One hot encoder python

from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
# apply on df
color_1hot = encoder.fit_transform(df['ocean_proximity'].values.reshape(-1,1))
color_1hot_df = pd.DataFrame(color_1hot.toarray())
df = pd.concat([df.drop('ocean_proximity', axis=1), color_1hot_df], axis=1)
# can allso use the pandas dummies
df = pd.get_dummies(df, columns=['ocean_proximity'])

# apply on np.array
X[:, 2] = le.fit_transform(X[:, 2])

Onehotencoder = OneHotEncoder(categorical_features = [1]) X = onehotencoder.fit_transform(X).toarray() X = X[:, 1:]

from sklearn.compose import ColumnTransformer

ct = ColumnTransformer([('encoder', OneHotEncoder(), [1])], remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype=np.float)

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