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Python Pandas - Return a Series containing counts of unique values from Index object considering NaN values as well
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
Updated on: 13-Oct-2021
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