Pandas DataFrame.map: Apply a Function Element-wise to a DataFrame

Pandas DataFrame.map

The DataFrame.map method in pandas is used to apply a function element-wise to a DataFrame. It is primarily used for transforming or mapping values in a DataFrame using a specified function.

Syntax

The syntax for DataFrame.map is:

DataFrame.map(func, na_action=None, **kwargs)

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

Parameters

ParameterDescription
funcThe function to apply to each element of the DataFrame. This can be a built-in function, a lambda function, or a custom function.
na_actionSpecifies how to handle missing values (NaN). If set to 'ignore', the function will not be applied to missing values. Defaults to None.
**kwargsAdditional keyword arguments to pass to the function.

Returns

A DataFrame with the same shape as the original, where each element has been transformed by the specified function.

Examples

Applying a Function to Each Element of a DataFrame

This example demonstrates how to use map 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 function to each element of the DataFrame
result = df.map(lambda x: x * 2)
print(result)

Output

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

Handling Missing Values in a DataFrame

This example shows how to use the na_action parameter to ignore missing values when applying a function to a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame with missing values
df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None], 'C': [7, 8, 9]})

# Apply a function to each element, ignoring missing values
result = df.map(lambda x: x * 2, na_action='ignore')
print(result)

Output

      A     B   C
0   2.0   8.0  14
1   NaN  10.0  16
2   6.0   NaN  18

Applying a Custom Function to a DataFrame

This example demonstrates how to use a custom function with map to transform each element 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 custom function to square each element
def square(x):
    return x ** 2

# Apply the custom function to each element of the DataFrame
result = df.map(square)
print(result)

Output

   A   B   C
0  1  16  49
1  4  25  64
2  9  36  81

Summary

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

  • Using map to apply a function element-wise to a DataFrame.
  • Handling missing values with the na_action parameter.
  • Applying custom functions to transform DataFrame elements.

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