I am using Python/requests to gather data from a website. Ideally I only want the latest 'banking' information, which always at the top of the page.
The code I have currently does that, but then it attempts to keep going and hits an index out of range error. I am not very good with aspx pages, but is it possible to only gather the data under the 'banking' heading?
Here's what I have so far:
import requests
from bs4 import BeautifulSoup
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'}
print('Scraping South Dakota Banking Activity Actions...')
url2 = 'https://dlr.sd.gov/banking/monthly_activity_reports/monthly_activity_reports.aspx'
r2 = requests.get(url2, headers=headers)
soup = BeautifulSoup(r2.text, 'html.parser')
mylist5 = []
for tr in soup.find_all('tr')[2:]:
tds = tr.find_all('td')
print(tds[0].text, tds[1].text)
Ideally I'd be able to slice the information as well so I can only show the activity or approval status, etc.
With bs4 4.7.1 + you can use :contains to isolate the latest month by filtering out the later months. I explain the principle of filtering out later general siblings using :not in this SO answer. In short, find the row containing "August 2019" (this month is determined dynamically) and grab it and all its siblings, then find the row containing "July 2019" and all its general siblings and remove the latter from the former.
import requests, re
from bs4 import BeautifulSoup as bs
import pandas as pd
r = requests.get('https://dlr.sd.gov/banking/monthly_activity_reports/monthly_activity_reports.aspx')
soup = bs(r.content, 'lxml')
months = [i.text for i in soup.select('[colspan="2"]:has(a)')][0::2]
latest_month = months[0]
next_month = months[1]
rows_of_interest = soup.select(f'tr:contains("{latest_month}"), tr:contains("{latest_month}") ~ tr:not(:contains("{next_month}"), :contains("{next_month}") ~ tr)')
results = []
for row in rows_of_interest:
data = [re.sub('\xa0|\s{2,}',' ',td.text) for td in row.select('td')]
if len(data) == 1:
data.extend([''])
results.append(data)
df = pd.DataFrame(results)
print(df)
Same as before
import requests
from bs4 import BeautifulSoup, Tag
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'}
url = 'https://dlr.sd.gov/banking/monthly_activity_reports/monthly_activity_reports.aspx'
print('Scraping South Dakota Banking Activity Actions...')
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.text, 'html.parser')
Inspecting data source, we can find the id of the element you need (the table of values).
banking = soup.find(id='secondarycontent')
After this, we filter out elements of soup that aren't tags (like NavigableString or others). You can see how to get texts too (for other options, check Tag doc).
blocks = [b for b in banking.table.contents if type(b) is Tag] # filter out NavigableString
texts = [b.text for b in blocks]
Now, if it's the goal you're achieving when you talk about latest, we must determine which month is latest and which is the month before.
current_month_idx, last_month_idx = None, None
current_month, last_month = 'August 2019', 'July 2019' # can parse with datetime too
for i, b in enumerate(blocks):
if current_month in b.text:
current_month_idx = i
elif last_month in b.text:
last_month_idx = i
if all(idx is not None for idx in (current_month_idx, last_month_idx)):
break # break when both indeces are not null
assert current_month_idx < last_month_idx
curr_month_blocks = [b for i, b in enumerate(blocks) if current_month_idx < i < last_month_idx]
curr_month_texts = [b.text for b in curr_month_blocks]
Related
I have the list of links, each link has an id that is in the Id list
How to change the code so that when iterating the link, the corresponding id is substituted into the string:
All code is below:
import pandas as pd
from bs4 import BeautifulSoup
import requests
HEADERS = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/81.0.4044.138 Safari/537.36 OPR/68.0.3618.125', 'accept': '*/*'}
links = ['https://www..ie', 'https://www..ch', 'https://www..com']
Id = ['164240372761e5178f0488d', '164240372661e5178e1b377', '164240365661e517481a1e6']
def get_html(url, params=None):
r = requests.get(url, headers=HEADERS, params=params)
def get_data_no_products(html):
data = []
soup = BeautifulSoup(html, 'html.parser')
items = soup.find_all('div', id= '') # How to iteration paste id???????
for item in items:
data.append({'pn': item.find('a').get('href')})
return print(data)
def parse():
for i in links:
html = get_html(i)
get_data_no_products(html.text)
parse()
Parametrise your code:
def get_data_no_products(html, id_):
data = []
soup = BeautifulSoup(html, 'html.parser')
items = soup.find_all('div', id=id_)
And then use zip():
for link, id_ in zip(links, ids):
get_data_no_producs(link, id_)
Note that there's a likely bug in your code: you return print(data) which will always be none. You likely just want to return data.
PS
There is another solution to this which you will frequently encounter from people beginning in python:
for i in range(len(links)):
link = links[i]
id_ = ids[i]
...
This... works. It might even be easier or more natural, if you are coming from e.g. C. (Then again I'd likely use pointers...). Style is very much personal, but if you're going to write in a high level language like python you might as well avoid thinking about things like 'the index of the current item' as much as possible. Just my £0.02.
I am having some problems with extracting tags from a websites:
r = req.get(web+"?pg=news&tf=G&page={}/".format(num))
soup = BeautifulSoup(r.content, 'html.parser')
results = [
(
x.select_one("h3.d-flex").text,
x.select_one("div.i").text,
x.select_one("div.a").a.text,
x.select_one("div.entry-content").p.text,
) for x in soup.findAll("section")
]
I need to scrape relevant information such as headlines, preview of content, date and link.
When I print the above tags, I get empty lists. Since I have no a lot of experience in selecting tags and I am not sure about classes I selected above, I would ask you if you could have a look and tell me which one(s) is wrong.
I hope this code help you. assume url http://gentedellarete.it/?pg=news&tf=G&page=1
import requests
from bs4 import BeautifulSoup
URL = "https://www.centrepointstores.com/sa/en/Women/Fashion-Accessories/Watches/CENTREPOINT-Citizen-Women%27s-Rose-Gold-Analog-Metal-Strap-Watch-EU-6039-86A/p/EU603986AGold"
HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36"
}
r = requests.get("http://www.gentedellarete.it/?pg=news&tf=G&page={}/".format(1), headers=HEADERS)
soup = BeautifulSoup(r.content, 'html.parser')
for x in soup.findAll('div', {'class',"text py-5 pl-md-5"}):
print('\n',x.select_one("div > a:nth-child(2) h3").text, sep='\n') #heading ok
print('\n', x.select_one('p').text) #under h3 ok
print('\n', x.select('p')[1].text) # body ok
print('\n', x.select('p')[1].text.split('(')[1].strip(')')) # date ok?
New to screen scraping here and this is my first time posting on stackoverflow. Aplogies in advance for any formatting errors in this post. Attempting to extract data from multiple pages with URL:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
For instance, page 1 is:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-1
Page 2:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-2
and so on...
My script is running without errors. However, my Pandas exported csv only contains 1 row with the first extracted value. At the time of this posting, the first value is:
14.01 Acres  Vestaburg, Montcalm County, MI$275,000
My intent is to create a spreadsheet with hundreds of rows that pull the property description from the URLs.
Here is my code:
import requests
from requests import get
from bs4 import BeautifulSoup
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'
}
)
n_pages = 0
desc = []
for page in range(1,900):
n_pages += 1
sapo_url = 'https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
r=get(sapo_url, headers=headers)
page_html = BeautifulSoup(r.text, 'html.parser')
house_containers = page_html.find_all('div', class_="propName")
if house_containers != []:
for container in house_containers:
desc = container.getText(strip=True)
else:
break
print('you scraped {} pages containing {} Properties'.format(n_pages, len(desc)))
import pandas as pd
df = pd.DataFrame({'description': [desc]})
df.to_csv('test4.csv', encoding = 'utf-8')
I suspect the problem is with the line reading desc = container.getText(strip=True) and have tried changing the line but keep getting errors when running.
Any help is appreciated.
I believe the mistake is in the line:
desc = container.getText(strip=True)
Every time it loops, the value in desc is replaced, not added on. To add items into the list, do:
desc.append(container.getText(strip=True))
Also, since it is already a list, you can remove the brackets from the DataFrame creation like so:
df = pd.DataFrame({'description': desc})
The cause is that no data is being added in the loop, so only the final data is being saved. For testing purposes, this code is now on page 2, so please fix it.
import requests
from requests import get
from bs4 import BeautifulSoup
import pandas as pd
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'
}
)
n_pages = 0
desc = []
all_data = pd.DataFrame(index=[], columns=['description'])
for page in range(1,3):
n_pages += 1
sapo_url = 'https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
r=get(sapo_url, headers=headers)
page_html = BeautifulSoup(r.text, 'html.parser')
house_containers = page_html.find_all('div', class_="propName")
if house_containers != []:
for container in house_containers:
desc = container.getText(strip=True)
df = pd.DataFrame({'description': [desc]})
all_data = pd.concat([all_data, df], ignore_index=True)
else:
break
all_data.to_csv('test4.csv', encoding = 'utf-8')
print('you scraped {} pages containing {} Properties'.format(n_pages, len(desc)))
A year ago I learned some python in one of my classes but haven't had to use much since then so this may or may not be a simple question.
I'm trying to web-scrape the top grossing films of all time table from Box Office Mojo and I want to grab the rank, title, and gross for the top 10 films in the 2010s. I've been playing around in python and I can get the entire table into python but I don't know how to manipulate it from there, let alone write out a csv file. Any guidance/tips?
Here is what will print the entire table for me (the first few lines are copied from an old web-scraping assignment to get me started):
import bs4
import requests
from bs4 import BeautifulSoup as soup
url = "https://www.boxofficemojo.com/chart/top_lifetime_gross/"
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML,
like Gecko) Chrome/71.0.3578.98 Safari/537.36'}
page_html = requests.get(url, headers=headers)
page_soup = soup(page_html.text, "html.parser")
boxofficemojo_table = page_soup.find("div", {"class": "a-section imdb-scroll-table-inner"})
complete_table = boxofficemojo_table.get_text()
print(complete_table)`
You Can use pd.read_html for this.
import pandas as pd
Data = pd.read_html(r'https://www.boxofficemojo.com/chart/top_lifetime_gross/')
for data in Data:
data.to_csv('Data.csv', ',')
2.Using Bs4
import pandas as pd
from bs4 import BeautifulSoup
import requests
URL = r'https://www.boxofficemojo.com/chart/top_lifetime_gross/'
print('\n>> Exctracting Data using Beautiful Soup for :'+ URL)
try:
res = requests.get(URL)
except Exception as e:
print(repr(e))
print('\n<> URL present status Code = ',(res.status_code))
soup = BeautifulSoup(res.text,"lxml")
table = soup.find('table')
list_of_rows = []
for row in table.findAll('tr'):
list_of_cells = []
for cell in row.findAll(["td"]):
text = cell.text
list_of_cells.append(text)
list_of_rows.append(list_of_cells)
for item in list_of_rows:
' '.join(item)
Data = pd.DataFrame(list_of_rows)
Data.dropna(axis = 0, how = 'all',inplace = True)
print(Data.head(10))
Data.to_csv('Table.csv')
I am working with lobbying data from opensecrets.org, in particular industry data. I want to have a time series of lobby expenditures for each industry going back since the 90's.
I want to web-scrape the data automatically. Urls where the data is have the following format:
https://www.opensecrets.org/lobby/indusclient.php?id=H04&year=2019
which are pretty easy to embed in a loop, the problem is that the data I need is not in an easy format in the webpage. It is inside a bar graph, and when I inspect the graph I do not know how to get the data since it is not in the html code. I am familiar with web-scraping in python when the data is in the html code, but in this case I am not sure how to proceed.
If there is an API, that your best bet as mentioned above. But the data is able to be parsed anyway provided you get the right url/query parameters:
I've managed to iterate through it with the links for you to grab each table. I stored it in a dictionary with the key being the Firm name, and the value being the table/data. You can change it up to anyway you'd like. Maybe just store as json, or save each as csv.
Code:
import requests
from bs4 import BeautifulSoup
import pandas as pd
url = 'https://www.opensecrets.org/lobby/indusclient.php?id=H04&year=2019'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36'}
data = requests.get(url, headers=headers)
soup = BeautifulSoup(data.text, 'html.parser')
links = soup.find_all('a', href=True)
root_url = 'https://www.opensecrets.org/lobby/include/IMG_client_year_comp.php?'
links_dict = {}
for each in links:
if 'clientsum.php?' in each['href']:
w=1
firms = each.text
link = root_url + each['href'].split('?')[-1].split('&')[0].strip() + '&type=c'
links_dict[firms] = link
all_tables = {}
n=1
tot = len(links_dict)
for firms, link in links_dict.items():
print ('%s of %s ---- %s' %(n, tot, firms))
data = requests.get(link)
soup = BeautifulSoup(data.text, 'html.parser')
results = pd.DataFrame()
graph = soup.find_all('set')
for each in graph:
year = each['label']
total = each['value']
temp_df = pd.DataFrame([[year, total]], columns=['year','$mil'])
results = results.append(temp_df,sort=True).reset_index(drop=True)
all_tables[firms] = results
n+=1
*Output:**
Not going to print as there are 347 tables, but just so you see the structure: