Web scraping with pyhton - table with mutliple tbody elements-CodePudding

I'm trying to scrape the data from

CodePudding user response：

You can extract all data using pandas

import pandas as pd
import requests 
from bs4 import BeautifulSoup

url='https://www.eliteprospects.com/league/nhl/stats/2021-2022'
req=requests.get(url).text
soup=BeautifulSoup(req,'lxml')
table=soup.select_one('table[]')
table_data =pd.read_html(str(table))[0]
print(table_data)

Output:

 #                    Player                   Team    GP  ...    TP   PPG   PIM  
  /-
0      1.0        Connor McDavid (C)        Edmonton Oilers  64.0  ...  95.0  1.48  37.0  
18.0
1      2.0      Leon Draisaitl (C/W)        Edmonton Oilers  65.0  ...  90.0  1.38  40.0  
20.0
2      3.0   Jonathan Huberdeau (LW)       Florida Panthers  63.0  ...  88.0  1.40  40.0  
25.0
3      4.0      Johnny Gaudreau (LW)         Calgary Flames  64.0  ...  85.0  1.33  22.0  
45.0
4      5.0          Kyle Connor (LW)          Winnipeg Jets  66.0  ...  82.0  1.24   4.0  
 3.0
..     ...                       ...                    ...   ...  ...   ...   ...   ...  
 ...
104   96.0        Ryan Strome (C/RW)       New York Rangers  61.0  ...  45.0  0.74  63.0  
 6.0
105   97.0  Alexander Kerfoot (C/LW)    Toronto Maple Leafs  63.0  ...  45.0  0.71  14.0  
16.0
106   98.0   Andrew Mangiapane (W/C)         Calgary Flames  64.0  ...  44.0  0.69  26.0  
17.0
107   99.0          Brock Nelson (C)     New York Islanders  53.0  ...  44.0  0.83  33.0  
 3.0
108  100.0          Boone Jenner (C)  Columbus Blue Jackets  59.0  ...  44.0  0.75  22.0 -11.0

[109 rows x 10 columns]

If you need next page pagination,then you can follow the next example:

import pandas as pd
import requests 
from bs4 import BeautifulSoup

url='https://www.eliteprospects.com/league/nhl/stats/2021-2022?page={page}'
table_data=[]
for page in range(1,11):

    req=requests.get(url.format(page=page)).text
    soup=BeautifulSoup(req,'lxml')
    table=soup.select_one('table[]')
    tables =pd.read_html(str(table))[0]
    tables=tables.dropna(how='all').reset_index(drop=True)
    #print(table_data)
    table_data.append(tables)

df=pd.concat(table_data)
print(df)

Output:

#                   Player                 Team    GP  ...    TP   PPG   PIM    /-
0       1.0       Connor McDavid (C)      Edmonton Oilers  64.0  ...  95.0  1.48  37.0  18.0
1       2.0     Leon Draisaitl (C/W)      Edmonton Oilers  65.0  ...  90.0  1.38  40.0  20.0
2       3.0  Jonathan Huberdeau (LW)     Florida Panthers  63.0  ...  88.0   1.4  40.0  25.0
3       4.0     Johnny Gaudreau (LW)       Calgary Flames  64.0  ...  85.0  1.33  22.0  45.0
4       5.0         Kyle Connor (LW)        Winnipeg Jets  66.0  ...  82.0  1.24   4.0   3.0
...     ...                      ...                  ...   ...  ...   ...   ...   ...   ...
1146  987.0  Micheal Ferland (LW/RW)    Vancouver Canucks     -  ...     -     -     -   NaN  
1147  988.0        Oscar Klefbom (D)      Edmonton Oilers     -  ...     -     -     -   NaN  
1148  989.0           Shea Weber (D)   Montréal Canadiens     -  ...     -     -     -   NaN  
1149  990.0    Brandon Sutter (C/RW)    Vancouver Canucks     -  ...     -     -     -   NaN  
1150  991.0       Brent Seabrook (D)  Tampa Bay Lightning     -  ...     -     -     -   NaN  

[1151 rows x 10 columns]