I would need to scrape information regarding Elenco dei comuni per regione on Wikipedia. I would like to create an array that can allow me to associate each comune to the corresponding region, i.e. something like this:
'Abbateggio': 'Pescara' -> Abruzzo
I tried to get information using BeautifulSoup and requests as follows:
from bs4 import BeautifulSoup as bs
import requests
with requests.Session() as s: # use session object for efficiency of tcp re-use
s.headers = {'User-Agent': 'Mozilla/5.0'}
r = s.get('https://it.wikipedia.org/wiki/Comuni_d%27Italia')
soup = bs(r.text, 'html.parser')
for ele in soup.find_all('h3')[:6]:
tx = bs(str(ele),'html.parser').find('span', attrs={'class': "mw-headline"})
if tx is not None:
print(tx['id'])
however it does not work (it returns me an empty list).
The information that I have looked at using Inspect of Google Chrome are the following:
<span class="mw-headline" id="Elenco_dei_comuni_per_regione">Elenco dei comuni per regione</span> (table)
Comuni dell'Abruzzo
(this field should change for each region)
then <table class="wikitable sortable query-tablesortes">
Could you please give me advice on how to get such results?
Any help and suggestion will be appreciated.
EDIT:
Example:
I have a word: comunediabbateggio. This word includes Abbateggio. I would like to know which region can be associated with that city, if it exists.
Information from Wikipedia needs to create a dataset that can allow me to check the field and associate to comuni/cities a region.
What I should expect is:
WORD REGION/STATE
comunediabbateggio Pescara
I hope this can help you. Sorry if it was not clear.
Another example for English speaker that might be slightly better for understanding is the following:
Instead of the Italian link above, you can also consider the following: https://en.wikipedia.org/wiki/List_of_comuni_of_Italy . For each region (Lombardia, Veneto, Sicily, ... ) I would need to collect information about the list of communes of the Provinces.
if you click in a link of List of Communes of ... , there is a table that list the comune, e.g. https://en.wikipedia.org/wiki/List_of_communes_of_the_Province_of_Agrigento.
import re
import requests
from bs4 import BeautifulSoup
import pandas as pd
from tqdm import tqdm
target = "https://en.wikipedia.org/wiki/List_of_comuni_of_Italy"
def main(url):
with requests.Session() as req:
r = req.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
provinces = [item.find_next("span").text for item in soup.findAll(
"span", class_="tocnumber", text=re.compile(r"\d[.]\d"))]
search = [item.replace(
" ", "_") if " " in item else item for item in provinces]
nested = []
for item in search:
for a in soup.findAll("span", id=item):
goes = [b.text.split("of ")[-1]
for b in a.find_next("ul").findAll("a")]
nested.append(goes)
dictionary = dict(zip(provinces, nested))
urls = [f'{url[:24]}{b.get("href")}' for item in search for a in soup.findAll(
"span", id=item) for b in a.find_next("ul").findAll("a")]
return urls, dictionary
def parser():
links, dics = main(target)
com = []
for link in tqdm(links):
try:
df = pd.read_html(link)[0]
com.append(df[df.columns[1]].to_list()[:-1])
except ValueError:
com.append(["N/A"])
com = iter(com)
for x in dics:
b = dics[x]
dics[x] = dict(zip(b, com))
print(dics)
parser()
Related
i am trying to scrape news from reuters but there is a click to view more at the bottom on the website. I could not know how to load the hidden results by using beautiful soup.
from bs4 import BeautifulSoup
import urllib.request
def scrape_reuters_news(ticker):
url = "https://www.reuters.com/search/news?sortBy=relevance&dateRange=pastWeek&blob="+ticker
scraped_data = urllib.request.urlopen(url)
scraped_data = scraped_data.read()
parsed_articles = BeautifulSoup(scraped_data, 'lxml')
links = parsed_articles.find_all("h3")
articles = []
titles = []
title_class = "Text__text___3eVx1j Text__dark-grey___AS2I_p Text__medium___1ocDap Text__heading_2___sUlNJP Heading__base___1dDlXY Heading__heading_2___3f_bIW ArticleHeader__heading___3ibi0Q"
for link in links:
paragraphs = ""
url = "https://www.reuters.com/"+str(link)[41:63]
scraped_data = urllib.request.urlopen(url)
scraped_data = scraped_data.read()
parsed_article = BeautifulSoup(scraped_data, 'lxml')
article = parsed_article.find_all("p")
title = parsed_article.select("h1", {"class": title_class})
titles.append(title[0].text.strip())
for paragraph in article:
paragraphs += paragraph.text + " "
articles.append(paragraphs)
return titles, articles
# edit
ticker = "apple"
news = scrape_reuters_news(ticker)
When you click the load more a callback is issued that you can find in the network tab. If you grab the number of results from the search page, you can add this into the callback to get all results in one go. I then use regex to extract the id to reconstruct each detail page url and the title (headline)
You would then visit each link to get the paragraph info.
Please note:
There is some de-duplication work to do. There exist different ids which lead to same content. So perhaps exclude based on title?
You may need to consider whether any pre-processing of ticker needs to happen e.g. convert to lowercase, replace spaces with "-". I don't know all your use cases.
from bs4 import BeautifulSoup as bs
import requests, re
ticker = 'apple'
with requests.Session() as s:
r = s.get(f'https://www.reuters.com/search/news?sortBy=relevance&dateRange=pastWeek&blob={ticker}')
soup = bs(r.content, 'lxml')
num_results = soup.select_one('.search-result-count-num').text
r = s.get(f'https://www.reuters.com/assets/searchArticleLoadMoreJson?blob={ticker}&bigOrSmall=big&articleWithBlog=true&sortBy=relevance&dateRange=pastWeek&numResultsToShow={num_results}&pn=&callback=addMoreNewsResults')
p = re.compile(r'id: "(.*?)"')
p2 = re.compile(r'headline: "(.*?)"')
links = [f'https://www.reuters.com/article/id{i}' for i in p.findall(r.text)]
headlines = [bs(i, 'lxml').get_text() for i in p2.findall(r.text)]
print(len(links), len(headlines))
From the detail pages you can get the paragraphs with
paras = ' '.join([i.get_text() for i in soup.select('[data-testid*=paragraph-]')])
I am trying to extract drug information from this website: https://www.medindia.net/doctors/drug_information/abacavir.htm.
So far, i am able to extract the drug information (abacavir)for one web-page. Now, i need advice on how to construct a loop to extract all drug information i need for other drug from other web-pages in the same website (www.medindia.net). The code is as below.
import pandas as pd
import requests
import urllib.request
import time
from bs4 import BeautifulSoup
url = 'https://www.medindia.net/doctors/drug_information/abacavir.htm'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
drug = soup.find(class_='mi-container__fluid')
print(drug)
# whole page contain drug content
items = drug.find_all(class_='report-content drug-widget')
print(items)
# extract drug information from drug content into individual variable
trade_name = items[0].find(class_='drug-content').get_text(strip=True).replace("\n", "")
function = items[1].find(class_='drug-content').get_text(strip=True).replace("\n", "")
Contraindications = items[2].find(class_='drug-content').get_text(strip=True).replace("\n", "")
Dosage = items[3].find(class_='drug-content').get_text(strip=True).replace("\n", "")
how_to_use = items[4].find(class_='drug-content').get_text(strip=True).replace("\n", "")
warnings = items[5].find(class_='drug-content').get_text(strip=True).replace("\n", "")
storage = items[7].find(class_='drug-content').get_text(strip=True).replace("\n", "")
drug_stuff = pd.DataFrame(
{
'trade_name':[trade_name],
'function': [function],
'Contraindications': [Contraindications],
'Dosage': [Dosage],
'how_to_use':[how_to_use],
'warnings':[warnings],
'storage':[storage],
})
print(drug_stuff)
Create a function!
def extract_drug_info(url):
""" Extracts drug information of a given medindia.com website. """
# ... rest of the code you posted above here ...
return drug_stuff
Then loop through some URLs:
urls = ["https://www.medindia.net/doctors/drug_information/abacavir.htm", "..."]
for url in urls:
print(extract_drug_info(url))
I've created a script in Python to fetch different product links from a webpage. Although I know the content of that site are dynamic, I tried conventional way to let you inform that I tried. I looked for APIs in the dev tools but could not find one. Ain't there any way to get those links using requests?
Site Link
I've written so far:
import requests
from bs4 import BeautifulSoup
link = "https://www.amazon.com/stores/node/10699640011"
def fetch_product_links(url):
res = requests.get(url,headers={"User-Agent":"Mozilla/5.0"})
soup = BeautifulSoup(res.text,"lxml")
for item_link in soup.select("[id^='ProductGrid-'] li[class^='style__itemOuter__'] > a"):
print(item_link.get("href"))
if __name__ == '__main__':
fetch_product_links(link)
How can I fetch different product links from that site using requests?
I think you only need the asins which you can collect from another url construct you can see in network tab i.e. you can significantly shorten the final urls. You do however need to make a request to your original url to pick up an identifier to use in second url. Returns 146 links.
import requests, re, json
node = '10699640011'
with requests.Session() as s:
r = s.get(f'https://www.amazon.com/stores/node/{node}')
p = re.compile(r'var slotsStr = "\[(.*?,){3} share\]";')
identifier = p.findall(r.text)[0]
identifier = identifier.strip()[:-1]
r = s.get(f'https://www.amazon.com/stores/slot/{identifier}?node={node}')
p = re.compile(r'var config = (.*?);')
data = json.loads(p.findall(r.text)[0])
asins = data['content']['ASINList']
links = [f'https://www.amazon.com/dp/{asin}' for asin in asins]
print(links)
EDIT:
With two given nodes:
import requests, re, json
from bs4 import BeautifulSoup as bs
nodes = ['3039806011','10699640011']
with requests.Session() as s:
for node in nodes:
r = s.get(f'https://www.amazon.com/stores/node/{node}')
soup = bs(r.content, 'lxml')
identifier = soup.select('.stores-widget-btf:not([id=share],[id*=RECOMMENDATION])')[-1]['id']
r = s.get(f'https://www.amazon.com/stores/slot/{identifier}?node={node}')
p = re.compile(r'var config = (.*?);')
data = json.loads(p.findall(r.text)[0])
asins = data['content']['ASINList']
links = [f'https://www.amazon.com/dp/{asin}' for asin in asins]
print(links)
I'm trying to scrape information about the datasets available on this website.
I want to collect the URLs to the resources and at least the title of the dataset.
Using this resource as an example, I want to capture the URL embedded in "Go to resource" and the title listed in the table:
I have created a basic scraper, but it doesn't seem work:
import requests
import csv
from bs4 import BeautifulSoup
site = requests.get('https://data.nsw.gov.au/data/dataset');
data_list=[]
if site.status_code is 200:
content = BeautifulSoup(site.content, 'html.parser')
internals = content.select('.resource-url-analytics')
for url in internals:
title = internals.select=('.resource-url-analytics')[0].get_text()
link = internals.select=('.resource-url-analytics')[0].get('href')
new_data = {"title": title, "link": link}
data_list.append(new_data)
with open ('selector.csv','w') as file:
writer = csv.DictWriter(file, fieldnames = ["dataset", "link"], delimiter = ';')
writer.writeheader()
for row in data_list:
writer.writerow(row)
I would like to write the output to a CSV with columns for the URLs and the titles.
This is an example of the desired output
Greatly appreciative for any assistance
Have a look at the API for the datasets that will likely be the easiest way to do this.
In the meantime, here is how you can get the API links at id level from those pages and store the entire package info for all packages in one list, data_sets, and just the info of interest in another variable (results). Be sure to review the API documentation in case there is a better method - for example, it would be nice if ids could be submitted in batches rather than per id.
Answer below is taking advantage of the endpoint detailed in the documentation which is used to get a full JSON representation of a dataset, resource or other object
Taking the current first result on landing page of:
Vegetation of the Guyra 1:25000 map sheet VIS_ID 240.
We want the last child a of parent h3 with a parent having class .dataset-item. In the below, the spaces between selectors are descendant combinators.
.dataset-item h3 a:last-child
You can shorten this to h3 a:last-child for a small efficiency gain.
This relationship reliably selects all relevant links on page.
Continuing with this example, visiting that retrieved url for first listed item, we can find the id using api endpoint (which retrieves json related to this package), via an attribute=value selector with contains, *, operator. We know this particular api endpoint has a common string so we substring match on the href attribute value:
[href*="/api/3/action/package_show?id="]
The domain can vary and some retrieved links are relative so we have to test if relative and add the appropriate domain.
First page html for that match:
Notes:
data_sets is a list containing all the package data for each package and is extensive. I did this in case you are interest in looking at what is in those packages (besides reviewing the API documentation)
You can get total number of pages from soup object on a page via
num_pages = int(soup.select('[href^="/data/dataset?page="]')[-2].text)
You can alter the loop for less pages.
Session object is used for efficiency of re-using connection. I'm sure there are other improvements to be made. In particular I would look for any method which reduced the number of requests (why I mentioned looking for a batch id endpoint for example).
There can be none to more than one resource url within a returned package. See example here. You can edit code to handle this.
Python:
from bs4 import BeautifulSoup as bs
import requests
import csv
from urllib.parse import urlparse
json_api_links = []
data_sets = []
def get_links(s, url, css_selector):
r = s.get(url)
soup = bs(r.content, 'lxml')
base = '{uri.scheme}://{uri.netloc}'.format(uri=urlparse(url))
links = [base + item['href'] if item['href'][0] == '/' else item['href'] for item in soup.select(css_selector)]
return links
results = []
#debug = []
with requests.Session() as s:
for page in range(1,2): #you decide how many pages to loop
links = get_links(s, 'https://data.nsw.gov.au/data/dataset?page={}'.format(page), '.dataset-item h3 a:last-child')
for link in links:
data = get_links(s, link, '[href*="/api/3/action/package_show?id="]')
json_api_links.append(data)
#debug.append((link, data))
resources = list(set([item.replace('opendata','') for sublist in json_api_links for item in sublist])) #can just leave as set
for link in resources:
try:
r = s.get(link).json() #entire package info
data_sets.append(r)
title = r['result']['title'] #certain items
if 'resources' in r['result']:
urls = ' , '.join([item['url'] for item in r['result']['resources']])
else:
urls = 'N/A'
except:
title = 'N/A'
urls = 'N/A'
results.append((title, urls))
with open('data.csv','w', newline='') as f:
w = csv.writer(f)
w.writerow(['Title','Resource Url'])
for row in results:
w.writerow(row)
All pages
(very long running so consider threading/asyncio):
from bs4 import BeautifulSoup as bs
import requests
import csv
from urllib.parse import urlparse
json_api_links = []
data_sets = []
def get_links(s, url, css_selector):
r = s.get(url)
soup = bs(r.content, 'lxml')
base = '{uri.scheme}://{uri.netloc}'.format(uri=urlparse(url))
links = [base + item['href'] if item['href'][0] == '/' else item['href'] for item in soup.select(css_selector)]
return links
results = []
#debug = []
with requests.Session() as s:
r = s.get('https://data.nsw.gov.au/data/dataset')
soup = bs(r.content, 'lxml')
num_pages = int(soup.select('[href^="/data/dataset?page="]')[-2].text)
links = [item['href'] for item in soup.select('.dataset-item h3 a:last-child')]
for link in links:
data = get_links(s, link, '[href*="/api/3/action/package_show?id="]')
json_api_links.append(data)
#debug.append((link, data))
if num_pages > 1:
for page in range(1, num_pages + 1): #you decide how many pages to loop
links = get_links(s, 'https://data.nsw.gov.au/data/dataset?page={}'.format(page), '.dataset-item h3 a:last-child')
for link in links:
data = get_links(s, link, '[href*="/api/3/action/package_show?id="]')
json_api_links.append(data)
#debug.append((link, data))
resources = list(set([item.replace('opendata','') for sublist in json_api_links for item in sublist])) #can just leave as set
for link in resources:
try:
r = s.get(link).json() #entire package info
data_sets.append(r)
title = r['result']['title'] #certain items
if 'resources' in r['result']:
urls = ' , '.join([item['url'] for item in r['result']['resources']])
else:
urls = 'N/A'
except:
title = 'N/A'
urls = 'N/A'
results.append((title, urls))
with open('data.csv','w', newline='') as f:
w = csv.writer(f)
w.writerow(['Title','Resource Url'])
for row in results:
w.writerow(row)
For simplicity use selenium package:
from selenium import webdriver
import os
# initialise browser
browser = webdriver.Chrome(os.getcwd() + '/chromedriver')
browser.get('https://data.nsw.gov.au/data/dataset')
# find all elements by xpath
get_elements = browser.find_elements_by_xpath('//*[#id="content"]/div/div/section/div/ul/li/div/h3/a[2]')
# collect data
data = []
for item in get_elements:
data.append((item.text, item.get_attribute('href')))
Output:
('Vegetation of the Guyra 1:25000 map sheet VIS_ID 240', 'https://datasets.seed.nsw.gov.au/dataset/vegetation-of-the-guyra-1-25000-map-sheet-vis_id-2401ee52')
('State Vegetation Type Map: Riverina Region Version v1.2 - VIS_ID 4469', 'https://datasets.seed.nsw.gov.au/dataset/riverina-regional-native-vegetation-map-version-v1-0-vis_id-4449')
('Temperate Highland Peat Swamps on Sandstone (THPSS) spatial distribution maps...', 'https://datasets.seed.nsw.gov.au/dataset/temperate-highland-peat-swamps-on-sandstone-thpss-vegetation-maps-vis-ids-4480-to-4485')
('Environmental Planning Instrument - Flood', 'https://www.planningportal.nsw.gov.au/opendata/dataset/epi-flood')
and so on
<div class="columns small-5 medium-4 cell header">Ref No.</div>
<div class="columns small-7 medium-8 cell">110B60329</div>
Website is https://www.saa.gov.uk/search/?SEARCHED=1&ST=&SEARCH_TERM=city+of+edinburgh%2C+BOSWALL+PARKWAY%2C+EDINBURGH&ASSESSOR_ID=&SEARCH_TABLE=valuation_roll_cpsplit&DISPLAY_COUNT=10&TYPE_FLAG=CP&ORDER_BY=PROPERTY_ADDRESS&H_ORDER_BY=SET+DESC&DRILL_SEARCH_TERM=BOSWALL+PARKWAY%2C+EDINBURGH&DD_TOWN=EDINBURGH&DD_STREET=BOSWALL+PARKWAY&UARN=110B60329&PPRN=000000000001745&ASSESSOR_IDX=10&DISPLAY_MODE=FULL#results
I would like to run a loop and return '110B60329'. I have ran beautiful soup and done a find_all(div), I then define the 2 different tags as head and data based on their class. I then ran iteration through the 'head' tags hoping it would return the info in the div tag i have defined as data .
Python returns a blank (cmd prompt reprinted the filepth).
Would anyone kindly know how i might fix this. My full code is.....thanks
import requests
from bs4 import BeautifulSoup as soup
import csv
url = 'https://www.saa.gov.uk/search/?SEARCHED=1&ST=&SEARCH_TERM=city+of+edinburgh%2C+BOSWALL+PARKWAY%2C+EDINBURGH&ASSESSOR_ID=&SEARCH_TABLE=valuation_roll_cpsplit&DISPLAY_COUNT=10&TYPE_FLAG=CP&ORDER_BY=PROPERTY_ADDRESS&H_ORDER_BY=SET+DESC&DRILL_SEARCH_TERM=BOSWALL+PARKWAY%2C+EDINBURGH&DD_TOWN=EDINBURGH&DD_STREET=BOSWALL+PARKWAY&UARN=110B60329&PPRN=000000000001745&ASSESSOR_IDX=10&DISPLAY_MODE=FULL#results'
baseurl = 'https://www.saa.gov.uk'
session = requests.session()
response = session.get(url)
# content of search page in soup
html= soup(response.content,"lxml")
properties_col = html.find_all('div')
for col in properties_col:
ref = 'n/a'
des = 'n/a'
head = col.find_all("div",{"class": "columns small-5 medium-4 cell header"})
data = col.find_all("div",{"class":"columns small-7 medium-8 cell"})
for i,elem in enumerate(head):
#for i in range(elems):
if head [i].text == "Ref No.":
ref = data[i].text
print ref
You can do this by two ways.
1) If you are sure that the website that your are scraping won't change its content you can find all divs by that class and get the content by providing an index.
2) Find all left side divs (The titles) and if one of them matches what you want get the next sibling to get the text.
Example:
import requests
from bs4 import BeautifulSoup as soup
url = 'https://www.saa.gov.uk/search/?SEARCHED=1&ST=&SEARCH_TERM=city+of+edinburgh%2C+BOSWALL+PARKWAY%2C+EDINBURGH&ASSESSOR_ID=&SEARCH_TABLE=valuation_roll_cpsplit&DISPLAY_COUNT=10&TYPE_FLAG=CP&ORDER_BY=PROPERTY_ADDRESS&H_ORDER_BY=SET+DESC&DRILL_SEARCH_TERM=BOSWALL+PARKWAY%2C+EDINBURGH&DD_TOWN=EDINBURGH&DD_STREET=BOSWALL+PARKWAY&UARN=110B60329&PPRN=000000000001745&ASSESSOR_IDX=10&DISPLAY_MODE=FULL#results'
baseurl = 'https://www.saa.gov.uk'
session = requests.session()
response = session.get(url)
# content of search page in soup
html = soup(response.content,"lxml")
#Method 1
LeftBlockData = html.find_all("div", class_="columns small-7 medium-8 cell")
Reference = LeftBlockData[0].get_text().strip()
Description = LeftBlockData[2].get_text().strip()
print(Reference)
print(Description)
#Method 2
for column in html.find_all("div", class_="columns small-5 medium-4 cell header"):
RightColumn = column.next_sibling.next_sibling.get_text().strip()
if "Ref No." in column.get_text().strip():
print (RightColumn)
if "Description" in column.get_text().strip():
print (RightColumn)
The prints will output (in order):
110B60329
STORE
110B60329
STORE
Your problem is that you are trying to match a node text that have a lot of tabs with a non-spaced string.
For example your head [i].textvariable contains
Ref No., so if you compare it with Ref No. it'll give a false result. Striping it will solve.
import requests
from bs4 import BeautifulSoup
r = requests.get("https://www.saa.gov.uk/search/?SEARCHED=1&ST=&SEARCH_TERM=city+of+edinburgh%2C+BOSWALL+PARKWAY%2C+EDINBURGH&ASSESSOR_ID=&SEARCH_TABLE=valuation_roll_cpsplit&DISPLAY_COUNT=10&TYPE_FLAG=CP&ORDER_BY=PROPERTY_ADDRESS&H_ORDER_BY=SET+DESC&DRILL_SEARCH_TERM=BOSWALL+PARKWAY%2C+EDINBURGH&DD_TOWN=EDINBURGH&DD_STREET=BOSWALL+PARKWAY&UARN=110B60329&PPRN=000000000001745&ASSESSOR_IDX=10&DISPLAY_MODE=FULL#results")
soup = BeautifulSoup(r.text, 'lxml')
for row in soup.find_all(class_='table-row'):
print(row.get_text(strip=True, separator='|').split('|'))
out:
['Ref No.', '110B60329']
['Office', 'LOTHIAN VJB']
['Description', 'STORE']
['Property Address', '29 BOSWALL PARKWAY', 'EDINBURGH', 'EH5 2BR']
['Proprietor', 'SCOTTISH MIDLAND CO-OP SOCIETY LTD.']
['Tenant', 'PROPRIETOR']
['Occupier']
['Net Annual Value', '£1,750']
['Marker']
['Rateable Value', '£1,750']
['Effective Date', '01-APR-10']
['Other Appeal', 'NO']
['Reval Appeal', 'NO']
get_text() is very powerful tool, you can strip the white space and put separator in the text.
You can use this method to get clean data and filter it.