Python | Pandas Series.ne()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series.ne() is used to compare every element of Caller series with passed series. It returns True for every element which is Not Equal to the element in passed series. Note: The results are returned on the basis of comparison caller series != other series.

Syntax: Series.ne(other, level=None, fill_value=None) Parameters: other: other series to be compared with level: int or name of level in case of multi level fill_value: Value to be replaced instead of NaN Return type: Boolean series

Example #1: Handling Null Values In this example, two series are created using pd.Series(). The series contains some Null values and some equal values at same indices too. The series are compared using .ne() method and 5 is passed to fill_value parameter to replace NaN values by 5. Python3 1==

# importing pandas module  
import pandas as pd  
  
# importing numpy module 
import numpy as np 
  
# creating series 1 
series1 = pd.Series([70, 5, 0, 225, 1, 16, np.nan, 10, np.nan]) 
  
# creating series 2 
series2 = pd.Series([27, np.nan, 2, 23, 1, 95, 53, 10, 5]) 

# NaN replacement
replace_nan = 5

# calling and returning to result variable
result = series1.ne(series2, fill_value = replace_nan)

# display 
result 

Output: As shown in output, True was returned wherever value in caller series was Not Equal to value in passed series. Also it can be seen Null values were replaced by 5 and the comparison was made using that value.   Example #2: Calling on Series with str objects In this example, two series are created using pd.Series(). The series contains some string values too. In case of strings, the comparison is made with their ASCII values. Python3 1==

# importing pandas module  
import pandas as pd  
  
# importing numpy module 
import numpy as np 
  
# creating series 1 
series1 = pd.Series(['Aaa', 0, 'cat', 43, 9, 'Dog', np.nan, 'x', np.nan]) 
  
# creating series 2 
series2 = pd.Series(['vaa', np.nan, 'Cat', 23, 5, 'Dog', 54, 'x', np.nan]) 

# NaN replacement
replace_nan = 14

# calling and returning to result variable
result = series1.ne(series2, fill_value = replace_nan)

# display 
result 

Output: As it can be seen in output, in case of strings, the comparison was made using their ASCII values. True was returned wherever the string in Caller series wasn't equal to string in passed series.