Fill missing values with means

Q: Fill missing values with means

This question evaluates proficiency in missing-data imputation and pandas DataFrame manipulation, testing skills in column-wise aggregation, group-wise operations, and edge-case handling within the Data Manipulation (SQL/Python) domain.

Q: How do I approach Data Manipulation (SQL/Python) interview questions?

Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

Question

You are given a pandas DataFrame df with missing values in multiple numeric columns. Write a concise pandas expression to replace NaNs in each numeric column with that column's mean while leaving non-numeric columns unchanged. Also explain how you would: (a) perform the operation in-place, (b) compute and apply per-group means based on a key column (e.g., 'group'), and (c) skip columns whose values are all NaN.

Fill missing values with means

Overview

Comments (0)