How to Add a New Column with Grouped Summation in Pandas Using `transform()`?

Creating a New Column Based on Grouped Summation in Pandas

Problem Statement

When attempting to create a new column based on the summation of a value grouped by date using pandas' groupby(), NaN results are encountered. The objective is to add a column that displays the total sum of a specific value for all dates, regardless of the number of rows associated with that date.

Solution

To achieve this, the transform() function is employed. Unlike the apply() function, which operates row-by-row, transform() performs computations on grouped data and returns a series aligned with the original dataframe.

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

Here's a step-by-step breakdown:

• df['Data3'].groupby(df['Date']): This line groups the 'Data3' column by 'Date'.
• transform('sum'): The 'transform' function is applied to the grouped object, calculating the sum of 'Data3' for each date group.
• The result is a series aligned with the original dataframe, allowing it to be added as a new column named 'Data4'.

Example Usage

Consider the following dataframe:

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

Applying the transform() function:

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

Results in:

         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 evident from the output, the 'Data4' column now holds the sum of 'Data3' for each unique 'Date' value.

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