Python | Pandas dataframe.ne()
Last Updated :
29 Jul, 2021
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.
Python3
import pandas as pd
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]})
df1
|
Let’s create the series
Python3
import pandas as pd
sr = pd.Series([3, 2, 4, 5, 6])
sr
|
Lets use the dataframe.ne() function to evaluate for inequality
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 dataframes. One dataframe contains NA values.
Python3
import pandas as pd
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]})
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]})
df2
|
Let’s use the dataframe.ne() function.
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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