def parse(self, response):
title, stock_code = self._parse_title_and_stock_code(response)
XPATH_ROOT = '//*[@id="SysJustIFRAMEDIV"]/table/tr[2]/td[2]/table[1]/tr/td/table/tr'
rows = response.xpath(XPATH_ROOT)
# Parse the first row. It is the content of statement dates. The first
# entry is the name and then the following entries are statement dates.
name_and_statement_dates = rows[0].xpath('td//text()').extract()
# Parse the following rest rows. Each row is containing of metrics of
# different statement dates. The first entry is the name and then the
# following entries are values.
for i in range(1, len(rows)):
name_and_values = rows[i].xpath('td//text()').extract()
for j in range(1, len(name_and_values)):
item = FinancialStatementEntryItem()
item['title'] = title
item['statement_date'] = datetime_utils. \
build_datetime_from_roc_era_with_quarter(name_and_statement_dates[j])
item['stock_code'] = stock_code
item['metric_index'] = i - 1
item['metric_name'] = name_and_values[0].strip()
item['metric_value'] = \
metric_value_utils.normalize(name_and_values[j])
yield item
yield EndOfDocumentItem()
def parse(self, response):
json_response = json.loads(response.body_as_unicode())
stock_code = self._parse_stock_code(json_response)
# Parse metric names. The first one represents the year of the trading
# date and the second one represents the month of the trading date.
metric_names = json_response['fields']
for metric_values in json_response['data']:
if len(metric_values) != len(metric_names):
raise ValueError(u'Could not parse metric values: {0}'.format(
metric_values))
statement_date = datetime_utils. \
build_datetime_from_roc_era_and_month(metric_values[0], metric_values[1])
for i in range(2, len(metric_names)):
item = FinancialStatementEntryItem()
item['title'] = u'個股月成交資訊'
item['statement_date'] = statement_date
item['stock_code'] = stock_code
item['metric_index'] = i - 2
item['metric_name'] = metric_names[i].strip()
item['metric_value'] = metric_value_utils.normalize(
metric_values[i])
yield item
yield EndOfDocumentItem()
def parse(self, response):
title, stock_code = self._parse_title_and_stock_code(response)
XPATH_ROOT = '//*[@id="oMainTable"]/tr[not(@id="oScrollHead")]'
rows = response.xpath(XPATH_ROOT)
# Parse the first row. The first entry is the date frame, and then the
# following entries are metric names.
date_frame_and_metric_names = rows[0].xpath('td//text()').extract()
# Parse the following rest rows. Each row is containing of metric
# values on different month. The first entry is the statement date (a
# specific month) and then the following entries are metric values.
for i in range(1, len(rows)):
statement_date_and_metric_values = rows[i].xpath(
'td//text()').extract()
for j in range(1, len(statement_date_and_metric_values)):
item = FinancialStatementEntryItem()
item['title'] = title
item['statement_date'] = datetime_utils. \
build_datetime_from_roc_era_with_month(statement_date_and_metric_values[0])
item['stock_code'] = stock_code
item['metric_index'] = j - 1
item['metric_name'] = date_frame_and_metric_names[j].strip()
item['metric_value'] = \
metric_value_utils.normalize(statement_date_and_metric_values[j])
yield item
yield EndOfDocumentItem()
def parse(self, response):
title, stock_code = self._parse_title_and_stock_code(response)
XPATH_ROOT = '//*[@id="SysJustIFRAMEDIV"]/table/tr/td/table/tr/td/table/tr'
rows = response.xpath(XPATH_ROOT)
# The first row is header and we skip it. Then parse the second row:
# The first entry is the date frame, and then the following entries are
# metric names.
date_frame_and_metric_names = rows[1].xpath('td/text()').extract()
# Parse the following rest rows except the last two rows (ignore pie
# charts gracefully). Each row is containing of metric values on
# different year. The first entry is the statement date (a specific
# year) and then the following entries are metric values.
for i in range(2, len(rows) - 2):
statement_date_and_metric_values = rows[i].xpath(
'td/text()').extract()
for j in range(1, len(statement_date_and_metric_values)):
item = FinancialStatementEntryItem()
item['title'] = title
item['statement_date'] = datetime_utils. \
build_datetime_from_roc_era(statement_date_and_metric_values[0])
item['stock_code'] = stock_code
item['metric_index'] = j - 1
item['metric_name'] = date_frame_and_metric_names[j].strip()
item['metric_value'] = \
metric_value_utils.normalize(statement_date_and_metric_values[j])
yield item
yield EndOfDocumentItem()