Introduction
The GroupBy object in pandas is a powerful
tool for grouping and analyzing data. With methods like
aggregate(), filter(),
transform(), and apply(), you can
efficiently perform operations on subsets of your dataset.
Below, we use real-world examples such as sales analysis, payroll calculations, and customer data insights.
aggregate()
The aggregate() method applies aggregation functions like
sum, mean, or count to groups
of data.
Real-World Example: Calculate the total and average sales by category.
import pandas as pd
# Sales data
data = {'Category': ['Electronics', 'Clothing', 'Electronics', 'Clothing'],
'Sales': [1500, 1200, 1700, 800]}
df = pd.DataFrame(data)
# Grouping by category
grouped = df.groupby('Category')
result = grouped.aggregate(['sum', 'mean'])
print(result)
Output:
Sales
sum mean
Category
Clothing 2000 1000.0
Electronics 3200 1600.0
filter()
Use filter() to include or exclude groups based on a
condition.
Real-World Example: Display categories with total sales greater than 2500.
# Filter categories with total sales > 2500
filtered = grouped.filter(lambda x: x['Sales'].sum() > 2500)
print(filtered)
Output:
Category Sales
Electronics 1500
Electronics 1700
transform()
The transform() method applies a function to each group
and returns a DataFrame with the same shape as the original.
Real-World Example: Calculate each sale's percentage contribution to total sales in its category.
# Calculate sales percentage
df['Percent'] = grouped['Sales'].transform(lambda x: x / x.sum() * 100)
print(df)
Output:
Category Sales Percent
Electronics 1500 46.88
Electronics 1700 53.12
Clothing 1200 60.00
Clothing 800 40.00
apply()
The apply() method applies a custom function to each
group and returns the result.
Real-World Example: Find the largest sales difference within each category.
# Calculate the largest sales difference in each category
result = grouped.apply(lambda x: x['Sales'].max() - x['Sales'].min())
print(result)
Output:
Category
Clothing 400
Electronics 200
Summary
| Method | Description |
|---|---|
| aggregate() | Applies aggregation functions like sum and mean on grouped data. |
| filter() | Filters groups based on a condition. |
| transform() | Applies a function to each group without changing the original data shape. |
| apply() | Applies a custom function to each group of data. |