def search_fs_table(tables: List, fs_tp: Tuple[str] = ('bs', 'is', 'cis', 'cf'),
separate: bool = False) -> Dict[str, dict]:
"""
페이지의 재무제표 테이블을 검색하는 함수
Parameters
----------
tables: list of ResultSet
page 내부에서 검색된 모든 Tables
fs_tp: tuple of str
'bs' 재무상태표, 'is' 손익계산서, 'cis' 포괄손익계산서, 'cf' 현금흐름표
separate: bool
개별 재무제표 여부
Returns
-------
dict of {str : dict }
검색된 재무제표 결과
"""
fs_table = OrderedDict()
# 순서대로 검색 (순서 변경 금지)
queryset = {
'bs': str_insert_whitespace('재무상태표') + ' OR ' + str_insert_whitespace('대차대조표'),
'is': str_insert_whitespace('손익계산서'),
'cis': str_insert_whitespace('포괄손익계산서'),
'cf': str_insert_whitespace('현금흐름표'),
}
for key, query in queryset.items():
if key not in fs_tp:
continue
# 연결재무제표 검색시 사용할 query 구문
excludes = None
if not separate:
query = query + ' AND ' + str_insert_whitespace('연결')
else:
excludes = str_insert_whitespace('연결')
if key == 'is':
if excludes:
excludes += ' OR ' + str_insert_whitespace('포괄')
else:
excludes = str_insert_whitespace('포괄')
if excludes:
excludes = str_to_regex(excludes)
regex = str_to_regex(query)
title, header, tb = seek_table(tables=tables, includes=regex, excludes=excludes)
fs_table[key] = {'title': title, 'header': header, 'table': tb}
return fs_table
def get_currency_str(unit: str) -> Union[str, None]:
regex_str = ' OR '.join(CURRENCY.keys())
str_unit = str_to_regex(regex_str).search(unit)
if str_unit:
str_unit = str_unit.group(0)
return CURRENCY[str_unit]
regex_str = ' OR '.join([v for _, v in CURRENCY.items()])
str_unit = str_to_regex(regex_str).search(unit)
if str_unit:
return str_unit.group(0)
return None
def find_all_columns(df: DataFrame, query: str) -> list:
"""
DataFrame의 column을 검색어를 통해 검색하는 함수
Parameters
----------
df: DataFrame
검색할 DataFrame
query: str
검색어
Returns
-------
tuple of str
검색된 DataFrame의 column
"""
regex = str_to_regex(query)
if df is None:
return []
columns = df.columns.tolist()
results = []
for column in columns:
for item in column:
if isinstance(item, str) and regex.search(item):
results.append(column)
else:
if regex.search(' '.join(item)):
results.append(column)
return results
def convert_tbody_to_dataframe(columns: list, fs_table: dict):
""" Html의 tbody를 DataFrame으로 변환하는 함수"""
column_matrix = OrderedDict()
for idx, column in enumerate(columns):
key = tuple(column)
if column_matrix.get(key):
column_matrix[key].append(idx)
else:
column_matrix[key] = []
column_matrix[key].append(idx)
deduplicated = [key for key in column_matrix]
df_columns = pd.MultiIndex.from_tuples(deduplicated)
df = pd.DataFrame(columns=df_columns)
tbody = fs_table['table'].tbody
regex = str_to_regex('label_ko OR comment')
str_unit = extract_unit_from_header(fs_table['header'])
unit = str_unit_to_number_unit(str_unit)
unit_regex = re.compile(r'\(단위\s*?:\s*([a-zA-Zㄱ-힣])\)')
for idx, tr in enumerate(tbody.find_all('tr')):
extracted = [
re.sub(r'\s+|=+', '', td.text) for td in tr.find_all('td')
]
row = {key: 0 for key in deduplicated}
for key, index_list in column_matrix.items():
for index in index_list:
if len(extracted) <= index:
row[key] = None
elif isinstance(key[1], str):
row[key] = extracted[index]
elif regex.search(' '.join(key[1])):
value = extracted[index]
row[key] = value
else:
value = str_to_float(extracted[index], unit)
row[key] += value
if isinstance(row[key], float):
if abs(row[key]) < 1e-10:
row[key] = ''
else:
row[key] = row[key] * unit
ordered_list = []
for column in df_columns.tolist():
ordered_list.append(row.get(column, None))
row_unit = unit_regex.search(ordered_list[0])
if row_unit:
row_unit = str_unit_to_number_unit(row_unit.group(1))
for jdx, value in enumerate(ordered_list):
if isinstance(value, str):
pass
else:
ordered_list[jdx] = ordered_list[jdx] / unit * row_unit
df.loc[idx] = ordered_list
return df
def cls_label_check(cls, query):
""" classification label에 특정 단어가 포함된지 검색하는 함수
Parameters
----------
cls: cls
classification
query: str
검색어
Returns
-------
bool
질의내용 포함시 True / 미포함시 False
"""
if query is None:
return True
regex = str_to_regex(query)
label = ''
for qname in cls['label']:
label = label + cls['label'][qname]['ko'] + cls['label'][qname]['en']
if regex.search(label):
return True
return False
def test_str_to_regex():
query = '삼성 OR ( 하이 AND 닉스)'
regex = str_to_regex(query=query)
actual = regex.search('하이닉')
expected = None
assert actual == expected
def test_str_to_regex_2():
query = '삼성 OR 하이'
regex = str_to_regex(query=query)
actual = regex.search('삼성이닉스').group(0)
expected = '삼성'
assert actual == expected
def determinant(value):
det1 = str_to_regex(includes).search(value) if includes else True
det2 = not str_to_regex(excludes).search(value) if excludes else True
return det1 and det2
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 = str_to_regex('원 OR USD').search(str_unit)
if str_unit:
str_unit = str_unit.group(0)
str_unit = 'KRW' if str_compare('원', str_unit) else 'USD'
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
