Parsing tags using Beautiful Soup and Python

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This is my code so far:

# URL page we will scraping (see image above)
url = "https://www.basketball-reference.com/leagues/NBA_2019_per_game.html"
# this is the HTML from the given URL
html = urlopen(url)
soup = BeautifulSoup(html)
soup.findAll('tr', limit=10)

It returns

<th aria-label="Personal Fouls Per Game" class=" poptip hide_non_quals center" data-stat="pf_per_g" data-tip="Personal Fouls Per Game" scope="col">PF</th>
 <th aria-label="Points Per Game" class=" poptip hide_non_quals center" data-stat="pts_per_g" data-tip="Points Per Game" scope="col">PTS</th>
 </tr>,
 <tr class="full_table"><th class="right " csk="1" data-stat="ranker" scope="row">1</th><td class="left " csk="Abrines,Álex" data-append-csv="abrinal01" data-stat="player"><a href="/players/a/abrinal01.html">Álex Abrines</a></td><td class="center " data-stat="pos">SG</td><td class="right " data-stat="age">25</td><td class="left " data-stat="team_id"><a href="/teams/OKC/2019.html">OKC</a></td><td class="right " data-stat="g">31</td><td class="right " data-stat="gs">2</td><td class="right non_qual" data-stat="mp_per_g">19.0</td><td class="right non_qual" data-stat="fg_per_g">1.8</td><td class="right non_qual" data-stat="fga_per_g">5.1</td><td class="right non_qual" data-stat="fg_pct">.357</td><td class="right non_qual" data-stat="fg3_per_g">1.3</td><td class="right non_qual" data-stat="fg3a_per_g">4.1</td><td class="right non_qual" data-stat="fg3_pct">.323</td><td class="right non_qual" data-stat="fg2_per_g">0.5</td><td class="right non_qual" data-stat="fg2a_per_g">1.0</td><td class="right non_qual" data-stat="fg2_pct">.500</td><td class="right non_qual" data-stat="efg_pct">.487</td><td class="right non_qual" data-stat="ft_per_g">0.4</td><td class="right non_qual" data-stat="fta_per_g">0.4</td><td class="right non_qual" data-stat="ft_pct">.923</td><td class="right non_qual" data-stat="orb_per_g">0.2</td><td class="right non_qual" data-stat="drb_per_g">1.4</td><td class="right non_qual" data-stat="trb_per_g">1.5</td><td class="right non_qual" data-stat="ast_per_g">0.6</td><td class="right non_qual" data-stat="stl_per_g">0.5</td><td class="right non_qual" data-stat="blk_per_g">0.2</td><td class="right non_qual" data-stat="tov_per_g">0.5</td><td class="right non_qual" data-stat="pf_per_g">1.7</td><td class="right non_qual" data-stat="pts_per_g">5.3</td></tr>,
 <tr class="full_table"><th class="right " csk="2" data-stat="ranker" scope="row">2</th><td class="left " csk="Acy,Quincy" data-append-csv="acyqu01" data-stat="player"><a href="/players/a/acyqu01.html">Quincy Acy</a></td><td class="center " data-stat="pos">PF</td><td class="right " data-stat="age">28</td><td class="left " data-stat="team_id"><a href="/teams/PHO/2019.html">PHO</a></td><td class="right " data-stat="g">10</td><td class="right iz" data-stat="gs">0</td><td class="right non_qual" data-stat="mp_per_g">12.3</td><td class="right non_qual" data-stat="fg_per_g">0.4</td><td class="right non_qual" data-stat="fga_per_g">1.8</td><td class="right non_qual" data-stat="fg_pct">.222</td><td class="right non_qual" data-stat="fg3_per_g">0.2</td><td class="right non_qual" data-stat="fg3a_per_g">1.5</td><td class="right non_qual" data-stat="fg3_pct">.133</td><td class="right non_qual" data-stat="fg2_per_g">0.2</td><td class="right non_qual" data-stat="fg2a_per_g">0.3</td><td class="right non_qual" data-stat="fg2_pct">.667</td><td class="right non_qual" data-stat="efg_pct">.278</td><td class="right non_qual" data-stat="ft_per_g">0.7</td><td class="right non_qual" data-stat="fta_per_g">1.0</td><td class="right non_qual" data-stat="ft_pct">.700</td><td class="right non_qual" data-stat="orb_per_g">0.3</td><td class="right non_qual" data-stat="drb_per_g">2.2</td><td class="right non_qual" data-stat="trb_per_g">2.5</td><td class="right non_qual" data-stat="ast_per_g">0.8</td><td class="right non_qual" data-stat="stl_per_g">0.1</td><td class="right non_qual" data-stat="blk_per_g">0.4</td><td class="right non_qual" data-stat="tov_per_g">0.4</td><td class="right non_qual" data-stat="pf_per_g">2.4</td><td class="right non_qual" data-stat="pts_per_g">1.7</td></tr>,
 <tr class="full_table"><th class="right " csk="3" data-stat="ranker" scope="row">3</th><td class="left " csk="Adams,Jaylen" data-append-csv="adamsja01" data-stat="player"><a href="/players/a/adamsja01.html">Jaylen Adams</a></td><td class="center " data-stat="pos">PG</td><td class="right " data-stat="age">22</td><td class="left " data-stat="team_id"><a href="/teams/ATL/2019.html">ATL</a></td><td class="right " data-stat="g">34</td><td class="right " data-stat="gs">1</td><td class="right non_qual" data-stat="mp_per_g">12.6</td><td class="right non_qual" data-stat="fg_per_g">1.1</td><td class="right non_qual" data-stat="fga_per_g">3.2</td><td class="right non_qual" data-stat="fg_pct">.345</td><td class="right non_qual" data-stat="fg3_per_g">0.7</td><td class="right non_qual" data-stat="fg3a_per_g">2.2</td><td class="right non_qual" data-stat="fg3_pct">.338</td><td class="right non_qual" data-stat="fg2_per_g">0.4</td><td class="right non_qual" data-stat="fg2a_per_g">1.1</td><td class="right non_qual" data-stat="fg2_pct">.361</td><td class="right non_qual" data-stat="efg_pct">.459</td><td class="right non_qual" data-stat="ft_per_g">0.2</td><td class="right non_qual" data-stat="fta_per_g">0.3</td><td class="right non_qual" data-stat="ft_pct">.778</td><td class="right non_qual" data-stat="orb_per_g">0.3</td><td class="right non_qual" data-stat="drb_per_g">1.4</td><td class="right non_qual" data-stat="trb_per_g">1.8</td><td class="right non_qual" data-stat="ast_per_g">1.9</td><td class="right non_qual" data-stat="stl_per_g">0.4</td><td class="right non_qual" data-stat="blk_per_g">0.1</td><td class="right non_qual" data-stat="tov_per_g">0.8</td><td class="right non_qual" data-stat="pf_per_g">1.3</td><td class="right non_qual" data-stat="pts_per_g">3.2</td></tr>,
 <tr class="full_table"><th class="right " csk="4" data-stat="ranker" scope="row">4</th><td class="left " csk="Adams,Steven" data-append-csv="adamsst01"

I want to know how for each tr class, I can get the a href and data-append-csv. So for example the first tr class, the data-append-csv is abrinal01.

For quick solution you can try something like:

import re

tags = page_soup.find_all('tr', limit=10)

for tag in tags:
    m = re.match('.+" data-append-csv="([^\"]+)" ', str(tag))
    if m:
        ge = m.groups()
        print(ge[0])

Same approach with href. For global / reuse solution you need more accurate code with soup parsing or more accurate regular expression

Beautiful Soup Documentation — Beautiful Soup 4.9.0 documentation, Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching ,� Please edit your question and include a sample of the HTML you want to parse. And please also follow common Python conventions and use four spaces for each level of indention. – user1907906 Jun 1 '15 at 5:53

If you want only the data-append-csv and href values, then you may use my code. I am using list comprehensions with find.

Code

from bs4 import BeautifulSoup
import requests

txt = '''
<th aria-label="Personal Fouls Per Game" class=" poptip hide_non_quals center" data-stat="pf_per_g" data-tip="Personal Fouls Per Game" scope="col">PF</th>
 <th aria-label="Points Per Game" class=" poptip hide_non_quals center" data-stat="pts_per_g" data-tip="Points Per Game" scope="col">PTS</th>
 </tr>,
 <tr class="full_table"><th class="right " csk="1" data-stat="ranker" scope="row">1</th><td class="left " csk="Abrines,Álex" data-append-csv="abrinal01" data-stat="player"><a href="/players/a/abrinal01.html">Álex Abrines</a></td><td class="center " data-stat="pos">SG</td><td class="right " data-stat="age">25</td><td class="left " data-stat="team_id"><a href="/teams/OKC/2019.html">OKC</a></td><td class="right " data-stat="g">31</td><td class="right " data-stat="gs">2</td><td class="right non_qual" data-stat="mp_per_g">19.0</td><td class="right non_qual" data-stat="fg_per_g">1.8</td><td class="right non_qual" data-stat="fga_per_g">5.1</td><td class="right non_qual" data-stat="fg_pct">.357</td><td class="right non_qual" data-stat="fg3_per_g">1.3</td><td class="right non_qual" data-stat="fg3a_per_g">4.1</td><td class="right non_qual" data-stat="fg3_pct">.323</td><td class="right non_qual" data-stat="fg2_per_g">0.5</td><td class="right non_qual" data-stat="fg2a_per_g">1.0</td><td class="right non_qual" data-stat="fg2_pct">.500</td><td class="right non_qual" data-stat="efg_pct">.487</td><td class="right non_qual" data-stat="ft_per_g">0.4</td><td class="right non_qual" data-stat="fta_per_g">0.4</td><td class="right non_qual" data-stat="ft_pct">.923</td><td class="right non_qual" data-stat="orb_per_g">0.2</td><td class="right non_qual" data-stat="drb_per_g">1.4</td><td class="right non_qual" data-stat="trb_per_g">1.5</td><td class="right non_qual" data-stat="ast_per_g">0.6</td><td class="right non_qual" data-stat="stl_per_g">0.5</td><td class="right non_qual" data-stat="blk_per_g">0.2</td><td class="right non_qual" data-stat="tov_per_g">0.5</td><td class="right non_qual" data-stat="pf_per_g">1.7</td><td class="right non_qual" data-stat="pts_per_g">5.3</td></tr>,
 <tr class="full_table"><th class="right " csk="2" data-stat="ranker" scope="row">2</th><td class="left " csk="Acy,Quincy" data-append-csv="acyqu01" data-stat="player"><a href="/players/a/acyqu01.html">Quincy Acy</a></td><td class="center " data-stat="pos">PF</td><td class="right " data-stat="age">28</td><td class="left " data-stat="team_id"><a href="/teams/PHO/2019.html">PHO</a></td><td class="right " data-stat="g">10</td><td class="right iz" data-stat="gs">0</td><td class="right non_qual" data-stat="mp_per_g">12.3</td><td class="right non_qual" data-stat="fg_per_g">0.4</td><td class="right non_qual" data-stat="fga_per_g">1.8</td><td class="right non_qual" data-stat="fg_pct">.222</td><td class="right non_qual" data-stat="fg3_per_g">0.2</td><td class="right non_qual" data-stat="fg3a_per_g">1.5</td><td class="right non_qual" data-stat="fg3_pct">.133</td><td class="right non_qual" data-stat="fg2_per_g">0.2</td><td class="right non_qual" data-stat="fg2a_per_g">0.3</td><td class="right non_qual" data-stat="fg2_pct">.667</td><td class="right non_qual" data-stat="efg_pct">.278</td><td class="right non_qual" data-stat="ft_per_g">0.7</td><td class="right non_qual" data-stat="fta_per_g">1.0</td><td class="right non_qual" data-stat="ft_pct">.700</td><td class="right non_qual" data-stat="orb_per_g">0.3</td><td class="right non_qual" data-stat="drb_per_g">2.2</td><td class="right non_qual" data-stat="trb_per_g">2.5</td><td class="right non_qual" data-stat="ast_per_g">0.8</td><td class="right non_qual" data-stat="stl_per_g">0.1</td><td class="right non_qual" data-stat="blk_per_g">0.4</td><td class="right non_qual" data-stat="tov_per_g">0.4</td><td class="right non_qual" data-stat="pf_per_g">2.4</td><td class="right non_qual" data-stat="pts_per_g">1.7</td></tr>,
 <tr class="full_table"><th class="right " csk="3" data-stat="ranker" scope="row">3</th><td class="left " csk="Adams,Jaylen" data-append-csv="adamsja01" data-stat="player"><a href="/players/a/adamsja01.html">Jaylen Adams</a></td><td class="center " data-stat="pos">PG</td><td class="right " data-stat="age">22</td><td class="left " data-stat="team_id"><a href="/teams/ATL/2019.html">ATL</a></td><td class="right " data-stat="g">34</td><td class="right " data-stat="gs">1</td><td class="right non_qual" data-stat="mp_per_g">12.6</td><td class="right non_qual" data-stat="fg_per_g">1.1</td><td class="right non_qual" data-stat="fga_per_g">3.2</td><td class="right non_qual" data-stat="fg_pct">.345</td><td class="right non_qual" data-stat="fg3_per_g">0.7</td><td class="right non_qual" data-stat="fg3a_per_g">2.2</td><td class="right non_qual" data-stat="fg3_pct">.338</td><td class="right non_qual" data-stat="fg2_per_g">0.4</td><td class="right non_qual" data-stat="fg2a_per_g">1.1</td><td class="right non_qual" data-stat="fg2_pct">.361</td><td class="right non_qual" data-stat="efg_pct">.459</td><td class="right non_qual" data-stat="ft_per_g">0.2</td><td class="right non_qual" data-stat="fta_per_g">0.3</td><td class="right non_qual" data-stat="ft_pct">.778</td><td class="right non_qual" data-stat="orb_per_g">0.3</td><td class="right non_qual" data-stat="drb_per_g">1.4</td><td class="right non_qual" data-stat="trb_per_g">1.8</td><td class="right non_qual" data-stat="ast_per_g">1.9</td><td class="right non_qual" data-stat="stl_per_g">0.4</td><td class="right non_qual" data-stat="blk_per_g">0.1</td><td class="right non_qual" data-stat="tov_per_g">0.8</td><td class="right non_qual" data-stat="pf_per_g">1.3</td><td class="right non_qual" data-stat="pts_per_g">3.2</td></tr>,
 <tr class="full_table"><th class="right " csk="4" data-stat="ranker" scope="row">4</th><td class="left " csk="Adams,Steven" data-append-csv="adamsst01"
 '''

#main scrape
bs = BeautifulSoup(txt, 'lxml')

#you may uncomment the following three lines to scrape directly from your url, the print results will be different
#url = 'https://www.basketball-reference.com/leagues/NBA_2019_per_game.html'
#html = requests.get(url)
#bs = BeautifulSoup(html.content, 'lxml')

tr = bs.find_all('tr')

#data-append-csv is part of <td class='left', ..., data-append-csv=...>
dacsv = [_.find('td', {'class':'left'})['data-append-csv'] if _.find('td') is not None else None for _ in tr]

#href is part of <a href=...>
href = [_.find('a')['href'] if _.find('a') is not None else None for _ in tr]

print(list(zip(dacsv, href)))

#[('abrinal01', '/players/a/abrinal01.html'), ('acyqu01', '/players/a/acyqu01.html'), ('adamsja01', '/players/a/adamsja01.html'), ('adamsst01', None)]

Note: If you want to see all attributes from an ID, you can do the following (then call the attribute you want)

temp = [_.find('td', {'class':'left'}).attrs if _.find('td') is not None else None for _ in tr]

print(temp)
#[{'class': ['left'], 'csk': 'Abrines,Álex', 'data-append-csv': 'abrinal01', 'data-stat': 'player'}, {'class': ['left'], 'csk': 'Acy,Quincy', 'data-append-csv': 'acyqu01', 'data-stat': 'player'}, {'class': ['left'], 'csk': 'Adams,Jaylen', 'data-append-csv': 'adamsja01', 'data-stat': 'player'}, {'class': ['left'], 'csk': 'Adams,Steven', 'data-append-csv': 'adamsst01'}]

Using BeautifulSoup to parse HTML and extract press briefings URLs, Python. In the above example, we added two a tags. a tags are links, and tell the We can use the BeautifulSoup library to parse this document, and extract the� BeautifulSoup is a Python library for parsing HTML and XML documents. for web scraping. BeautifulSoup transforms a complex HTML document into a complex tree of Python objects, such as tag, navigable string, or comment.

If you simply want to extract those data_append_csv and href you could zip the two matched lists then extract in loop. I would investigate whether, with full html, you can remove the .left class selector.

from bs4 import BeautifulSoup as bs

html = '''
<html>
 <head></head>
 <body>
  <table> 
   <tbody>
    <tr> 
     <th aria-label="Personal Fouls Per Game" class=" poptip hide_non_quals center" data-stat="pf_per_g" data-tip="Personal Fouls Per Game" scope="col">PF</th> 
     <th aria-label="Points Per Game" class=" poptip hide_non_quals center" data-stat="pts_per_g" data-tip="Points Per Game" scope="col">PTS</th> 
    </tr> 
    <tr class="full_table"> 
     <th class="right " csk="1" data-stat="ranker" scope="row">1</th> 
     <td class="left " csk="Abrines,Álex" data-append-csv="abrinal01" data-stat="player"><a href="/players/a/abrinal01.html">Álex Abrines</a></td> 
     <td class="center " data-stat="pos">SG</td> 
     <td class="right " data-stat="age">25</td> 
     <td class="left " data-stat="team_id"><a href="/teams/OKC/2019.html">OKC</a></td> 
     <td class="right " data-stat="g">31</td> 
     <td class="right " data-stat="gs">2</td> 
     <td class="right non_qual" data-stat="mp_per_g">19.0</td> 
     <td class="right non_qual" data-stat="fg_per_g">1.8</td> 
     <td class="right non_qual" data-stat="fga_per_g">5.1</td> 
     <td class="right non_qual" data-stat="fg_pct">.357</td> 
     <td class="right non_qual" data-stat="fg3_per_g">1.3</td> 
     <td class="right non_qual" data-stat="fg3a_per_g">4.1</td> 
     <td class="right non_qual" data-stat="fg3_pct">.323</td> 
     <td class="right non_qual" data-stat="fg2_per_g">0.5</td> 
     <td class="right non_qual" data-stat="fg2a_per_g">1.0</td> 
     <td class="right non_qual" data-stat="fg2_pct">.500</td> 
     <td class="right non_qual" data-stat="efg_pct">.487</td> 
     <td class="right non_qual" data-stat="ft_per_g">0.4</td> 
     <td class="right non_qual" data-stat="fta_per_g">0.4</td> 
     <td class="right non_qual" data-stat="ft_pct">.923</td> 
     <td class="right non_qual" data-stat="orb_per_g">0.2</td> 
     <td class="right non_qual" data-stat="drb_per_g">1.4</td> 
     <td class="right non_qual" data-stat="trb_per_g">1.5</td> 
     <td class="right non_qual" data-stat="ast_per_g">0.6</td> 
     <td class="right non_qual" data-stat="stl_per_g">0.5</td> 
     <td class="right non_qual" data-stat="blk_per_g">0.2</td> 
     <td class="right non_qual" data-stat="tov_per_g">0.5</td> 
     <td class="right non_qual" data-stat="pf_per_g">1.7</td> 
     <td class="right non_qual" data-stat="pts_per_g">5.3</td> 
    </tr> 
    <tr class="full_table"> 
     <th class="right " csk="2" data-stat="ranker" scope="row">2</th> 
     <td class="left " csk="Acy,Quincy" data-append-csv="acyqu01" data-stat="player"><a href="/players/a/acyqu01.html">Quincy Acy</a></td> 
     <td class="center " data-stat="pos">PF</td> 
     <td class="right " data-stat="age">28</td> 
     <td class="left " data-stat="team_id"><a href="/teams/PHO/2019.html">PHO</a></td> 
     <td class="right " data-stat="g">10</td> 
     <td class="right iz" data-stat="gs">0</td> 
     <td class="right non_qual" data-stat="mp_per_g">12.3</td> 
     <td class="right non_qual" data-stat="fg_per_g">0.4</td> 
     <td class="right non_qual" data-stat="fga_per_g">1.8</td> 
     <td class="right non_qual" data-stat="fg_pct">.222</td> 
     <td class="right non_qual" data-stat="fg3_per_g">0.2</td> 
     <td class="right non_qual" data-stat="fg3a_per_g">1.5</td> 
     <td class="right non_qual" data-stat="fg3_pct">.133</td> 
     <td class="right non_qual" data-stat="fg2_per_g">0.2</td> 
     <td class="right non_qual" data-stat="fg2a_per_g">0.3</td> 
     <td class="right non_qual" data-stat="fg2_pct">.667</td> 
     <td class="right non_qual" data-stat="efg_pct">.278</td> 
     <td class="right non_qual" data-stat="ft_per_g">0.7</td> 
     <td class="right non_qual" data-stat="fta_per_g">1.0</td> 
     <td class="right non_qual" data-stat="ft_pct">.700</td> 
     <td class="right non_qual" data-stat="orb_per_g">0.3</td> 
     <td class="right non_qual" data-stat="drb_per_g">2.2</td> 
     <td class="right non_qual" data-stat="trb_per_g">2.5</td> 
     <td class="right non_qual" data-stat="ast_per_g">0.8</td> 
     <td class="right non_qual" data-stat="stl_per_g">0.1</td> 
     <td class="right non_qual" data-stat="blk_per_g">0.4</td> 
     <td class="right non_qual" data-stat="tov_per_g">0.4</td> 
     <td class="right non_qual" data-stat="pf_per_g">2.4</td> 
     <td class="right non_qual" data-stat="pts_per_g">1.7</td> 
    </tr> 
    <tr class="full_table"> 
     <th class="right " csk="3" data-stat="ranker" scope="row">3</th> 
     <td class="left " csk="Adams,Jaylen" data-append-csv="adamsja01" data-stat="player"><a href="/players/a/adamsja01.html">Jaylen Adams</a></td> 
     <td class="center " data-stat="pos">PG</td> 
     <td class="right " data-stat="age">22</td> 
     <td class="left " data-stat="team_id"><a href="/teams/ATL/2019.html">ATL</a></td> 
     <td class="right " data-stat="g">34</td> 
     <td class="right " data-stat="gs">1</td> 
     <td class="right non_qual" data-stat="mp_per_g">12.6</td> 
     <td class="right non_qual" data-stat="fg_per_g">1.1</td> 
     <td class="right non_qual" data-stat="fga_per_g">3.2</td> 
     <td class="right non_qual" data-stat="fg_pct">.345</td> 
     <td class="right non_qual" data-stat="fg3_per_g">0.7</td> 
     <td class="right non_qual" data-stat="fg3a_per_g">2.2</td> 
     <td class="right non_qual" data-stat="fg3_pct">.338</td> 
     <td class="right non_qual" data-stat="fg2_per_g">0.4</td> 
     <td class="right non_qual" data-stat="fg2a_per_g">1.1</td> 
     <td class="right non_qual" data-stat="fg2_pct">.361</td> 
     <td class="right non_qual" data-stat="efg_pct">.459</td> 
     <td class="right non_qual" data-stat="ft_per_g">0.2</td> 
     <td class="right non_qual" data-stat="fta_per_g">0.3</td> 
     <td class="right non_qual" data-stat="ft_pct">.778</td> 
     <td class="right non_qual" data-stat="orb_per_g">0.3</td> 
     <td class="right non_qual" data-stat="drb_per_g">1.4</td> 
     <td class="right non_qual" data-stat="trb_per_g">1.8</td> 
     <td class="right non_qual" data-stat="ast_per_g">1.9</td> 
     <td class="right non_qual" data-stat="stl_per_g">0.4</td> 
     <td class="right non_qual" data-stat="blk_per_g">0.1</td> 
     <td class="right non_qual" data-stat="tov_per_g">0.8</td> 
     <td class="right non_qual" data-stat="pf_per_g">1.3</td> 
     <td class="right non_qual" data-stat="pts_per_g">3.2</td> 
    </tr>
   </tbody>
  </table>
 </body>
</html>
'''
soup = bs(html, 'lxml')

for name, link in zip(soup.select('[data-append-csv].left'),soup.select('[data-append-csv].left a')): #you may wish to add td in
    print(name['data-append-csv'], link['href'])

Tutorial: Python Web Scraping Using BeautifulSoup –, Using Requests to scrape data for Beautiful Soup to parse With this soup object, you can navigate and search through the HTML for data that you want. For example, if you run soup. title after the previous code in a Python shell you'll get the title of the web page. If you run print(soup. The Python libraries requests and Beautiful Soup are powerful tools for the job. If you like to learn with hands-on examples and you have a basic understanding of Python and HTML, then this tutorial is for you. In this tutorial, you’ll learn how to: Use requests and Beautiful Soup for scraping and parsing data from the Web

Web Scraping and Parsing HTML in Python with Beautiful Soup , In the next tutorial, we're going to cover working with tables and XML. The next tutorial: Parsing tables and XML with Beautiful Soup 4. Beautiful Soup doesn't mimic a client. Javascript is code that runs on the client. With Python, we simply make a request to the server, and get the server's response, which is the starting text, along of course with the javascript, but it's the browser that reads and runs that javascript.

Navigation with Beautiful Soup 4, Use BeautifulSoup to find the particular element from the response and extract the text. HTML content can also contain CSS instructions within style tag to add 1 soup = BeautifulSoup(content.text, 'html.parser'). python. parser — a string consisting of the name of the parser to be used; here we will use python’s default parser: “html.parser” Note that we named the first parameter as “markup_string” instead of “html_string” because BeautifulSoup can be used with other markup languages as well, not just HTML, but we need to specify an appropriate

Web Scraping with Beautiful Soup, BeautifulSoup is one popular library provided by Python to scrape data Fetching and parsing the data using Beautifulsoup and maintain the data in Analyzing the HTML tags and their attributes, such as class, id, and other� In this course, you will learn how to perform web scraping using Python 3 and the Beautiful Soup, a free open-source library written in Python for parsing HTML. We will use lxml, which is an extensive library for parsing XML and HTML documents very quickly; it can even handle messed up tags.

Comments
  • if you're familiar with CSS selectors like in jquery, BS supports those. so something like soup.select("tr...") would work, once you get the CSS selector to work in a browser's dev console. Attributes are a bit more involved. see crummy.com/software/BeautifulSoup/bs4/doc/#css-selectors