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one hot encoding python

Innocent Iguana answered on January 29, 2023 Popularity 9/10 Helpfulness 4/10

answer one hot encoding python

one hot encoding python

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# Binary Encoding
df["cat_col"] = df["cat_col"].apply(lambda val: 1 if val == "y" else 0)

# One-hot-encoding on categorical variable
df_onehot = pd.get_dummies(df, columns=['cat'], prefix='C')
df_dummy = pd.get_dummies(df, columns=['cat'], drop_first=True, prefix='C')

# Alternative approach-2
from sklearn import preprocessing
encoder = preprocessing.OneHotEncoder()
onehot_transformed = encoder.fit_transform(df['cat_col'].values.reshape(-1,1))
# Convert into dataframe
onehot_df = pd.DataFrame(onehot_transformed.toarray())
# Add the encoded columns with original dataset, 
df = pd.concat([df, onehot_df], axis=1)
# Drop the original column that you used for encoding 
df = df.drop('cat_col', axis=1)

# Label encoding : Turning string labels into numeric values
from sklearn import preprocessing
encoder_lvl = preprocessing.LabelEncoder()
# Specify the unique categories in the column to apply one-hot encoding
encoder_lvl.fit([ 'LOW', 'NORMAL', 'HIGH'])
# Apply one hot encoding on the third column of the dataset
df[:,2] = encoder_lvl.transform(df[:,2]) 

# Alternative approach : DictVectorizer
from sklearn.feature_extraction import DictVectorizer

df_dict = df.to_dict("records") # Convert df into a list of dictionary
dv = DictVectorizer(sparse = False)
df_encoded = dv.fit_transform(df_dict)
print(df_encoded[:5,:]) # Print first five rows
# Print the vocabulary (how the features are mapped to columns in the resulting matrix.)
print(dv.vocabulary_)

Popularity 9/10 Helpfulness 4/10 Language python

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Contributed on Jan 29 2023

Innocent Iguana

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one hot encoding python

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