Exploring text vectors, part 1

Let's expand on the text vector exploration method we just learned about, using the volunteer dataset's title tf/idf vectors. In this first part of text vector exploration, we're going to add to that function we learned about in the slides. We'll return a list of numbers with the function. In the next exercise, we'll write another function to collect the top words across all documents, extract them, and then use that list to filter down our text_tfidf vector.

This exercise is part of the course

Preprocessing for Machine Learning in Python

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Exercise instructions

Add parameters called original_vocab, for the tfidf_vec.vocabulary_, and top_n.
Call pd.Series() on the zipped dictionary. This will make it easier to operate on.
Use the .sort_values() function to sort the series and slice the index up to top_n words.
Call the function, setting original_vocab=tfidf_vec.vocabulary_, setting vector_index=8 to grab the 9th row, and setting top_n=3, to grab the top 3 weighted words.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Add in the rest of the arguments
def return_weights(vocab, ____, vector, vector_index, ____):
    zipped = dict(zip(vector[vector_index].indices, vector[vector_index].data))
    
    # Transform that zipped dict into a series
    zipped_series = ____({vocab[i]:zipped[i] for i in vector[vector_index].indices})
    
    # Sort the series to pull out the top n weighted words
    zipped_index = zipped_series.____(ascending=False)[:____].index
    return [original_vocab[i] for i in zipped_index]

# Print out the weighted words
print(return_weights(vocab, ____, text_tfidf, ____, ____))

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