Pandas Series - le() function

The Pandas le() function compares series and other, element-wise for less than equal to and returns the comparison result. It is equivalent to series <= other, but with support to substitute a fill_value for missing data as one of the parameters.

Syntax

Series.le(other, level=None, fill_value=None)

Parameters

Return Value

Returns the result of the comparison.

Example: Comparing Series with a scalar value

In the example below, the le() function is used to compare a Series with a given scalar value.

import pandas as pd
import numpy as np

x = pd.Series([10, 20, 30, 40, 50])

print("The Series contains:")
print(x)

#comparing Series <= 30
print("\nx.le(30) returns:")
print(x.le(30))

The output of the above code will be:

The Series contains:
0    10
1    20
2    30
3    40
4    50
dtype: int64

x.le(30) returns:
0     True
1     True
2     True
3    False
4    False
dtype: bool

Example: Comparing Series with a Series

A series can be compared with another series element-wise for less than equal to of. Consider the following example:

import pandas as pd
import numpy as np

x = pd.Series([10, np.NaN, 30, 40, 50], 
              index=['A', 'B', 'C', 'D', 'E'])
y = pd.Series([5, 20, 30, np.NaN, 65], 
              index=['A', 'B', 'C', 'D', 'E'])

print("The x contains:")
print(x)

print("\nThe y contains:")
print(y)

#calculating x <= y
print("\nx.le(y, fill_value=0) returns:")
print(x.le(y, fill_value=0))

The output of the above code will be:

The x contains:
A    10.0
B     NaN
C    30.0
D    40.0
E    50.0
dtype: float64

The y contains:
A     5.0
B    20.0
C    30.0
D     NaN
E    65.0
dtype: float64

x.le(y, fill_value=0) returns:
A    False
B     True
C     True
D    False
E     True
dtype: bool

Example: using le() on columns of a DataDrame

The le() function can be applied in a DataFrame to get the result of comparing for less than equal to of two series/column element-wise. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "col1": [10, 20, 30, 40, 50],
  "col2": [5, 15, 30, 45, 55]
})

print("The DataFrame is:")
print(df)

#calculating 'col1' <= 'col2'
df['Result'] = df['col1'].le(df['col2'])

print("\nThe DataFrame is:")
print(df)

The output of the above code will be:

The DataFrame is:
   col1  col2
0    10     5
1    20    15
2    30    30
3    40    45
4    50    55

The DataFrame is:
   col1  col2  Result
0    10     5   False
1    20    15   False
2    30    30    True
3    40    45    True
4    50    55    True