How to Sum Specific DataFrame Rows in Pandas?

How to Sum DataFrame Rows for Specific Columns in Pandas

For a given DataFrame, it can be necessary to calculate the sum of values across specific rows. While attempting to achieve this through df[['a', 'b', 'd']].map(sum), you may encounter issues.

The appropriate operation for this task involves using sum() with axis=1. This operation calculates the sum of each row, ignoring non-numeric columns. To ensure accuracy, it's recommended to specify numeric_only=True, especially in Pandas versions 2.0 and above.

For example, consider a DataFrame with columns 'a', 'b', 'c', and 'd', where 'c' is a non-numeric column:

df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': ['dd', 'ee', 'ff'], 'd': [5, 9, 1]})

To calculate the sum of columns 'a', 'b', and 'd', we can use:

df['e'] = df.sum(axis=1, numeric_only=True)

This will add a column 'e' containing the sum of the desired columns.

If you wish to sum specific columns while excluding others, you can specify a list of columns and remove any unwanted ones using col_list.remove(column_name).

col_list = list(df)
col_list.remove('d')
df['e'] = df[col_list].sum(axis=1)

This will create a new column 'e' with the sum of the specified columns.

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