Combining text columns for tokenization

In order to get a bag-of-words representation for all of the text data in our DataFrame, you must first convert the text data in each row of the DataFrame into a single string.

In the previous exercise, this wasn't necessary because you only looked at one column of data, so each row was already just a single string. CountVectorizer expects each row to just be a single string, so in order to use all of the text columns, you'll need a method to turn a list of strings into a single string.

In this exercise, you'll complete the function definition combine_text_columns(). When completed, this function will convert all training text data in your DataFrame to a single string per row that can be passed to the vectorizer object and made into a bag-of-words using the .fit_transform() method.

Note that the function uses NUMERIC_COLUMNS and LABELS to determine which columns to drop. These lists have been loaded into the workspace.

Use the .drop() method on data_frame with to_drop and axis= as arguments to drop the non-text data. Save the result as text_data.
Fill in missing values (inplace) in text_data with blanks (""), using the .fillna() method.
Complete the .apply() method by writing a lambda function that uses the .join() method to join all the items in a row with a space in between.

Exercise

Combining text columns for tokenization

Instructions