使用 Python 抓取 HTML 中的特定元素：BeautifulSoup4
Grab specific element in HTML using Python : BeautifulSoup4
我正在使用 beautifulsoup 抓取网站数据。我正在了解如何抓取网页上显示的内容，但是，我想抓取的 html 中嵌入了一个没有标题的唯一标识符。例如：
<tbody><tr ><th scope="row" class="right " data-stat="ranker" csk="1" >1</th><td class="left " data-stat="pos" csk="1" ><strong>C</strong></td><td class="left " data-append-csv="mccanja02" data-stat="player" csk="McCann,James" ><strong><a href="/players/m/mccanja02.shtml">James McCann</a></strong></td><td class="right " data-stat="age" >32</td><td class="right " data-stat="G" >13</td><td class="right " data-stat="PA" >42</td><td class="right " data-stat="AB" >36</td><td class="right " data-stat="R" >5</td><td class="right " data-stat="H" >7</td><td class="right " data-stat="2B" >2</td><td class="right iz" data-stat="3B" >0</td><td class="right " data-stat="HR" >1</td><td class="right " data-stat="RBI" >5</td><td class="right " data-stat="SB" >1</td><td class="right iz" data-stat="CS" >0</td><td class="right " data-stat="BB" >2</td><td class="right " data-stat="SO" >7</td><td class="right " data-stat="batting_avg" >.194</td><td class="right " data-stat="onbase_perc" >.286</td><td class="right " data-stat="slugging_perc" >.333</td><td class="right " data-stat="onbase_plus_slugging" >.619</td><td class="right " data-stat="onbase_plus_slugging_plus" >87</td><td class="right " data-stat="TB" >12</td><td class="right " data-stat="GIDP" >1</td><td class="right " data-stat="HBP" >3</td><td class="right iz" data-stat="SH" >0</td><td class="right " data-stat="SF" >1</td><td class="right iz" data-stat="IBB" >0</td></tr>
我只想获取“mccanja02”，因为它可用于添加到 URL 并指向玩家特定页面。到目前为止，我已经尝试过这样的事情：
# grab players UID
rowsUID = tableTeamBatting.find_all('tr')
for rowUID in rowsUID:
playerUID = rowUID.find('td', {'data-append-csv'})
if playerUID:
playerUID = playerUID.text
print(playerUID)
但是没有标题可以与之关联，就像我想获取玩家的名字一样：
# grab players name
rows = tableTeamBatting.find_all('tr')
for row in rows:
players = []
player = row.find('td', {'data-stat' : 'player'})
if player:
player = player.text
print(player)
我无法准确输出@F.Hoque的解决方案，所以我做了这个怪物：
# grab players UID
rowsUID = tableTeamBatting.find_all('tr')
for rowUID in rowsUID:
playerUID = rowUID.select('a[href]')
playerUID = playerUID if playerUID else None
if playerUID == None:
continue
else:
pUID = str(playerUID)
pUID = pUID.split('/')
for p in range(len(pUID)):
if '.shtml' in pUID[p]:
stor = pUID[p].split('.shtml')
print(stor[0])
这给了我正在寻找的 pUID。我无法使用评论中的代码的原因是因为它会 return this:
<td class="left" csk="McCann,James" data-append-csv="mccanja02" data-stat="player"><strong><a href="/players/m/mccanja02.shtml">James McCann</a></strong></td>
<td class="left" csk="Alonso,Pete" data-append-csv="alonspe01" data-stat="player"><strong><a href="/players/a/alonspe01.shtml">Pete Alonso</a></strong></td>
<td class="left" csk="McNeil,Jeff" data-append-csv="mcneije01" data-stat="player"><strong><a href="/players/m/mcneije01.shtml">Jeff McNeil</a>*</strong></td>
<td class="left" csk="Lindor,Francisco" data-append-csv="lindofr01" data-stat="player"><strong><a href="/players/l/lindofr01.shtml">Francisco Lindor</a>#</strong></td>...
我只是在寻找 data-append-csv=pUID。不过，我很感谢您的帮助，我深入研究了一些文档并找到了一些东西。我乐于接受有关如何改进这一点的任何建议。
mccanja02是data-append-csv的一个属性值。所以不能调用.text来抢。您可以使用 css 选择器获取它，如下所示：
html='''
<html>
<body>
<tbody>
<tr>
<th class="right" csk="1" data-stat="ranker" scope="row">
1
</th>
<td class="left" csk="1" data-stat="pos">
<strong>
C
</strong>
</td>
<td class="left" csk="McCann,James" data-append-csv="mccanja02" data-stat="player">
<strong>
<a href="/players/m/mccanja02.shtml">
James McCann
</a>
</strong>
</td>
<td class="right" data-stat="age">
32
</td>
<td class="right" data-stat="G">
13
</td>
<td class="right" data-stat="PA">
42
</td>
<td class="right" data-stat="AB">
36
</td>
<td class="right" data-stat="R">
5
</td>
<td class="right" data-stat="H">
7
</td>
<td class="right" data-stat="2B">
2
</td>
<td class="right iz" data-stat="3B">
0
</td>
<td class="right" data-stat="HR">
1
</td>
<td class="right" data-stat="RBI">
5
</td>
<td class="right" data-stat="SB">
1
</td>
<td class="right iz" data-stat="CS">
0
</td>
<td class="right" data-stat="BB">
2
</td>
<td class="right" data-stat="SO">
7
</td>
<td class="right" data-stat="batting_avg">
.194
</td>
<td class="right" data-stat="onbase_perc">
.286
</td>
<td class="right" data-stat="slugging_perc">
.333
</td>
<td class="right" data-stat="onbase_plus_slugging">
.619
</td>
<td class="right" data-stat="onbase_plus_slugging_plus">
87
</td>
<td class="right" data-stat="TB">
12
</td>
<td class="right" data-stat="GIDP">
1
</td>
<td class="right" data-stat="HBP">
3
</td>
<td class="right iz" data-stat="SH">
0
</td>
<td class="right" data-stat="SF">
1
</td>
<td class="right iz" data-stat="IBB">
0
</td>
</tr>
</tbody>
</body>
</html>
'''
from bs4 import BeautifulSoup
tableTeamBatting=BeautifulSoup(html,'lxml')
#print(soup.prettify())
rowsUID = tableTeamBatting.select('tr')
for rowUID in rowsUID:
playerUID = rowUID.select_one('td[data-append-csv]')
playerUID = playerUID.get('data-append-csv')if playerUID else None
print(playerUID)
输出：
mccanja02
我认为您可以将 data-stat 属性用于 select 由击球手 id 锚定的 tbody 子项。
import requests
from bs4 import BeautifulSoup as bs
r = requests.get('https://www.baseball-reference.com/teams/NYM/2022.shtml', headers = {'User-Agent':'Mozilla/5.0'})
soup = bs(r.content, 'lxml')
players = {i['csk']:i['data-append-csv'] for i in soup.select('#div_team_batting tbody td[data-stat="player"]')}
更好的方法是使用玩家的 ID 并通过注册页面（重定向到玩家的 url）。您可以从 the sup_players_search_list.csv.
的一个请求中获取全部
那么就按照https://www.baseball-reference.com/register/player.fcgi?id=的格式。
例如 James McCann 'mccanja02' playerId 是 'mccann002jam'
代码：
import pandas as pd
player_df = pd.read_csv('https://www.baseball-reference.com/short/inc/sup_players_search_list.csv', header=None)
player_df = player_df.rename(columns={0:'id'})
playerId = 'mccann002jam'
baseUrl = 'https://www.baseball-reference.com/register/player.fcgi?id='
url = f'{baseUrl}{playerId}'
# Get the data
response = requests.get(url)
html = response.text.replace('<!--', '').replace('-->', '')
soup = BeautifulSoup(html, 'html.parser')
tables_dict = {}
tables = soup.find_all('table')
for table in tables:
stat_type = table.find('caption').text.strip()
df = pd.read_html(str(table))[0]
tables_dict[stat_type] = df
for tableName, table in tables_dict.items():
print(f'\n\n*** {tableName} ***')
print(table)
输出：
csv：
print(player_df)
id 1 2 3 4 5 6 7 8
0 aardsm001dav David Aardsma 2004-2015 0 NaN NaN NaN NaN 2025.78
1 aaron-001hen Henry Aaron 1954-1976 0 NaN NaN NaN NaN 2142.01
2 aaron-001tom Tommie Aaron 1962-1971 0 NaN NaN NaN NaN 1975.23
3 aase--001don Don Aase 1977-1990 0 NaN NaN NaN NaN 2018.02
4 abad--001fau Andy Abad 2001-2006 0 NaN NaN NaN NaN 2008.65
... ... ... .. .. .. .. .. ...
22671 zupo--001fra Frank Zupo 1957-1961 0 NaN NaN NaN NaN 1963.93
22672 zuvell001pau Paul Zuvella 1982-1991 0 NaN NaN NaN NaN 1997.64
22673 zuveri001geo George Zuverink 1951-1959 0 NaN NaN NaN NaN 1975.83
22674 zwilli001edw Dutch Zwilling 1910-1916 0 NaN NaN NaN NaN 1928.49
22675 zych--001ton Tony Zych 2015-2017 0 NaN NaN NaN NaN 2021.59
[22676 rows x 9 columns]
詹姆斯·麦肯的数据：
*** Futures Game ***
0 1
0 2013 Futures Game U.S.
*** Register Batting ***
Year Age ... SF IBB
0 2010 20 ... 0 0
1 2011 21 ... 2 NaN
2 2011 21 ... 1 0
3 2011 21 ... 1 0
4 2011 21 ... 0 0
5 2012 22 ... 3 1
6 2012 22 ... 1 0
7 2012 22 ... 2 1
8 2012 22 ... 0 0
9 2013 23 ... 7 1
10 2013-14 23 ... 0 0
11 2014 24 ... 7 0
12 2014 24 ... 0 0
13 2015 25 ... 1 0
14 2016 26 ... 0 0
15 2016 26 ... 3 0
16 2017 27 ... 0 0
17 2017 27 ... 3 0
18 2018 28 ... 2 0
19 2019 29 ... 0 1
20 2020 30 ... 2 0
21 2021 31 ... 3 1
22 2022 32 ... 2 0
23 Year Age ... SF IBB
24 (1 season) (1 season) ... 0 0
25 Majors (9 seasons) Majors (9 seasons) ... 16 2
26 Minors (6 seasons) Minors (6 seasons) ... 18 2
27 Foreign (1 season) Foreign (1 season) ... 0 0
28 College (1 season) College (1 season) ... 2 NaN
29 Other (1 season) Other (1 season) ... 0 0
30 All Levels (13 Seasons) All Levels (13 Seasons) ... 36 4
31 NaN NaN ... NaN NaN
32 AAA (3 seasons) AAA (3 seasons) ... 7 0
33 AA (2 seasons) AA (2 seasons) ... 8 1
34 A+ (1 season) A+ (1 season) ... 2 1
35 A (1 season) A (1 season) ... 1 0
36 Rk (1 season) Rk (1 season) ... 0 0
[37 rows x 30 columns]
*** Register Fielding ***
Year Age ... lgCS% PO.1
0 2010 20 ... NaN NaN
1 2011 21 ... NaN NaN
2 2011 21 ... NaN NaN
3 2011 21 ... NaN NaN
4 2012 22 ... NaN NaN
5 2012 22 ... NaN NaN
6 2012 22 ... NaN NaN
7 2012 22 ... NaN NaN
8 2013 23 ... NaN NaN
9 2013-14 23 ... NaN NaN
10 2014 24 ... NaN NaN
11 2014 24 ... NaN NaN
12 2014 24 ... NaN NaN
13 2015 25 ... NaN NaN
14 2016 26 ... NaN NaN
15 2016 26 ... NaN NaN
16 2017 27 ... NaN NaN
17 2017 27 ... NaN NaN
18 2018 28 ... NaN NaN
19 2019 29 ... NaN NaN
20 2020 30 ... NaN NaN
21 2021 31 ... NaN NaN
22 2021 31 ... NaN NaN
23 2022 32 ... NaN NaN
24 Year Age ... lgCS% PO
25 (1 season) (1 season) ... NaN NaN
26 Majors (1 season) Majors (1 season) ... NaN NaN
27 Majors (9 seasons) Majors (9 seasons) ... NaN NaN
28 Minors (1 season) Minors (1 season) ... NaN NaN
29 Minors (6 seasons) Minors (6 seasons) ... NaN NaN
30 Foreign (1 season) Foreign (1 season) ... NaN NaN
31 Other (1 season) Other (1 season) ... NaN NaN
32 All Levels (1 Season) All Levels (1 Season) ... NaN NaN
33 All Levels (1 Season) All Levels (1 Season) ... NaN NaN
34 All Levels (13 Seasons) All Levels (13 Seasons) ... NaN NaN
[35 rows x 26 columns]
*** Teams Played For ***
Year Age Tm ... Stint From To
0 2010 20 Cotuit Kettleers ... NaN 2010-06-26 2010-08-04
1 2011 21 Arkansas Razorbacks ... NaN NaN NaN
2 2011 21 GCL Tigers ... NaN 2011-08-13 2011-08-19
3 2011 21 West Michigan Whitecaps ... NaN 2011-08-21 2011-09-03
4 2012 22 Lakeland Flying Tigers ... NaN 2012-04-05 2012-06-04
5 2012 22 Erie SeaWolves ... NaN 2012-06-07 2012-09-03
6 2012 22 Mesa Solar Sox ... NaN 2012-10-10 2012-11-15
7 2013 23 Erie SeaWolves ... NaN 2013-04-04 2013-09-02
8 2013 23 Leones del Escogido ... NaN 2013-10-19 2013-11-16
9 2014 24 Toledo Mud Hens ... NaN 2014-04-04 2014-08-29
10 2014 24 Detroit Tigers ... 1.0 2014-09-01 2014-09-27
11 2015 25 Detroit Tigers ... 1.0 2015-04-08 2015-10-04
12 2016 26 Detroit Tigers ... 1.0 2016-04-05 2016-10-02
13 2016 26 Toledo Mud Hens ... NaN 2016-04-26 2016-05-01
14 2017 27 Detroit Tigers ... 1.0 2017-04-04 2017-09-30
15 2017 27 Toledo Mud Hens ... NaN 2017-06-06 2017-06-07
16 2018 28 Detroit Tigers ... 1.0 2018-03-30 2018-09-30
17 2019 29 Chicago White Sox ... 1.0 2019-03-28 2019-09-28
18 2020 30 Chicago White Sox ... 1.0 2020-07-25 2020-09-26
19 2021 31 New York Mets ... 1.0 2021-04-05 2021-10-02
20 2022 32 New York Mets ... 1.0 2022-04-07 2022-05-10
[21 rows x 9 columns]
我正在使用 beautifulsoup 抓取网站数据。我正在了解如何抓取网页上显示的内容，但是，我想抓取的 html 中嵌入了一个没有标题的唯一标识符。例如：
<tbody><tr ><th scope="row" class="right " data-stat="ranker" csk="1" >1</th><td class="left " data-stat="pos" csk="1" ><strong>C</strong></td><td class="left " data-append-csv="mccanja02" data-stat="player" csk="McCann,James" ><strong><a href="/players/m/mccanja02.shtml">James McCann</a></strong></td><td class="right " data-stat="age" >32</td><td class="right " data-stat="G" >13</td><td class="right " data-stat="PA" >42</td><td class="right " data-stat="AB" >36</td><td class="right " data-stat="R" >5</td><td class="right " data-stat="H" >7</td><td class="right " data-stat="2B" >2</td><td class="right iz" data-stat="3B" >0</td><td class="right " data-stat="HR" >1</td><td class="right " data-stat="RBI" >5</td><td class="right " data-stat="SB" >1</td><td class="right iz" data-stat="CS" >0</td><td class="right " data-stat="BB" >2</td><td class="right " data-stat="SO" >7</td><td class="right " data-stat="batting_avg" >.194</td><td class="right " data-stat="onbase_perc" >.286</td><td class="right " data-stat="slugging_perc" >.333</td><td class="right " data-stat="onbase_plus_slugging" >.619</td><td class="right " data-stat="onbase_plus_slugging_plus" >87</td><td class="right " data-stat="TB" >12</td><td class="right " data-stat="GIDP" >1</td><td class="right " data-stat="HBP" >3</td><td class="right iz" data-stat="SH" >0</td><td class="right " data-stat="SF" >1</td><td class="right iz" data-stat="IBB" >0</td></tr>
我只想获取“mccanja02”，因为它可用于添加到 URL 并指向玩家特定页面。到目前为止，我已经尝试过这样的事情：
# grab players UID
rowsUID = tableTeamBatting.find_all('tr')
for rowUID in rowsUID:
playerUID = rowUID.find('td', {'data-append-csv'})
if playerUID:
playerUID = playerUID.text
print(playerUID)
但是没有标题可以与之关联，就像我想获取玩家的名字一样：
# grab players name
rows = tableTeamBatting.find_all('tr')
for row in rows:
players = []
player = row.find('td', {'data-stat' : 'player'})
if player:
player = player.text
print(player)
我无法准确输出@F.Hoque的解决方案，所以我做了这个怪物：
# grab players UID
rowsUID = tableTeamBatting.find_all('tr')
for rowUID in rowsUID:
playerUID = rowUID.select('a[href]')
playerUID = playerUID if playerUID else None
if playerUID == None:
continue
else:
pUID = str(playerUID)
pUID = pUID.split('/')
for p in range(len(pUID)):
if '.shtml' in pUID[p]:
stor = pUID[p].split('.shtml')
print(stor[0])
这给了我正在寻找的 pUID。我无法使用评论中的代码的原因是因为它会 return this:
<td class="left" csk="McCann,James" data-append-csv="mccanja02" data-stat="player"><strong><a href="/players/m/mccanja02.shtml">James McCann</a></strong></td>
<td class="left" csk="Alonso,Pete" data-append-csv="alonspe01" data-stat="player"><strong><a href="/players/a/alonspe01.shtml">Pete Alonso</a></strong></td>
<td class="left" csk="McNeil,Jeff" data-append-csv="mcneije01" data-stat="player"><strong><a href="/players/m/mcneije01.shtml">Jeff McNeil</a>*</strong></td>
<td class="left" csk="Lindor,Francisco" data-append-csv="lindofr01" data-stat="player"><strong><a href="/players/l/lindofr01.shtml">Francisco Lindor</a>#</strong></td>...
我只是在寻找 data-append-csv=pUID。不过，我很感谢您的帮助，我深入研究了一些文档并找到了一些东西。我乐于接受有关如何改进这一点的任何建议。
mccanja02是data-append-csv的一个属性值。所以不能调用.text来抢。您可以使用 css 选择器获取它，如下所示：
html='''
<html>
<body>
<tbody>
<tr>
<th class="right" csk="1" data-stat="ranker" scope="row">
1
</th>
<td class="left" csk="1" data-stat="pos">
<strong>
C
</strong>
</td>
<td class="left" csk="McCann,James" data-append-csv="mccanja02" data-stat="player">
<strong>
<a href="/players/m/mccanja02.shtml">
James McCann
</a>
</strong>
</td>
<td class="right" data-stat="age">
32
</td>
<td class="right" data-stat="G">
13
</td>
<td class="right" data-stat="PA">
42
</td>
<td class="right" data-stat="AB">
36
</td>
<td class="right" data-stat="R">
5
</td>
<td class="right" data-stat="H">
7
</td>
<td class="right" data-stat="2B">
2
</td>
<td class="right iz" data-stat="3B">
0
</td>
<td class="right" data-stat="HR">
1
</td>
<td class="right" data-stat="RBI">
5
</td>
<td class="right" data-stat="SB">
1
</td>
<td class="right iz" data-stat="CS">
0
</td>
<td class="right" data-stat="BB">
2
</td>
<td class="right" data-stat="SO">
7
</td>
<td class="right" data-stat="batting_avg">
.194
</td>
<td class="right" data-stat="onbase_perc">
.286
</td>
<td class="right" data-stat="slugging_perc">
.333
</td>
<td class="right" data-stat="onbase_plus_slugging">
.619
</td>
<td class="right" data-stat="onbase_plus_slugging_plus">
87
</td>
<td class="right" data-stat="TB">
12
</td>
<td class="right" data-stat="GIDP">
1
</td>
<td class="right" data-stat="HBP">
3
</td>
<td class="right iz" data-stat="SH">
0
</td>
<td class="right" data-stat="SF">
1
</td>
<td class="right iz" data-stat="IBB">
0
</td>
</tr>
</tbody>
</body>
</html>
'''
from bs4 import BeautifulSoup
tableTeamBatting=BeautifulSoup(html,'lxml')
#print(soup.prettify())
rowsUID = tableTeamBatting.select('tr')
for rowUID in rowsUID:
playerUID = rowUID.select_one('td[data-append-csv]')
playerUID = playerUID.get('data-append-csv')if playerUID else None
print(playerUID)
输出：
mccanja02
我认为您可以将 data-stat 属性用于 select 由击球手 id 锚定的 tbody 子项。
import requests
from bs4 import BeautifulSoup as bs
r = requests.get('https://www.baseball-reference.com/teams/NYM/2022.shtml', headers = {'User-Agent':'Mozilla/5.0'})
soup = bs(r.content, 'lxml')
players = {i['csk']:i['data-append-csv'] for i in soup.select('#div_team_batting tbody td[data-stat="player"]')}
更好的方法是使用玩家的 ID 并通过注册页面（重定向到玩家的 url）。您可以从 the sup_players_search_list.csv.
那么就按照https://www.baseball-reference.com/register/player.fcgi?id=的格式。
例如 James McCann 'mccanja02' playerId 是 'mccann002jam'
代码：
import pandas as pd
player_df = pd.read_csv('https://www.baseball-reference.com/short/inc/sup_players_search_list.csv', header=None)
player_df = player_df.rename(columns={0:'id'})
playerId = 'mccann002jam'
baseUrl = 'https://www.baseball-reference.com/register/player.fcgi?id='
url = f'{baseUrl}{playerId}'
# Get the data
response = requests.get(url)
html = response.text.replace('<!--', '').replace('-->', '')
soup = BeautifulSoup(html, 'html.parser')
tables_dict = {}
tables = soup.find_all('table')
for table in tables:
stat_type = table.find('caption').text.strip()
df = pd.read_html(str(table))[0]
tables_dict[stat_type] = df
for tableName, table in tables_dict.items():
print(f'\n\n*** {tableName} ***')
print(table)
输出：
csv：
print(player_df)
id 1 2 3 4 5 6 7 8
0 aardsm001dav David Aardsma 2004-2015 0 NaN NaN NaN NaN 2025.78
1 aaron-001hen Henry Aaron 1954-1976 0 NaN NaN NaN NaN 2142.01
2 aaron-001tom Tommie Aaron 1962-1971 0 NaN NaN NaN NaN 1975.23
3 aase--001don Don Aase 1977-1990 0 NaN NaN NaN NaN 2018.02
4 abad--001fau Andy Abad 2001-2006 0 NaN NaN NaN NaN 2008.65
... ... ... .. .. .. .. .. ...
22671 zupo--001fra Frank Zupo 1957-1961 0 NaN NaN NaN NaN 1963.93
22672 zuvell001pau Paul Zuvella 1982-1991 0 NaN NaN NaN NaN 1997.64
22673 zuveri001geo George Zuverink 1951-1959 0 NaN NaN NaN NaN 1975.83
22674 zwilli001edw Dutch Zwilling 1910-1916 0 NaN NaN NaN NaN 1928.49
22675 zych--001ton Tony Zych 2015-2017 0 NaN NaN NaN NaN 2021.59
[22676 rows x 9 columns]
詹姆斯·麦肯的数据：
*** Futures Game ***
0 1
0 2013 Futures Game U.S.
*** Register Batting ***
Year Age ... SF IBB
0 2010 20 ... 0 0
1 2011 21 ... 2 NaN
2 2011 21 ... 1 0
3 2011 21 ... 1 0
4 2011 21 ... 0 0
5 2012 22 ... 3 1
6 2012 22 ... 1 0
7 2012 22 ... 2 1
8 2012 22 ... 0 0
9 2013 23 ... 7 1
10 2013-14 23 ... 0 0
11 2014 24 ... 7 0
12 2014 24 ... 0 0
13 2015 25 ... 1 0
14 2016 26 ... 0 0
15 2016 26 ... 3 0
16 2017 27 ... 0 0
17 2017 27 ... 3 0
18 2018 28 ... 2 0
19 2019 29 ... 0 1
20 2020 30 ... 2 0
21 2021 31 ... 3 1
22 2022 32 ... 2 0
23 Year Age ... SF IBB
24 (1 season) (1 season) ... 0 0
25 Majors (9 seasons) Majors (9 seasons) ... 16 2
26 Minors (6 seasons) Minors (6 seasons) ... 18 2
27 Foreign (1 season) Foreign (1 season) ... 0 0
28 College (1 season) College (1 season) ... 2 NaN
29 Other (1 season) Other (1 season) ... 0 0
30 All Levels (13 Seasons) All Levels (13 Seasons) ... 36 4
31 NaN NaN ... NaN NaN
32 AAA (3 seasons) AAA (3 seasons) ... 7 0
33 AA (2 seasons) AA (2 seasons) ... 8 1
34 A+ (1 season) A+ (1 season) ... 2 1
35 A (1 season) A (1 season) ... 1 0
36 Rk (1 season) Rk (1 season) ... 0 0
[37 rows x 30 columns]
*** Register Fielding ***
Year Age ... lgCS% PO.1
0 2010 20 ... NaN NaN
1 2011 21 ... NaN NaN
2 2011 21 ... NaN NaN
3 2011 21 ... NaN NaN
4 2012 22 ... NaN NaN
5 2012 22 ... NaN NaN
6 2012 22 ... NaN NaN
7 2012 22 ... NaN NaN
8 2013 23 ... NaN NaN
9 2013-14 23 ... NaN NaN
10 2014 24 ... NaN NaN
11 2014 24 ... NaN NaN
12 2014 24 ... NaN NaN
13 2015 25 ... NaN NaN
14 2016 26 ... NaN NaN
15 2016 26 ... NaN NaN
16 2017 27 ... NaN NaN
17 2017 27 ... NaN NaN
18 2018 28 ... NaN NaN
19 2019 29 ... NaN NaN
20 2020 30 ... NaN NaN
21 2021 31 ... NaN NaN
22 2021 31 ... NaN NaN
23 2022 32 ... NaN NaN
24 Year Age ... lgCS% PO
25 (1 season) (1 season) ... NaN NaN
26 Majors (1 season) Majors (1 season) ... NaN NaN
27 Majors (9 seasons) Majors (9 seasons) ... NaN NaN
28 Minors (1 season) Minors (1 season) ... NaN NaN
29 Minors (6 seasons) Minors (6 seasons) ... NaN NaN
30 Foreign (1 season) Foreign (1 season) ... NaN NaN
31 Other (1 season) Other (1 season) ... NaN NaN
32 All Levels (1 Season) All Levels (1 Season) ... NaN NaN
33 All Levels (1 Season) All Levels (1 Season) ... NaN NaN
34 All Levels (13 Seasons) All Levels (13 Seasons) ... NaN NaN
[35 rows x 26 columns]
*** Teams Played For ***
Year Age Tm ... Stint From To
0 2010 20 Cotuit Kettleers ... NaN 2010-06-26 2010-08-04
1 2011 21 Arkansas Razorbacks ... NaN NaN NaN
2 2011 21 GCL Tigers ... NaN 2011-08-13 2011-08-19
3 2011 21 West Michigan Whitecaps ... NaN 2011-08-21 2011-09-03
4 2012 22 Lakeland Flying Tigers ... NaN 2012-04-05 2012-06-04
5 2012 22 Erie SeaWolves ... NaN 2012-06-07 2012-09-03
6 2012 22 Mesa Solar Sox ... NaN 2012-10-10 2012-11-15
7 2013 23 Erie SeaWolves ... NaN 2013-04-04 2013-09-02
8 2013 23 Leones del Escogido ... NaN 2013-10-19 2013-11-16
9 2014 24 Toledo Mud Hens ... NaN 2014-04-04 2014-08-29
10 2014 24 Detroit Tigers ... 1.0 2014-09-01 2014-09-27
11 2015 25 Detroit Tigers ... 1.0 2015-04-08 2015-10-04
12 2016 26 Detroit Tigers ... 1.0 2016-04-05 2016-10-02
13 2016 26 Toledo Mud Hens ... NaN 2016-04-26 2016-05-01
14 2017 27 Detroit Tigers ... 1.0 2017-04-04 2017-09-30
15 2017 27 Toledo Mud Hens ... NaN 2017-06-06 2017-06-07
16 2018 28 Detroit Tigers ... 1.0 2018-03-30 2018-09-30
17 2019 29 Chicago White Sox ... 1.0 2019-03-28 2019-09-28
18 2020 30 Chicago White Sox ... 1.0 2020-07-25 2020-09-26
19 2021 31 New York Mets ... 1.0 2021-04-05 2021-10-02
20 2022 32 New York Mets ... 1.0 2022-04-07 2022-05-10
[21 rows x 9 columns]