OneHotEncoding mapping issue between training data and test data - pandas

Question

I've transformed training and test data set by sklearn OneHotEncoding method. However, trnsformed results have different type shape. So It is impossible to apply to other algorithms like logistic regression.
How do I reshape the test data in accordance with the training data set's shape?
Best Regardings, Chris
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
def data_transformation(data, dummy):
le = LabelEncoder()
# Encoding the columns with multiple categorical levels
for col1 in dummy:
le.fit(data[col1])
data[col1] = le.transform(data[col1])
dummy_data = np.array(data[dummy])
enc = OneHotEncoder()
enc.fit(dummy_data)
dummy_data = enc.transform(dummy_data).toarray()
if __name__ == '__main__':
data = pd.read_csv('train.data', delimiter=',')
data_test = pd.read_csv('test.data', delimiter=',')
dummy_columns = ['Column1', 'Column2']
data = data_transformation(data, dummy_columns)
data_test = data_transformation(data_test, dummy_columns)
# result
# data shape : (200000, 71 )
# data_test shape : ( 15000, 32)

Answer 1

Thank you so much, Vivek! I've solved this issue due to your help.
def data_transformation2(data, data_test, dummy):
le = LabelEncoder()
# Encoding the columns with multiple categorical levels
for col in dummy:
le.fit(data[col])
data[col] = le.transform(data[col])
for col in dummy:
le.fit(data_test[col])
data_test[col] = le.transform(data_test[col])
enc = OneHotEncoder()
dummy_data = np.array(data[dummy])
dummy_data_test = np.array(data_test[dummy])
enc.fit(dummy_data)
dummy_data = enc.transform(dummy_data).toarray()
dummy_data_test = enc.transform(dummy_data_test).toarray()
print(dummy_data.shape)
print(dummy_data_test.shape)