def _set_data(self, **kwargs):
""" SearchResults 결과의 parameter를 저장하는 함수"""
data = kwargs.get('data')
check_err_code(**data)
self._page_no = data['page_no']
self._page_set = data['page_set']
self._total_count = data['total_count']
self._total_page = data['total_page']
self._report_list = [Report(**r) for r in data['report_list']]
self._params = kwargs.get('params')
def report_find_all(report: Report, query: dict, fs_tp: Tuple[str],
separate: bool) -> Tuple[int, Dict[str, Dict]]:
"""
Report의 Page 중 Query 조건에 맞는 페이지 검색후 모든 재무제표 Table 추출
Parameters
----------
report: Report
Report
query: dict
검색 조건
fs_tp: tuple of str
검색할 재무제표 타입
separate: bool
개별 재무제표 여부
Returns
-------
"""
count = 0
fs_table = None
searched_end = False
searched = report.find_all(**query)
for key in searched:
for page in searched[key]:
non_break_space = u'\xa0'
html = page.html.replace(non_break_space, ' ')
soup = BeautifulSoup(html, 'html.parser')
tables = soup.find_all('table', border='1')
fs_table = search_fs_table(tables=tables,
fs_tp=fs_tp,
separate=separate)
count = sum(
[fs_table[fs_tp]['table'] is not None for fs_tp in fs_table])
if count > 0:
searched_end = True
break
if searched_end:
break
return count, fs_table
def merge_fs(fs_df: Dict[str, DataFrame], label_df: Dict[str, DataFrame],
report: Report, fs_tp: Tuple[str] = ('fs', 'is', 'ci', 'cf'),
lang: str = 'ko', separate: bool = False):
"""
재무제표 DataFrame과 Report의 데이터를 합쳐주는 Method
Parameters
----------
fs_df: dict of {str: DataFrame}
재무제표
label_df: dict of {str: DataFrame}
재무제표 검색결과시 추출된 값의 Label
report: Report
Report
fs_tp: tuple of str, optional
'fs' 재무상태표, 'is' 손익계산서, 'ci' 포괄손익계산서, 'cf' 현금흐름표
lang: str, optional
'ko' 한글, 'en' 영문
separate: bool, optional
개별재무제표 여부
Returns
-------
tuple of dict of {str: DataFrame}
재무제표, 추출된 Label 리스트
"""
try:
global additional_comparison_function
# 보고서의 웹페이지에서 재무제표 추출
nfs_df = analyze_html(report=report, fs_tp=fs_tp, lang=lang, separate=separate)
if label_df is None:
label_df = {tp: None for tp in fs_tp}
for tp in fs_df:
if tp in fs_tp:
# 추가될 재무제표의 DataFrame
df = fs_df[tp]
# 새로 추가할 재무제표
ndf = nfs_df[tp]
# 재무제표가 없을시 추가 검색 X
if df is None:
if ndf is None:
continue
else:
fs_df[tp] = ndf.copy(deep=True)
df = fs_df[tp]
# 검색된 재무제표가 없을시 추가 검색 X
if ndf is None:
continue
# label_df가 없을시 초기화
if label_df.get(tp) is None:
concept_column = find_all_columns(df, r'concept_id')
ko_column = find_all_columns(df, r'label_ko')
# Label_ko 가 없을시 Table 오류 이므로 None 처리
if len(ko_column) == 0:
fs_df[tp] = None
continue
else:
ko_column = ko_column[0]
date_columns = find_all_columns(df, r'\d{8}')
label_columns = []
if len(concept_column) == 1:
label_columns.append(('default', 'concept_id',))
for column in date_columns:
label_columns.append(column)
nlabel_columns = pd.MultiIndex.from_tuples(label_columns)
label_df[tp] = pd.DataFrame(columns=nlabel_columns)
if len(concept_column) == 1:
label_df[tp][label_columns[0]] = [extract_account_title(x) for x in list(df[concept_column[0]])]
for column in date_columns:
label_df[tp][column] = list(df[ko_column])
df_columns = set(df.columns.tolist())
ndf_columns = set(ndf.columns.tolist())
overlap = df_columns.intersection(ndf_columns)
date_regex = re.compile(r'\d{8}')
diff = [x for x in (ndf_columns - overlap) if date_regex.search(x[0])]
diff.sort(key=lambda x: date_regex.findall(x[0])[0], reverse=True)
# Data가 동일할 경우 Continue
if len(diff) == 0:
continue
for column in diff:
ndata = [None for _ in range(len(df))]
nlabels = ['' for _ in range(len(df))]
if len(overlap) > 0:
ndata, nlabels = compare_df_and_ndf_value(column, df, ndf, ndata, nlabels)
for compare_func in additional_comparison_function:
ndata, nlabels = compare_func(column, df, ndf, label_df[tp], ndata, nlabels)
label_df[tp][column] = nlabels
fs_df[tp][column] = ndata
return fs_df, label_df
except Exception:
msg = 'An error occurred while fetching or analyzing {}.'.format(report.to_dict())
raise RuntimeError(msg)