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dataframe onehotencoder

JJSSEEXX answered on November 20, 2022 Popularity 9/10 Helpfulness 6/10

answer dataframe onehotencoder

dataframe onehotencoder

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from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder, MinMaxScaler

# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))

# One-hot encoding removed index; put it back
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index

# Remove categorical columns (will replace with one-hot encoding)
numeric_X_train = X_train.drop(low_cardinality_cols, axis=1)
numeric_X_valid = X_valid.drop(low_cardinality_cols, axis=1)

# Add one-hot encoded columns to numerical features
new_X_train = pd.concat([numeric_X_train, OH_cols_train], axis=1)
new_X_valid = pd.concat([numeric_X_valid, OH_cols_valid], axis=1)
print(new_X_train)

# Label Encoder

from sklearn.preprocessing import LabelEncoder
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

Popularity 9/10 Helpfulness 6/10 Language python

Source: stackoverflow.com

Tags: dataframe python

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Contributed on Nov 20 2022

JJSSEEXX

0 Answers Avg Quality 2/10

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