Answer
Identify missing values with isnull/notnull.
Fill with mean/median/mode or custom values.
Drop rows or columns when appropriate.
Fill with mean/median/mode or custom values.
Drop rows or columns when appropriate.
Quick Answer
Handle missing data in pandas: detect with df.isna() or df.isnull(). Drop: df.dropna(axis=0) rows, dropna(axis=1) columns, dropna(subset=["col"]). Fill: df.fillna(value), fillna(df.mean()), fillna(method="ffill") forward fill, bfill backward fill. Interpolate: df.interpolate(). For ML: use SimpleImputer from scikit-learn to fill with mean/median/mode in pipelines.
S
SugharaIQ Editorial Team
Verified Answer
This answer has been peer-reviewed by industry experts holding senior engineering roles to ensure technical accuracy and relevance for modern interview standards.
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Source: SugharaIQ