beautifulsoup espn table, can't find the proper tag, pictures within

beautifulsoup tutorial
espn scraper
web scraping espn
espn api
nfl scraper python
python extract table from webpage
beautifulsoup extract
beautifulsoup attributes

I am trying to scrape a table from the espn site. I just seem not to be able to find the right name to access it.

url="https://www.espn.com/nba/stats/player/_/table/offensive/sort/avgAssists/dir/desc"
import requests
from bs4 import BeautifulSoup
headers={'User-Agent': 'Mozilla/5.0'}
response=requests.get(url,headers=headers)
soup=BeautifulSoup(response.content, 'html.parser')
soup.find_all('table',class_ ="ResponsiveTable ResponsiveTable--fixed-left mt4 Table2__title--remove-capitalization")

The code only gives me an empty list :(

If you have table tags, let Pandas do the work for you. It uses BeautifulSoup under the hood.

import pandas as pd

url = "https://www.espn.com/nba/stats/player/_/table/offensive/sort/avgAssists/dir/desc"

dfs = pd.read_html(url)

df = dfs[0].join(dfs[1])
df[['Name','Team']] = df['Name'].str.extract('^(.*?)([A-Z]+)$', expand=True)

Output:

print(df.head(5).to_string())
   RK          Name POS  GP   MIN   PTS  FGM   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  REB   AST  STL  BLK   TO  DD2  TD3    PER Team
0   1  LeBron James  SF  35  35.1  24.9  9.6  19.7  48.6  2.0  6.0  33.8  3.7  5.5  67.7  7.9  11.0  1.3  0.5  3.7   28    9  26.10  LAL
1   2   Ricky Rubio  PG  30  32.0  13.6  4.9  11.9  41.3  1.2  3.7  31.8  2.6  3.1  83.7  4.6   9.3  1.3  0.2  2.5   12    1  16.40  PHX
2   3   Luka Doncic  SF  32  32.8  29.7  9.6  20.2  47.5  3.1  9.4  33.1  7.3  9.1  80.5  9.7   8.9  1.2  0.2  4.2   22   11  31.74  DAL
3   4   Ben Simmons  PG  36  35.4  14.9  6.1  10.8  56.3  0.1  0.1  40.0  2.7  4.6  59.0  7.5   8.6  2.2  0.7  3.6   19    3  19.49  PHI
4   5    Trae Young  PG  34  35.1  28.9  9.3  20.8  44.8  3.5  9.4  37.5  6.7  7.9  85.0  4.3   8.4  1.2  0.1  4.8   11    1  23.47  ATL

Web Scraping MLB Stats with Python and Beautiful Soup, Python web scraping libraries like BeautifulSoup can automate the ESPN designed the table with this header repeated every 10 players, so it� Watch ESPN and over 100 live channels with fuboTV. Get rid of cable today. DVR included. No contracts. No fees. Cancel anytime.

Why not just get the flex class and then get the table of players..

import requests
from bs4 import BeautifulSoup

url="https://www.espn.com/nba/stats/player/_/table/offensive/sort/avgAssists/dir/desc"


headers={'User-Agent': 'Mozilla/5.0'}
response=requests.get(url, headers=headers)
soup=BeautifulSoup(response.content, 'html.parser')

all_tables = soup.find('div', {'class':'flex'})
all_tables.find('table') # To get all players name

Python BeautifulSoup Tutorial: Web scraping in 20 lines of code, Using Python and BeautifulSoup, we can quickly, and efficiently, scrape data from a In this example, we are scraping college footballer data from ESPN website. (CTRL + U in Chrome) we note that the page uses a table to display the data,� Is there anyway I can scrape the whole table properly, where RK, TEAM, FG%, FT%, 3PM, REB, AST, STL, BLK, TO, PTS, LAST, MOVES are all stored on one row (categoryList)? It seems silly that ESPN even put these values on different rows. Moreover, if I could get this whole table into one matrix, it would be immensely help. Desired Output:

The tag you are selecting with:

soup.find_all('table',class_ ="ResponsiveTable ResponsiveTable--fixed-left mt4 Table2__title--remove-capitalization")

Should be not 'table' but 'section':

soup.find_all('section',class_ ="ResponsiveTable ResponsiveTable--fixed-left mt4 Table2__title--remove-capitalization")

To get all data, you can use this example:

import requests
from bs4 import BeautifulSoup

url="https://www.espn.com/nba/stats/player/_/table/offensive/sort/avgAssists/dir/desc"
headers={'User-Agent': 'Mozilla/5.0'}
response=requests.get(url,headers=headers)
soup=BeautifulSoup(response.content, 'html.parser')

for tr1, tr2 in zip(soup.select('table.Table.Table--align-right.Table--fixed.Table--fixed-left tr'),
                    soup.select('table.Table.Table--align-right.Table--fixed.Table--fixed-left ~ div tr')):
    data = tr1.select('td') + tr2.select('td')
    if not data:
        continue
    print('{:<25}'.format(data[1].get_text(strip=True, separator='-').split()[-1]), end=' ')
    for td in data[2:]:
        print('{:<6}'.format(td.get_text(strip=True)), end=' ')
    print()

Prints:

James-LAL                 SF     30     34.9   25.7   9.9    20.2   49.1   2.2    6.4    34.4   3.6    5.3    67.9   7.6    10.6   1.2    0.6    3.9    23     7      26.33  
Rubio-PHX                 PG     25     31.8   13.8   5.0    12.2   41.0   1.1    3.7    30.1   2.7    3.2    84.8   4.8    9.2    1.2    0.2    2.6    11     1      16.30  
Doncic-DAL                SF     26     32.2   29.1   9.4    19.8   47.7   3.0    9.2    32.2   7.3    9.1    79.7   9.6    8.8    1.2    0.1    4.3    17     8      31.43  
Simmons-PHI               PG     32     34.9   14.3   5.9    10.4   56.3   0.1    0.2    40.0   2.5    4.3    58.3   7.0    8.6    2.2    0.6    3.7    15     2      18.92  
Young-ATL                 PG     31     34.9   28.5   9.3    20.9   44.4   3.4    9.3    36.8   6.5    7.7    84.5   4.3    8.3    1.2    0.1    4.7    9      1      23.21  
Graham-CHA                PG     34     34.7   19.2   6.1    15.9   38.2   3.8    9.5    39.8   3.2    4.1    79.7   3.9    7.6    0.8    0.3    3.0    9      0      17.20  
Brogdon-IND               PG     26     31.4   18.3   6.6    14.5   45.2   1.4    4.3    33.3   3.8    4.0    93.3   4.5    7.6    0.9    0.2    2.7    7      0      20.31  
Harden-HOU                SG     31     37.6   38.1   11.1   24.5   45.2   5.1    13.8   37.2   10.9   12.4   87.5   5.8    7.5    1.9    0.7    4.7    9      0      31.72  
Lillard-POR               PG     30     36.7   26.9   8.4    19.0   44.3   3.4    9.4    35.8   6.6    7.4    89.6   4.2    7.5    1.0    0.4    2.9    6      0      24.42  
Westbrook-HOU             PG     28     35.3   24.1   8.9    20.9   42.6   1.2    5.1    23.8   5.1    6.5    79.1   8.1    7.1    1.5    0.4    4.4    12     6      18.68  
VanVleet-TOR              SG     26     36.3   18.1   5.9    14.5   40.5   2.4    6.6    36.8   3.9    4.5    87.2   3.9    7.0    2.0    0.2    2.6    5      0      16.82  
Jokic-DEN                 C      30     31.3   17.6   7.0    14.4   48.5   1.3    4.1    30.6   2.4    3.0    82.0   10.0   6.8    1.0    0.6    2.5    17     6      23.01  

...and so on.

Scraping ESPN Fantasy Football (in Python), EDIT: ESPN changed their Fantasy API to v3 in early 2019, so lots of Again this can take us into a gray area, but to my knowledge there is We create soup , a BeautifulSoup object that allows us to soup.find the HTML table� The good news is that Python web scraping libraries like Beautiful Soup can automate the collection of data from websites. Codecademy has a new course introducing you to the basics of webscraping and Beautiful Soup. In this article, we will walk through an example of how to use Beautiful Soup to collect MLB player stats from the 2018 season.

You can also use the same API that the webpage uses to populate its table with player information. If you make a direct GET-request to that API (with the correct headers and query string), you will receive all the player information you could ever want in a JSON-compliant format.

The URL to the API, the relevant headers and query string GET-Parameters are all visible in Google Chrome's Network Log (most modern browsers have something equivalent). I was able to find them by applying a filter and retaining only XMLHttpRequest (XHR) resources, and then clicking the "Show More" button at the bottom of the table.

I've set the "limit" GET-Parameter to "3", because I was only interested in printing data pertaining to the first three players. Changing this string to "50", for example, will query the API for the first fifty players.

def main():

    import requests

    headers = {
        "accept": "application/json, text/plain, */*",
        "origin": "https://www.espn.com",
        "user-agent": "Mozilla/5.0"
    }

    params = {
        "region": "us",
        "lang": "en",
        "contentorigin": "espn",
        "isqualified": "true",
        "page": "1",
        "limit": "3",
        "sort": "offensive.avgAssists:desc"
    }

    base_url = "https://site.web.api.espn.com/apis/common/v3/sports/basketball/nba/statistics/byathlete"

    response = requests.get(base_url, headers=headers, params=params)
    response.raise_for_status()

    data = response.json()
    print(data["athletes"])

    return 0


if __name__ == "__main__":
    import sys
    sys.exit(main())

Gentle Intro to Python Web scraping 04, In this video, we will be learning how to scrape an html table from the ESPN site. This is a Duration: 22:52 Posted: 12 Jul 2017 You can use it not only to extract tables and lists but you can also use to to pull out very specific elements like a paragraph with a green font color. To briefly illustrate this functionality and in honor of the upcoming World Cup we will use BeautifulSoup on world soccer rankings.

Example of web scraping using Python and BeautifulSoup. � GitHub, http://www.espn.com/college-sports/football/recruiting/databaseresults/_/sportid/ 24/class/2006/sort/school/ The script will loop through a defined number of pages to extract footballer data. loop through the table, get data from the columns. By looking at the table, we can see the specific HTML tags that we will be using to extract the data: Now, we go back to BeautifulSoup . By using findAll() , we can get the first 2 rows (limit = 2) and pass the element we want as the first argument, in this case ‘ tr ’, which is the HTML tag for table row.

Web Scraping with Beautiful Soup in Python, Looking at the HTML code for ESPN, I can see that the data I want is in the first table on the page. table = soup.find('table'). I create a new� With our BeautifulSoup object i.e., soup we can move on and collect the required table data. Before going to the actual code, let's first play with the soup object and print some basic information from it:

Scraping a table, altering the data and inserting into MySQL DB , I am trying to scrape golf scores from ESPN.com, I am able to insert the data I need from the given url html = urlopen(url) par = 72 soup = BeautifulSoup(html) soup. Yes, I could have just imported it all table by table into excel and then use� Parsing tables and XML with Beautiful Soup 4 Welcome to part 3 of the web scraping with Beautiful Soup 4 tutorial mini-series. In this tutorial, we're going to talk more about scraping what you want, specifically with a table example, as well as scraping XML documents.

Comments
  • Thank you but it always throws me an exception when I run it , even with limit: '1"
  • What kind of exception?