def _html_to_pe_df(lst):
del lst[0]
del lst[0]
del lst[0]
lst.pop()
l = len(lst)
dates = np.empty(l, dtype = 'datetime64[D]')
arr = np.empty([3,l])
c_cnt = 0
r_cnt = 0
for r in lst:
c_cnt = 0
for c in r.find_all('td'):
try:
val = float(c.get_text().replace(',','').replace(' ',''))
except:
val = np.NaN
if c_cnt == 0:
dates[r_cnt] = str_to_date(c.text)
elif c_cnt > 0 and c_cnt <= 3 :
arr[c_cnt - 1][r_cnt] = val
c_cnt += 1
r_cnt += 1
df = pd.DataFrame()
df['P/E'] = arr[0]
df['P/B'] = arr[1]
df['Div Yield'] = arr[2]
df.index = dates
return df
def _html_to_pe_df(lst):
del lst[0]
del lst[0]
del lst[0]
lst.pop()
l = len(lst)
dates = np.empty(l, dtype = 'datetime64[D]')
arr = np.empty([3,l])
c_cnt = 0
r_cnt = 0
for r in lst:
c_cnt = 0
for c in r.find_all('td'):
try:
val = float(c.get_text().replace(',','').replace(' ',''))
except:
val = np.NaN
if c_cnt == 0:
dates[r_cnt] = str_to_date(c.text)
elif c_cnt > 0 and c_cnt <= 3 :
arr[c_cnt - 1][r_cnt] = val
c_cnt += 1
r_cnt += 1
df = pd.DataFrame()
df['P/E'] = arr[0]
df['P/B'] = arr[1]
df['Div Yield'] = arr[2]
df.index = dates
return df
def _html_to_index_df(lst):
''' delete top 3 rows which contain text headers'''
del lst[0]
del lst[0]
del lst[0]
lst.pop()
l = len(lst)
dates = np.empty(l, dtype = 'datetime64[D]')
arr = np.empty([6,l])
c_cnt = 0
r_cnt = 0
for r in lst:
c_cnt = 0
for c in r.find_all('td'):
try:
val = float(c.get_text().replace(',','').replace(' ',''))
except:
val = np.NaN
if c_cnt == 0:
dates[r_cnt] = str_to_date(c.text)
elif c_cnt > 0 and c_cnt <= 6 :
arr[c_cnt - 1][r_cnt] = val
c_cnt += 1
r_cnt += 1
df = pd.DataFrame()
df['Open'] = arr[0]
df['High'] = arr[1]
df['Low'] = arr[2]
df['Close'] = arr[3]
df['Shares Traded'] = arr[4]
df['Turnover'] = arr[5]
df.index = dates
return df
def _html_to_index_df(lst):
''' delete top 3 rows which contain text headers'''
del lst[0]
del lst[0]
del lst[0]
lst.pop()
l = len(lst)
dates = np.empty(l, dtype = 'datetime64[D]')
arr = np.empty([6,l])
c_cnt = 0
r_cnt = 0
for r in lst:
c_cnt = 0
for c in r.find_all('td'):
try:
val = float(c.get_text().replace(',','').replace(' ',''))
except:
val = np.NaN
if c_cnt == 0:
dates[r_cnt] = str_to_date(c.text)
elif c_cnt > 0 and c_cnt <= 6 :
arr[c_cnt - 1][r_cnt] = val
c_cnt += 1
r_cnt += 1
df = pd.DataFrame()
df['Open'] = arr[0]
df['High'] = arr[1]
df['Low'] = arr[2]
df['Close'] = arr[3]
df['Shares Traded'] = arr[4]
df['Turnover'] = arr[5]
df.index = dates
return df
