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t = pd.DataFrame({'a': range(0, 10000), 'b': range(10000, 20000)})
B = []
C = []
A = time.time()
for i,r in t.iterrows():
    C.append((r['a'], r['b']))
B.append(time.time()-A)

C = []
A = time.time()

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tushare 数据规整画图
from matplotlib.pylab import date2num
import datetime
 
# 对tushare获取到的数据转换成candlestick_ohlc()方法可读取的格式
data_list = []
hist_data = ts.get_hist_data('600199')
for dates,row in hist_data.iterrows():
    # 将时间转换为数字
    date_time = datetime.datetime.strptime(dates,'%Y-%m-%d')

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import pandas as pd
from pandas import DataFrame
fro WindPy import *
w.start

###
t1=w.wset('sectorconstituent','date=20170501;sector=全部A股')
a=DataFrame({'code':t1.Data[1],'name':t1.Data[2]})

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>>> keys = ['a', 'b', 'c']
>>> values = [1, 2, 3]
>>> dictionary = dict(zip(keys, values))
>>> print(dictionary)
{'a': 1, 'b': 2, 'c': 3}

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from WindPy import *
import pandas as pd
from pandas import DataFrame

w.start()
all_data={}
a=w.weqs('tp')
for ticker in a.Data[0]:
    all_data[ticker]=w.wsd(ticker,'close','20170101')
price=DataFrame({tic:data.Data[0] for tic,data in all_data.items()},index=all_data[next(iter(all_data))].Times)  # next (iter(my_dict))get firt key of a dict

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import WindPy
from WindPy import *
import pandas as pd
from pandas import DataFrame

w.start()
all_data={}
a=w.weqs('tp')
for ticker in a.Data[0]:
    all_data[ticker]=w.wsd(ticker,'close','20170101')

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#coding:utf-8
import tushare as ts
import pynance as pn
ticker=input('input your ticker:')
t=ts.get_k_data(ticker)


x=t.set_index('date')#设置日期为索引
x.index=x.index.to_datetime()#将索引转为日期格式
x.index.names=['Date']#为索引指定名称

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# -*- coding:utf-8 -*-
# Author:jqzhang
# Editdate:2015-12-21
from WindPy import w
import pymssql
from datetime import datetime

server = 'WIND-PUB010\SQLEXPRESS'
user = 'jqzhang'
password = 'wind'

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import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot') ## 采用ggplot格式

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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tushare as ts
import pynance as pn
from pandas import DataFrame,Series

a=ts.get_today_all()
b=a[(a.nmc>5e+5)&(a.nmc<15e+5)&(a.changepercent>5)&(a.turnoverratio>2)]# 涨幅大于5%且自由流通市值在50到150亿之间的个股