label encoding

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1# Basic syntax:
2df_onehot = pd.get_dummies(df, columns=['col_name'], prefix=['one_hot'])
3# Where:
4#	- get_dummies creates a one-hot encoding for each unique categorical
5#		value in the column named col_name
6#	- The prefix is added at the beginning of each categorical value 
7#		to create new column names for the one-hot columns
8
9# Example usage:
10# Build example dataframe:
11df = pd.DataFrame(['sunny', 'rainy', 'cloudy'], columns=['weather'])
12print(df)
13  weather
140   sunny
151   rainy
162  cloudy
17
18# Convert categorical weather variable to one-hot encoding:
19df_onehot = pd.get_dummies(df, columns=['weather'], prefix=['one_hot'])
20print(df_onehot)
21	one_hot_cloudy	 one_hot_rainy   one_hot_sunny
220                0               0               1
231                0               1               0
242                1               0               0

1from sklearn.preprocessing import LabelEncoder
2
3le = LabelEncoder()
4companydata.ShelveLoc = le.fit_transform(companydata.ShelveLoc)

1
2obj_df["body_style"] = obj_df["body_style"].astype('category')
3obj_df.dtypes
4
5obj_df["body_style_cat"] = obj_df["body_style"].cat.codes
6obj_df.head()
7

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