这是我目前的代码:
# 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)
它回来了
<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"
我想知道如何为每个tr类,我可以得到a href和数据附加csv。例如,第一个tr类,data append csv是abrinal01。你知道吗
如果您只想提取这些数据,您可以压缩两个匹配的列表,然后在循环中提取。我将研究是否可以使用完整的html删除
.left
类选择器。你知道吗要快速解决问题,您可以尝试以下方法:
与href的方法相同。对于全局/重用解决方案,您需要更精确的soup解析代码或更精确的正则表达式
如果只需要
data-append-csv
和href
值,则可以使用我的代码。我将列表理解与find
一起使用。你知道吗代码
注意:如果您想查看ID中的所有属性,可以执行以下操作(然后调用所需的属性)
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