Pandas DataFrame.eq

The DataFrame.eq method in pandas is used to perform element-wise equality comparison between a DataFrame and another DataFrame, Series, or scalar value. It returns a DataFrame of the same shape with True where elements are equal and False otherwise.

Syntax

The syntax for DataFrame.eq is:

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

Here, DataFrame refers to the pandas DataFrame being compared.

Parameters

Parameter	Description
`other`	Value to compare against. Can be a scalar, Series, or DataFrame.
`axis`	Specifies whether to compare by rows or columns. Defaults to `'columns'`. Use `0` or `'index'` for row-wise comparison and `1` or `'columns'` for column-wise comparison.
`level`	Broadcast across a specified level, matching labels on a MultiIndex.

Returns

A DataFrame of the same shape as the original, with boolean values indicating element-wise equality.

Examples

Comparing with a Scalar

Compare all elements in a DataFrame to 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 all elements with a scalar value
print("Comparing all elements with 30:")
result = df.eq(30)
print(result)

Output

Comparing all elements with 30:
    Name    Age  Salary
0  False  False   False
1  False   True   False
2  False  False   False

Row-Wise Comparison

Compare elements of a DataFrame with a Series row-wise using axis=0.

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 row-wise with a Series
row_comparison = pd.Series({'Name': 'Arjun', 'Age': 25, 'Salary': 70000})
print("Row-wise comparison:")
result = df.eq(row_comparison, axis=1)
print(result)

Output

Row-wise comparison:
    Name   Age  Salary
0   True  True    True
1  False False   False
2  False False   False

Column-Wise Comparison

Compare elements of a DataFrame with a Series column-wise using axis=1.

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 column-wise with a Series
column_comparison = pd.Series(['Arjun', 30, 80000], index=['Name', 'Age', 'Salary'])
print("Column-wise comparison:")
result = df.eq(column_comparison, axis=0)
print(result)

Output

Column-wise comparison:
    Name    Age  Salary
0   True  False   False
1  False   True    True
2  False  False   False

Comparing with Another DataFrame

Compare two DataFrames 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', 'Ram', 'Suresh'],
    'Age': [25, 29, 35],
    'Salary': [70000, 75000, 90000]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Compare two DataFrames
print("Element-wise comparison between two DataFrames:")
result = df1.eq(df2)
print(result)

Output

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

Summary

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

Using eq for element-wise equality comparisons with scalars, Series, or other DataFrames.
Performing row-wise or column-wise comparisons with the axis parameter.
Handling element-wise comparisons with flexible inputs.

The DataFrame.eq method is a powerful tool for equality checks and filtering data in pandas.