Pandas DataFrame.le

Pandas DataFrame.le

The DataFrame.le method in pandas is used to perform an element-wise less-than-or-equal-to comparison between a DataFrame and another DataFrame, Series, or scalar value. The method returns a DataFrame of boolean values, where each element indicates if the corresponding element in the original DataFrame is less than or equal to the specified value.

Syntax

The syntax for DataFrame.le is:

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

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

Parameters

Returns

A DataFrame of boolean values indicating the result of the less-than-or-equal-to comparison.

Examples

Comparing with a Scalar

Perform a less-than-or-equal-to comparison between a DataFrame and 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 to a scalar value
print("Comparing with scalar value 30:")
result = df[['Age', 'Salary']].le(30)
print(result)

Output

Comparing with scalar value 30:
     Age  Salary
0   True   False
1   True   False
2  False   False

Comparing with Another DataFrame

Perform a less-than-or-equal-to comparison between two DataFrames.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
data2 = {
    'Age': [30, 25, 40],
    'Salary': [75000, 75000, 95000]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

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

Output

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

Comparing Along a Specific Axis

Perform a less-than-or-equal-to comparison 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)

# Compare row-wise with a Series
series = pd.Series([30, 75000], index=['Age', 'Salary'])
print("Row-wise comparison with a Series:")
result = df[['Age', 'Salary']].le(series, axis='columns')
print(result)

Output

Row-wise comparison with a Series:
     Age  Salary
0   True   True
1   True  False
2  False  False

Using MultiIndex and Level

Use the level parameter to compare specific levels of a MultiIndex DataFrame.

Python Program

import pandas as pd

# Create a MultiIndex DataFrame
data = {
    'Value': [10, 20, 30, 40]
}
index = pd.MultiIndex.from_tuples([
    ('A', 1), ('A', 2), ('B', 1), ('B', 2)
], names=['Group', 'Number'])
df = pd.DataFrame(data, index=index)

# Compare values at level 'Number'
print("Comparison at level 'Number':")
result = df.le(20, level='Number')
print(result)

Output

Comparison at level 'Number':
               Value
Group Number       
A     1        True
      2       False
B     1        True
      2       False

Summary

In this tutorial, we explored the DataFrame.le method in pandas. Key points include:

• Comparing a DataFrame with a scalar, Series, or another DataFrame.
• Using the axis parameter for row-wise or column-wise comparisons.
• Handling MultiIndex DataFrames with the level parameter.

The DataFrame.le method is a powerful tool for element-wise comparisons in pandas, useful for filtering and conditional operations.