def get_value_from_dataset(
classification,
dataset,
concept_id,
label_ko=None,
lang='ko',
):
""" dataset에서 값을 추출하는 함수 """
def str_to_float(val):
try:
return float(val)
except ValueError:
return val
if isinstance(classification, dict):
classification = [classification]
# XBRL 내부 주당이익에서 발생하는 오류 수정을 위한 코드
currency_unit = None
if label_ko is not None:
regex = re.compile(r'\(단위:(.*)\)')
unit = regex.search(label_ko)
if unit is not None:
unit = unit.group(0)
currency = get_currency_str(unit)
if currency is not None:
currency_unit = str_unit_to_number_unit(currency)
results = list()
added_title = list()
for cls in classification:
value = float('nan')
for data in dataset[cls['cls_id']]:
if str_compare(data.concept.id, concept_id):
value = str_to_float(data.value)
# XBRL 내부 주당이익에서 발생하는 오류 수정을 위한 코드
if currency_unit is not None:
decimals = str_to_float(data.decimals)
# decimals이 없을 경우 0으로 처리
if math.isinf(decimals) or math.isnan(decimals):
decimals = 0
value = value * pow(10, decimals)
value = value * currency_unit
break
title = get_title(cls, lang)
if title in added_title:
index = added_title.index(title)
if not math.isnan(value):
results[index] = value
else:
results.append(value)
added_title.append(title)
return results
def convert_thead_into_columns(fs_tp: str, fs_table: dict, separate: bool = False,
lang: str = 'ko'):
""" thead에서 DataFrame의 columns을 추출하는 Method"""
def column_ko_to_en(ko):
ko_to_en = {
'과목': 'label_ko',
'주석': 'comment'
}
en = ko_to_en.get(ko)
return en if en else ko
thead = fs_table['table'].thead
if thead is None:
tt = fs_table['table'].tbody.tr.extract()
thead = BeautifulSoup('<thead></thead>', 'html.parser')
thead.thead.append(tt)
for td in thead.tr.find_all('td'):
td.name = 'th'
th_colspan_list = [int(th.attrs.get('colspan', 1)) for th in thead.tr.find_all('th')]
date_info = extract_date_from_header(fs_table['header'])
# Regular Expression for title
regex = str_to_regex('과목 OR 주석')
fs_string = {
'bs': 'Statement of financial position',
'is': 'Income statement',
'cis': 'Statement of comprehensive income',
'cf': 'Statement of cash flows'
}
str_unit = extract_unit_from_header(fs_table['header'])
str_unit = get_currency_str(str_unit)
if str_unit:
for key in fs_string:
fs_string[key] = fs_string[key] + '(Unit: {})'.format(str_unit)
label = {
'ko': {
True: '별도재무제표',
False: '연결재무제표'
},
'en': {
True: 'Separate',
False: 'Consolidated'
}
}
# 최대 Col
col_length = sum(th_colspan_list)
# 최대 Row
row_length = len(thead.find_all('tr'))
row_length = row_length + 1 if row_length == 1 else row_length
# row-sapn, col-span을 처리하기 위한 Matrix
columns_matrix = [[None for _y in range(col_length)] for _x in range(row_length)]
for idx, tr in enumerate(thead.find_all('tr')):
start_idx = 0
for ele_idx, element in enumerate(columns_matrix[idx]):
if element is None:
start_idx = ele_idx
break
for jdx, th in enumerate(tr.find_all('th')):
row_span = int(th.attrs.get('rowspan', 1))
col_span = int(th.attrs.get('colspan', 1))
text = re.sub(r'\s+', '', th.text)
date_list = [datetime(1900, 1, 1)]
if idx == 0:
if jdx == 0:
text = '과목'
elif regex.search(text) is None:
if len(date_info) > 0:
date_list = date_info.pop(0)
else:
import warnings
date = '-'.join([date.strftime('%Y%m%d') for date in date_list])
warnings_text = "Date data length does not match table header."\
+ "So last date was set using last data({}). ".format(date)
warnings.warn(warnings_text, RuntimeWarning)
text = '-'.join([date.strftime('%Y%m%d') for date in date_list])
if regex.search(text):
row_span = 2
for mdx in range(row_span):
for ndx in range(col_span):
new_text = text
if mdx == 0 and regex.search(text):
new_text = fs_string[fs_tp]
columns_matrix[idx + mdx][start_idx + ndx] = new_text
start_idx = start_idx + ndx + 1
regex_3month = re.compile(r'3개월')
regex_total = str_to_regex(r'누적 OR 금액')
columns = []
for jdx in range(len(columns_matrix[0])):
column = []
sec_item = []
for idx in range(len(columns_matrix)):
item = columns_matrix[idx][jdx]
if idx == 0:
column.append(item)
continue
elif idx == 1 and (item is None or regex.search(item) is None):
sec_item.append(label[lang][separate])
else:
pass
if item is None:
pass
elif str_compare(column[0], item):
continue
elif regex_3month.search(item):
# extract date info
date_info = [datetime.strptime(date_str, '%Y%m%d') for date_str in column[0].split('-')]
# calculating start_dt
delta = relativedelta(months=3)
start_dt = date_info[1] - delta
start_dt = start_dt.replace(day=1)
end_dt = date_info[1]
column[0] = '-'.join([date.strftime('%Y%m%d') for date in [start_dt, end_dt]])
elif regex_total.search(item):
pass
else:
sec_item.append(column_ko_to_en(item))
if sec_item[0] in ['label_ko', 'comment']:
column.append(sec_item[0])
else:
column.append(tuple(sec_item))
columns.append(column)
return columns