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The Pandas lt() function is used to get less than of dataframe and argument, element-wise (binary operator lt). It is equivalent to dataframe < other, but with support to choose axis (rows or columns) and level for comparison. The syntax for using this function is given below:

Syntax

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

Parameters

other Required. Specify any single or multiple element data structure, or list-like object.
axis Optional. Specify whether to compare by the index (0 or 'index') or columns (1 or 'columns').
level Optional. Specify int or label to broadcast across a level, matching Index values on the passed MultiIndex level. Default is None.

Return Value

Returns the result of the comparison.

Example: Using lt() on whole DataFrame

In the below example, a DataFrame df is created. The lt() function is used to get the result of comparing for less than of the whole data frame with given scalar value.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

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

#comparing for less than for all entries 
#of the DataFrame by 4
print("\ndf.lt(4) returns:")
print(df.lt(4))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.lt(4) returns:
       Bonus  Salary
John   False   False
Marry   True   False
Sam     True   False
Jo     False   False

Example: Comparing different column with different value

Different column can be compared with different scalar value by providing other argument as a list. Consider the following example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

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

#comparing all entries of Bonus column by 4
#comparing all entries of Salary column by 62
print("\ndf.lt([4,62]) returns:")
print(df.lt([4,62]))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.lt([4,62]) returns:
       Bonus  Salary
John   False    True
Marry   True   False
Sam     True   False
Jo     False    True

Example: Using lt() on selected columns

Instead of whole data frame, the lt() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Last Salary": [58, 60, 63, 57],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

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

#comparing all entries of Salary column by 62
print("\ndf['Salary'].lt(62) returns:")
print(df["Salary"].lt(62))

#comparing all entries of Bonus column by 4
#comparing all entries of Salary column by 62
print("\ndf[['Salary', 'Bonus']].lt([62,4]) returns:")
print(df[["Salary", "Bonus"]].lt([62,4]))

The output of the above code will be:

The DataFrame is:
       Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].lt(62) returns:
John      True
Marry    False
Sam      False
Jo        True
Name: Salary, dtype: bool

df[['Salary', 'Bonus']].lt([62,4]) returns:
       Salary  Bonus
John     True  False
Marry   False   True
Sam     False   True
Jo       True  False

Example: Using lt() on columns of a DataDrame

The lt() function can be applied in a data frame to get the result of comparing for less than 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'].lt(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   False
3    40    45    True
4    50    55    True

❮ Pandas DataFrame - Functions