Python · easy

How do you handle missing values in a pandas DataFrame?

Asked in Data Analyst interviews, in the Python round.

Short answer

fillna (mean/median/mode/forward-fill), dropna, or model-based imputation depending on context.

How to answer it

First find out why they are missing; the fix follows from the reason.

• Missing because it does not apply (no discount on this order): fill with a meaningful value, usually 0 or "none".
• Missing at random from a sensor or a form: impute. Median for skewed numerics, mode for categoricals, forward-fill for time series where the last value persists.
• Missing because of a bug upstream: fix upstream, do not paper over it.
• Missing rarely and in rows you can afford to lose: drop them, and report how many.

missing = df.isna().mean().sort_values(ascending=False)   # share missing per column, first
df["discount"] = df["discount"].fillna(0)
df["income"]   = df["income"].fillna(df["income"].median())
df["price"]    = df.sort_values("ts").groupby("sku")["price"].ffill()
df = df.dropna(subset=["user_id"])                          # a row with no key is not a row

For a model, add a "was missing" indicator column before imputing: missingness is often predictive, and imputation erases it. And compute the fill value on the training set only, then apply it to validation and test, or you have leaked.

• What they are checking: that you ask why before you fill, and that you know imputation must be fit on train only.
• Common mistake: fillna(0) on everything. A zero income and a missing income are different facts.

Related questions

• Merge two DataFrames on a key and keep unmatched rows from the left.
• Given a pandas DataFrame, compute the 30-day moving average of a column.
• Explain the difference between .apply(), .map(), and vectorized ops in pandas.
• Detect and remove outliers using the IQR method in pandas.
• Write a function to compute a rolling session count with a 30-min timeout.
• What are Python generators and when would you use them?

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