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 Index.nunique() function return number of unique elements in the object. It returns a scalar value which is the count of all the unique values in the Index. By default the NaN values are not included in the count. If dropna parameter is set to be False then it includes NaN value in the count.
Syntax: Index.nunique(dropna=True)
Parameters :
dropna : Don’t include NaN in the count.Returns : nunique : int
Example #1: Use Index.nunique()() function to find the count of unique values in the Index. Do not include NaN values in the count.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Beagle', 'Pug', 'Labrador', 'Pug', 'Mastiff', None, 'Beagle']) # Print the Index idx |
Output :
Let’s find the count of unique values in the Index.
# to find the count of unique values. idx.nunique(dropna = True) |
Output :
As we can see in the output, the function has returned 4 indicating that there are only 4 unique values in the Index.
Example #2: Use Index.nunique() function find out all the unique values in the Index. Also include the missing values i.e. NaN values in the count.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Beagle', 'Pug', 'Labrador', 'Pug', 'Mastiff', None, 'Beagle']) # Print the Index idx |
Output :
Let’s find the count of unique values in the Index.
# to find the count of unique values. idx.nunique(dropna = False) |
Output :
As we can see in the output, the function has returned 5 indicating that there are only 5 unique values in the Index. We have also included the missing values in the count.
