def QA_fetch_financial_report(code, report_date, ltype='EN', db=DATABASE):
"""获取专业财务报表
Arguments:
code {[type]} -- [description]
report_date {[type]} -- [description]
Keyword Arguments:
ltype {str} -- [description] (default: {'EN'})
db {[type]} -- [description] (default: {DATABASE})
Raises:
e -- [description]
Returns:
pd.DataFrame -- [description]
"""
if isinstance(code, str):
code = [code]
if isinstance(report_date, str):
report_date = [QA_util_date_str2int(report_date)]
elif isinstance(report_date, int):
report_date = [report_date]
elif isinstance(report_date, list):
report_date = [QA_util_date_str2int(item) for item in report_date]
collection = db.financial
CH_columns = [item[3:] for item in list(financial_dict.keys())]
CH_columns.extend(['277', '278', '279', '280', '281', '282', '_id', 'code',
'report_date'])
CH_columns = pd.Index(CH_columns)
EN_columns = list(financial_dict.values())
EN_columns.extend(['277', '278', '279', '280', '281', '282', '_id', 'code',
'report_date'])
EN_columns = pd.Index(EN_columns)
try:
if code is not None and report_date is not None:
data = [item for item in collection.find(
{'code': {'$in': code}, 'report_date': {'$in': report_date}})]
elif code is None and report_date is not None:
data = [item for item in collection.find(
{'report_date': {'$in': report_date}})]
elif code is not None and report_date is None:
data = [item for item in collection.find({'code': {'$in': code}})]
else:
data = [item for item in collection.find()]
if len(data) > 0:
res_pd = pd.DataFrame(data)
if ltype in ['CH', 'CN']:
res_pd.columns = CH_columns
elif ltype is 'EN':
res_pd.columns = EN_columns
return res_pd.replace(-4.039810335e+34, numpy.nan).set_index(['report_date', 'code'], drop=False)
else:
return None
except Exception as e:
raise e
def QA_fetch_financial_report(code, report_date, ltype='EN', db=DATABASE):
"""获取专业财务报表
Arguments:
code {[type]} -- [description]
report_date {[type]} -- [description]
Keyword Arguments:
ltype {str} -- [description] (default: {'EN'})
db {[type]} -- [description] (default: {DATABASE})
Raises:
e -- [description]
Returns:
pd.DataFrame -- [description]
"""
if isinstance(code, str):
code = [code]
if isinstance(report_date, str):
report_date = [QA_util_date_str2int(report_date)]
elif isinstance(report_date, int):
report_date = [report_date]
elif isinstance(report_date, list):
report_date = [QA_util_date_str2int(item) for item in report_date]
collection = db.financial
CH_columns = [item[3:] for item in list(financial_dict.keys())]
CH_columns.extend(['277', '278', '279', '280', '281', '282', '_id', 'code',
'report_date'])
CH_columns = pd.Index(CH_columns)
EN_columns = list(financial_dict.values())
EN_columns.extend(['277', '278', '279', '280', '281', '282', '_id', 'code',
'report_date'])
EN_columns = pd.Index(EN_columns)
try:
if code is not None and report_date is not None:
data = [item for item in collection.find(
{'code': {'$in': code}, 'report_date': {'$in': report_date}})]
elif code is None and report_date is not None:
data = [item for item in collection.find(
{'report_date': {'$in': report_date}})]
elif code is not None and report_date is None:
data = [item for item in collection.find({'code': {'$in': code}})]
else:
data = [item for item in collection.find()]
if len(data)>0:
res_pd = pd.DataFrame(data)
if ltype in ['CH','CN']:
res_pd.columns=CH_columns
elif ltype is 'EN':
res_pd.columns=EN_columns
return res_pd.replace(-4.039810335e+34,numpy.nan).set_index(['report_date','code'],drop=False)
else:
return None
except Exception as e:
raise e
def __init__(self, data):
self.data = data
# keys for CN, values for EN
self.colunms_en = list(financial_dict.values())
self.colunms_cn = list(financial_dict.keys())
def QA_fetch_financial_report(code, report_date, ltype='EN', db=DATABASE):
"""获取专业财务报表
Arguments:
code {[type]} -- [description]
report_date {[type]} -- [description]
Keyword Arguments:
ltype {str} -- [description] (default: {'EN'})
db {[type]} -- [description] (default: {DATABASE})
Raises:
e -- [description]
Returns:
pd.DataFrame -- [description]
"""
if isinstance(code, str):
code = [code]
if isinstance(report_date, str):
report_date = [QA_util_date_str2int(report_date)]
elif isinstance(report_date, int):
report_date = [report_date]
elif isinstance(report_date, list):
report_date = [QA_util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
#num_columns.extend(['283', '_id', 'code', 'report_date'])
# CH_columns.extend(['283', '_id', 'code', 'report_date'])
#CH_columns = pd.Index(CH_columns)
#EN_columns = list(financial_dict.values())
#EN_columns.extend(['283', '_id', 'code', 'report_date'])
#EN_columns = pd.Index(EN_columns)
try:
if code is not None and report_date is not None:
data = [item for item in collection.find(
{'code': {'$in': code}, 'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is None and report_date is not None:
data = [item for item in collection.find(
{'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is not None and report_date is None:
data = [item for item in collection.find(
{'code': {'$in': code}}, {"_id": 0}, batch_size=10000)]
else:
data = [item for item in collection.find({}, {"_id": 0})]
if len(data) > 0:
res_pd = pd.DataFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['283'] = '283'
try:
cndict['284'] = '284'
cndict['285'] = '285'
cndict['286'] = '286'
except:
pass
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['283'] = '283'
try:
endict['284'] = '284'
endict['285'] = '285'
endict['286'] = '286'
except:
pass
endict['code'] = 'code'
endict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: endict[x])
if res_pd.report_date.dtype == numpy.int64:
res_pd.report_date = pd.to_datetime(
res_pd.report_date.apply(QA_util_date_int2str))
else:
res_pd.report_date = pd.to_datetime(res_pd.report_date)
return res_pd.replace(-4.039810335e+34, numpy.nan).set_index(['report_date', 'code'], drop=False)
else:
return None
except Exception as e:
raise e
def __init__(self, data):
self.data = data
self.colunms_cn = list(financial_dict.values())
self.colunms_en = list(financial_dict.keys())
def QA_fetch_financial_report(code, report_date, ltype='EN', db=DATABASE):
"""获取专业财务报表
Arguments:
code {[type]} -- [description]
report_date {[type]} -- [description]
Keyword Arguments:
ltype {str} -- [description] (default: {'EN'})
db {[type]} -- [description] (default: {DATABASE})
Raises:
e -- [description]
Returns:
pd.DataFrame -- [description]
"""
if isinstance(code, str):
code = [code]
if isinstance(report_date, str):
report_date = [QA_util_date_str2int(report_date)]
elif isinstance(report_date, int):
report_date = [report_date]
elif isinstance(report_date, list):
report_date = [QA_util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
#num_columns.extend(['283', '_id', 'code', 'report_date'])
# CH_columns.extend(['283', '_id', 'code', 'report_date'])
#CH_columns = pd.Index(CH_columns)
#EN_columns = list(financial_dict.values())
#EN_columns.extend(['283', '_id', 'code', 'report_date'])
#EN_columns = pd.Index(EN_columns)
try:
if code is not None and report_date is not None:
data = [item for item in collection.find(
{'code': {'$in': code}, 'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is None and report_date is not None:
data = [item for item in collection.find(
{'report_date': {'$in': report_date}}, {"_id": 0}, batch_size=10000)]
elif code is not None and report_date is None:
data = [item for item in collection.find(
{'code': {'$in': code}}, {"_id": 0}, batch_size=10000)]
else:
data = [item for item in collection.find({}, {"_id": 0})]
if len(data) > 0:
res_pd = pd.DataFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['283'] = '283'
try:
cndict['284'] = '284'
cndict['285'] = '285'
cndict['286'] = '286'
except:
pass
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['283'] = '283'
try:
endict['284'] = '284'
endict['285'] = '285'
endict['286'] = '286'
except:
pass
endict['code'] = 'code'
endict['report_date'] = 'report_date'
res_pd.columns = res_pd.columns.map(lambda x: endict[x])
if res_pd.report_date.dtype == numpy.int64:
res_pd.report_date = pd.to_datetime(
res_pd.report_date.apply(QA_util_date_int2str))
else:
res_pd.report_date = pd.to_datetime(res_pd.report_date)
return res_pd.replace(-4.039810335e+34, numpy.nan).set_index(['report_date', 'code'], drop=False)
else:
return None
except Exception as e:
raise e
