Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.factorize() function encode the object as an enumerated type or categorical variable. This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values.
Syntax: Series.factorize(sort=False, na_sentinel=-1)
Parameter :
sort : Sort uniques and shuffle labels to maintain the relationship.
na_sentinel : Value to mark “not found”.Returns :
labels : ndarray
uniques : ndarray, Index, or Categorical
Example #1: Use Series.factorize() function to encode the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', None, 'Rio']) # Create the Index sr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.factorize() function to encode the underlying data of the given series object.
# encode the values result = sr.factorize() # Print the result print(result) |
Output :
As we can see in the output, the Series.factorize() function has successfully encoded the underlying data of the given series object. Notice missing values has been assigned a code of -1.
Example #2 : Use Series.factorize() function to encode the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([80, 25, 3, 80, 24, 25]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.factorize() function to encode the underlying data of the given series object.
# encode the values result = sr.factorize() # Print the result print(result) |
Output :
As we can see in the output, the Series.factorize() function has successfully encoded the underlying data of the given series object.
