How to Avoid NaN Values When Adding Grouped Sums as a New Column in Pandas?

Creating a New Column from Grouped Summation

Problem

When attempting to create a new column in a DataFrame from the results of a groupby sum operation using pandas, some users encounter NaN values in the new column. The primary issue arises when trying to assign group-specific sums to individual rows.

Solution

The key to resolving this issue is to employ the transform function, which returns a Series with its index aligned to the DataFrame. By using transform, you can add the result as a new column to your DataFrame.

Consider the following code snippet:

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]
})

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

print(df)

Output:

         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

As illustrated, each row in the new column, Data4, now reflects the sum of Data3 values for the corresponding date group, effectively addressing the initial problem of NaN values.

The above is the detailed content of How to Avoid NaN Values When Adding Grouped Sums as a New Column in Pandas?. For more information, please follow other related articles on the PHP Chinese website!