刚学了一点python，写了一个简单的爬虫来抓取股票信息，最后将数据储存在excel文件中。功能比较简单，拓展性较差，以后可能会用scrapy重写。
话不多说直接上代码：
# -*- coding: utf-8 -*-
import urllib2
import json
import datetime
import pandas as pd
'''
根据url的response可以看出，这是一个js文件，可以将其转变为json格式后再进行处理。
'''
def pages_num():
url = "http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._A&sty=FCOIATA&sortType=C" \
"&sortRule=-1&page=1&pageSize=20&js=var%20quote_123%3d{rank:[(x)],pages:(pc)}" \
"&token=7bc05d0d4c3c22ef9fca8c2a912d779c&jsName=quote_123&_g=0.5072298033821419"
request = urllib2.Request(url)
response = urllib2.urlopen(request)
data = response.read()
start_pos = data.index('=')
js_object = data[start_pos + 1:]
# print js_object[0] + '"' +js_object[1:5] + '"' + js_object[5:-10] + '"' +js_object[-10:-5] +'"' +js_object[-5:]
json_data = js_object[0] + '"' + js_object[1:5] + '"' + js_object[5:-10] + '"' + js_object[ -10:-5] + '"' \
+ js_object[-5:]
dict = json.loads(json_data)
pages = dict['pages']
return pages
#获取url池
def get_urls():
url_list = []
start = 1
while (start <= pages_num()):
url = "http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._A&sty=FCOIATA"
url += "&sortType=C&sortRule=-1&page=%d&pageSize=20" % start
url += "&js=var%20quote_123%3d{rank:[(x)],pages:(pc)}&token=7bc05d0d4c3c22ef9fca8c2a912d779c"
url += "&jsName=quote_123&_g=0.5072298033821419"
url_list.append(url)
start += 1
return url_list
#创建文件，文件名根据当前系统日期生成。文件目录可以自己更改，应该可以自动生成，但是这里没有去实现
today =datetime.date.today()
str_date = '%Y%m%d'
str_today = today.strftime(str_date)
headers = ['代码','名称','最新价','涨跌额','涨跌幅','振幅（%）','成交量（手）','成交额','昨收','今开','最高','最低',
'量比','换手（%）','市盈']
f = open('D:/stock_profit/stock_pool_%s.xlsx' % str_today,'a+')
f.close()
'''
主程序，因为获取的数据是list里的string，没有key，pandas生成dataframe的时候是没有列头的，
可以使用列的index选择要储存的列，并用header重命名
'''
url_list = get_urls()
print url_list
bd = pd.DataFrame()
for url in url_list:
request = urllib2.Request(url)
response = urllib2.urlopen(request)
data = response.read()
start_pos = data.index('=')
js_object = data[start_pos + 1:]
json_data = js_object[0]+'"'+js_object[1:5]+'"'+js_object[5:-10] + '"' + js_object[-10:-5] + '"' + js_object[-5:]
dict = json.loads(json_data)
list = dict['rank']
pd_data = []
for x in list:
stock_meg = x.split(',')
stock_pool = []
for y in stock_meg:
stock_pool.append(y)
pd_data.append(stock_pool)
df = pd.DataFrame(pd_data)
bd = bd.append(df,ignore_index=True)
print bd
#write data to excel
pd.DataFrame.to_excel(bd,excel_writer='D:/stock_profit/stock_pool_%s.xlsx' % str_today,index=None,
columns=[1,2,3,4,5,6,7,8,9,10,11,12,22,23,24],header=headers)
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