如何在Python中显示来自数据框的wordcloud

Question

当前，我有一个包含单词和权重（tf * idf）的数据框，并且我想显示在wordcloud中按权重排列的单词。

数据框在左侧图像上。

def generate_wordcloud(words_tem):
    word_cloud = WordCloud(width = 512, height = 512, background_color='white', stopwords= None, max_words=20).generate(words_tem)
    plt.figure(figsize=(10,8),facecolor = 'white', edgecolor='blue')
    plt.imshow(word_cloud, interpolation='bilinear')
    plt.axis('off')
    plt.tight_layout(pad=0)
    plt.show()


tfidf = TfidfVectorizer(data, lowercase = False)
tfs = tfidf.fit_transform([data]) 

feature_names = tfidf.get_feature_names()

df = pd.DataFrame(tfs.T.toarray(), index=feature_names, columns= ['weight'])
df = df.sort_values(by = 'weight', ascending = False)
word_lists = df.index.values
unique_str  = ' '.join(word_lists)
print(df[0:20])
generate_wordcloud(unique_str)

Answer 1

最常用的软件包称为wordcloud。看到 https://github.com/amueller/word_cloud/blob/master/README.md

pip install wordcloud

您可以使用

from PIL import Image
from wordcloud import WordCloud

import matplotlib.pyplot as plt
% matplotlib inline # only if using notebooks


 text = your_text_data

# Generate a word cloud image
wordcloud = WordCloud().generate(text)

# Display the generated image:
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

您的流程与上面类似，而不是文字     ＃从gensim.models的TF-IDF模型开始的步骤将导入TfidfModel，但是您也可以使用，因为我们只对（term，weight）作一个元组。

tfidf = TfidfModel(vectors)

# Get TF-IDF weights

weights = tfidf[vectors[0]]


# Get terms from the dictionary and pair with weights

weights = [(dictionary[pair[0]], pair[1]) for pair in weights]


# Generate the cloud

wc = WordCloud()
wc.generate_from_frequencies(weights)
...