Question: Python offers many ways to convert a variable to categorical. import numpy as np import pandas as pd mydata = pd.Series(['A', 'B', 'B', 'C']) mydata 0 A 1 B 2 B 3 C dtype: object pd.factorize(mydata) # Outputs a tuple with an array and an index (array([0, 1, 1, 2], dtype=int64), Index(['A', 'B', 'C'], dtype='object')) pd.Categorical(mydata) # Outputs a pandas. How do you extract categories? ['A', 'B', 'B', 'C'] Categories (3, object): ['A', 'B', 'C'] mydata.astype('category') # Outputs a series. How do you extract categories? 0 A 1 B 2 B 3 C dtype: category Categories (3, object): ['A', 'B', 'C']  There are more alternatives such as sklearn LabelEncoder and keras to_categorical, which are used with pipelines because they preserve the conversions and allow to reapply them to new data. Could you please explain the differences, advantages or limitations of these methods, and they different applications: factorize(), categorical() and astype("category")? For example if I want to use them for a decision tree model or I just want to calculate a frequency table. Which one is easier to use or more versatile? For example if afterwards I want to modify a category or add a new one or change a value or merge two categories.

Answer: The methods factorize(), categorical(), and astype("category") in Python offer different ways to convert variables to categorical data:  pandas.factorize docs : This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values. It returns a tuple containing an array of encoded values and an index mapping the original categories to the encoded values. The advantage of factorize() is that it provides a simple and efficient way to convert categorical data into numerical form. However, it does not directly provide the categorical data itself, only the encoded values. pandas.Categorical docs: This method creates a pandas Categorical object(limited, and usually fixed, number of possible values (categories)), which represents a categorical variable. It returns a pandas Categorical series that contains the original data along with the categories. The advantage of Categorical() is that it explicitly represents the categories and allows you to extract them using the .categories attribute. It also provides additional functionalities like ordering and renaming categories. However, it does not encode the data into numerical form like factorize(). astype("category") : this method converts a pandas series or column to a categorical data type. It returns a series with the data type set to 'category'. The advantage of astype("category") is that it allows you to efficiently store and manipulate categorical data within a pandas DataFrame. It also provides some basic functionalities like accessing the categories using the .cat.categories attribute. However, it does not provide direct encoding or explicit representation of categories like the other methods.  For a decision tree model, you can use any of these methods depending on your specific requirements.  If you only need the encoded values, factorize() would be the simplest option. If you need to explicitly represent the categories and perform operations on them, Categorical() would be more suitable. If you want to efficiently store and manipulate categorical data within a DataFrame, astype("category") would be a good choice.  In terms of modifying categories or adding new ones, both Categorical() and astype("category") allow you to do so.  With Categorical(), you can use the .rename_categories() method to change category names, and the .add_categories() method to add new categories. With astype("category"), you can directly assign new categories to the .cat.categories attribute.  Overall, the choice of method depends on your specific needs and the functionalities you require for model.