Quick Guide to Pandas Pivot Table & Crosstab

Quick Guide to Pandas Pivot Table & Crosstab

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Pivot table or crosstab? Let?s see panda?s description.

Crosstab: ?Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed.?

Pivot Table: ?Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (Hierarchical indexes on the index and columns of the result DataFrame.

Both create tables and compare features/columns of data. Let?s see some examples.

Import Pandas and Create a Data Frame

#Import Pandasimport pandas as pd#Sample data I created and saved to dfdf = pd.read_csv(‘sample_car_data.csv’)Image for postSample Data of Cars

Pivot Tables

We?ll begin with a simple pivot table:

pd.pivot_table(df, index=’Car’, aggfunc=’mean’)#Deleting aggfunc=’mean’ will yield the same value.Image for postCar with Average Values of MPG & Price

With just the bare minimum the numerical values are automatically filled while the non-numerical values are omitted. Each cell is the averages/mean of the car?s MPG and Price. The default aggfunc is mean.

Create multiple indexes and specify its value and aggfunc.

pd.pivot_table(df, index=[‘Car’, ‘Color’], values=’Price’, aggfunc=’sum’)Image for postCar & Color with Summed Values of Price

Adding multiple columns to the pivot tables

pd.pivot_table(df, index=’Car’, columns=[‘Color’,’MPG’], aggfunc=’sum’)Image for postCar with Color and MPG and Summed price values

Crosstab

Create the bare minimum crosstab below. This compares the index and column to get the count.

pd.crosstab(index=df[‘Car’], columns=df[‘Color’])Image for postCar and Color

Crosstab of multiple indexes and a column. Additionally, it shows the total count of each row and column.

pd.crosstab(index=[df[‘Car’],df[‘MPG’]], columns=df[‘Color’], margins=True)Image for postCar & MPG with Color

Crosstab of multiple columns also showing the total count of each row and column.

pd.crosstab(index=df[‘Car’], columns=[df[‘Color’],df[‘Price’]], margins=True)Image for post

Something Interesting!

Depending how we specify the parameters we can create the same values for pivot tables and crosstabs.

Pivot Table <==> Crosstab

#Pivot Tablepd.pivot_table(df, index=’Car’, columns=’Color’, values=’MPG’, aggfunc=’count’, fill_value=0)#For the values parameter, I could have used ‘Name’ or ‘Priceand still get the same result.#Crosstabpd.crosstab(index=df[‘Car’], columns=df[‘Color’])Image for postPivot Table and Crosstab#Pivot Tablepd.pivot_table(df, index=’Car’, columns=’Color’, values=’Price’, margins=True)#Crosstabpd.crosstab(index=df[‘Car’], columns=df[‘Color’], values=df[‘Price’], margins=True, aggfunc=’mean’)Image for postPivot Table and Crosstab

Pivot Table or Crosstab?

Choose how you want to represent the data. At bare minimum, the pivot table compares the index and yields numerical values. Additionally, the crosstab compares the index and columns which yield the count.

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