Python词云图绘制—看博客大佬们的写作热点

矫情吗；* 2022-06-03 01:15 258阅读 0赞

在[《Python可视化展现》][Python]中，我们使用了**Matplotlib**可视化了一些博客大佬们的博客发表年份与数量的关系，接下来我们再看下这些博客文章的热点都有哪些。  
我们仅对文章的标题进行分词处理，然后统计分词结果，并绘制出博客文章词云，我们使用了**jieba**和**thulac**进行中文分词，结果总体差不多，但**thulac**速度更显得慢。

重新定义walk\_tree

def walk_tree_j(html, num):
        for li in html.find_all("li"):
            num = num + 1
            print("%s %s %s%s" % (num, li.h3.a.string, CSDN_BLOG_URL, li.h3.a["href"]))
            k_list = jieba.cut(li.h3.a.string)
            # k_list = thulac.thulac().cut(li.h3.a.string)
            for keyword in k_list:
                # for thulac
                # keyword = str.strip(keyword[0])
                # for jieba
                keyword = str.strip(keyword)
                if len(keyword) < 2:
                    pass
                elif keyword_dict.get(keyword, 0) == 0:
                    keyword_dict[keyword] = 1
                else:
                    keyword_dict[keyword] = keyword_dict[keyword] + 1
    
            for d in li.find_all("div"):
                if "class" in d.attrs and str.strip(d["class"][0]) == "unit-control":
                    print(d.div.find_all("div")[0].string + "，发表时间：" + d.div.find_all("div")[1].string + "，阅读量：" +
                          d.div.find_all("div")[2].span.string + "，评论数：" + d.div.find_all("div")[3].span.string)
                    t_value = d.div.find_all("div")[1].string
                    year = int(str.strip(t_value)[0:4])
                    if article_dict.get(year, 0) == 0:
                        article_dict[year] = 1
                    else:
                        article_dict[year] = article_dict[year] + 1
    
        print(keyword_dict)
        return num

对文章标题进行分词处理，注意，为了简化处理，我们仅去掉单个字符的单词：

> k\_list = jieba.cut(li.h3.a.string)  
> \# k\_list = thulac.thulac().cut(li.h3.a.string)  
> for keyword in k\_list:  
>     \# for thulac  
>     \# keyword = str.strip(keyword\[0\])  
>     \# for jieba  
>     keyword = str.strip(keyword)  
>     if len(keyword) < 2:  
>         pass  
>     elif keyword\_dict.get(keyword, 0) == 0:  
>         keyword\_dict\[keyword\] = 1  
>     else:  
>         keyword\_dict\[keyword\] = keyword\_dict\[keyword\] + 1

获取到分词结果之后，我们使用wordcloud进行词云绘制

def generate_dict(dic):
        fullTermsDict = multidict.MultiDict()
        for key, value in dic.items():
            fullTermsDict.add(key, value)
        return fullTermsDict
    
    
    image_path = '1.jpg'
    d = path.dirname(__file__)
    image = imread(path.join(d, image_path))
    
    wc = WordCloud(background_color="white", max_words=1000, font_path="C:/Windows/Fonts/simkai.ttf", mask=image)
    # generate word cloud
    fullTermsDict = multidict.MultiDict()
    wc.generate_from_frequencies(generate_dict(keyword_dict))
    # show
    plt.imshow(wc, interpolation="bilinear")
    plt.axis("off")
    plt.show()

### 结果查看： ###

#### **比如，对于：[http://blog.csdn.net/phphot][http_blog.csdn.net_phphot]** ####

![这里写图片描述][SouthEast]

#### **比如，对于：[http://blog.csdn.net/littletigerat][http_blog.csdn.net_littletigerat]** ####

![这里写图片描述][SouthEast 1]

[Python]: http://blog.csdn.net/kangkanglou/article/details/78796123
[http_blog.csdn.net_phphot]: http://blog.csdn.net/phphot
[SouthEast]: /images/20220603/8d5935da6ea84d789ae3de0ffa7441d2.png
[http_blog.csdn.net_littletigerat]: http://blog.csdn.net/littletigerat
[SouthEast 1]: /images/20220603/2920a4cd35c943bb839c83185bbd767c.png