Sex_of_Casualty \\\n", "0 -1 -1 1 1 \n", "1 2 1 1 1 \n", "2 -1 -1 1 2 \n", "3 1 13 1 2 \n", "4 -1 -1 1 2 \n", "\n", " Age_of_Casualty Casualty_Severity Car_Passenger Date \n", "0 54 3 0 1/9/2015 \n", "1 54 3 0 1/9/2015 \n", "2 20 3 0 2/23/2015 \n", "3 20 3 0 2/23/2015 \n", "4 20 3 0 2/23/2015 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OBS=n in SAS determines the number of observations used as input." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c_04_display_1st_5_obs.sas */\n", " /******************************************************/\n", " 39 proc print data = uk_accidents (obs=5);\n", "\n", " The output from PROC PRINT is not displayed here. \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scoping output by columns is shown in the cell below. The column list is analogous to the VAR statement in PROC PRINT. Note the double set of square brackets for this syntax. This is slicing operating by column label. The square braces ([]) is the slicing operator. The details are explained here " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example uses the slicing operator to request columns by labels. Slicers work along rows as well." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Sex_of_DriverTime
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1119:00
2118:30
3218:30
4118:30
5117:50
6117:50
717:05
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9112:30
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" ], "text/plain": [ " Sex_of_Driver Time\n", "0 1 19:00\n", "1 1 19:00\n", "2 1 18:30\n", "3 2 18:30\n", "4 1 18:30\n", "5 1 17:50\n", "6 1 17:50\n", "7 1 7:05\n", "8 1 7:05\n", "9 1 12:30" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Sex_of_Driver', 'Time']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the DataFrame default index (incrementing from 0 to 9). This is analogous to the SAS automatic variable \\_n\\_. Later, we illustrate using other columns in the DataFrame as the index. \n", "\n", "Below is the SAS program to print the first 10 observations of a data set along with the variables 'sex_of_driver' and 'time'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_scoping_obs_and_variables.sas */\n", " /******************************************************/\n", " 40 proc print data = uk_accidents (obs=10);\n", " 41 var sex_of_driver time;\n", "\n", " The output from PROC PRINT is not displayed here.\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling Missing Data\n", "\n", "Before analyzing data a common task is dealing with missing data. pandas uses two designations to indicate missing data, NaN (not a number) and the Python None object.\n", "\n", "Consider cells #15, #16, and #17 below. Cell #15 uses the Python None object to represent a missing value in the array. In turn, Python infers the data type for the array to be an object. Unfortuantely, the use of a Python None object with an aggregation function for arrays raises an error. Cell #17 addresses the error raised in cell #16." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([32, None, 17, 109, 201], dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = np.array([32, None, 17, 109, 201])\n", "s1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\Users\\randy\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'" ] } ], "source": [ "s1.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To aliviate the error raised above, use the np.nan (missing value indicator) in the array example below. Also notice how Python chose floating point (or up-casting) for the array compared to the same example two cells above. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 32. nan 17. 109. 201.]\n" ] }, { "data": { "text/plain": [ "dtype('float64')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = np.array([32, np.nan, 17, 109, 201])\n", "print(s1)\n", "s1.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not all arithmetic operations using NaN's will result in a NaN. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrast the Python program in the cell above for calculating the mean of the array elements with the SAS example below. SAS excludes the missing value and utilizes the remaining array elements to calculate a mean." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_mean_of_array_elements.sas */\n", " /******************************************************/\n", " 4 data _null_;\n", " 5 \n", " 6 array s2 {5} (32 . 17 109 201);\n", " 7 avg = mean(of s2[*]);\n", " 8 \n", " 9 put avg;\n", "\n", " 89.75\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Value Identification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Returning to our DataFrame, we need an analysis of missing values for all the columns. Pandas provide four methods for the detection and replacement of missing values. They are:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
 Method Action Taken isnull() generates a boolean mask to indicate missing values notnull() opposite of isnull() dropna() returns a filtered version of the data fillna() returns a copy of data with missing values filled or imputed
\n", "\n", "We will look at each of these in detail below. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A typical SAS-programming approach to address the missing data analysis is to write a program to traverses all columns using counter variables with IF/THEN testing for missing values. \n", "\n", "This can be along the lines of the example in cell #19 below. df.columns returns the sequence of column names in the DataFrame. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accident_Severity---->0\n", "Number_of_Vehicles---->0\n", "Number_of_Casualties---->0\n", "Day_of_Week---->0\n", "Time---->24\n", "Road_Type---->0\n", "Speed_limit---->0\n", "Junction_Detail---->0\n", "Light_Conditions---->0\n", "Weather_Conditions---->0\n", "Road_Surface_Conditions---->0\n", "Urban_or_Rural_Area---->0\n", "Vehicle_Reference---->0\n", "Vehicle_Type---->0\n", "Skidding_and_Overturning---->0\n", "Was_Vehicle_Left_Hand_Drive_---->0\n", "Sex_of_Driver---->0\n", "Age_of_Driver---->0\n", "Engine_Capacity__CC_---->0\n", "Propulsion_Code---->0\n", "Age_of_Vehicle---->0\n", "Casualty_Class---->0\n", "Sex_of_Casualty---->0\n", "Age_of_Casualty---->0\n", "Casualty_Severity---->0\n", "Car_Passenger---->0\n", "Date---->0\n" ] } ], "source": [ "for col_name in df.columns:\n", " print (col_name, end=\"---->\")\n", " print (sum(df[col_name].isnull()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this give the desired results, there is a better approach. \n", "\n", "As an aside, if you find yourself thinking of solving a pandas' operation (or Python for that matter) using iterative processing, stop and take a little time to do research. Chances are, a method or function already exists!\n", "\n", "Case-in-point is illustrated below. It chains the .sum() attribute to the .isnull() attribute to return a count of the missing values for the columns in the DataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The .isnull() method returns True for missing values. By chaining the .sum() method to the .isnull() method it produces a count of the missing values for each columns." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Accident_Severity 0\n", "Number_of_Vehicles 0\n", "Number_of_Casualties 0\n", "Day_of_Week 0\n", "Time 24\n", "Road_Type 0\n", "Speed_limit 0\n", "Junction_Detail 0\n", "Light_Conditions 0\n", "Weather_Conditions 0\n", "Road_Surface_Conditions 0\n", "Urban_or_Rural_Area 0\n", "Vehicle_Reference 0\n", "Vehicle_Type 0\n", "Skidding_and_Overturning 0\n", "Was_Vehicle_Left_Hand_Drive_ 0\n", "Sex_of_Driver 0\n", "Age_of_Driver 0\n", "Engine_Capacity__CC_ 0\n", "Propulsion_Code 0\n", "Age_of_Vehicle 0\n", "Casualty_Class 0\n", "Sex_of_Casualty 0\n", "Age_of_Casualty 0\n", "Casualty_Severity 0\n", "Car_Passenger 0\n", "Date 0\n", "dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To identify missing values the SAS example below uses PROC Format to bin missing and non-missing values. Missing values are represented by default as (*.*) for numeric and blank (' ') for character variables. Therefore, a user-defined format is needed for both types. \n", "\n", "PROC FREQ is used with the automatic variables \\_CHARACTER\\_ and \\_NUMERIC\\_ to produce a frequency listing for each variable type. \n", "\n", "Only a portion of the SAS output is shown since separate output is produced for each variable. As with the example in cell # 19 above, the 'time' variable is the only variable with missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_find_missing_numerics_characters.sas */\n", " /******************************************************/\n", " 26 proc format;\n", " 27 value $missfmt ' '='Missing' other='Not Missing';\n", " 28 value missfmt . ='Missing' other='Not Missing';\n", " 29 run;\n", "\n", " 30 \n", " 31 proc freq data=uk_accidents;\n", " 32 format _CHARACTER_$missfmt.;\n", " 33 tables _CHARACTER_ / missing missprint nocum nopercent;\n", " 34 \n", " 35 format _NUMERIC_ missfmt.;\n", " 36 tables _NUMERIC_ / missing missprint nocum nopercent;\n", "" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Anaconda3\\\\output\\\\freq.JPG')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another method for detecting missing values is to search column-wise by using the axis=1 parameter to the chained attributes .isnull().any(). The operation is then performed along columns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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116693212NaN130211...-121482122244311/5/2015
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5 rows Ã— 27 columns

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" ], "text/plain": [ " Accident_Severity Number_of_Vehicles Number_of_Casualties \\\n", "11669 3 2 1 \n", "12473 3 1 1 \n", "12671 3 2 1 \n", "55179 3 1 1 \n", "55187 3 2 1 \n", "\n", " Day_of_Week Time Road_Type Speed_limit Junction_Detail \\\n", "11669 2 NaN 1 30 2 \n", "12473 4 NaN 6 30 0 \n", "12671 5 NaN 6 30 0 \n", "55179 3 NaN 6 60 9 \n", "55187 7 NaN 1 30 1 \n", "\n", " Light_Conditions Weather_Conditions ... Age_of_Driver \\\n", "11669 1 1 ... -1 \n", "12473 1 2 ... 51 \n", "12671 1 1 ... 43 \n", "55179 1 8 ... 48 \n", "55187 4 1 ... 54 \n", "\n", " Engine_Capacity__CC_ Propulsion_Code Age_of_Vehicle Casualty_Class \\\n", "11669 2148 2 12 2 \n", "12473 -1 -1 -1 1 \n", "12671 -1 -1 -1 1 \n", "55179 1560 2 1 3 \n", "55187 749 1 17 1 \n", "\n", " Sex_of_Casualty Age_of_Casualty Casualty_Severity Car_Passenger \\\n", "11669 2 44 3 1 \n", "12473 1 18 3 0 \n", "12671 1 29 3 0 \n", "55179 2 70 2 0 \n", "55187 2 21 3 0 \n", "\n", " Date \n", "11669 1/5/2015 \n", "12473 7/8/2015 \n", "12671 9/3/2015 \n", "55179 1/20/2015 \n", "55187 1/24/2015 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_data = df[df.isnull().any(axis=1)]\n", "null_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Value Replacement\n", "\n", "The code below is used to render multiple objects side-by-side. It is from Essential Tools for Working With Data, by Jake VanderPlas found here . It displays the 'before' and 'after' effects of changes to objects together. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class display(object):\n", " \"\"\"Display HTML representation of multiple objects\"\"\"\n", " template = \"\"\"
\n", "

{0}

{1}\n", "
\"\"\"\n", " def __init__(self, *args):\n", " self.args = args\n", "\n", " def _repr_html_(self):\n", " return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n", " for a in self.args)\n", "\n", " def __repr__(self):\n", " return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n", " for a in self.args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To illustrate the .fillna() method, consider the following to create a DataFrame. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.DataFrame([['cold','slow', np.nan, 2., 6., 3.], \n", " ['warm', 'medium', 4, 5, 7, 9],\n", " ['hot', 'fast', 9, 4, np.nan, 6],\n", " ['cool', None, np.nan, np.nan, 17, 89],\n", " ['cool', 'medium', 16, 44, 21, 13],\n", " ['cold', 'slow', np.nan, 29, 33, 17]],\n", " columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'],\n", " index=(list('abcdef')))\n", "display(\"df2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
\n", "
\n", "

df_tf

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1col2col3col4col5col6
aFalseFalseTrueFalseFalseFalse
bFalseFalseFalseFalseFalseFalse
cFalseFalseFalseFalseTrueFalse
dFalseTrueTrueTrueFalseFalse
eFalseFalseFalseFalseFalseFalse
fFalseFalseTrueFalseFalseFalse
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df_tf\n", " col1 col2 col3 col4 col5 col6\n", "a False False True False False False\n", "b False False False False False False\n", "c False False False False True False\n", "d False True True True False False\n", "e False False False False False False\n", "f False False True False False False" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tf = df2.isnull()\n", "display(\"df2\", \"df_tf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default the .dropna() method drops either the entire row or column in which any null value is found." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
\n", "
\n", "

df3

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1col2col3col4col5col6
bwarmmedium4.05.07.09.0
ecoolmedium16.044.021.013.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df3\n", " col1 col2 col3 col4 col5 col6\n", "b warm medium 4.0 5.0 7.0 9.0\n", "e cool medium 16.0 44.0 21.0 13.0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df2.dropna()\n", "display(\"df2\", \"df3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The .dropna() method also works along a column axis. axis = 1 or axis = 'columns' is equivalent." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
\n", "
\n", "

df4

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1col6
acold3.0
bwarm9.0
chot6.0
dcool89.0
ecool13.0
fcold17.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df4\n", " col1 col6\n", "a cold 3.0\n", "b warm 9.0\n", "c hot 6.0\n", "d cool 89.0\n", "e cool 13.0\n", "f cold 17.0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df4 = df2.dropna(axis='columns')\n", "display(\"df2\", \"df4\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly this drops a fair amount of 'good' data. The thresh parameter allows you to specify a minimum of non-null values to be kept for the row or column. In this case, row 'd' is dropped because it contains only 3 non-null values." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
\n", "
\n", "

df5

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df5\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5 = df2.dropna(thresh=5)\n", "display(\"df2\", \"df5\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than dropping rows and columns, missing values can be imputed or replaced. The .fillna() method returns either a Series or a DataFrame with null values replaced. The example below replaces all NaN's with zero." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
\n", "
\n", "

df6

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col1col2col3col4col5col6
acoldslow0.02.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.00.06.0
dcool00.00.017.089.0
ecoolmedium16.044.021.013.0
fcoldslow0.029.033.017.0
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\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df6\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow 0.0 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 0.0 6.0\n", "d cool 0 0.0 0.0 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow 0.0 29.0 33.0 17.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df6 = df2.fillna(0)\n", "display(\"df2\", \"df6\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the example in cell #28 above, the .fillna() method is applied to all DataFrame cells. We may not wish to have missing values in df['col2'] replaced with zeros since they are strings. The method is applied to a list of target columns using the .loc method. The details for .loc method are discussed in Chapter 05--Understanding Indexes ." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
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\n", "
\n", "
\n", "

df7

\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col3col4col5col6
a0.02.06.03.0
b4.05.07.09.0
c9.04.00.06.0
d0.00.017.089.0
e16.044.021.013.0
f0.029.033.017.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df7\n", " col3 col4 col5 col6\n", "a 0.0 2.0 6.0 3.0\n", "b 4.0 5.0 7.0 9.0\n", "c 9.0 4.0 0.0 6.0\n", "d 0.0 0.0 17.0 89.0\n", "e 16.0 44.0 21.0 13.0\n", "f 0.0 29.0 33.0 17.0" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df7 = df2[['col3', 'col4', 'col5', 'col6']].fillna(0)\n", "display(\"df2\", \"df7\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An imputation method based on the mean value of df['col6'] is shown below. The .fillna() method finds and then replaces all occurences of NaN with this calculated value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
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\n", "
\n", "
\n", "

df8

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col3col4col5
a22.8333332.0000006.000000
b4.0000005.0000007.000000
c9.0000004.00000022.833333
d22.83333322.83333317.000000
e16.00000044.00000021.000000
f22.83333329.00000033.000000
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df8\n", " col3 col4 col5\n", "a 22.833333 2.000000 6.000000\n", "b 4.000000 5.000000 7.000000\n", "c 9.000000 4.000000 22.833333\n", "d 22.833333 22.833333 17.000000\n", "e 16.000000 44.000000 21.000000\n", "f 22.833333 29.000000 33.000000" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df8 = df2[[\"col3\", \"col4\", \"col5\"]].fillna(df2.col6.mean())\n", "display(\"df2\", \"df8\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The corresponding SAS program is shown below. The PROC SQL SELECT INTO clause stores the calculated mean for the variable \"col6\" into the macro variable &col6_mean. This is followed by a Data Step iterating the array 'x' for \"col3 - col5\" replacing missing values with &col6_mean. \n", "\n", "A more detailed example of replacing missing values with group means is located here. \n", "\n", "SAS/Stat has PROC MI for imputation of missing values with a range of methods described \n", "here. PROC MI is outside the scope of these examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " /******************************************************/\n", " /* c04_replace_missing_with_mean_values.sas */\n", " /******************************************************/\n", " 4 data df;\n", " 5 infile cards dlm=',';\n", " 6 \n", " 7 input col1 $\n", " 8 col2$\n", " 9 col3\n", " 10 col4\n", " 11 col5\n", " 12 col6 ;\n", " 13 \n", " 14 datalines;\n", " 15 cold, slow, ., 2, 6, 3\n", " 16 warm, medium, 4, 5, 7, 9\n", " 17 hot, fast, 9, 4, ., 6\n", " 18 cool, , ., ., 17, 89\n", " 19 cool, medium, 16, 44, 21, 13\n", " 20 cold, slow, . ,29, 33, 17\n", " 21 ;;;;\n", "\n", " 22 proc sql;\n", " 23 select mean(col6) into :col6_mean\n", " 24 from df;\n", " 25 quit;\n", "\n", " 26 \n", " 27 data df2;\n", " 28 set df;\n", " 29 array x {3} col3-col5 ;\n", " 30 \n", " 31 do i = 1 to 3;\n", " 32 if x(i) = . then x(i) = &col6_mean;\n", " 33 end;\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The .fillna(method='ffill') is a 'forward' fill method. NaN's are replaced by the adjacent cell above traversing 'down' the columns. Cell #32 below constrasts the DataFrame df2, created in cell #24 above with the DataFrame df9 created with the 'forward' fill method." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
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\n", "
\n", "
\n", "

df9

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.07.06.0
dcoolfast9.04.017.089.0
ecoolmedium16.044.021.013.0
fcoldslow16.029.033.017.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df9\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 7.0 6.0\n", "d cool fast 9.0 4.0 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow 16.0 29.0 33.0 17.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df9 = df2.fillna(method='ffill')\n", "display(\"df2\", \"df9\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simalarly, the .fillna(bfill) is a 'backwards' fill method. NaN's are replaced by the adjecent cell traversing 'up' the columns. Cell #32 constrasts the DataFrame df2, created in cell #23 above with the DataFrame df10 created with the 'backward' fill method." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df2

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.0NaN6.0
dcoolNoneNaNNaN17.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
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\n", "
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\n", "

df10

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col1col2col3col4col5col6
acoldslow4.02.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.017.06.0
dcoolmedium16.044.017.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "
" ], "text/plain": [ "df2\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow NaN 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 NaN 6.0\n", "d cool None NaN NaN 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0\n", "\n", "df10\n", " col1 col2 col3 col4 col5 col6\n", "a cold slow 4.0 2.0 6.0 3.0\n", "b warm medium 4.0 5.0 7.0 9.0\n", "c hot fast 9.0 4.0 17.0 6.0\n", "d cool medium 16.0 44.0 17.0 89.0\n", "e cool medium 16.0 44.0 21.0 13.0\n", "f cold slow NaN 29.0 33.0 17.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df10 = df2.fillna(method='bfill')\n", "display(\"df2\", \"df10\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cell #34 contrasts DataFrame df9 created in cell #32 using the 'forward' fill method with DataFrame df10 created in cell #33 with the 'backward' fill method." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "

df9

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col1col2col3col4col5col6
acoldslowNaN2.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.07.06.0
dcoolfast9.04.017.089.0
ecoolmedium16.044.021.013.0
fcoldslow16.029.033.017.0
\n", "
\n", "
\n", "
\n", "

df10

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col1col2col3col4col5col6
acoldslow4.02.06.03.0
bwarmmedium4.05.07.09.0
chotfast9.04.017.06.0
dcoolmedium16.044.017.089.0
ecoolmedium16.044.021.013.0
fcoldslowNaN29.033.017.0
\n", "
\n", "