Clean numeric text before aggregation

The problem

Use pandas to calculate the mean of numeric input values. Invalid strings and null are missing; return null when no numeric observation remains.

Function: def numeric_mean(values):

Example input (a JSON list of arguments): [["2", "bad", null, "4"]]
Expected output: 3.0

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. Coerce invalid strings and missing values to NaN, then average observed numeric values.
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 numeric_mean(values):
    import pandas as pd
    s=pd.to_numeric(pd.Series(values,dtype='object'),errors='coerce').dropna()
    return float(s.mean()) if len(s) else None

Why it works

Coerce invalid strings and missing values to NaN, then average observed numeric values. An entirely missing series returns null rather than inventing a zero-valued observation.

Complexity: O(n) time / O(n) series space.

Time: O(n) time · Space: O(n) series space