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
# importing pandas as pdimport 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 dataframedf1 |
Let’s create the series
Python3
# importing pandas as pdimport pandas as pd# create seriessr = pd.Series([3, 2, 4, 5, 6])# Print seriessr |
Lets use the dataframe.ne() function to evaluate for inequality
Python3
# evaluate inequality over the index axisdf.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 dataframes. One dataframe contains NA values.
Python3
# importing pandas as pdimport 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> valuedf2=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 dataframedf2 |
Let’s use the dataframe.ne() function.
Python3
# 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.
