Python | From GroupBy to Pivot Tables: Data Analysis Made Easy with Pandas in Python

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Group by to pivot table

You can convert a pandas groupby object into a pivot table using the pivot_table() method in pandas. Here's an example:

import pandas as pd

# create a pandas DataFrame

df = pd.DataFrame({'Region': ['North', 'North', 'South', 'South', 'West', 'West'],

'Salesperson': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve', 'Frank'],

'Sales': [100, 200, 150, 50, 75, 125]})

# group by region and salesperson and calculate the total sales for each group

grouped = df.groupby(['Region', 'Salesperson']).sum()

# convert the groupby object to a pivot table

pivot = grouped.pivot_table(values='Sales', index=['Region'], columns=['Salesperson'], aggfunc=sum)

print(pivot)

This will group the DataFrame by region and salesperson and calculate the total sales for each group. Then, the pivot_table() method is used to convert the resulting groupby object into a pivot table that shows the total sales for each salesperson broken down by region. The resulting output will be the same as the previous example:

Salesperson Alice Bob Charlie Dave Eve Frank

Region

North 100 200 NaN NaN NaN NaN

South NaN NaN 150.0 50.0 NaN NaN

West NaN NaN NaN NaN 75.0 125.0

Note that the parameters passed to the pivot_table() method are similar to those used in the previous example. The values parameter specifies the column to summarize (in this case, 'Sales'), the index parameter specifies the rows to group by (in this case, 'Region'), and the columns parameter specifies the columns to group by (in this case, 'Salesperson'). The aggfunc parameter specifies the function to use to summarize the data (in this case, sum(), which is the default function).