How to Group Data by Key in Python

Group a list of dictionaries by a specified key using a defaultdict and compute per-group averages.

Easy Python 3.9+ Aug 9, 2026 Data pipelines & processing 15 views 0 copies

grouping defaultdict data-pipelines dictionaries

Python code

23 lines

Python 3.9+

from collections import defaultdict

def group_by_key(data, key):
    grouped = defaultdict(list)
    for item in data:
        grouped[item[key]].append(item)
    return dict(grouped)

if __name__ == "__main__":
    records = [
        {"name": "Alice", "dept": "Engineering", "score": 85},
        {"name": "Bob", "dept": "Sales", "score": 72},
        {"name": "Charlie", "dept": "Engineering", "score": 91},
        {"name": "Diana", "dept": "Sales", "score": 64},
        {"name": "Eve", "dept": "Marketing", "score": 78},
    ]

    by_dept = group_by_key(records, "dept")

    for dept, members in by_dept.items():
        names = ", ".join(m["name"] for m in members)
        avg_score = sum(m["score"] for m in members) / len(members)
        print(f"{dept}: {names} (avg score: {avg_score:.1f})")

Output

stdout

Engineering: Alice, Charlie (avg score: 88.0)
Sales: Bob, Diana (avg score: 68.0)
Marketing: Eve (avg score: 78.0)

How it works

The group_by_key function iterates over each dictionary and uses a defaultdict(list) to automatically create a list for each new key. Appending each item to the list groups records without needing to check if the key exists. Converting back to a regular dict at the end returns a clean dictionary of lists. The main block then iterates over each group, joins member names, and calculates the average score, demonstrating a common ETL pattern.

Common mistakes

Using a regular dict and forgetting to initialize empty lists, causing KeyError
Assuming all items have the grouping key, leading to KeyError for missing keys
Not converting defaultdict back to dict before returning, exposing unexpected behavior

Variations

Use pandas' groupby for larger datasets, e.g., pd.DataFrame(records).groupby('dept').mean()
Use itertools.groupby after sorting the data by the key

Real-world use cases

Grouping event logs by user ID to compute session durations in analytics pipelines.
Categorizing support tickets by department to aggregate response times and priorities.
Batching orders by warehouse region for inventory and shipping processing.

How to Group Data by Key in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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