Pandas DataFrame.apply: Apply a Function to Rows or Columns of a DataFrame

Pandas DataFrame.apply

The DataFrame.apply method in pandas is used to apply a function along the axis (rows or columns) of a DataFrame. This method allows you to perform complex operations on DataFrame elements, rows, or columns.

Syntax

The syntax for DataFrame.apply is:

DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), by_row='compat', engine='python', engine_kwargs=None, **kwargs)

Here, DataFrame refers to the pandas DataFrame on which the function is applied.

Parameters

ParameterDescription
funcThe function to apply to each row or column of the DataFrame. This can be a built-in function, a lambda function, or a custom function.
axisSpecifies the axis along which the function is applied. Use 0 or 'index' to apply the function to each column, and 1 or 'columns' to apply the function to each row. Defaults to 0.
rawDetermines whether the function receives a Series (False) or a NumPy array (True). Defaults to False.
result_typeSpecifies the type of the result. Options include 'expand', 'reduce', 'broadcast', or None. Defaults to None.
argsA tuple of additional arguments to pass to the function.
by_rowIf 'compat', the function is applied to each row or column as a Series. If False, the function is applied to the entire DataFrame at once. Defaults to 'compat'.
engineSpecifies the engine to use for computation. Options include 'python' or 'numba'. Defaults to 'python'.
engine_kwargsA dictionary of additional keyword arguments to pass to the engine.
**kwargsAdditional keyword arguments to pass to the function.

Returns

A Series or DataFrame resulting from applying the function to the specified axis of the DataFrame.

Examples

Applying a Function to Each Column of a DataFrame

This example demonstrates how to use apply to calculate the sum of each column in a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Define a function to calculate the sum of a column
def column_sum(column):
    return column.sum()

# Apply the function to each column of the DataFrame
result = df.apply(column_sum)
print(result)

Output

A     6
B    15
C    24
dtype: int64

Applying a Function to Each Row of a DataFrame

This example shows how to use apply to calculate the sum of each row in a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Define a function to calculate the sum of a row
def row_sum(row):
    return row.sum()

# Apply the function to each row of the DataFrame
result = df.apply(row_sum, axis=1)
print(result)

Output

0    12
1    15
2    18
dtype: int64

Applying a Lambda Function to a DataFrame

This example demonstrates how to use a lambda function with apply to multiply each element in a DataFrame by 2.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Apply a lambda function to each element of the DataFrame
result = df.apply(lambda x: x * 2)
print(result)

Output

   A   B   C
0  2   8  14
1  4  10  16
2  6  12  18

Applying a Function with Additional Arguments to a DataFrame

This example shows how to pass additional arguments to a function using apply. The function adds a specified value to each element in the DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Define a function to add a value to each element
def add_value(element, value):
    return element + value

# Apply the function with an additional argument
result = df.apply(add_value, args=(10,))
print(result)

Output

    A   B   C
0  11  14  17
1  12  15  18
2  13  16  19

Summary

In this tutorial, we explored the DataFrame.apply method in pandas. Key takeaways include:

  • Using apply to apply a function to each row or column of a DataFrame.
  • Applying lambda functions for quick, inline operations.
  • Passing additional arguments to the function using the args parameter.
  • Understanding the flexibility of apply for performing complex operations on DataFrames.
  • Using the by_row, engine, and engine_kwargs parameters for advanced use cases.

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