Aeonea·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Interviewed for a Software Engineer role at Aeonea and got hit with a pandas data cleaning question that had a few layers to it. Not the hardest thing in the world but the follow-up parts tripped me up a bit.

Questions Asked (1)

Given a pandas DataFrame with missing values across multiple numeric columns, write a concise expression to fill NaNs in each numeric column with that column's mean, without touching non-numeric columns. Then explain how you'd do it in-place, how you'd use per-group means based on a key column, and how you'd skip columns that are entirely NaN.

Technical Trade-offsAlgorithms & Data Structures

Author's notes

The base case I had down pretty quickly.

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AI HintsAI Generated

Suggested Approach

Start by writing the concise expression using `df.fillna(df.mean(numeric_only=True))` to fill NaNs in numeric columns with their means. Then explain the in-place variant with `inplace=True` or reassignment, and discuss per-group means using `groupby` and `transform`, and skipping all-NaN columns by filtering them out.

Pro tip: Mention that `df.mean(numeric_only=True)` automatically excludes non-numeric columns, and that using `transform` for group-wise filling preserves the original index alignment. Also note that all-NaN columns are skipped by default because their mean is NaN, but you can explicitly drop them to avoid warnings.

1. Concise expression for overall mean imputation

Write `df.fillna(df.mean(numeric_only=True))` to fill NaNs in each numeric column with that column's mean, leaving non-numeric columns untouched.

2. In-place imputation

Explain that you can use `df.fillna(df.mean(numeric_only=True), inplace=True)` or reassign `df = df.fillna(...)` to modify the DataFrame in place.

3. Per-group mean imputation

Describe using `df.groupby('key').transform(lambda x: x.fillna(x.mean()))` to fill NaNs with group-specific means, ensuring alignment with the original DataFrame.

4. Skipping entirely NaN columns

Mention that columns with all NaNs are automatically skipped because their mean is NaN, but you can explicitly drop them with `df.dropna(axis=1, how='all')` before imputation.

5. Handling non-numeric columns

Emphasize that `numeric_only=True` ensures only numeric columns are considered, so non-numeric columns remain unchanged.

Key Points to Mention

Use of `numeric_only=True` in `df.mean()` to avoid errors with non-numeric columns.
In-place modification via `inplace=True` or reassignment, and the trade-offs (e.g., chaining, memory).
Group-wise imputation using `groupby` and `transform` to maintain index alignment.
All-NaN columns are naturally skipped because their mean is NaN, but can be explicitly dropped.
Potential pitfalls: mean imputation can distort distributions and is sensitive to outliers.
Alternative approaches like using `SimpleImputer` from scikit-learn for more complex strategies.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.