def stockProfiles(self, retry=3, pause=0.001):
"""
获取上市公司基本情况
Parameters
--------
retry : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
--------
DataFrame or List: [{'code':, 'name':, ...}, ...]
code,代码
name,名称
city,所在城市
staff,员工人数
date,上市日期
industry,行业分类
pro_type,产品类型
main,主营业务
"""
self._data = pd.DataFrame()
self._writeHead()
date = '%s-12-31' % Utility.getYear()
self._data = pd.DataFrame()
self._data = self.__handleStockProfiles(self._data, date, 1, retry,
pause)
return self._result()
def restrictedLift(self, year=None, month=None, retry=3, pause=0.001):
"""
获取限售股解禁数据
Parameters
--------
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame or List: [{'code':, 'name':, ...}, ...]
code:股票代码
name:名称
date:解禁日期
count:解禁数量（万股）
ratio:占总盘比率
"""
self._data = pd.DataFrame()
year = Utility.getYear() if year is None else year
month = Utility.getMonth() if month is None else month
for _ in range(retry):
time.sleep(pause)
try:
# http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx?type=FD&sty=BST&st=3&sr=true&fd=2019&stat=1
request = self._session.get(cf.RL_URL % (year, month),
timeout=10)
if self._PY3:
request.encoding = 'utf-8'
lines = request.text
except Exception as e:
print(e)
else:
da = lines[3:len(lines) - 3]
list = []
for row in da.split('","'):
list.append([data for data in row.split(',')])
self._data = pd.DataFrame(list)
self._data = self._data[[1, 3, 4, 5, 6]]
for col in [5, 6]:
self._data[col] = self._data[col].astype(float)
self._data[5] = self._data[5] / 10000
self._data[6] = self._data[6] * 100
self._data[5] = self._data[5].map(cf.FORMAT)
self._data[6] = self._data[6].map(cf.FORMAT)
self._data.columns = cf.RL_COLS
return self._result()
raise IOError(cf.NETWORK_URL_ERROR_MSG)
def __parseExcel(self, year, datatype, lab, column):
year = Utility.getYear() if year is None else year
lab = lab.encode('utf-8') if self._PY3 else lab
try:
df = pd.read_excel( cf.SHIBOR_DATA_URL % (datatype, year, lab, year), skiprows=[0] )
df.columns = column
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype('datetime64[ns]')
except Exception as e:
print(e)
else:
return df