Calculate total and average sales per region in pandas

WHAT THIS TESTS: The interviewer wants to see if you understand pandas groupby aggregation beyond simple single-statistic summaries. Specifically, they are checking whether you know how to compute multiple aggregates on the same column in a single expression without resorting to iterative or multi-pass approaches. At the senior level, this reveals fluency with the split-apply-combine paradigm, awareness of API ergonomics, and understanding that vectorized operations outperform Python-level loops. They also care whether you can produce readable, maintainable code that colleagues can immediately understand.

A GOOD ANSWER COVERS: First, mention grouping by Region using df.groupby("Region"). Second, specify selecting the Sales_Amount column to avoid unnecessary computations on other columns. Third, call the agg method with either a dictionary mapping the column to a list of functions like {"Sales_Amount": ["sum", "mean"]} or use named aggregation via .agg(total_sales=("Sales_Amount", "sum"), avg_sales=("Sales_Amount", "mean")). Fourth, note that this returns a DataFrame indexed by Region with both columns produced in one vectorized pass through the data. Optionally mention that named aggregation yields cleaner column names than the default MultiIndex columns produced by the dictionary approach, which saves a cleanup step.

COMMON WRONG ANSWERS: A red flag is proposing two separate groupby operations, such as df.groupby("Region")["Sales_Amount"].sum() followed by a second call for mean, then merging the results manually. Another red flag is using apply with a custom lambda that returns a Series, which is far slower than the built-in aggregation engine and harder to read. Iterating over rows or groups with a for loop is also unacceptable at this level because it abandons vectorization entirely and consumes far more memory. Finally, grouping the entire DataFrame without column selection first is not wrong but shows less precision, since it can trigger unnecessary computation on irrelevant columns or unexpected type issues.

LIKELY FOLLOW-UPS: The interviewer might ask how you would rename the resulting columns cleanly, which is a natural segue into named aggregation or post-hoc column flattening. They might ask how to handle missing values, prompting a discussion of whether to pass skipna behavior or pre-filter the data. Another follow-up could involve adding Product_Category to the groupby keys, testing whether you understand multi-index grouping and hierarchical aggregation. A performance-oriented follow-up might contrast agg with apply or ask how the operation scales to datasets that do not fit in memory, moving toward Dask or Polars.

ONE CONCRETE EXAMPLE: Suppose the DataFrame is named sales. The concise expression is sales.groupby("Region")["Sales_Amount"].agg(total="sum", average="mean"). This groups by Region, isolates Sales_Amount, and computes both sum and mean in one call, returning a DataFrame with columns total and average and an index of unique regions. If you prefer the dictionary syntax, sales.groupby("Region")["Sales_Amount"].agg({"Sales_Amount": ["sum", "mean"]}) works but creates a MultiIndex column that often requires cleanup afterward. For a senior candidate, recommending the named aggregation version demonstrates modern pandas best practices.

Source: pandas.pydata.org