统计词频，使用matplotlib画图验证齐普夫定律

    以下我是用了人民日报的数据和英文数据进行分词，这里我是用的是pkuseg这个分词工具，原因是当时我读了一篇关于翟天临的文章，关于计算相似度，里面使用的是这个工具，然后我是用的是哈工大的停用此表，统计词频、排序等进行一系列的文本与预处理。接下就是使用matplotlib画图啦。

#-*-coding:utf-8-*-
from collections import Counter
import pkuseg
import matplotlib.pyplot as plt
import numpy
#导入文件
with open("The People's Daily.txt",encoding='utf-8') as fp:
    with open("English.txt",encoding='utf-8-sig') as fp1:
     data = fp.read()
     fp.close()
     data1=fp1.read()
     fp1.close()
#人民日报数据清理
def cleanWord(content):
    seg = pkuseg.pkuseg()
    text = seg.cut(content)
    stopwords = []
    with open("哈工大停用词表.txt",encoding="utf-8") as f:
        stopwords = f.read()
    new_text = []
    for w in text:
        if len(w)>1 and len(w)<3 and w >= u'\u4e00' and w <= u'\u9fa5'and w not in stopwords:
         new_text.append(w)
    return new_text
#英文数据处理
def cleanWord2(english):
    seg = pkuseg.pkuseg()
    text = seg.cut(english)
    stopwords2 = []
    with open("哈工大停用词表.txt",encoding="utf-8") as f:
        stopwords2 = f.read()
    yingwen_text = []
    for yingwen in text:
        if  yingwen not in stopwords2:
            yingwen_text.append(yingwen)
    return yingwen_text
#主函数部分
if __name__ == "__main__":
    #整理
    word = cleanWord(data)
    word1=cleanWord2(data1)
    #统计
    counter=Counter(word)
    counter1 = Counter(word1)
    #排序
    counter_top=counter.most_common()
    counter_top1=counter1.most_common()
    print("人民日报词频统计：",counter)
    print("人民日报词语长度：",len(counter_top))
    print("英文词频统计:",counter1)
    print("英文词的长度:",len(counter_top1))
    # 绘图
    #第一张图
    plt.figure('散点图')
    plt.rc('font',family='SimHei',size=13)
    y=list(map(lambda y:y[1], counter_top[:]))
    y1 = list(map(lambda y: y[1], counter_top1[:]))
    plt.xlabel('rank')
    plt.ylabel('frequency')
    plt.scatter(range(len(y)),y ,s=1,c='red')
    plt.scatter(range(len(y1)), y1, s=1, c='black')
    plt.title('Word frequency vs rank')
    #第二张图
    plt.figure("折线图 ")
    plt.rc('font', family='SimHei', size=13)  
    y = list(map(lambda y: y[1], counter_top[:])) 
    y1 = list(map(lambda y1: y1[1], counter_top1[:]))
    numpy.seterr(divide='ignore')
    a=numpy.log(range(len(y)))
    b=numpy.log(y)
    c=numpy.log(range(len(y1)))
    d=numpy.log(y1)
    plt.xlabel('log(rank)')
    plt.ylabel('log(frequency)')
    line, = plt.plot(a,b, label='人民日报', color='red') 
    line1, = plt.plot(c,d, label='英文单词', color='black')
    plt.legend(handles=[line,line1, ], loc='best')  
    plt.title('Word log(frequency) vs log(rank)')
    plt.show()

由于某种原因
图片敬请上传！！！