Pandas — GroupBy

Basic groupby

import pandas as pd

df = pd.DataFrame({
    'department': ['HR', 'HR', 'IT', 'IT', 'Sales', 'Sales'],
    'salary': [50000, 55000, 60000, 65000, 70000, 75000]
})

# Group and aggregate
print(df.groupby('department')['salary'].mean())

Multiple aggregations

df.groupby('department')['salary'].agg(['mean', 'min', 'max', 'count'])

Custom aggregation

df.groupby('department')['salary'].agg(lambda x: x.max() - x.min())

Multiple columns

df.groupby(['department', 'level'])['salary'].mean()

Iteration

for name, group in df.groupby('department'):
    print(name)
    print(group)

Mini Practice

1. Group by column
2. Apply aggregations
3. Use multiple aggregations
4. Iterate over groups

Up Next

Continue with Merge - Combining DataFrames.