#Vectorization for Data Visualization
def vectorization(table):
    #CountVectorizer will convert a collection of text documents to a matrix of token counts
    #Produces a sparse representation of the counts
    #Initialize
    vector = CountVectorizer()
    #We fit and transform the vector created
    frequency_matrix = vector.fit_transform(table.tweet)
    #Sum all the frequencies for each word
    sum_frequencies = np.sum(frequency_matrix, axis=0)
    #Now we use squeeze to remove single-dimensional entries from the shape of an array that we got from applying np.asarray to
    #the sum of frequencies.
    frequency = np.squeeze(np.asarray(sum_frequencies))
    #Now we get into a dataframe all the frequencies and the words that they correspond to
    frequency_df = pd.DataFrame([frequency], columns=vector.get_feature_names()).transpose()
    return frequency_df