How do I calculate the row sum of specific columns in a Pandas DataFrame?

Row Summation of Given Columns in Pandas DataFrame

In Python's Pandas library, we often encounter the need to calculate the sum of specific columns in a DataFrame. To effectively achieve this, we must consider the appropriate parameters and operations.

Let's consider the following DataFrame:

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

Our objective is to add a column 'e' that represents the sum of columns 'a', 'b', and 'd'. While intuitively, one might approach this with something like:

df['e'] = df[['a', 'b', 'd']].map(sum)

this method fails to produce the desired output.

The correct approach involves utilizing the sum() function with the following parameters:

• axis=1: Specifies that the summation should be performed along the rows (horizontally).
• numeric_only=True: Ensures that only numeric columns are considered in the operation, excluding non-numeric columns like 'c'.

Applying this approach yields the following result:

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

Output:

   a  b   c  d   e
0  1  2  dd  5   8
1  2  3  ee  9  14
2  3  4  ff  1   8

Alternatively, if we desire to calculate the sum of only specific columns, we can create a list of those columns and eliminate the ones we don't need using the remove() method.

col_list = list(df)
col_list.remove('d')

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

Output:

   a  b   c  d  e
0  1  2  dd  5  3
1  2  3  ee  9  5
2  3  4  ff  1  7

By utilizing these operations, we can effectively sum rows for specified columns in a Pandas DataFrame, ensuring accurate and efficient data analysis.

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