label encoding

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03 Oct 2016
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
9# Example usage:
10# Build example dataframe:
11df = pd.DataFrame(['sunny', 'rainy', 'cloudy'], columns=['weather'])
13  weather
140   sunny
151   rainy
162  cloudy
18# Convert categorical weather variable to one-hot encoding:
19df_onehot = pd.get_dummies(df, columns=['weather'], prefix=['one_hot'])
21	one_hot_cloudy	 one_hot_rainy   one_hot_sunny
220                0               0               1
231                0               1               0
242                1               0               0
20 Jul 2016
1from sklearn.preprocessing import LabelEncoder
3le = LabelEncoder()
4companydata.ShelveLoc = le.fit_transform(companydata.ShelveLoc)
21 Nov 2018
2obj_df["body_style"] = obj_df["body_style"].astype('category')
5obj_df["body_style_cat"] = obj_df["body_style"]
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