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Finding the most common words

Its always advisable once you have created your features to inspect them to ensure that they are as you would expect. This will allow you to catch errors early, and perhaps influence what further feature engineering you will need to do.

The vectorizer (cv) you fit in the last exercise and the sparse array consisting of word counts (cv_trigram) is available in your workspace.

Cet exercice fait partie du cours

Feature Engineering for Machine Learning in Python

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Instructions

  • Create a DataFrame of the features (word counts).
  • Add the counts of word occurrences and print the top 5 most occurring words.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create a DataFrame of the features
cv_tri_df = ____(____, 
                 columns=cv_trigram_vec.get_feature_names()).add_prefix('Counts_')

# Print the top 5 words in the sorted output
print(cv_tri_df.sum().____(ascending=____).head())
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