def get_datalist(html_output, current_url_date):
class_of_table = "table1" #div class of table with the data
data_list = []
location_columns = []
soup = BeautifulSoup(html_output)
table_html =soup.find('table', attrs={'class':'text_psinormal'})
#grabs all tr in table 'text_psinormal'
rows = table_html.findAll('tr')
#rows[0] is useless header
#rows[1], second row i table, contains location names
locations = rows[1].findChildren('th')
for each_location in locations:
location_columns.append(each_location.text.strip())
#rows[2] to rows[26] is the 24 hour readings :)
for count in range(2,26):
#grab all columns from each row
cols = rows[count].findAll('td')
#cols[0] is the time
spans = cols[0].find_all('span')
time = spans[0].text
#cols[1] to cols[6] is the actual reading data (neglect column 7)
for nested_count in range (1,6):
data = ApiReading (current_url_date,
"Singapore", #State
location_columns[nested_count-1], #Region/Town..etc
time, #Time--taken from the header of the column -- modified before insertion into DB
strip_html(cols[nested_count].text.strip())) #reading itself -- modified before insertion into DB
#print_ApiReading(data)
data_list.append(data)
return data_list
def get_datalist(html_output, current_url_date):
class_of_table = "table1" #div class of table with the data
data_list = []
soup = BeautifulSoup(html_output)
table_html =soup.find('table', attrs={'class':class_of_table})
# grabs the top row
rows = table_html.findAll('tr')
is_Header_Row = True #set this to true, before entering for loop below, the first row is the header row
#loop through al the rows
for tr in rows:
if is_Header_Row:
headers = getHeaders(tr) #get the headers from the first row
is_Header_Row = False #set it to fail the if check before all data rows (only first row is header row)
else:
cols = tr.findAll('td')
location_columns = []
data_row = ''
column_count = 0
for td in cols:
if column_count < 2 :
location_columns.append( td.text.strip() )
else:
data = ApiReading(current_url_date,
location_columns[0], #State
location_columns[1], #Region/Town..etc
str(headers[column_count]), #Time--taken from the header of the column -- modified before insertion into DB
strip_html(td.text.strip())) #reading itself -- modified before insertion into DB
data_list.append(data)
column_count = column_count + 1
return data_list
def get_datalist(html_output, current_url_date):
class_of_table = "table1" #div class of table with the data
data_list = []
location_columns = []
soup = BeautifulSoup(html_output, "lxml")
table_html =soup.find('table', attrs={'class':'text_psinormal'})
#grabs all tr in table 'text_psinormal'
rows = table_html.findAll('tr')
#rows[0] is useless header
#rows[1], second row i table, contains location names
locations = rows[1].findChildren('th')
for each_location in locations:
location_columns.append(each_location.text.strip())
#rows[2] to rows[26] is the 24 hour readings :)
for count in range(2,26):
#grab all columns from each row
cols = rows[count].findAll('td')
#cols[0] is the time
spans = cols[0].find_all('span')
time = spans[0].text
#cols[1] to cols[6] is the actual reading data (neglect column 7)
for nested_count in range (1,6):
data = ApiReading (current_url_date,
"Singapore", #State
location_columns[nested_count-1], #Region/Town..etc
time, #Time--taken from the header of the column -- modified before insertion into DB
check_values(strip_html(cols[nested_count].text.strip()))) #reading itself -- modified before insertion into DB
#print_ApiReading(data)
data_list.append(data)
return data_list