Pandas GroupBy Metric Aggregationeasy

Pandas GroupBy Metric Aggregation

easy NumPy & Data Wrangling 10–15 min

NumPy allowed · plain Python earns 10 bonus points

A vector transforms

Matrix multiplication is the first computation behind every model.

Open lesson →

01vector

02matrix

03transform

04result

[2, −1] × [[1, 2], [−.5, 1]] = [0, −2]

Computed matrix × vector transformation

Group tabular records by category and compute aggregate metrics (count, sum, mean) using Pandas.

Related Skills

Data Aggregation & Grouping

Task

Implement groupby_aggregate(records, group_col, val_col). Given tabular rows, group by group_col and compute for each group its count (int), sum (float rounded to 2 decimals), and mean (float rounded to 2 decimals). Return a dictionary mapping group names to their metric dict.

Function Signature

groupby_aggregate(records: list[dict], group_col: str, val_col: str) -> dict

Examples

Example 1: Regional Sales Aggregation
Input: records = [{'region': 'East', 'sales': 100}, {'region': 'West', 'sales': 200}, {'region': 'East', 'sales': 150}]
group_col = 'region'
val_col = 'sales'
Output: {'East': {'count': 2, 'sum': 250, 'mean': 125}, 'West': {'count': 1, 'sum': 200, 'mean': 200}}

Example 2: Logins by Role
Input: records = [{'role': 'admin', 'logins': 5}, {'role': 'user', 'logins': 2}, {'role': 'user', 'logins': 4}]
group_col = 'role'
val_col = 'logins'
Output: {'admin': {'count': 1, 'sum': 5, 'mean': 5}, 'user': {'count': 2, 'sum': 6, 'mean': 3}}

Constraints

records is a list of dictionaries.
group_col and val_col exist in all records.
val_col values are numeric.
Returns empty dict {} for empty records.

Python3Saved ✓

import pandas as pd

def groupby_aggregate(records: list[dict], group_col: str, val_col: str) -> dict:

"""Group records by group_col and compute count, sum, and mean for val_col.

Round sum and mean to 2 decimal places.

Return a dictionary mapping each group name (str) to:

{'count': int, 'sum': float, 'mean': float}"""

# Your implementation here

pass

Case 1: Regional Sales Aggregation

Input: records = [{'region': 'East', 'sales': 100}, {'region': 'West', 'sales': 200}, {'region': 'East', 'sales': 150}]
group_col = 'region'
val_col = 'sales'

Expected: {'East': {'count': 2, 'sum': 250, 'mean': 125}, 'West': {'count': 1, 'sum': 200, 'mean': 200}}

Case 2: Logins by Role

Input: records = [{'role': 'admin', 'logins': 5}, {'role': 'user', 'logins': 2}, {'role': 'user', 'logins': 4}]
group_col = 'role'
val_col = 'logins'

Expected: {'admin': {'count': 1, 'sum': 5, 'mean': 5}, 'user': {'count': 2, 'sum': 6, 'mean': 3}}