def macro_info_to_sql():
create_classify_table()
a = ts.get_cpi()
b = ts.get_ppi()
c = ts.get_money_supply()
c = c.iloc[:, [0, 1, 3, 5]]
b = b.iloc[:, [0, 2]]
result = pd.merge(a,
b,
how='left',
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=('_x', '_y'),
copy=True,
indicator=False)
result = pd.merge(result,
c,
how='left',
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=('_x', '_y'),
copy=True,
indicator=False)
df_to_mysql('anack_macro_data', result)
def get_gdp_ppi_info():
df = ts.get_ppi()
if df is not None:
res = df.to_sql(microE_ppi, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取工业品出厂价格指数: ' + msg + '\n')
else:
print('获取工业品出厂价格指数: ' + 'None' + '\n')
def get_ppi():
try:
df = ts.get_ppi()
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('ppi', engine, if_exists='append')
print "message"
except Exception, e:
e.message
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 call_ppi():
key = 'ppi'
stores = pd.HDFStore(COMMEN_FILE_PATH)
if key not in stores:
print('CALL TS GET PPI...')
df = ts.get_ppi()
stores[key] = df
else:
df = stores[key]
stores.close()
return df
def stat_all(tmp_datetime):
# 存款利率
data = ts.get_deposit_rate()
common.insert_db(data, "ts_deposit_rate", False, "`date`,`deposit_type`")
# 贷款利率
data = ts.get_loan_rate()
common.insert_db(data, "ts_loan_rate", False, "`date`,`loan_type`")
# 存款准备金率
data = ts.get_rrr()
common.insert_db(data, "ts_rrr", False, "`date`")
# 货币供应量
data = ts.get_money_supply()
common.insert_db(data, "ts_money_supply", False, "`month`")
# 货币供应量(年底余额)
data = ts.get_money_supply_bal()
common.insert_db(data, "ts_money_supply_bal", False, "`year`")
# 国内生产总值(年度)
data = ts.get_gdp_year()
common.insert_db(data, "ts_gdp_year", False, "`year`")
# 国内生产总值(季度)
data = ts.get_gdp_quarter()
common.insert_db(data, "ts_get_gdp_quarter", False, "`quarter`")
# 三大需求对GDP贡献
data = ts.get_gdp_for()
common.insert_db(data, "ts_gdp_for", False, "`year`")
# 三大产业对GDP拉动
data = ts.get_gdp_pull()
common.insert_db(data, "ts_gdp_pull", False, "`year`")
# 三大产业贡献率
data = ts.get_gdp_contrib()
common.insert_db(data, "ts_gdp_contrib", False, "`year`")
# 居民消费价格指数
data = ts.get_cpi()
common.insert_db(data, "ts_cpi", False, "`month`")
# 工业品出厂价格指数
data = ts.get_ppi()
common.insert_db(data, "ts_ppi", False, "`month`")
#############################基本面数据 http://tushare.org/fundamental.html
# 股票列表
data = ts.get_stock_basics()
print(data.index)
common.insert_db(data, "ts_stock_basics", True, "`code`")
def add_ppi_page(canvas_para, length):
"""
函数功能：工业品出厂价格指数
:param canvas_para:
:return:
"""
c = canvas_para
ppi_df = ts.get_ppi()
ppi_df['month'] = ppi_df.apply(lambda x:stdMonthDate(x['month']), axis=1)
ppi_df = ppi_df.sort_values(by='month',ascending=False).head(length).sort_values(by='month',ascending=True)
ppiip = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='ppiip', timeAxis='month')
ppi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='ppi', timeAxis='month')
qm = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='qm', timeAxis='month')
rmi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='rmi', timeAxis='month')
pi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='pi', timeAxis='month')
ppi_industry_drawing = gen_lp_drawing([tuple(ppiip), tuple(ppi), tuple(qm), tuple(rmi), tuple(pi)],
data_note=['工业品出厂价格指数',
'生产资料价格指数',
'采掘工业价格指数',
'原材料工业价格指数',
'加工工业价格指数'],
time_axis='month')
renderPDF.draw(drawing=ppi_industry_drawing, canvas=c, x=10, y=letter[1] * 0.6)
cg = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='cg', timeAxis='month')
food = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='food', timeAxis='month')
clothing = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='clothing', timeAxis='month')
roeu = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='roeu', timeAxis='month')
dcg = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='dcg', timeAxis='month')
ppi_life_drawing = gen_lp_drawing([tuple(cg), tuple(food), tuple(clothing), tuple(roeu), tuple(dcg)],
data_note=['生活资料价格指数',
'食品类价格指数',
'衣着类价格指数',
'一般日用品价格指数',
'耐用消费品价格指数'],
time_axis='month')
renderPDF.draw(drawing=ppi_life_drawing, canvas=c, x=10, y=letter[1] * 0.2)
c.showPage()
return c
def get_macro():
Macro={}
Macro['Depo']=ts.get_deposit_rate()
Macro['Loan']=ts.get_loan_rate()
Macro['RRR']=ts.get_rrr()
Macro['MoneySupply']=ts.get_money_supply()
Macro['MoneyBalance']=ts.get_money_supply_bal()
Macro['GDPYOY']=ts.get_gdp_year()
Macro['GDPQOQ']=ts.get_gdp_quarter()
Macro['GDPFOR']=ts.get_gdp_for()
Macro['GDPPULL']=ts.get_gdp_pull()
Macro['GDPCON']=ts.get_gdp_contrib()
Macro['CPI']=ts.get_cpi()
Macro['PPI']=ts.get_ppi()
Macro['SHIBO']=ts.shibor_data()
return Macro
def return_ppi(self):
'''
工业品出厂价格指数
'''
df = ts.get_ppi()
detail = {}
for col in df.columns:
lt = df[col].values.tolist()
lt.reverse()
for idx in xrange(0, len(lt)):
try:
if math.isnan(lt[idx]):
lt[idx] = None
except:
pass
detail[col] = lt
self.reply(detail=detail)
def core_function(self, type):
self.set_data()
print(type)
mongo = MongoClient("127.0.0.1", 27017)
if (type == 'gdp_year'):
print("gdp_year")
df = fd.get_gdp_year()
elif (type == 'gdp_quarter'):
print("gdp_quarter")
df = fd.get_gdp_quarter()
elif (type == 'gdp_for'):
print("gdp_for")
df = fd.get_gdp_for()
elif (type == 'gdp_pull'):
print("gdp_pull")
df = fd.get_gdp_pull()
elif (type == 'get_money_supply_bal'):
print("get_money_supply_bal")
df = fd.get_money_supply_bal()
elif (type == 'gdp_contrib'):
print("gdp_contrib")
df = fd.get_gdp_contrib()
elif (type == 'get_cpi'):
print("get_cpi")
df = ts.get_cpi()
elif (type == 'get_ppi'):
print("get_ppi")
df = ts.get_ppi()
elif (type == 'get_rrr'):
print("get_rrr")
df = ts.get_rrr()
elif (type == 'money_supply'):
print("money_supply")
df = ts.get_money_supply()
elif (type == 'money_supply_bal'):
print("money_supply_bal")
df = ts.get_money_supply_bal()
else:
df = {}
print(df)
insert_string = df.to_json(orient='records')
items = json.loads(insert_string)
mongo.macro.gdp_year.insert(items)
def get_ppi():
"""工业品出厂价格指数"""
logger.info('Begin get PPI.')
try:
data_df = ts.get_ppi()
except Exception as e:
logger.exception('Error get PPI.')
return None
else:
data_dicts = []
if data_df.empty:
logger.warn('Empty get PPI.')
else:
data_dicts = [{'month': row[0], 'ppiip': row[1],
'ppi': row[2], 'qm': row[3], 'rmi': row[4],
'pi': row[5], 'cg': row[6], 'food': row[7], 'clothing': row[8],
'roeu': row[9], 'dcg': row[10]}
for row in data_df.values]
logger.info('Success get PPI.')
return data_dicts
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
'''存款利率'''
deposit_rate = ts.get_deposit_rate()
self.base.batchwri(deposit_rate, 'deposit_rate', self.financial_data)
'''贷款利率'''
loan_rate = ts.get_loan_rate()
self.base.batchwri(loan_rate, 'loan_rate', self.financial_data)
'''存款准备金率'''
rrr = ts.get_rrr()
self.base.batchwri(rrr, 'RatioOfDeposit', self.financial_data)
'''货币供应量'''
money_supply = ts.get_money_supply()
self.base.batchwri(money_supply, 'money_supply', self.financial_data)
'''货币供应量（年底余额)'''
money_supply_bal = ts.get_money_supply_bal()
self.base.batchwri(money_supply_bal, 'money_supply_bal',
self.financial_data)
'''国内生产总值(年度）'''
gdp_year = ts.get_gdp_year()
self.base.batchwri(gdp_year, 'gdp_year', self.financial_data)
'''国内生产总值（季度）'''
gdp_quarter = ts.get_gdp_quarter()
self.base.batchwri(gdp_quarter, 'gdp_quarter', self.financial_data)
'''三大需求对GDP贡献'''
gdp_for = ts.get_gdp_for()
self.base.batchwri(gdp_for, 'gdp_for', self.financial_data)
'''三大产业对GDP拉动'''
gdp_pull = ts.get_gdp_pull()
self.base.batchwri(gdp_pull, 'gdp_pull', self.financial_data)
'''三大产业贡献率'''
gdp_contrib = ts.get_gdp_contrib()
self.base.batchwri(gdp_contrib, 'gdp_contrib', self.financial_data)
'''居民消费价格指数'''
cpi = ts.get_cpi()
self.base.batchwri(cpi, 'cpi', self.financial_data)
'''工业品出场价格指数'''
ppi = ts.get_ppi()
self.base.batchwri(ppi, 'ppi', self.financial_data)
def getPPI(ann_dt_label):
raw_data = ts.get_ppi()
year_month_label = 'year_month'
raw_data = raw_data.rename({'month': year_month_label}, axis=1)
raw_data.loc[:, 'year'] = raw_data[year_month_label].apply(
lambda x: x.split('.')[0]).astype('int')
raw_data.loc[:, 'month'] = raw_data[year_month_label].apply(
lambda x: x.split('.')[1]).astype('int')
raw_data = raw_data.drop(year_month_label, axis=1)
# ========= change datatype
# raw_data = chgDataType(raw_data, ['year', 'month'])
# ========== announcement date
tmp_data = raw_data.loc[raw_data['month'] !=
12] # announced month is 1 month later
tmp_data.loc[:, 'announced_month'] = tmp_data['month'] + 1
tmp_data.loc[:, 'announced_year'] = tmp_data['year']
processed_data = tmp_data.copy()
tmp_data = raw_data.loc[raw_data[
'month'] == 12] # December, adding 1 month = January in the next year
tmp_data.loc[:, 'announced_month'] = 1
tmp_data.loc[:, 'announced_year'] = tmp_data['year'] + 1
processed_data = processed_data.append(tmp_data)
processed_data.loc[:, ann_dt_label] = processed_data.apply(
lambda x: datetime.strftime(
datetime(int(x['announced_year']), int(x['announced_month']), 15),
'%Y-%m-%d'),
axis=1)
processed_data = processed_data.drop(['announced_year', 'announced_month'],
axis=1)
return processed_data
gdp_y = ts.get_gdp_year()
gdp_q = ts.get_gdp_quarter()
#三大需求对GDP贡献
gdp_for = ts.get_gdp_for()
#三大产业对GDP拉动
gdp_pull = ts.get_gdp_pull()
#三大产业贡献率
gdp_contrib = ts.get_gdp_contrib()
cpi = ts.get_cpi()
ppi = ts.get_ppi()
df = ts.shibor_data() #取当前年份的数据
#df = ts.shibor_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
df = ts.shibor_quote_data() #取当前年份的数据
#df = ts.shibor_quote_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
#shibo均值
df = ts.shibor_ma_data() #取当前年份的数据
#df = ts.shibor_ma_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
#贷款基础利率
def getPPI(self):
file_name = 'ppi.csv'
path = self.index + self.index_ppi + file_name
data = ts.get_ppi()
data.to_csv(path, encoding='utf-8')
print(file_name)
money_supply=ts.get_money_supply()
money_supply.to_csv('D:\\ts\\macro\\money_supply.csv', encoding='gbk')
money_supply_bal=ts.get_money_supply_bal()
money_supply_bal.to_csv('D:\\ts\\macro\\money_supply_bal.csv', encoding='gbk')
gdp_year=ts.get_gdp_year()
gdp_year.to_csv('D:\\ts\\macro\\gdp_year.csv', encoding='gbk')
gdp_quater=ts.get_gdp_quarter()
gdp_quater.to_csv('D:\\ts\\macro\\gdp_quater.csv', encoding='gbk')
gdp_for=ts.get_gdp_for()
gdp_for.to_csv('D:\\ts\\macro\\gdp_for.csv', encoding='gbk')
gdp_pull=ts.get_gdp_pull()
gdp_pull.to_csv('D:\\ts\\macro\\gdp_pull.csv', encoding='gbk')
gdp_contrib=ts.get_gdp_contrib()
gdp_contrib.to_csv('D:\\ts\\macro\\gdp_contrib.csv', encoding='gbk')
cpi=ts.get_cpi()
cpi.to_csv('D:\\ts\\macro\\cpi.csv', encoding='gbk')
ppi=ts.get_ppi()
ppi.to_csv('D:\\ts\\macro\\ppi.csv', encoding='gbk')
print 'all done'
def macro_type(macros_type):
if macros_type == 'deposit_rate':
deposit_rate = ts.get_deposit_rate()
if deposit_rate is not None:
deposit_rate.to_sql('macros_deposit_rate',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'loan_rate':
loan_rate = ts.get_loan_rate()
if loan_rate is not None:
loan_rate.to_sql('macros_loan_rate',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'rrr':
rrr = ts.get_rrr()
if rrr is not None:
rrr.to_sql('macros_rrr',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'money_supply':
money_supply = ts.get_money_supply()
if money_supply is not None:
money_supply.to_sql('macros_money_supply',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'money_supply_bal':
money_supply_bal = ts.get_money_supply_bal()
if money_supply_bal is not None:
money_supply_bal.to_sql('macros_money_supply_bal',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_year':
gdp_year = ts.get_gdp_year()
if gdp_year is not None:
gdp_year.to_sql('macros_gdp_year',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_quater':
gdp_quater = ts.get_gdp_quarter()
if gdp_quater is not None:
gdp_quater.to_sql('macros_gdp_quater',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_for':
gdp_for = ts.get_gdp_for()
if gdp_for is not None:
gdp_for.to_sql('macros_gdp_for',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_pull':
gdp_pull = ts.get_gdp_pull()
if gdp_pull is not None:
gdp_pull.to_sql('macros_gdp_pull',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_contrib':
gdp_contrib = ts.get_gdp_contrib()
if gdp_contrib is not None:
gdp_contrib.to_sql('macros_gdp_contrib',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'cpi':
cpi = ts.get_cpi()
if cpi is not None:
cpi.to_sql('macros_cpi',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'ppi':
ppi = ts.get_ppi()
if ppi is not None:
ppi.to_sql('macros_ppi',
engine,
flavor='mysql',
if_exists='replace')
# -*- coding:UTF-8 -*-
import tushare as ts
import pymongo
import json
db = "MacroData"
coll = "ppi"
conn = pymongo.MongoClient('127.0.0.1', port=27017)
df = ts.get_ppi()
# index data columns(X columns)
dicIndex = json.loads(df.to_json(orient='split'))
for i, ind in enumerate(dicIndex['index']):
d = dicIndex['data'][i][0]
jsonstr = {
'_id': d,
dicIndex['columns'][0]: d,
dicIndex['columns'][1]: dicIndex['data'][i][1],
dicIndex['columns'][2]: dicIndex['data'][i][2],
dicIndex['columns'][3]: dicIndex['data'][i][3],
dicIndex['columns'][4]: dicIndex['data'][i][4],
dicIndex['columns'][5]: dicIndex['data'][i][5],
dicIndex['columns'][6]: dicIndex['data'][i][6],
dicIndex['columns'][7]: dicIndex['data'][i][7],
dicIndex['columns'][8]: dicIndex['data'][i][8],
dicIndex['columns'][9]: dicIndex['data'][i][9]
}
try:
conn[db][coll].insert(jsonstr)
except:
'''
ts.get_gdp_contrib()
# 居民消费价格指数
'''
返回值说明：
month :统计月份
cpi :价格指数
'''
ts.get_cpi()
# 工业品出厂价格指数
'''
返回值说明：
month :统计月份
ppiip :工业品出厂价格指数
ppi :生产资料价格指数
qm:采掘工业价格指数
rmi:原材料工业价格指数
pi:加工工业价格指数
cg:生活资料价格指数
food:食品类价格指数
clothing:衣着类价格指数
roeu:一般日用品价格指数
dcg:耐用消费品价格指数
'''
ts.get_ppi()
def job_5():
try:
print("I'm working......宏观经济数据")
# 存款利率
deposit_rate = ts.get_deposit_rate()
data = pd.DataFrame(deposit_rate)
data.to_sql('deposit_rate',engine,index=True,if_exists='replace')
print("存款利率......done")
# 贷款利率
loan_rate = ts.get_loan_rate()
data = pd.DataFrame(loan_rate)
data.to_sql('loan_rate',engine,index=True,if_exists='replace')
print("贷款利率......done")
# 存款准备金率
rrr = ts.get_rrr()
data = pd.DataFrame(rrr)
data.to_sql('rrr',engine,index=True,if_exists='replace')
print("存款准备金率......done")
# 货币供应量
money_supply = ts.get_money_supply()
data = pd.DataFrame(money_supply)
data.to_sql('money_supply',engine,index=True,if_exists='replace')
print("货币供应量......done")
# 货币供应量(年底余额)
money_supply_bal = ts.get_money_supply_bal()
data = pd.DataFrame(money_supply_bal)
data.to_sql('money_supply_bal',engine,index=True,if_exists='replace')
print("货币供应量(年底余额)......done")
# 国内生产总值(年度)
gdp_year = ts.get_gdp_year()
data = pd.DataFrame(gdp_year)
data.to_sql('gdp_year',engine,index=True,if_exists='replace')
print("国内生产总值(年度)......done")
# 国内生产总值(季度)
gdp_quarter = ts.get_gdp_quarter()
data = pd.DataFrame(gdp_quarter)
data.to_sql('gdp_quarter',engine,index=True,if_exists='replace')
print("国内生产总值(季度)......done")
# 三大需求对GDP贡献
gdp_for = ts.get_gdp_for()
data = pd.DataFrame(gdp_for)
data.to_sql('gdp_for',engine,index=True,if_exists='replace')
print("三大需求对GDP贡献......done")
# 三大产业对GDP拉动
gdp_pull = ts.get_gdp_pull()
data = pd.DataFrame(gdp_pull)
data.to_sql('gdp_pull',engine,index=True,if_exists='replace')
print("三大产业对GDP拉动......done")
# 三大产业贡献率
gdp_contrib = ts.get_gdp_contrib()
data = pd.DataFrame(gdp_contrib)
data.to_sql('gdp_contrib',engine,index=True,if_exists='replace')
print("三大产业贡献率......done")
# 居民消费价格指数
cpi = ts.get_cpi()
data = pd.DataFrame(cpi)
data.to_sql('cpi',engine,index=True,if_exists='replace')
print("居民消费价格指数......done")
# 工业品出厂价格指数
ppi = ts.get_ppi()
data = pd.DataFrame(ppi)
data.to_sql('ppi',engine,index=True,if_exists='replace')
print("工业品出厂价格指数......done")
except Exception as e:
print(e)
def getppidb():
ppi = ts.get_ppi()
ppi.to_sql('ppi_data',ENGINE,if_exists = 'append')
def ppi():
return ts.get_ppi()
#g_y.sort_index(inplace=True) # g_y.sort_values(by='year',inplace=True) g_y.head() plt.plot(g_y.year,g_y.gdp) g_y1=g_y[g_y.year>=1990];g_y1 plt.plot(g_y1.year,g_y1.gdp) g_y2=g_y1[['pi','si','ti']] g_y2.index=g_y1.year; g_y2 g_y2.plot(kind='bar') g_y2.plot(kind='line') ###10.3.3 工业品出厂价格指数分析 g_p=ts.get_ppi() g_p.info() g_p g_p.sort_values(by='month',inplace=True); g_p 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。
def init(engine, session): tbl = "macro_deposit" tsl.log(tbl + " start...") df = ts.get_deposit_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_loan" tsl.log(tbl + " start...") df = ts.get_loan_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_rrr" tsl.log(tbl + " start...") df = ts.get_rrr() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply" tsl.log(tbl + " start...") df = ts.get_money_supply() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply_year" tsl.log(tbl + " start...") df = ts.get_money_supply_bal() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_year" tsl.log(tbl + " start...") df = ts.get_gdp_year() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_quarter" tsl.log(tbl + " start...") df = ts.get_gdp_quarter() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_for" tsl.log(tbl + " start...") df = ts.get_gdp_for() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_pull" tsl.log(tbl + " start...") df = ts.get_gdp_pull() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_contrib" tsl.log(tbl + " start...") df = ts.get_gdp_contrib() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_cpi" tsl.log(tbl + " start...") df = ts.get_cpi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_ppi" tsl.log(tbl + " start...") df = ts.get_ppi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "gold_and_foreign_reserves" tsl.log(tbl + " start...") df = ts.get_gold_and_foreign_reserves() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done")
def call_ppi():
df = ts.get_ppi()
return df
def load_macro_economy():
# 下载存款利率
try:
rs = ts.get_deposit_rate()
pd.DataFrame.to_sql(rs, "deposit_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载存款利率ok")
except:
print("下载存款利率出错")
# 下载贷款利率
try:
rs = ts.get_loan_rate()
pd.DataFrame.to_sql(rs, "loan_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载贷款利率ok")
except:
print("下载贷款利率出错")
# 下载存款准备金率
try:
rs = ts.get_rrr()
pd.DataFrame.to_sql(rs, "rrr", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载存款准备金率ok")
except:
print("下载存款准备金率出错")
# 下载货币供应量
try:
rs = ts.get_money_supply()
pd.DataFrame.to_sql(rs, "money_supply", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载货币供应量ok")
except:
print("下载货币供应量出错")
# 下载货币供应量(年底余额)
try:
rs = ts.get_money_supply_bal()
pd.DataFrame.to_sql(
rs, "money_supply_bal", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True
)
print("下载货币供应量(年底余额)ok")
except:
print("下载货币供应量(年底余额)出错")
# 下载国内生产总值(年度)
try:
rs = ts.get_gdp_year()
pd.DataFrame.to_sql(rs, "gdp_year", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载国内生产总值(年度)ok")
except:
print("下载国内生产总值(年度)出错")
# 下载国内生产总值(季度)
try:
rs = ts.get_gdp_quarter()
pd.DataFrame.to_sql(rs, "gdp_quarter", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载国内生产总值(季度)ok")
except:
print("下载国内生产总值(季度)出错")
# 下载三大需求对GDP贡献
try:
rs = ts.get_gdp_for()
pd.DataFrame.to_sql(rs, "gdp_for", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大需求对GDP贡献ok")
except:
print("下载三大需求对GDP贡献出错")
# 下载三大产业对GDP拉动
try:
rs = ts.get_gdp_pull()
pd.DataFrame.to_sql(rs, "gdp_pull", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大产业对GDP拉动ok")
except:
print("下载三大产业对GDP拉动出错")
# 下载三大产业贡献率
try:
rs = ts.get_gdp_contrib()
pd.DataFrame.to_sql(rs, "gdp_contrib", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大产业贡献率ok")
except:
print("下载三大产业贡献率出错")
# 下载居民消费价格指数
try:
rs = ts.get_cpi()
pd.DataFrame.to_sql(rs, "gdp_cpi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载居民消费价格指数ok")
except:
print("下载居民消费价格指数出错")
# 下载工业品出厂价格指数
try:
rs = ts.get_ppi()
pd.DataFrame.to_sql(rs, "gdp_ppi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载工业品出厂价格指数ok")
except:
print("下载工业品出厂价格指数出错")
