why one hot encoder should have a fit or fit transform code example

Example 1: 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

Example 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)