第10章 Python第三方库概览

1001.使用jieba.cut()对“Python是最有意思的编程语言”进行分词，输出结果，并将该迭代器转换为列表类型。

import jieba
text = "Python是最有意思的编程语言"
#jieba.cut()生成的是一个generator发生器，可以通过for循环来取里面的每一个词
ls = list(jieba.cut(text))
print(ls)

结果

['Python', '是', '最', '有意思', '的', '编程语言']

1002.使用jieba.cut()对“今天晚上我吃了意大利面”进行分词，输出结果，并使“意大利面”作为一个词出现在结果中。

import jieba
text = "今天晚上我吃了意大利面"
jieba.add_word('意大利面')
ls = list(jieba.cut(text))
print(ls)

结果

['今天', '晚上', '我', '吃', '了', '意大利面']

1003.自选一篇报告或者演讲稿，利用jieba分析出其词频排前5的关键词。

#这里选择马丁·路德·金的演讲《我有一个梦想》进行分析
import jieba
f = open('我有一个梦想.txt','r')
txt = f.read()
f.close()
words = jieba.lcut(txt)
d = {}
for word in words:
    if len(word) == 1:
        continue
    else:
        d[word] = d.get(word,0)+1
items = list(d.items())
items.sort(key = lambda x:x[1],reverse = True)
for i in range(5):
    word,count = items[i]
    print('{:<5}{:>5}'.format(word,count))

结果

我们      46
自由      27
黑人      18
今天      13
一个      12

1004.参考本章最后的实例，选择你喜欢的小说，统计出场人物词频排名。

#这里选择《雪中悍刀行》
import jieba
excludes = {"北凉","没有","一个","这个","不是","就是", \
            "自己","北莽","女子","只是","什么","那个", \
            "已经","江湖","年轻","知道","还是","不过", \
            "老人","这位","有些","问道","他们","说道", \
            "但是","可以","殿下","世子","骑军","离阳", \
            "那些","以后","如何","这些","然后","时候", \
            "看到","还有","天下","一声","当年","这么", \
            "轩辕","咱们","你们","将军","觉得","不会", \
            "其实","那位","不知","便是","怎么","如今", \
            "之后","如果","年轻人","一位","轻轻","身边", \
            "能够","藩王","北凉王","如此","那么","一起", \
            "一些","点头","更是","不敢","甚至","铁骑", \
            "师父","京城","最后","开始","只要","一点", \
            "许多","就要","高手","皇帝","因为","两人", \
            "突然","一下","所以","大将军","广陵","起身", \
            "公子","中原","双手","转头","这种","一直", \
            "一名","就算","看着","少年","除了","yin", \
            "缓缓","男子","那边","只有","朝廷","喜欢", \
            "为何","而是","一次","一样","摇头","坐在", \
            "哪怕","我们","那名","说话","幽州","事情", \
            "当时","眼神","离开","家伙","境界","这里", \
            "宗师"}
f = open("雪中悍刀行.txt", "r", encoding="utf-8")
txt = f.read()
f.close()
words  = jieba.lcut(txt)
counts = {}
for word in words:
    if len(word) == 1:  #排除单个字符的分词结果
        continue
    else:
        counts[word] = counts.get(word,0) + 1
for word in excludes:
    del counts[word]
items = list(counts.items())
items.sort(key=lambda x:x[1], reverse=True) 
for i in range(6):
    word, count = items[i]
    print ("{0:<5}{1:>5}".format(word, count))

结果

徐凤年  19255
徐骁    1996
王仙芝   1151
菩萨    1145
陈芝豹   1008
曹长卿   1001

1005.续上题，将上题结果以词云的方式展现，并尝试美化生成的词云图片。

import jieba
from wordcloud import WordCloud
excludes = {"北凉","没有","一个","这个","不是","就是", \
            "自己","北莽","女子","只是","什么","那个", \
            "已经","江湖","年轻","知道","还是","不过", \
            "老人","这位","有些","问道","他们","说道", \
            "但是","可以","殿下","世子","骑军","离阳", \
            "那些","以后","如何","这些","然后","时候", \
            "看到","还有","天下","一声","当年","这么", \
            "轩辕","咱们","你们","将军","觉得","不会", \
            "其实","那位","不知","便是","怎么","如今", \
            "之后","如果","年轻人","一位","轻轻","身边", \
            "能够","藩王","北凉王","如此","那么","一起", \
            "一些","点头","更是","不敢","甚至","铁骑", \
            "师父","京城","最后","开始","只要","一点", \
            "许多","就要","高手","皇帝","因为","两人", \
            "突然","一下","所以","大将军","广陵","起身", \
            "公子","中原","双手","转头","这种","一直", \
            "一名","就算","看着","少年","除了","yin", \
            "缓缓","男子","那边","只有","朝廷","喜欢", \
            "为何","而是","一次","一样","摇头","坐在", \
            "哪怕","我们","那名","说话","幽州","事情", \
            "当时","眼神","离开","家伙","境界","这里", \
            "宗师"}
f = open("雪中悍刀行.txt", "r", encoding="utf-8")
txt = f.read()
f.close()
words  = jieba.lcut(txt)
newtxt = ' '.join(words)
wordcloud = WordCloud(background_color="white", \
                          width=800, \
                          height=600, \
                          font_path="msyh.ttc", \
                          max_words=200, \
                          max_font_size=80, \
                          stopwords = excludes, \
                          ).generate(newtxt)
wordcloud.to_file("雪中悍刀行基本词云.png")

结果