Exercise

Inspecting the vectors

To get a better idea of how the vectors work, you'll investigate them by converting them into pandas DataFrames.

Here, you'll use the same data structures you created in the previous two exercises (count_train, count_vectorizer, tfidf_train, tfidf_vectorizer) as well as pandas, which is imported as pd.

Instructions

100 XP
  • Create the DataFrames count_df and tfidf_df by using pd.DataFrame() and specifying the values as the first argument and the columns (or features) as the second argument.
    • The values can be accessed by using the .A attribute of, respectively, count_train and tfidf_train.
    • The columns can be accessed using the .get_feature_names() methods of count_vectorizer and tfidf_vectorizer.
  • Print the head of each DataFrame to investigate their structure. This has been done for you.
  • Test if the column names are the same for each DataFrame by creating a new object called difference to see the difference between the columns that count_df has from tfidf_df. Columns can be accessed using the .columns attribute of a DataFrame. Subtract the set of tfidf_df.columns from the set of count_df.columns.
  • Test if the two DataFrames are equivalent by using the .equals() method on count_df with tfidf_df as the argument.