How to Create a New Column in Pandas DataFrame Using Groupby().sum() and .transform()?

Creating a New Column from the Output of Pandas Groupby().sum()

When working with data in Python, it's often necessary to perform calculations and create new columns in a DataFrame based on existing values. In this example, we're looking to create a new column (Data4) containing the sum of Data3 for each Date.

Using .transform()

To achieve this, we can utilize the .transform() method on the grouped Data3 column. .transform() applies a function to each group and returns a Series with the index aligned to the original DataFrame. This allows us to add the calculated values as a new column.

df['Data4'] = df['Data3'].groupby(df['Date']).transform('sum')

In the example DataFrame provided:

import pandas as pd

df = pd.DataFrame({
    'Date': ['2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05',
             '2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05'],
    'Sym': ['aapl', 'aapl', 'aapl', 'aapl', 'aaww', 'aaww', 'aaww', 'aaww'],
    'Data2': [11, 8, 10, 15, 110, 60, 100, 40],
    'Data3': [5, 8, 6, 1, 50, 100, 60, 120]
})

Using .transform(), we calculate the sum of Data3 for each Date and assign it to the new column Data4:

df['Data4'] = df['Data3'].groupby(df['Date']).transform('sum')

The resulting DataFrame will have the desired Data4 column:

         Date   Sym  Data2  Data3  Data4
0  2015-05-08  aapl     11      5     55
1  2015-05-07  aapl      8      8    108
2  2015-05-06  aapl     10      6     66
3  2015-05-05  aapl     15      1    121
4  2015-05-08  aaww    110     50     55
5  2015-05-07  aaww     60    100    108
6  2015-05-06  aaww    100     60     66
7  2015-05-05  aaww     40    120    121

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