How to Iterate Over Groups in pandas: Simple Guide

Use DataFrame.groupby() to split data into groups, then iterate over these groups with a for loop. Each loop returns a tuple with the group name and the group data as a DataFrame.

Syntax

The basic syntax to iterate over groups in pandas is:

• grouped = df.groupby('column_name') creates groups based on unique values in the specified column.
• for group_name, group_data in grouped: loops over each group.
• group_name is the unique value of the group.
• group_data is a DataFrame containing rows of that group.

python

grouped = df.groupby('column_name')
for group_name, group_data in grouped:
    print(group_name)
    print(group_data)

Example

This example shows how to group a DataFrame by the 'Category' column and iterate over each group to print the group name and its rows.

python

import pandas as pd

data = {'Category': ['A', 'B', 'A', 'B', 'C'],
        'Value': [10, 20, 15, 25, 30]}
df = pd.DataFrame(data)

grouped = df.groupby('Category')

for group_name, group_data in grouped:
    print(f"Group: {group_name}")
    print(group_data)
    print('---')

Output

Group: A
  Category  Value
0        A     10
2        A     15
---
Group: B
  Category  Value
1        B     20
3        B     25
---
Group: C
  Category  Value
4        C     30
---

Common Pitfalls

Common mistakes when iterating over groups include:

• Forgetting that group_data is a DataFrame, so you can use all DataFrame operations on it.
• Trying to modify the original DataFrame inside the loop without using apply or other methods.
• Assuming groups are sorted; groups appear in sorted order by default.

python

import pandas as pd

data = {'Category': ['A', 'B', 'A', 'B'], 'Value': [1, 2, 3, 4]}
df = pd.DataFrame(data)

grouped = df.groupby('Category')

# Wrong: trying to modify original df inside loop
for name, group in grouped:
    group['Value'] = group['Value'] * 2  # This does NOT change df

print(df)

# Right: use transform to modify original df

df['Value'] = grouped['Value'].transform(lambda x: x * 2)
print(df)

Output

  Category  Value
0        A      1
1        B      2
2        A      3
3        B      4
  Category  Value
0        A      2
1        B      4
2        A      6
3        B      8

Quick Reference

Summary tips for iterating over groups in pandas:

• Use df.groupby('col') to create groups.
• Loop with for name, group in grouped:.
• group is a DataFrame for that group.
• Use apply or transform to modify data efficiently.
• Groups are sorted by default.

Key Takeaways

Use DataFrame.groupby() to split data into groups by column values.

Iterate groups with a for loop receiving group name and group DataFrame.

Group data is a DataFrame; you can apply all DataFrame operations on it.

Modifying original data inside a loop requires transform or apply, not direct assignment.

Groups are sorted by default when iterating.