Pandas DataFrame.ne

Pandas DataFrame.ne

The DataFrame.ne method in pandas is used to compare a DataFrame element-wise with another object (such as a scalar, Series, or DataFrame) for inequality (!=). It returns a DataFrame of boolean values indicating where the inequality holds true.

Syntax

The syntax for DataFrame.ne is:

DataFrame.ne(other, axis='columns', level=None)

Here, DataFrame refers to the pandas DataFrame being compared for inequality.

Parameters

Returns

A DataFrame of boolean values indicating where the inequality holds true.

Examples

Comparing with a Scalar

Compare all elements of a DataFrame with a scalar 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)

# Compare DataFrame elements with a scalar value
print("Inequality comparison with scalar 30:")
result = df.ne(30)
print(result)

Output

Inequality comparison with scalar 30:
    Name   Age  Salary
0   True  True    True
1   True  False    True
2   True  True    True

Comparing with Another DataFrame

Compare a DataFrame with another DataFrame element-wise.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
data2 = {
    'Name': ['Arjun', 'Raj', 'Priya'],
    'Age': [25, 32, 35],
    'Salary': [70000, 85000, 90000]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Compare the two DataFrames
print("Element-wise inequality between two DataFrames:")
result = df1.ne(df2)
print(result)

Output

Element-wise inequality between two DataFrames:
    Name    Age  Salary
0  False  False   False
1   True   True    True
2  False  False   False

Comparing with a Series

Compare a DataFrame with a Series element-wise along a specific axis.

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)

# Create a Series for comparison
series = pd.Series([25, 80000, 'Priya'], index=['Age', 'Salary', 'Name'])

# Compare with the Series
print("Element-wise inequality with Series:")
result = df.ne(series, axis='columns')
print(result)

Output

Element-wise inequality with Series:
    Name    Age  Salary
0   True  False    True
1   True   True   False
2  False   True    True

Handling MultiIndex with level Parameter

Use the level parameter to compare along a particular level in a MultiIndex DataFrame.

Python Program

import pandas as pd

# Create a MultiIndex DataFrame
data = {
    'Value': [100, 200, 300, 400]
}
index = pd.MultiIndex.from_tuples(
    [('A', 'x'), ('A', 'y'), ('B', 'x'), ('B', 'y')],
    names=['Group', 'Subgroup']
)
df = pd.DataFrame(data, index=index)

# Compare with a scalar using level
print("Inequality comparison along MultiIndex level:")
result = df.ne(200, level='Subgroup')
print(result)

Output

Inequality comparison along MultiIndex level:
               Value
Group Subgroup       
A     x         True
      y        False
B     x         True
      y        False

Summary

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

• Using ne to perform element-wise inequality comparison with scalars, Series, or DataFrames.
• Handling MultiIndex DataFrames using the level parameter.
• Specifying the axis to control the direction of the comparison.

The DataFrame.ne method is a powerful tool for inequality comparison in pandas, enabling flexible element-wise operations.