Web scraping with Python Web scraping is an automated, programmatic process through which data can be constantly 'scraped' off webpages. Also known as screen scraping or web harvesting, web scraping can provide instant data from any publicly accessible webpage. On some websites, web scraping. I’ve recently had to perform some web scraping from a site that required login. It wasn’t very straight forward as I expected so I’ve decided to write a tutorial for it. For this tutorial we will scrape a list of projects from our bitbucket account. The code from this tutorial can be found on my Github. We will perform the following steps.
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Loading Web Pages with 'request' The requests module allows you to send HTTP requests using. Navigate to the folder called PythonWebScrape that you downloaded to your desktop and double-click on the folder Within the PythonWebScrape folder, double-click on the file with the word “BLANK” in the name (PythonWebScrapeBLANK.ipynb). A pop-up window will ask you to Select Kernal — you should select the Python.
| importurllib2 |
| frombs4importBeautifulSoup |
| # http://segfault.in/2010/07/parsing-html-table-in-python-with-beautifulsoup/ |
| f=open('cricket-data.txt','w') |
| linksFile=open('linksSource.txt') |
| lines=list(linksFile.readlines()) |
| foriinlines[12:108]: #12:108 |
| url='http://www.gunnercricket.com/'+str(i) |
| try: |
| page=urllib2.urlopen(url) |
| except: |
| continue |
| soup=BeautifulSoup(page) |
| title=soup.title |
| date=title.string[:4]+','#take first 4 characters from title |
| try: |
| table=soup.find('table') |
| rows=table.findAll('tr') |
| fortrinrows: |
| cols=tr.findAll('td') |
| text_data= [] |
| fortdincols: |
| text='.join(td) |
| utftext=str(text.encode('utf-8')) |
| text_data.append(utftext) # EDIT |
| text=date+','.join(text_data) |
| f.write(text+'n') |
| except: |
| pass |
| f.close() |
commented Jan 15, 2018 •
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import pandas as pd from bs4 import BeautifulSoup Nortek cameras. import requests import lxml url = 'http://espn.go.com/college-football/bcs/_/year/2013 ' result = requests.get(url) c= result.content soup.prettify() Broadcom sound cards & media devices driver download. summary = soup.find('table',attrs = {'class':'tablehead'}) #tables = summary.fins_all('td' /'tr') data =[] rows = tables[0].findAll('tr') list_of_rows=[] for row in table.findAll('tr')[0:]: outfile = open('./Rankings.csv', 'wb') Can please you help me with this code? Am using python 3.5 |
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| importurllib.request |
| frombs4importBeautifulSoup |
| wiki='https://pt.wikipedia.org/wiki/Lista_de_capitais_do_Brasil_por_%C3%A1rea' |
| page=urllib.request.urlopen(wiki) |
| soup=BeautifulSoup(page, 'html.parser') |
| table=soup.find('table') |
| A=[] |
| B=[] |
| C=[] |
| D=[] |
| E=[] |
| forrowintable.findAll('tr'): |
| cells=row.findAll('td') |
| iflen(cells)5: |
| A.append(cells[0].find(text=True)) |
| B.append(cells[1].find(text=True)) |
| C.append(cells[2].find(text=True)) |
| D.append(cells[3].find('a').text) |
| E.append(cells[4].find(text=True)) |
| importpandasaspd |
| df=pd.DataFrame(index=A, columns=['Posição']) |
| df['Posição']=A |
| df['Estado']=B |
| df['Código/IBGE']=C |
| df['Capital']=D |
| df['Área']=E |
| df |
commented Nov 9, 2018
Existe um artigo explicando os detalhes. Acesse aqui! |
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