How to Change Column Values in pandas DataFrame

To change column values in pandas, assign new values directly using df['column'] = new_values. You can also use conditions with loc or apply functions with apply() to modify values selectively.

Syntax

Here are common ways to change column values in a pandas DataFrame:

• df['column'] = new_values: Replace entire column values.
• df.loc[condition, 'column'] = new_value: Change values where condition is true.
• df['column'] = df['column'].apply(function): Apply a function to each value in the column.

python

df['column'] = new_values

df.loc[condition, 'column'] = new_value

df['column'] = df['column'].apply(function)

Example

This example shows how to change all values in a column, update values conditionally, and modify values using a function.

python

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Change entire 'Age' column to 20

df['Age'] = 20

# Change 'Age' to 40 where Name is 'Bob'
df.loc[df['Name'] == 'Bob', 'Age'] = 40

# Add 5 years to all ages using apply

df['Age'] = df['Age'].apply(lambda x: x + 5)

print(df)

Output

      Name  Age
0    Alice   25
1      Bob   45
2  Charlie   25

Common Pitfalls

Common mistakes when changing column values include:

• Trying to change values without using loc for conditional updates, which can cause SettingWithCopyWarning.
• Assigning a list or array with a different length than the DataFrame column.
• Forgetting to assign the result of apply() back to the column.

python

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# Wrong: This may cause a warning or no change
# df[df['Name'] == 'Bob']['Age'] = 40

# Right: Use loc for conditional assignment

df.loc[df['Name'] == 'Bob', 'Age'] = 40

print(df)

Output

    Name  Age
0  Alice   25
1    Bob   40

Quick Reference

Operation	Syntax	Description
Replace entire column	df['col'] = new_values	Set all values in the column to new_values
Conditional update	df.loc[condition, 'col'] = value	Change values where condition is True
Apply function	df['col'] = df['col'].apply(func)	Modify each value using a function
Add constant	df['col'] += constant	Increase all values by a constant
Replace with map	df['col'] = df['col'].map(mapping_dict)	Replace values using a dictionary mapping

Key Takeaways

Use direct assignment to change entire column values in pandas.

Use df.loc with a condition to update values selectively without warnings.

Apply functions with df['col'].apply() to modify column values flexibly.

Avoid SettingWithCopyWarning by using loc for conditional changes.

Ensure new values match the column length when assigning directly.