1.正则的使用匹配

2.dic.setdefault()的使用

3、内建函数enumerate(sequence,start=0)的使用

4、内建函数sorted(),key,reversed参数设置

5、str.lower()string大小写转换

#coding:utf-8
import re
import os
import time
import codecs

PATH = os.path.dirname(__file__)

s = u'what a Beautiful woRld'.lower()
pattern = re.compile(u'[^a-z]+',re.U)#在非英文出进行切割
for con in pattern.split(s.lower()):#将所有英文转化为小写
    if len(con) <= 1:
        continue
    else:
        print con

def get_english_words():
    '''过滤出语料中夹杂在汉语中的英文单词，并统计出现的词频'''
    eng_freq_dic = {}
    pattern = re.compile(u'[^a-z]+',re.U)
    cut_filename = r'E:\SVN\linguistic_model\data\combine_msg_comment.txt'
    with codecs.open(cut_filename,encoding='utf-8') as f:
        for line in f.readlines():
            for con in pattern.split(line.lower()):
                if len(con) <= 1:#过滤掉单字母
                    continue
                else:
                    count = eng_freq_dic.setdefault(con,0) + 1 #若没有该key，则保存该key且设value其为0。若有则value加1
                    eng_freq_dic[con] = count#整个英文单词及其出现的频度
    eng_filename = os.path.join(PATH,'english_words_original.txt')
    eng_to_write_list = sorted([(k,v) for (k,v) in eng_freq_dic.items()],key=lambda x:x[1],reverse=True)#按照词频的高低进行倒序排列
    codecs.open(eng_filename,mode='wb',encoding='utf-8').writelines([item[0]+'\t'+str(item[1])+'\n' for item in eng_to_write_list])#词频为int类型，转化为str类型以后写入到本地文件中

def chose_top_n():
    '''筛选出top2000,并写入到文件'''
    line_list = []
    filename = os.path.join(PATH,'english_words_original.txt')
    with codecs.open(filename,encoding='utf-8') as f:
        for index,line in enumerate(f.readlines(),start=1):#enumerate(sequence,start=0)用法，显示可迭代序列中元素及其位置，start参数可以确定起始下标，默认情况下为0
            print index,line.strip()
            time.sleep(1)
            line_list.append(line)
            if index == 2000:
                top_filename = os.path.join(PATH,'top_2000_english_words.txt')
                codecs.open(top_filename,encoding='utf-8').writelines(line_list)
                break