Count Unique Values from Index Object in Pandas

To return a Series containing counts of unique values from Index object considering NaN values as well with the index.value_counts() method. Set the parameter dropna with value False.

At first, import the required libraries -

import pandas as pd
import numpy as np

Creating Pandas index with some NaN values as well −

index = pd.Index([50, 10, 70, np.nan, 90, 50, np.nan, np.nan, 30])

Display the Pandas index −

print("Pandas Index...\n",index)

Count of unique values using value_counts(). Considering NaN as well using the "False" value of the "dropna" parameter −

index.value_counts(dropna=False)

Example

Following is the code −

import pandas as pd
import numpy as np

# Creating Pandas index with some NaN values as well
index = pd.Index([50, 10, 70, np.nan, 90, 50, np.nan, np.nan, 30])

# Display the Pandas index
print("Pandas Index...\n",index)

# Return the number of elements in the Index
print("\nNumber of elements in the index...\n",index.size)

# Return the dtype of the data
print("\nThe dtype object...\n",index.dtype)

# count of unique values using value_counts()
# considering NaN as well using the "False" value of the "dropna" parameter
print("\nGet the count of unique values with NaN...\n",index.value_counts(dropna=False))

Output

This will produce the following output −

Pandas Index...
Float64Index([50.0, 10.0, 70.0, nan, 90.0, 50.0, nan, nan, 30.0], dtype='float64')

Number of elements in the index...
9

The dtype object...
float64

Get the count of unique values with NaN...
NaN   3
50.0  2
10.0  1
70.0  1
90.0  1
30.0  1
dtype: int64