Grouping and Aggregating

groupby, agg and transform

groupby splits rows by key, applies an aggregation and combines results: df.groupby("city")["revenue"].sum(). Named aggregation (agg(orders=("units", "size"), ...)) produces several clearly named statistics at once. transform returns a result aligned to the original rows, which is ideal for shares of a group total or group-wise normalisation.

Group, join, reshape, resample

Most analysis questions are a combination of grouping, joining, reshaping and time-based aggregation.

Revenue by city and each order's share, run

I ran this with Python 3.12.3 and pandas 3.0.6. Totals per city, named aggregations, and transform computing each order's share of its city's revenue.

import pandas as pd

df = pd.DataFrame({
    "city": ["Delhi", "Mumbai", "Pune", "Delhi", "Pune", "Mumbai"],
    "product": ["pen", "mug", "pen", "lamp", "mug", "pen"],
    "units": [10, 4, 7, 1, 3, 5],
    "revenue": [200.0, 1000.0, 140.0, 1499.0, 750.0, 100.0],
})
print(df.groupby("city")["revenue"].sum())
print(df.groupby("city").agg(
    orders=("units", "size"),
    units=("units", "sum"),
    avg_revenue=("revenue", "mean"),
).round(1))
df["city_share"] = (df["revenue"] / df.groupby("city")["revenue"].transform("sum")).round(2)
print(df[["city", "revenue", "city_share"]])

Output:

city
Delhi     1699.0
Mumbai    1100.0
Pune       890.0
Name: revenue, dtype: float64
        orders  units  avg_revenue
city                              
Delhi        2     11        849.5
Mumbai       2      9        550.0
Pune         2     10        445.0
     city  revenue  city_share
0   Delhi    200.0        0.12
1  Mumbai   1000.0        0.91
2    Pune    140.0        0.16
3   Delhi   1499.0        0.88
4    Pune    750.0        0.84
5  Mumbai    100.0        0.09

Avoid apply when a built-in exists

Built-in aggregations (sum, mean, size, nunique) are much faster than apply with a Python function.

त्वरित जाँच: Which groupby method returns results aligned to the original rows?

• agg
• transform
• size
• describe