Challenge: Creating a Bag of Words

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Your task is to display the vector for the 'graphic design' bigram in a BoW model:

Import the CountVectorizer class to create a BoW model.
Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.
Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.
Display the vector for 'graphic design' as an array, rather than as a pandas Series.

# Import CountVectorizer

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

corpus = pd.read_csv(

'https://content-media-cdn.codefinity.com/courses/c68c1f2e-2c90-4d5d-8db9-1e97ca89d15e/section_3/chapter_4/example_corpus.csv')

# Instantiate CountVectorizer

count_vectorizer = CountVectorizer(ngram_range=(1, 2))

# Generate a BoW matrix

bow_matrix = count_vectorizer.fit_transform(corpus['Document'])

# Convert the resulting matrix to a DataFrame

bow_df = pd.DataFrame(bow_matrix.toarray(), columns=count_vectorizer.get_feature_names_out())

# Print the vector for "graphic design"

print(bow_df['graphic design'].values)

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 5

# Import CountVectorizer

from ___ import ___

import pandas as pd

corpus = pd.read_csv(

'https://content-media-cdn.codefinity.com/courses/c68c1f2e-2c90-4d5d-8db9-1e97ca89d15e/section_3/chapter_4/example_corpus.csv')

# Instantiate CountVectorizer

count_vectorizer = ___

# Generate a BoW matrix

bow_matrix = count_vectorizer.___

# Convert the resulting matrix to a DataFrame

bow_df = pd.___(bow_matrix.___, columns=___)

# Print the vector for "graphic design"

print(___)

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