def save_income_df_to_db(engine, dataframe ):
#pd.set_option('display.max_columns', 200)
#pd.reset_option('display.max_columns')
a = nr.clean_df_db_dups(dataframe, 'Income', engine, ['id'] )
a.to_sql( 'Income', con = engine , index=False, if_exists='append')
def save_XrXd_df_to_db(engine, df):
##conn = engine.connect()
##trans = conn.begin()
##try:
## start_d = '%d-01-01' % year
## end_d = '%d-01-01' % (year +1)
## s = alch_text(
## '''
## delete from XrXd
## where report_date between :A and :B
## '''
## )
## conn.execute( s, A = year, B = compo_m )
##
## trans.commit()
##except Exception as e:
## trans.rollback()
## raise e
## 我们只关心‘已经实施’的分红除权记录，而‘已经实施’的记录是不会再变的，因此无须删除DB里原有记录
a = nr.clean_df_db_dups(df ,'XrXd', engine, ['id'] )
a.to_sql( 'XrXd', con = engine , index=False, if_exists='append')
def save_valuation_df_to_db(engine, dataframe ):
if dataframe is None:
return
#pd.set_option('display.max_columns', 200)
#pd.reset_option('display.max_columns')
a = nr.clean_df_db_dups(dataframe, 'Valuation', engine, ['id'] )
a.to_sql( 'Valuation', con = engine , index=False, if_exists='append')
def save_balance_df_to_db(engine, dataframe ):
#pd.set_option('display.max_columns', 200)
# a = dataframe.drop( 'statDate.1' , axis =1 ) # 这列文档里没有
#pd.reset_option('display.max_columns')
a = nr.clean_df_db_dups(dataframe ,'BalanceSheetDay', engine, ['id'] )
a.to_sql( 'BalanceSheetDay', con = engine , index=False, if_exists='append')
def save_daily_line_to_db(engine, code, dataframe ):
#pd.set_option('display.max_columns', 200)
#pd.reset_option('display.max_columns')
if dataframe is None:
return
a= dataframe
a.insert(0, 'code', code) # 加一列'code'，都设为code
a['t_day'] = a.index # 加一列't_day'，设为该行的index，也就是 datetime64
a['t_day'] = a['t_day'].apply(date_only) # 对于所有行的't_day'列，执行一次'date_only'函数
#print a
a = nr.clean_df_db_dups(a , 'DailyLine', engine, ['code', 't_day'] )
a.to_sql( 'DailyLine', con = engine , index=False, if_exists='append')
def save_forcast_df_to_db(engine, df):
a = nr.clean_df_db_dups(df ,'Forcast', engine, ['id'] )
a.to_sql( 'Forcast', con = engine , index=False, if_exists='append')