如果只输入10个数据，输出如下所示。
如果cnt> 10：#kk的大小约为2000。 开始CountChangePercentMT1于2016-06-16 15：19：30.706000：end-in 0 2016-06-16 15：19：33.291000 end-in 1 2016-06-16 15：19：34.990000 end-in 2 2016-06- 16 15：19：36.921000 end 0 end 1 end 2 end-in 3 2016-06-16 15：19：38.748000 end 3 End CountChangePercentMT1 at 2016-06-16 15：20：06.665000：
如果输入q 20数据，输出如下：
如果cnt> 20：#kk的大小约为2000。 开始CountChangePercentMT1于2016-06-16 15：35：36.661000：end-in 0 2016-06-16 15：35：39.330000 end-in 1 2016-06-16 15：35：40.954000 end-in 2 2016-06- 16 15:35:42.828000结束0结束1结束2结束3 2016-06-16 15：35：44.669000
如果输入q 50数据，输出如下：
如果cnt> 50：#kk的大小约为2000。 开始CountChangePercentMT1于2016-06-16 15：36：54.518000：end-in 0 2016-06-16 15：36：57.583000 end-in 1 2016-06-16 15：36：58.886000 end-in 2 2016-06- 16 15：37：00.757000 end-in 3 2016-06-16 15：37：02.648000
代码如下：
def do_somthing(q_out,q,i,trueStartDate,trueEndDate,preStartDate,allExistTables,stock_gainian):
#Init conn
conn = MySQLdb.connect(host='localhost',db='tushare',user='root',passwd='',port=3306,charset='UTF8')
#Init df_Cc
df_Cc = pd.DataFrame()
df_Cc['ticker'] = pd.Series(dtype=numpy.int64,index=df_Cc.index)
df_Cc['secShortName'] = pd.Series(dtype=numpy.object,index=df_Cc.index)
df_Cc['percent'] = pd.Series(dtype=numpy.float64,index=df_Cc.index)
df_Cc['startPrice'] = pd.Series(dtype=numpy.float64,index=df_Cc.index)
df_Cc['endPrice'] = pd.Series(dtype=numpy.float64,index=df_Cc.index)
df_Cc['startDate'] = pd.Series(dtype=numpy.object,index=df_Cc.index)
df_Cc['endDate'] = pd.Series(dtype=numpy.object,index=df_Cc.index)
df_Cc['gainian'] = pd.Series(dtype=numpy.object,index=df_Cc.index)
df_Cc['shizhi'] = pd.Series(dtype=numpy.float64,index=df_Cc.index)
df_Cc['liutong'] = pd.Series(dtype=numpy.float64,index=df_Cc.index)
while True:
aa = q.get()
if aa != None:
ticker = aa[0]
secShortName = aa[1]
totalShares = aa[2]
nonrestfloatA = aa[3]
listDate = datetime.datetime.strptime(aa[4], "%Y-%m-%d")
stock_mktequd_name = '%s%06d' % (init.g_mktequd,ticker)
if (stock_mktequd_name in allExistTables) & (trueEndDate>=listDate>=trueStartDate)==False:
str = 'select * from %s where tradeDate>=\'%s\' and tradeDate<=\'%s\' order by tradeDate' % (stock_mktequd_name,trueStartDate.strftime("%Y-%m-%d"),trueEndDate.strftime("%Y-%m-%d"))
df = pd.read_sql(str,conn)
l = len(df_Cc)
df_Cc.at[l,'ticker'] = ticker
df_Cc.at[l,'secShortName'] = secShortName
nn = df.shape[0]
if nn>0:
#
startPrice = df.iloc[0]['preClosePrice']*GetQfq(ticker,preStartDate,conn) # if nn>=n else df.iloc[0]['preClosePrice']*1
endPrice = df.iloc[nn-1]['closePrice']*df.iloc[nn-1]['accumAdjFactor']
percent = (endPrice-startPrice)/startPrice*100 # if startPrice!=0 else -100
df_Cc.at[l,'startDate'] = df.iloc[0]['tradeDate']
df_Cc.at[l,'endDate'] = df.iloc[nn-1]['tradeDate']
df_Cc.at[l,'percent'] = percent
df_Cc.at[l,'startPrice'] = startPrice
df_Cc.at[l,'endPrice'] = endPrice
df_gn = stock_gainian.query('ticker==%s' % ticker)
if df_gn.shape[0]==1:
df_Cc.at[l,'gainian'] = df_gn.iloc[0]['ref_gainian']
df_Cc.at[l,'shizhi'] = totalShares*startPrice/100000000
df_Cc.at[l,'liutong'] = nonrestfloatA*startPrice/100000000
else:
break
q_out.put(df_Cc)
conn.close()
print 'end-in %s %s' % (i,datetime.datetime.now())
if __name__ == '__main__':
q = multiprocessing.Queue()
kk = allStocksBasicInfo.set_index(['ticker','secShortName','totalShares','nonrestfloatA','listDate']).T.to_dict()
cnt = 0
for k in kk:
q.put(k)
cnt = cnt+1
if cnt>10: # kk's size is about 2000.
break
#q_out
q_out = multiprocessing.Queue()
#fork NUM
NUM = multiprocessing.cpu_count()
t = [1]*NUM
#
for i in xrange(NUM):
q.put(None)
for i in xrange(NUM):
t[i] = multiprocessing.Process(target=do_somthing, args=(q_out,q,i,trueStartDate,trueEndDate,preStartDate,init.g_data.allExistTables,init.g_data.stockGainian))
t[i].start()
for i in xrange(NUM):
t[i].join()
print('end %s' % i)