To return the relative frequency from Index object, use the index.value_counts() method with parameter normalize as True.
At first, import the required libraries -
import pandas as pd
Creating Pandas index −
index = pd.Index([50, 10, 70, 110, 90, 50, 110, 90, 30])
Display the Pandas index −
print("Pandas Index...\n",index)Get the count of unique values using value_counts(). Set the parameter "normalize" to True to get the relative frequency −
print("\nGet the relative frequency by dividing all values by the sum of values...\n", index.value_counts(normalize=True))Example
Following is the code −
import pandas as pd
# Creating Pandas index
index = pd.Index([50, 10, 70, 110, 90, 50, 110, 90, 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)
# Get the count of unique values using value_counts()
# Set the parameter "normalize" to True to get the relative frequency
print("\nGet the relative frequency by dividing all values by the sum of values...\n", index.value_counts(normalize=True))Output
This will produce the following output −
Pandas Index... Int64Index([50, 10, 70, 110, 90, 50, 110, 90, 30], dtype='int64') Number of elements in the index... 9 The dtype object... int64 Get the relative frequency by dividing all values by the sum of values... 50 0.222222 110 0.222222 90 0.222222 10 0.111111 70 0.111111 30 0.111111 dtype: float64