Pandas DataFrame.mask

The DataFrame.mask method in pandas is used to replace values in a DataFrame where a specified condition is True. This method is useful for conditionally updating data in a DataFrame.

Syntax

The syntax for DataFrame.mask is:

DataFrame.mask(cond, other=, *, inplace=False, axis=None, level=None)

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

Parameters

Parameter	Description
`cond`	A boolean condition. Where True, values in the DataFrame will be replaced.
`other`	Optional. The value to replace with where the condition is True. Defaults to NaN.
`inplace`	If True, modifies the original DataFrame. If False, returns a new DataFrame. Defaults to False.
`axis`	Axis along which the condition is evaluated (0 for rows, 1 for columns). Defaults to None.
`level`	Used with a MultiIndex to specify the level to apply the mask on. Defaults to None.

Returns

A DataFrame with updated values where the condition is True. If inplace=True, returns None and modifies the original DataFrame.

Examples

Replacing Values Based on a Condition

Replace values greater than a threshold with NaN.

Python Program

import pandas as pd
import numpy as np

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Replace values greater than 75000 with NaN
print("DataFrame after masking values greater than 75000:")
df_masked = df.mask(df['Salary'] > 75000)
print(df_masked)

Output

DataFrame after masking values greater than 75000:
    Name  Age   Salary
0  Arjun   25  70000.0
1    Ram   30      NaN
2   Priya   35      NaN

Replacing Values with a Specific Value

Replace values that meet a condition with a specific value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Replace values greater than 75000 with 0
print("DataFrame after masking values greater than 75000 with 0:")
df_masked = df.mask(df['Salary'] > 75000, other=0)
print(df_masked)

Output

DataFrame after masking values greater than 75000 with 0:
    Name  Age   Salary
0  Arjun   25  70000.0
1    Ram   30      0.0
2   Priya   35      0.0

Masking with `inplace=True`

Modify the original DataFrame by using inplace=True.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Replace values where 'Age' is greater than 30 with NaN inplace
print("DataFrame after masking 'Age' greater than 30:")
df.mask(df['Age'] > 30, inplace=True)
print(df)

Output

DataFrame after masking 'Age' greater than 30:
    Name   Age   Salary
0  Arjun  25.0  70000.0
1    Ram  30.0  80000.0
2   Priya   NaN      NaN

Masking Along a Specific Axis

Apply the mask condition along rows or columns using the axis parameter.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Mask along columns (axis=1)
print("DataFrame after masking along columns:")
df_masked = df.mask(df > 75000, axis=1)
print(df_masked)

Output

DataFrame after masking along columns:
    Name   Age   Salary
0  Arjun  25.0  70000.0
1    Ram  30.0      NaN
2   Priya  35.0      NaN

Summary

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

Replacing values in a DataFrame where a condition is True.
Replacing with NaN by default or with a specific value using other.
Using inplace=True to modify the original DataFrame directly.
Applying the mask condition along rows or columns with axis.

The DataFrame.mask method is a powerful tool for conditionally updating data in pandas DataFrames.