One-Hot Encoding in Python with Pandas and Scikit-Learn
Introduction In computer science, data can be represented in a lot of different ways, and naturally, every single one of them has its advantages as well as disadvantages in certain fields. Since computers are unable to process categorical data as these categories have no meaning for them, this information has to be prepared if we want a computer to be able to process it. This action is called prep
pandas.get_dummies — pandas 2.2.3 documentation
pandas.get_dummies# pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)[source]# Convert categorical variable into dummy/indicator variables. Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the or
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pandasでダミー変数を作成する(get_dummies) - Qiita
