how to use one hot encoding in python
# Basic syntax:
df_onehot = pd.get_dummies(df, columns=['col_name'], prefix=['one_hot'])
# Where:
# - get_dummies creates a one-hot encoding for each unique categorical
# value in the column named col_name
# - The prefix is added at the beginning of each categorical value
# to create new column names for the one-hot columns
# Example usage:
# Build example dataframe:
df = pd.DataFrame(['sunny', 'rainy', 'cloudy'], columns=['weather'])
print(df)
weather
0 sunny
1 rainy
2 cloudy
# Convert categorical weather variable to one-hot encoding:
df_onehot = pd.get_dummies(df, columns=['weather'], prefix=['one_hot'])
print(df_onehot)
one_hot_cloudy one_hot_rainy one_hot_sunny
0 0 0 1
1 0 1 0
2 1 0 0
4
10
from numpy import as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# define example
data = ['cold', 'cold', 'warm', 'cold', 'hot',
'hot', 'warm', 'cold', 'warm', 'hot']
values = np.array(data)
# first apply label encoding
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
# now we can apply one hot encoding
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)
Thank you!
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