How to Sum Specific Columns in a Pandas DataFrame While Handling Non-Numeric Data?

Pandas: Summing DataFrame Rows for Specific Columns

Within a Pandas DataFrame, combining data from multiple rows for a given set of columns can be a common task. In this article, we'll address the query of calculating the sum of specific columns within DataFrame rows.

Initial Approach and Error:

One might attempt to use the following code to achieve the sum of columns 'a', 'b,' and 'd':

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

However, this approach fails due to the presence of non-numeric data in the 'c' column.

Correct Operation:

To account for non-numeric data and accurately sum the desired columns, we modify the code as follows:

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

Explanation:

The sum function is invoked with axis=1 to sum rows rather than columns. Additionally, numeric_only=True ensures that only numeric columns are considered in the calculation, excluding non-numeric columns such as 'c'.

Sum Specific Columns:

To sum only a subset of columns, create a list of the desired columns and exclude those you don't need:

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

This operation would sum the 'a,' 'b,' and 'c' columns, storing the result in the 'e' column.

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