Python · easy

Group a DataFrame by category and compute multiple aggregations at once.

Asked in Analytics Engineer interviews, in the Python round.

Short answer

df.groupby('cat').agg({'x':'sum','y':'mean'}).

How to answer it

Named aggregation gives each output column a name you chose, which is what makes the result usable downstream.

out = (
    df.groupby("category")
      .agg(
          revenue=("amount", "sum"),
          orders=("order_id", "nunique"),
          avg_amount=("amount", "mean"),
          first_seen=("order_date", "min"),
      )
      .reset_index()
)

The dict form, agg({"amount": ["sum", "mean"]}), works but yields a two-level column index that every later step has to flatten. Named aggregation avoids that. nunique on the order id rather than size is the difference between orders and line items when the frame has one row per item.

If a metric needs a custom function, pass a callable in the same tuple; if it needs several columns at once (revenue per order across a group), compute the row-level quantity first, then aggregate. groupby(...).apply on the whole group is the slow path and rarely necessary.

• What they are checking: named aggregation and the nunique versus size distinction.
• Common mistake: count where nunique was meant, giving a line-item count labelled "orders".

Related questions

• How do you handle missing values in a pandas DataFrame?
• 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.

Practice this for real

• Check your resume against a Analytics Engineer job description — free, no account needed.
• Every data interview question, by role and topic
• Scored AI mock interviews — two free, no card