Total selected columns

The problem

Return the total of each listed column as a float. Missing values are skipped; a column with no values totals 0.

Function: def column_totals(rows,columns):

Example input (a JSON list of arguments): [[{"a": 1, "b": 2.5}, {"a": 3, "b": null}], ["a", "b"]]
Expected output: {"a": 4.0, "b": 2.5}

Inputs satisfy the problem statement. Sequences contain at most 200 elements unless a smaller bound is stated. Use JSON values and the supplied function signature.

Hints

1. Build the DataFrame with the requested columns so a missing column or an empty input still gives a result.
2. Check the supplied examples, empty/boundary cases and the stated answer policy before optimizing.

Worked answer

Try it yourself first. When you are ready, open the answer below.

def column_totals(rows,columns):
    import pandas as pd
    totals=pd.DataFrame(rows,columns=columns).apply(pd.to_numeric).sum()
    return {column:float(totals[column]) for column in columns}

Why it works

Build the DataFrame with the requested columns so a missing column or an empty input still gives a result. pandas sum() skips missing values by default, and the total of an empty column is 0.

Complexity: O(n * c) time / O(n * c) space.

Time: O(n * c) time · Space: O(n * c) space