Python Tutorial
Pandas GroupBy
Split rows into groups, apply a function, and combine the results — counts, means, and custom aggregations.
Group and Aggregate
import pandas as pd
df = pd.DataFrame({
"city": ["Oslo", "Bergen", "Oslo", "Bergen"],
"score": [88, 92, 95, 80],
})
print(df.groupby("city")["score"].mean())
print(df.groupby("city")["score"].agg(["count", "mean", "max"]))Named Aggregations
print(df.groupby("city").agg(
avg_score=("score", "mean"),
n=("score", "size"),
))📘 Real-World Deep Dive
Knowing <strong>Pandas Groupby (pandas)</strong> well is what turns pandas from a curiosity into a daily tool — you'll reach for it in nearly every real project.
Real-Life Scenario
An end-to-end usage of Pandas Groupby that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.DataFrame({"team": ["a","a","b","b","c"], "score": [3, 5, 2, 8, 6]})
print(df.groupby("team")["score"].agg(["mean", "count", "max"]))Expected Output
(see source)Common mistakes
- A
DataFrameindexing pattern likedf[df.col > 5]returns a copy — use.loc[row_mask, col]for assignment to avoidSettingWithCopyWarning. - Pandas infers
objectdtype for CSVs with mixed numeric/text columns; cast withpd.to_numeric/astype("category")for big speed/memory wins. df.iterrows()is O(n) and slow; iterate withdf.itertuples()or vectorise column-wise.- Treating Pandas Groupby as a black box without reading the docs — the API has subtle defaults that bite when you scale.
🚀 Performance & Best Practices
- Enable the Arrow backend:
pd.read_csv("…", engine="pyarrow", dtype_backend="pyarrow")for faster, type-stable reads. - Use
categoricaldtype for columns with low-cardinality strings — sort/join/group-by speed up dramatically. - Switching a hot loop from row-wise Python to
df.eval("…")/df.query("…")often gives 5–50×. - When working with pandas, prefer vectorised / batched operations over Python loops.
🧪 Try It Yourself
- Reproduce the snippet on a representative slice of your own data.
- Profile the snippet with
cProfileortimeitand find the single biggest improvement. - Generalise the snippet into a small, reusable function you can drop into future projects.