可以把上面的写成一段能复制的代码形式吗

当然可以，下面是完整代码： ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer # Load the data df = pd.read_csv('fake_or_real_news.csv') # Create a series to store the labels: y y = df.label # Create training and test sets X_train, X_test, y_train, y_test = train_test_split(df['text'], y, test_size=0.33, random_state=53) # Initialize a CountVectorizer object: count_vectorizer count_vectorizer = CountVectorizer(stop_words='english') # Transform the training data using only the 'text' column values: count_train count_train = count_vectorizer.fit_transform(X_train) # Transform the test data using only the 'text' column values: count_test count_test = count_vectorizer.transform(X_test) # Initialize a TfidfVectorizer object: tfidf_vectorizer tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7) # Transform the training data: tfidf_train tfidf_train = tfidf_vectorizer.fit_transform(X_train) # Transform the test data: tfidf_test tfidf_test = tfidf_vectorizer.transform(X_test) # Create the CountVectorizer DataFrame: count_df count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names()) # Create the TfidfVectorizer DataFrame: tfidf_df tfidf_df = pd.DataFrame(tfidf_train.A, columns=tfidf_vectorizer.get_feature_names()) # Print the head of count_df print(count_df.head()) # Print the head of tfidf_df print(tfidf_df.head()) # Calculate the difference in columns: difference difference = set(count_df.columns) - set(tfidf_df.columns) print(difference) # Check whether the DataFrames are equal print(count_df.equals(tfidf_df)) ```