def movie():
df = ts.realtime_boxoffice()
return df[[
'Irank', # 排名
'MovieName', # 片名
'BoxOffice'
]] # 今日票房
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_realtime_boxoffice():
df = ts.realtime_boxoffice()
if df is not None:
res = df.to_sql(fun_realtime_box_office, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取获取实时电影票房数据: ' + msg + '\n')
else:
print('获取获取实时电影票房数据: ' + 'None' + '\n')
def sync_movie_data():
'''
movie boxoffice data
'''
df = ts.realtime_boxoffice()
# df.to_excel('movie.xlsx',encoding='utf-8')
# records = json.loads(df.T.to_json()).values()
DataFrameToMongo(df, MongoClient(mongourl)['stoinfo']['movie'], ['MovieName'])
def test():
ts.get_sz50s()
ts.get_hs300s()
ts.get_zz500s()
ts.realtime_boxoffice()
ts.get_latest_news()
ts.get_notices(tk)
ts.guba_sina()
ts.get_cpi()
ts.get_ppi()
ts.get_stock_basics()
ts.get_concept_classified()
ts.get_money_supply()
ts.get_gold_and_foreign_reserves()
ts.top_list() #每日龙虎榜列表
ts.cap_tops() #个股上榜统计
ts.broker_tops() #营业部上榜统计
ts.inst_tops() # 获取机构席位追踪统计数据
ts.inst_detail()
def movie():
df = tu.realtime_boxoffice()
df_data = nu.array(df)
df_list = df_data.tolist()
send_df_list = []
for i in df_list:
send_df_list.append('-'.join(i[:-1]))
send_df_list.insert(0, u"实时票房(万)-票房排名-影片名称-票房占比-上映天数-累计票房(万)")
str_send_df_list = '\n'.join(send_df_list)
return str_send_df_list
def main():
#ts.get_latest_news() #默认获取最近80条新闻数据，只提供新闻类型、链接和标题
# contents = ts.get_latest_news(top=5,show_content=True) #显示最新5条新闻，并打印出新闻内容
# print contents['title'][0],contents['time'][0]
# print contents['content'][0]
df = ts.realtime_boxoffice()
df.columns = [
'实时票房（万）', '排名', '影片名', '票房占比 （%）', '上映天数', '累计票房（万）', '数据获取时间'
]
print(df)
def prepare_data():
real_time_box_office = tushare.realtime_boxoffice()
if real_time_box_office is None or real_time_box_office.empty:
return
record_json = real_time_box_office.to_json(orient='records')
print(record_json)
collection = mongoConfig.get_collection_default("realtime_boxoffice")
current_date = time.strftime("%Y-%m-%d")
mongoConfig.clear_collection(collection)
# mongoConfig.remove(collection, {"time": {'$regex': current_date}})
mongoConfig.insert_json(collection, json.loads(record_json))
def movie():
df = ts.realtime_boxoffice()
content = '排名 电影名字 票房占比 上映时间 累计票房\n'
for i in range(0, 11):
content = content + str(
df['Irank'][i]) + ' ' + df['MovieName'][i] + ' ' + str(
df['boxPer'][i]) + '% ' + str(df['movieDay'][i]) + '天 ' + str(
df['sumBoxOffice'][i]) + '万\n'
#print(content)
itchat.send('实时票房：\n' + content, toUserName='******')
def updateRealtimeBoxoffice(con):
import share.model.dao.boxoffice.RealtimeBoxoffice as Model
logging.debug("Updating realtime boxoffice")
df = ts.realtime_boxoffice(retry_count=16)
res = []
for _, row in df.iterrows():
obj = Model.rowToORM(row)
if obj is not None:
res.append(obj)
Base.metadata.create_all(con.engine)
con.save_all(res)
return
def real_time_box_office():
df = ts.realtime_boxoffice()
f.write('### 实时票房\n\n')
table_head = '|影片名|排名|实时票房(万)|累计票房(万)|上映天数|\n|-|-|-|-|-|\n'
f.write(table_head)
for i in range(len(df)):
txt = '|' + df['MovieName'][i] + '|' + df['Irank'][i] + '|' + df['BoxOffice'][i] + '|' + \
df['sumBoxOffice'][i] + '|' + df['movieDay'][i] + '|\n'
f.write(txt)
pass
f.write('\n\n')
pass
def today_top_movies(self):
#获得当日电影实时票房数据DF
movie_df = ts.realtime_boxoffice()
#将票房数据转化为int和float以便调用
movie_df[['Irank','movieDay']] = movie_df[['Irank','movieDay']].astype(int)
movie_df[['BoxOffice','boxPer','sumBoxOffice']] = movie_df[['BoxOffice','boxPer','sumBoxOffice']].astype(float)
#计算前十名日均票房
movie_df['dayboxoffice'] = movie_df.sumBoxOffice/movie_df.movieDay
#返回所需数据,上映两周之内日均票房一千万以上，当日票房占比百分之十以上
result = movie_df.ix[(movie_df.movieDay<15) & (movie_df.dayboxoffice>1000) & (movie_df.boxPer>10)]
self.today_goods = [i.encode('utf8') for i in list(result.MovieName)]
return self.today_goods
def get_realtime_boxoffice(day=None):
try:
total = ts.realtime_boxoffice().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"
}
return result
def handle(text, mic, profile, wxbot=None):
"""
Arguments:
text -- user-input, typically transcribed speech
mic -- used to interact with the user (for both input and output)
profile -- contains information related to the user (e.g., phone
number)
wxbot -- wechat bot instance
"""
sys.path.append(mic.dingdangpath.LIB_PATH)
from app_utils import wechatUser
df = ts.realtime_boxoffice()
rs = []
# rs.append("实时票房（万）")
# rs.append("排名")
# rs.append("影片名")
# rs.append("票房占比 （%）")
# rs.append("上映天数")
# rs.append("累计票房（万）")
# rs.append("数据获取时间")
# rs.append("\n")
for index, row in df.iterrows():
for col_name in df.columns:
if col_name == "BoxOffice":
rs.append("实时票房:" + row[col_name] + "万")
elif col_name == "Irank":
rs.append("排名:" + row[col_name])
elif col_name == "MovieName":
rs.append("片名:" + row[col_name])
elif col_name == "boxPer":
rs.append("票房占比:" + row[col_name])
elif col_name == "movieDay":
rs.append("上映天数:" + row[col_name])
elif col_name == "sumBoxOffice":
rs.append("累计票房:" + row[col_name])
elif col_name == "time":
rs.append("获取时间:" + row[col_name])
rs.append('\n')
msg = ' '.join(rs)
tit = "电影票房实时排行榜"
t = mic.asyncSay("已获取" + tit + "," +
('将发送到您的微信' if wxbot != None else "篇幅较长，请登录微信收取"))
if wxbot != None:
wechatUser(profile, wxbot, tit, msg)
t.join()
def today_top_movies(self):
#获得当日电影实时票房数据DF
movie_df = ts.realtime_boxoffice()
#将票房数据转化为int和float以便调用
movie_df[['Irank', 'movieDay']] = movie_df[['Irank',
'movieDay']].astype(int)
movie_df[['BoxOffice', 'boxPer', 'sumBoxOffice'
]] = movie_df[['BoxOffice', 'boxPer',
'sumBoxOffice']].astype(float)
#计算前十名日均票房
movie_df['dayboxoffice'] = movie_df.sumBoxOffice / movie_df.movieDay
#返回所需数据,上映两周之内日均票房一千万以上，当日票房占比百分之十以上
result = movie_df.ix[(movie_df.movieDay < 15)
& (movie_df.dayboxoffice > 1000) &
(movie_df.boxPer > 10)]
self.today_goods = [i.encode('utf8') for i in list(result.MovieName)]
return self.today_goods
def show_stats_home():
"""大盘指数"""
df = ts.get_index()
market_index = df2DictList(df, True)
"""自选股数据"""
df = ts.get_realtime_quotes(
['000002', '300122', '002230', '300166', '603189', '000005'])
self_stock = df2DictList(df)
"""新闻数据"""
df = ts.get_latest_news()
news = df2DictList(df)
"""电影票房"""
df = ts.realtime_boxoffice()
boxoffice = df2DictList(df)
return dict(market_index=market_index,
self_stock=self_stock,
news=news,
boxoffice=boxoffice)
# -=-=-=-=-=-=-=-=-=-=-= # coding=UTF-8 # __author__='Guo Jun' # Version 1..0.0 # -=-=-=-=-=-=-=-=-=-=-= import tushare as ts df = ts.realtime_boxoffice() print(df) # ds = ts.get_notices() # print(ds) # da = ts.get_latest_news() #默认获取最近80条新闻数据，只提供新闻类型、链接和标题 # dc = ts.get_latest_news(top=5,show_content=True) #显示最新5条新闻，并打印出新闻内容 # # print(da) # print(dc)
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import tushare as ts
df_film = ts.realtime_boxoffice()
print(df_film)
df_film_date = ts.day_boxoffice('2017-05-17')
print(df_film_date)
import tushare
# ts = tushare.get_hist_data('601668')
# print(ts)
# rate = tushare.get_deposit_rate()
# print(rate)
movie = tushare.realtime_boxoffice()
print(movie)
# 股票列表 ts.get_stock_basics() # 业绩报告（主表） ts.get_report_data(2014,3) # 盈利能力 ts.get_profit_data(2014,3) # 营运能力 ts.get_operation_data(2014,3) # 成长能力 ts.get_growth_data(2014,3) # 偿债能力 ts.get_debtpaying_data(2014,3) # 现金流量 ts.get_cashflow_data(2014,3) # 宏观经济数据 # 存款利率 ts.get_deposit_rate() # 贷款利率 ts.get_loan_rate() # 存款准备金率 ts.get_rrr() # 货币供应量 ts.get_money_supply() # 货币供应量(年底余额) ts.get_money_supply_bal() # 国内生产总值(年度) ts.get_gdp_year() # 国内生产总值(季度) ts.get_gdp_quarter() # 三大需求对GDP贡献 ts.get_gdp_for() # 三大产业对GDP拉动 ts.get_gdp_pull() # 三大产业贡献率 ts.get_gdp_contrib() # 居民消费价格指数 ts.get_cpi() # 工业品出厂价格指数 ts.get_ppi() # 新闻事件数据 # 信息地雷 ts.get_notices() code:股票代码 date:信息公布日期 # 新浪股吧 ts.guba_sina() fts.realtime_boxoffice()
def get_realtime_boxoffice():
return ts.realtime_boxoffice().to_json()
def get_all_price(code_list):
'''''process all stock'''
df = ts.realtime_boxoffice()
print df
#!usr/bin/env python3
import tushare as ts
df_dayly = ts.realtime_boxoffice()
# df_mon = ts.month_boxoffice()
df_dayly.to_csv("D:/file.csv", encoding="utf_8_sig")
# -*- coding: utf-8 -*- import tushare as ts ''' 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。 返回值说明： BoxOffice 实时票房（万） Irank 排名 MovieName 影片名 boxPer 票房占比 （%） movieDay 上映天数 sumBoxOffice 累计票房（万） time 数据获取时间 ''' data = ts.realtime_boxoffice(); print (data[['Irank','MovieName', 'BoxOffice','movieDay', 'sumBoxOffice','boxPer','time']])
def BoxOffice():
return ts.realtime_boxoffice()
def GetRealtime_boxoffice():
df = ts.realtime_boxoffice()
df = df.to_json(force_ascii=False)
print(df)
return df
import time
# to analyse movie data
plt.axis([0, 48, 0, 6000])
plt.ion()
plt.grid(True)
index = 1
pre_movie_box1 = 0
pre_movie_box2 = 0
pre_movie_box3 = 0
pre_movie_box4 = 0
pre_movie_box5 = 0
while True:
movie_df = ts.realtime_boxoffice()
print(movie_df)
# t = datetime.datetime.now()
t = time.localtime()
shituxingzhe_df = movie_df[movie_df['MovieName'] == u'使徒行者']
weiweiyixiao_df = movie_df[movie_df['MovieName'] == u'微微一笑很倾城']
daomubiji_df = movie_df[movie_df['MovieName'] == u'盗墓笔记']
weicheng_df = movie_df[movie_df['MovieName'] == u'危城']
aichongdajimi_df = movie_df[movie_df['MovieName'] == u'爱宠大机密']
print index
print(str(float(shituxingzhe_df.iloc[0, 0]))+','+str(float(weiweiyixiao_df.iloc[0, 0]))+',' +
str(float(daomubiji_df.iloc[0, 0]))+','+str(float(weicheng_df.iloc[0, 0]))+',' +
str(float(aichongdajimi_df.iloc[0, 0])))
plt.plot([index-1, index], [pre_movie_box1, float(shituxingzhe_df.iloc[0, 0])], c='b')
plt.plot([index-1, index], [pre_movie_box2, float(weiweiyixiao_df.iloc[0, 0])], c='r')
plt.plot([index-1, index], [pre_movie_box3, float(daomubiji_df.iloc[0, 0])], c='g')
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!")
#coding:utf-8
import tushare as ts
from sqlalchemy import create_engine, insert
df = ts.realtime_boxoffice() #获取实时电影票房数据，30分钟更新一次票房数据
engine = create_engine('mysql://*****:*****@127.0.0.1/tushare?charset=utf8')
df.to_sql('movie_data', engine)
print df
def get_realtime_boxoffice(): return ts.realtime_boxoffice()
def test_boxoffice_1(self):
bf: pd.DataFrame = ts.realtime_boxoffice()
print(bf)
bf: pd.DataFrame = ts.day_boxoffice()
print(bf)
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()
