python convert categorical data to one-hot encoding
# Basic syntax: df_onehot = pd.get_dummies(df, columns=['col_name'], prefix=['one_hot']) # Where: # - get_dummies creates a one-hot encoding for each unique categorical # value in the column named col_name # - The prefix is added at the beginning of each categorical value # to create new column names for the one-hot columns # Example usage: # Build example dataframe: df = pd.DataFrame(['sunny', 'rainy', 'cloudy'], columns=['weather']) print(df) weather 0 sunny 1 rainy 2 cloudy # Convert categorical weather variable to one-hot encoding: df_onehot = pd.get_dummies(df, columns=['weather'], prefix=['one_hot']) print(df_onehot) one_hot_cloudy one_hot_rainy one_hot_sunny 0 0 0 1 1 0 1 0 2 1 0 0