My Data set contains categorical variables so I am using label encoding and one hot encoder and my code is as follows
can I use a loop to ensure that my code consists of lesser lines of code?
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_0 = LabelEncoder()
X[:, 0] = labelencoder_X_0.fit_transform(X[:, 0])
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
labelencoder_X_3 = LabelEncoder()
X[:, 3] = labelencoder_X_3.fit_transform(X[:, 3])
labelencoder_X_4 = LabelEncoder()
X[:, 4] = labelencoder_X_4.fit_transform(X[:, 4])
labelencoder_X_5 = LabelEncoder()
X[:, 5] = labelencoder_X_5.fit_transform(X[:, 5])
labelencoder_X_6 = LabelEncoder()
X[:, 6] = labelencoder_X_6.fit_transform(X[:, 6])
labelencoder_X_7 = LabelEncoder()
X[:, 7] = labelencoder_X_7.fit_transform(X[:, 7])
labelencoder_X_8 = LabelEncoder()
X[:, 8] = labelencoder_X_8.fit_transform(X[:, 8])
labelencoder_X_13 = LabelEncoder()
X[:, 13] = labelencoder_X_13.fit_transform(X[:, 13])
labelencoder_X_14 = LabelEncoder()
X[:, 14] = labelencoder_X_14.fit_transform(X[:, 14])
labelencoder_X_15 = LabelEncoder()
X[:, 15] = labelencoder_X_15.fit_transform(X[:, 15])
labelencoder_y_16 = LabelEncoder()
y[:, ] = labelencoder_y_16.fit_transform(y[:, ])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
onehotencoder = OneHotEncoder(categorical_features = [14])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
onehotencoder = OneHotEncoder(categorical_features = [27])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
onehotencoder = OneHotEncoder(categorical_features = [29])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
onehotencoder = OneHotEncoder(categorical_features = [38])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
onehotencoder = OneHotEncoder(categorical_features = [40])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
How can I use a for loop to optimise the number of lines of code ?? Please Help!
Yes of course! I recommend using a dictionary to store you encoders
label_encoders = {}
categorical_columns = [0, 1, 2, 3] # I would recommend using columns names here if you're using pandas. If you're using numpy then stick with range(n) instead
for column in categorical_columns:
label_encoders[column] = LabelEncoder()
X[column] = label_encoders[column].fit_transform(X[column]) # if numpy instead of pandas use X[:, column] instead