def get_day_cinema(day=None):
if day == None:
try:
result = ts.day_cinema().to_json()
except Exception as e:
result = json.dumps({"error":True,"message":str(e)})
else:
try:
result = ts.day_cinema(day).to_json()
except Exception as e:
result = json.dumps({"error":True,
"message":"can not get the data, format date as YYYY-M-D,error:{error}".format(error=e.message)})
return result
def job_9():
try:
print("I'm working......电影票房")
# 实时票房
realtime_boxoffice = ts.realtime_boxoffice()
data = pd.DataFrame(realtime_boxoffice)
data.to_sql('realtime_boxoffice',engine,index=True,if_exists='replace')
print("实时票房......done")
# 每日票房
day_boxoffice = ts.day_boxoffice()
data = pd.DataFrame(day_boxoffice)
data.to_sql('day_boxoffice',engine,index=True,if_exists='replace')
print("每日票房......done")
# 月度票房
month_boxoffice = ts.month_boxoffice()
data = pd.DataFrame(month_boxoffice)
data.to_sql('month_boxoffice',engine,index=True,if_exists='replace')
print("月度票房......done")
# 影院日度票房
day_cinema = ts.day_cinema()
data = pd.DataFrame(day_cinema)
data.to_sql('day_cinema',engine,index=True,if_exists='replace')
print("影院日度票房......done")
except Exception as e:
print(e)
def get_movie():
# mov = ts.realtime_boxoffice()
# print(mov.to_json())
df = ts.day_cinema() # 取上一日全国影院票房排行数据
# df = ts.day_cinema('2015-12-24') #取指定日期的数据
df.head(10)
print(df.to_json())
def cinema():
dd = tu.day_cinema()
dd_data = nu.array(dd)
dd_list = dd_data.tolist()
send_dd_list = []
for i in dd_list[:10]:
send_dd_list.append('-'.join(i))
send_dd_list.insert(0, u"上座率-场均人次-影院名称-排名-今日观众-今日票房-今日场次-场均票价")
str_send_dd_list = '\n'.join(send_dd_list)
return str_send_dd_list
def get_day_cinema(day=None):
df = ts.day_cinema(day)
if df is not None:
if day is None:
day = get_yesterday()
df.insert(0, 'date', day)
res = df.to_sql(fun_day_cinema, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取全国影院指定单日：{0}票房排行数据: {1} '.format(day, msg) + '\n')
else:
print('获取全国影院指定单日：{0}票房排行数据: {1} '.format(day, 'None') + '\n')
def updateDayCinema(con, date: datetime):
import share.model.dao.boxoffice.DayCinema as Model
dateString = date.strftime("%Y-%m-%d")
logging.debug("Updating day cinema of {}".format(dateString))
df = ts.day_cinema(date=dateString,retry_count=16)
res = []
for _, row in df.iterrows():
obj = Model.rowToORM(row, date=date)
if obj is not None:
res.append(obj)
Base.metadata.create_all(con.engine)
con.save_all(res)
return
def cinema_office((start_date,end_date)):
startdate = datetime .datetime .strptime(start_date ,'%Y-%m-%d').date()
enddate = datetime .datetime .strptime(end_date ,'%Y-%m-%d').date()
date_list = date_loop .date_loop_all(start_date= startdate ,end_date= enddate )
DTS = []
for date in date_list:
preDTS = ts.day_cinema(date=date)
preDTS['date'] = date
DTS.append(preDTS)
DTS = pd.concat(DTS)
return DTS
def get_day_cinema(day=None):
print(day)
if day == None:
try:
total = ts.day_cinema().to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {"error": "true", "message": str(e)}
else:
try:
total = ts.day_cinema(day).to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {
"error": "true",
"message": "can not get the data, format date as YYYY-M-D"
}
print("result")
print(result)
return result
import tushare as ts
# 实时票房
ts.realtime_boxoffice()
# 每日票房
ts.day_boxoffice() #取上一日的数据
ts.day_boxoffice('2010-01-01') #取指定日期的数据
# 月度票房
ts.month_boxoffice() #取上一月票房数据
ts.month_boxoffice('2019-02') #此月数据
# 影院日度票房
ts.day_cinema() #取上一日全国影院票房排行数据
df = ts.day_cinema('2019-02-05') # 指定日期的数据 2019年春节
df.to_excel('movie.xlsx') # pip install openpyxl
import tushare as ts
from sqlalchemy import create_engine
token = ts.set_token(
'c723069dd4a25402d05ea6afad36da2937111adf012f8258abb5f7e05936e575')
#engine = create_engine('mysql+pymysql://root:[email protected]/packageing?charset=utf8')
engine = create_engine(
'mysql+pymysql://root:[email protected]/gupiao?charset=utf8')
realtime = ts.realtime_boxoffice()
#realtime.to_sql('realtime_boxoffice',engine)
realtime.to_sql('realtime_boxoffice', engine, if_exists='append')
day = ts.day_boxoffice()
#day.to_sql('day_boxoffice',engine)
day.to_sql('day_boxoffice', engine, if_exists='append')
month = ts.month_boxoffice()
#month.to_sql('month_boxoffice',engine)
month.to_sql('month_boxoffice', engine, if_exists='append')
cinema = ts.day_cinema()
#cinema.to_sql('day_cinema',engine)
cinema.to_sql('day_cinema', engine, if_exists='append')
import tushare as ts
# 实时票房
# 获取实时电影票房数据，30分钟更新一次票房数据，可随时调用。
df = ts.realtime_boxoffice()
print(df)
# 每日票房
# 获取单日电影票房数据，默认为上一日的电影票房，可输入参数获取指定日期的票房。
# 似乎只能显示前一日的
df = ts.day_boxoffice("2015-01-01")
print(df)
# 月度票房
# 获取单月电影票房数据，默认为上一月，可输入月份参数获取指定月度的数据。
df = ts.month_boxoffice('2016-12')
print(df)
# 影院日度票房
# 获取全国影院单日票房排行数据，默认为上一日，可输入日期参数获取指定日期的数据。
df = ts.day_cinema('2015-12-24')
print(df)
g_p.index=g_p.month;g_p g_p.plot(); #工业品价格指数 g_p1=g_p[['ppiip','ppi','qm','rmi','pi']].dropna() g_p1.plot(); #生活价格指数 g_p2=g_p[['cg','food','clothing','roeu','dcg']].dropna();g_p2 g_p2.plot(grid=True) ###10.4 电影票房数据的实时分析 #实时票房 #获取实时电影票房数据，30分钟更新一次票房数据，可随时调用。 r_b = ts.realtime_boxoffice() r_b.info() r_b plt.barh(r_b.MovieName,r_b.BoxOffice.astype(float)); plt.pie(r_b.boxPer,labels=r_b.MovieName); #每日票房 d_b = ts.day_boxoffice() #取上一日的数据 d_b #影院日度票房 #获取全国影院单日票房排行数据，默认为上一日，可输入日期参数获取指定日期的数据。 d_c=ts.day_cinema() #取上一日全国影院票房排行数据 d_c.info() d_c[:10] plt.barh(d_c.CinemaName[:10],d_c.Attendance.astype(float)[:10]);
def capture_stock_data():
capture_date = datetime.datetime.now().strftime("%Y%m%d")
save_dir = "/home/dandelion/stock_data/" + capture_date
if not os.path.exists(save_dir):
os.mkdir(save_dir)
print("The save directory is created successfully!\n", save_dir)
print("The save directory is already exist!\n", save_dir)
# ======================Daily Command================================================================
# get the boxoffcie data of the last day and save as csvfile named as the capture command
ts.day_boxoffice().to_csv(
save_dir + "/" + capture_date + "_day_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("day_boxoffice data capture completed!")
# get the cinema data of the last day and save as csvfile named as the capture command
ts.day_cinema().to_csv(
save_dir + "/" + capture_date + "_day_cinema.csv",
header=True,
sep=",",
index=False,
)
print("day_cinema data capture completed!")
ts.month_boxoffice().to_csv(
save_dir + "/" + capture_date + "_month_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("month_boxoffice data capture completed!")
ts.realtime_boxoffice().to_csv(
save_dir + "/" + capture_date + "_realtime_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("realtime_boxoffice data capture completed!")
# get the stock data index of the last day and save as csvfile named as the capture command
ts.get_index().to_csv(
save_dir + "/" + capture_date + "_get_index.csv",
header=True,
sep=",",
index=False,
)
print("get_index data capture completed!")
# get the history cpi data and save as csvfile named as the capture command
ts.get_cpi().to_csv(
save_dir + "/" + capture_date + "_get_cpi.csv",
header=True,
sep=",",
index=False,
)
print("get_cpi data capture completed!")
# get the history gdp data by month and save as csvfile named as the capture command
ts.get_gdp_year().to_csv(
save_dir + "/" + capture_date + "_get_gdp_year.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_year data capture completed!")
# get today all stock data and save as csvfile named as the capture command
# ts.get_today_all().to_csv(save_dir+'/'+capture_date+'_get_today_all.csv',header=True,sep=',',index=False)
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.broker_tops().to_csv(
save_dir + "/" + capture_date + "_broker_tops.csv",
header=True,
sep=",",
index=False,
)
print("broker_tops data capture completed!")
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.cap_tops().to_csv(
save_dir + "/" + capture_date + "_cap_tops.csv",
header=True,
sep=",",
index=False,
)
print("cap_tops data capture completed!")
ts.get_area_classified().to_csv(
save_dir + "/" + capture_date + "_get_area_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_area_classified data capture completed!")
# ts.get_balance_sheet(code='').to_csv(save_dir+'/'+capture_date+'_get_balance_sheet.csv',header=True,sep=',',index=False)
# print('get_balance_sheet data capture completed!')
# ts.get_cash_flow(code='').to_csv(save_dir+'/'+capture_date+'_get_cash_flow.csv',header=True,sep=',',index=False)
# print('get_cash_flow data capture completed!')
ts.get_day_all().to_csv(
save_dir + "/" + capture_date + "_get_day_all.csv",
header=True,
sep=",",
index=False,
)
print("get_day_all data capture completed!")
ts.get_cashflow_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_cashflow_data.csv",
header=True,
sep=",",
index=False,
)
print("get_cashflow_data data capture completed!")
ts.get_concept_classified().to_csv(
save_dir + "/" + capture_date + "_get_concept_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_concept_classified data capture completed!")
ts.get_debtpaying_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_debtpaying_data.csv",
header=True,
sep=",",
index=False,
)
print("get_debtpaying_data data capture completed!")
ts.get_deposit_rate().to_csv(
save_dir + "/" + capture_date + "_get_deposit_rate.csv",
header=True,
sep=",",
index=False,
)
print("get_deposit_rate data capture completed!")
ts.get_gdp_contrib().to_csv(
save_dir + "/" + capture_date + "_get_gdp_contrib.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_for().to_csv(
save_dir + "/" + capture_date + "_get_gdp_for.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_pull().to_csv(
save_dir + "/" + capture_date + "_get_gdp_pull.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_quarter().to_csv(
save_dir + "/" + capture_date + "_get_gdp_quarter.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_ data capture completed!")
# ts.get_gdp_year().to_csv(save_dir+'/'+capture_date+'_get_gdp_year.csv',header=True,sep=',',index=False)
ts.get_gem_classified().to_csv(
save_dir + "/" + capture_date + "_get_gem_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_gold_and_foreign_reserves().to_csv(
save_dir + "/" + capture_date + "_get_gold_and_foreign_reserves.csv",
header=True,
sep=",",
index=False,
)
ts.get_growth_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_growth_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_industry_classified().to_csv(
save_dir + "/" + capture_date + "_get_industry_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_hs300s().to_csv(
save_dir + "/" + capture_date + "_get_hs300s.csv",
header=True,
sep=",",
index=False,
)
ts.get_sz50s().to_csv(
save_dir + "/" + capture_date + "_get_sz50s.csv",
header=True,
sep=",",
index=False,
)
ts.get_zz500s().to_csv(
save_dir + "/" + capture_date + "_get_zz500s.csv",
header=True,
sep=",",
index=False,
)
ts.get_operation_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_operation_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_stock_basics().to_csv(
save_dir + "/" + capture_date + "_get_stock_basics.csv",
header=True,
sep=",",
index=False,
)
ts.get_report_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_report_data.csv",
header=True,
sep=",",
index=False,
)
ts.inst_detail().to_csv(
save_dir + "/" + capture_date + "_inst_detail.csv",
header=True,
sep=",",
index=False,
)
ts.inst_tops().to_csv(
save_dir + "/" + capture_date + "_inst_tops.csv",
header=True,
sep=",",
index=False,
)
print("inst_tops data capture completed!")
ts.new_stocks().to_csv(
save_dir + "/" + capture_date + "_new_stocks.csv",
header=True,
sep=",",
index=False,
)
print("new_stocks data capture completed!")
ts.top_list().to_csv(
save_dir + "/" + capture_date + "_top_list.csv",
header=True,
sep=",",
index=False,
)
print("top_list data capture completed!")
def GetDayCinema():
df = ts.day_cinema()
df = df.head(10)
df = df.to_json(force_ascii=False)
print(df)
return df
for i in range(aaa.shape[0]):
print(aaa.iloc[i,1])
print("aaa result={result}".format(result=aaa[i,1]))
df=pd.DataFrame(columns=['month','cpi'])
for i in range(aaa.shape[0]):
gap=pd.DataFrame({"month":aaa.iloc[i,0],"cpi":(aaa.iloc[i,1].astype(float)-bbb.astype(float))})
df=df.append(gap)
#票房数据
piaofang=ts.day_cinema('2017-10-02')
pd.set_option
pd.set_option('display.max_columns', 200)
pd.set_option('display.width', 1000)
piaofang[piaofang['price'].max()]
rl_piaofang=ts.realtime_boxoffice()
last_m_pf=ts.month_boxoffice('2017-07')
piaofang_df=pd.DataFrame()
ticker = '000547'
# 获取历史行情数据
# finace=ts.get_hist_data(ticker)
# print("获取历史行情数据")
# print(finace)
#
# # 获取实时行情数据
# datato = ts.get_today_all(ticker)
# print("获取实时行情数据")
# print(datato)
# # 获取实时分笔数据
# data=ts.get_realtime_quotes(ticker)
# print("获取实时分笔数据")
# print(data)
# 获取当日历史分笔数据
data_today = ts.get_today_ticks(ticker)
print("获取当日历史分笔数据")
print(data_today)
data_today.to_csv('000547.csv')
# data_today.to_excel('000547.xlsx')
df = ts.realtime_boxoffice()
print(df)
dd = ts.day_cinema()
print(dd)
