How to Add Column to DataFrame in pandas: Simple Guide

To add a column to a pandas DataFrame, assign a new column name with values using df['new_column'] = values. The values can be a list, a scalar, or a pandas Series matching the DataFrame's length.

Syntax

Use df['new_column'] = values to add a new column to a DataFrame.

• df: your existing DataFrame
• 'new_column': the name of the new column as a string
• values: data for the new column; can be a list, scalar (single value), or Series matching the DataFrame length

python

df['new_column'] = values

Example

This example shows how to add a new column named Age to a DataFrame with a list of values.

python

import pandas as pd

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

# Add new column 'Age'
df['Age'] = [25, 30, 35]

print(df)

Output

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   35

Common Pitfalls

Common mistakes when adding columns include:

• Assigning a list or Series with a different length than the DataFrame rows causes an error.
• Using a scalar value adds the same value to all rows, which may be unintended.
• For conditional columns, use np.where or df.loc to avoid errors.

python

import pandas as pd
import numpy as np

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

# Wrong: list length mismatch
# df['Age'] = [25, 30]  # This will raise a ValueError

# Right: scalar value (same age for all)
df['Age'] = 28

# Right: conditional column

df['Is_Adult'] = np.where(df['Age'] >= 18, True, False)

print(df)

Output

      Name  Age  Is_Adult
0    Alice   28      True
1      Bob   28      True
2  Charlie   28      True

Quick Reference

Summary tips for adding columns:

Action	Syntax	Notes
Add column with list	df['col'] = [val1, val2, ...]	List length must match DataFrame rows
Add column with scalar	df['col'] = value	Same value for all rows
Add column with Series	df['col'] = pd.Series([...])	Series index aligns with DataFrame
Add conditional column	df['col'] = np.where(condition, val1, val2)	Use numpy for conditions

Key Takeaways

Use df['new_column'] = values to add a column to a pandas DataFrame.

Ensure the values length matches DataFrame rows or use a scalar for the same value.

Common errors come from mismatched lengths or unintended scalar assignments.

Use numpy or df.loc for adding columns based on conditions.

Adding columns is a simple and powerful way to enrich your DataFrame.