Pandas DataFrame.where

The DataFrame.where method in pandas is used to replace values in a DataFrame based on a condition. It is particularly useful for applying conditional operations where elements that do not satisfy a condition are replaced with a specified value.

Syntax

The syntax for DataFrame.where is:

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

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

Parameters

Parameter	Description
`cond`	A condition to evaluate. Elements satisfying the condition retain their original values; otherwise, they are replaced by `other`.
`other`	Value to replace elements not satisfying the condition. Defaults to `NaN`.
`inplace`	If `True`, performs the operation in place and modifies the DataFrame directly. Defaults to `False`.
`axis`	Axis along which to evaluate the condition. Defaults to `None`.
`level`	Used for multi-level indexing to specify which level to apply the operation on. Defaults to `None`.

Returns

A DataFrame with values replaced based on the condition.

Examples

Basic Usage of `where`

Use where to replace values that do not satisfy a condition 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 where Age < 30
print("DataFrame where Age < 30 is replaced with NaN:")
result = df.where(df['Age'] >= 30)
print(result)

Output

DataFrame where Age < 30 is replaced with NaN:
     Name   Age   Salary
0     NaN   NaN     NaN
1     Ram  30.0  80000.0
2   Priya  35.0  90000.0

Using the `other` Parameter

Specify a custom value to replace elements that do not satisfy the condition.

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 < 30 with 0
print("DataFrame where Age < 30 is replaced with 0:")
result = df.where(df['Age'] >= 30, other=0)
print(result)

Output

DataFrame where Age < 30 is replaced with 0:
     Name  Age  Salary
0       0    0       0
1     Ram   30   80000
2   Priya   35   90000

Using `inplace=True`

Modify the DataFrame directly using the inplace=True 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)

# Replace values where Age < 30 with NaN in place
print("Modifying DataFrame in place where Age < 30:")
df.where(df['Age'] >= 30, inplace=True)
print(df)

Output

Modifying DataFrame in place where Age < 30:
     Name   Age   Salary
0     NaN   NaN     NaN
1     Ram  30.0  80000.0
2   Priya  35.0  90000.0

Applying Conditions Across All Columns

Use where to apply a condition across multiple columns.

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 all values less than 80000 with NaN
print("DataFrame where all values < 80000 are replaced with NaN:")
result = df.where(df >= 80000)
print(result)

Output

DataFrame where all values < 80000 are replaced with NaN:
     Name     Age    Salary
0     NaN     NaN      NaN
1     NaN     NaN  80000.0
2   Priya    35.0  90000.0

Summary

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

Using where to replace values in a DataFrame based on conditions.
Specifying a custom replacement value with other.
Modifying DataFrames in place using inplace=True.
Applying conditions across all columns.

The DataFrame.where method is a powerful tool for conditional operations and is especially useful in data preprocessing and analysis.