Answers for "one hot encoding python code"

python convert categorical data to one-hot encoding

# 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

one hot encoding python pandas

y = pd.get_dummies(df.Countries, prefix='Country')
print(y.head())
# from here you can merge it onto your main DF

how to use one hot encoding in python

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)