March 2020
Beginner to intermediate
352 pages
8h 40m
English
Content preview from Hands-On Exploratory Data Analysis with Python
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Word cloud
In order to see the top few keywords that belong to each cluster, we need to create a function that provides us with the top 50 words from each of the clusters and plot the word cloud.
Check the function, as follows:
from wordcloud import WordCloud fig, ax = plt.subplots(4, sharex=True, figsize=(15,10*4))plt.rcParams["axes.grid"] = Falsedef high_frequency_keywords(data, clusters, labels, n_terms): df = pd.DataFrame(data.todense()).groupby(clusters).mean() for i,r in df.iterrows(): words = ','.join([labels[t] for t in np.argsort(r)[-n_terms:]]) print('Cluster {} \n'.format(i)) print(words) wordcloud = WordCloud(max_font_size=40, collocations=False, colormap = 'Reds', background_color = 'white').generate(words) ax[i].imshow(wordcloud, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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