Pandas DataFrame - ne() function

The Pandas ne() function compares dataframe and other, element-wise for not equal to and returns the comparison result. It is equivalent to dataframe != other, but with support to choose axis (rows or columns) and level for comparison.

Syntax

DataFrame.ne(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 ne() on whole DataFrame

In the example below, a DataFrame df is created. The ne() function is used to compare this DataFrame with a 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 Not equal to for all
#entries of the DataFrame by 4
print("\ndf.ne(4) returns:")
print(df.ne(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.ne(4) returns:
       Bonus  Salary
John    True    True
Marry   True    True
Sam     True    True
Jo     False    True

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

Example: using ne() on selected columns

Instead of whole DataFrame, the ne() 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'].ne(62) returns:")
print(df["Salary"].ne(62))

#comparing all entries of Bonus column by 4
#comparing all entries of Salary column by 62
print("\ndf[['Salary', 'Bonus']].ne([62,4]) returns:")
print(df[["Salary", "Bonus"]].ne([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'].ne(62) returns:
John      True
Marry    False
Sam       True
Jo        True
Name: Salary, dtype: bool

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

Example: using ne() on columns of a DataDrame

The ne() function can be applied in a DataFrame to get the result of comparing for Not 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": [10, 15, 30, 45, 55]
})

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

#calculating 'col1' != 'col2'
df['Result'] = df['col1'].ne(df['col2'])

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

The output of the above code will be:

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

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

❮ Pandas DataFrame - Functions