How to Use apply in pandas: Syntax, Examples, and Tips

In pandas, apply lets you run a custom function across rows or columns of a DataFrame or elements of a Series. Use df.apply(func, axis=0) to apply func to each column, or axis=1 to apply it to each row.

Syntax

The apply function in pandas is used like this:

• df.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

func: The function you want to apply to each row or column.
axis: 0 to apply to each column, 1 to apply to each row.
raw: If True, passes ndarray instead of Series to the function.
result_type: Controls the type of the final output.
args and kwargs: Additional arguments for the function.

python

df.apply(func, axis=0)

Example

This example shows how to use apply to calculate the range (max - min) for each row in a DataFrame.

python

import pandas as pd

data = {'A': [1, 4, 3], 'B': [4, 2, 5], 'C': [7, 1, 6]}
df = pd.DataFrame(data)

# Define a function to calculate range
def row_range(row):
    return row.max() - row.min()

# Apply function to each row
range_per_row = df.apply(row_range, axis=1)
print(range_per_row)

Output

0    6
1    3
2    3
dtype: int64

Common Pitfalls

Common mistakes when using apply include:

• Using the wrong axis value, which changes whether the function applies to rows or columns.
• Writing functions that do not handle Series input properly.
• Expecting apply to be very fast; it can be slower than vectorized operations.

Example of a wrong and right way:

python

import pandas as pd

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

# Wrong: axis=0 applies function to columns, but function expects row
 def add_one(row):
     return row + 1

print(df.apply(add_one, axis=0))  # Works here but changes columns

# Right: use axis=1 to apply to rows
print(df.apply(add_one, axis=1))

Output

   A  B
0  2  4
1  3  5
   A  B
0  2  4
1  3  5

Quick Reference

Parameter	Description
func	Function to apply to each row or column
axis	0 for columns, 1 for rows
raw	If True, passes ndarray instead of Series
result_type	Controls output type: 'expand', 'reduce', or 'broadcast'
args	Positional arguments to pass to func
kwargs	Keyword arguments to pass to func

Key Takeaways

Use df.apply(func, axis=1) to apply a function to each row and axis=0 for each column.

The function passed to apply should accept a pandas Series when axis is set.

apply is flexible but can be slower than built-in vectorized pandas functions.

Check the axis parameter carefully to avoid unexpected results.

Use additional args and kwargs to pass extra parameters to your function.