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Python | Pandas dataframe.ne()
  • Last Updated : 24 Nov, 2018

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 dataframe.ne() function checks for inequality of a dataframe element with a constant, series or other dataframe element-wise. If two values in comparison are not equal to each other, it returns a true else if they are equal it returns false.

Syntax: DataFrame.ne(other, axis=’columns’, level=None)

Parameters :
other : Series, DataFrame, or constant
axis : For Series input, axis to match Series index on
level :Broadcast across a level, matching Index values on the passed MultiIndex level

Returns : result : DataFrame



Example #1: Use ne() function to check for inequality between series and a dataframe.




# importing pandas as pd
import pandas as pd
  
# Creating the first dataframe 
df1=pd.DataFrame({"A":[14,4,5,4,1],
                  "B":[5,2,54,3,2],
                  "C":[20,20,7,3,8],
                  "D":[14,3,6,2,6]})
  
# Print the dataframe
df1

Let’s create the series




# importing pandas as pd
import pandas as pd
  
# create series
sr = pd.Series([3, 2, 4, 5, 6])
  
# Print series
sr

Lets use the dataframe.ne() function to evaluate for inequality




# evaluate inequality over the index axis
df.ne(sr, axis = 0)

Output :

All true value cells indicate that values in comparison are not equal to each other whereas, all the false values cells indicate that values in comparison are equal to each other.
 
Example #2: Use ne() function to check for inequality of two datframes. One dataframe contains NA values.




# importing pandas as pd
import pandas as pd
  
# Creating the first dataframe 
df1=pd.DataFrame({"A":[14,4,5,4,1],
                  "B":[5,2,54,3,2],
                  "C":[20,20,7,3,8],
                  "D":[14,3,6,2,6]})
  
# Creating the second dataframe with <code>Na</code> value
df2=pd.DataFrame({"A":[12,4,5,None,1],
                  "B":[7,2,54,3,None],
                  "C":[20,16,11,3,8],
                  "D":[14,3,None,2,6]})
  
# Print the second dataframe
df2

Let’s use the dataframe.ne() function.




# passing df2 to check for inequality with the df1 dataframe.
d1f.ne(df2)

Output :

All true value cells indicate that values in comparison are not equal to each other whereas, all the false values cells indicate that values in comparison are equal to each other.

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