Q:

How to parse nested table from HTML link using BeautifulSoup in Python?

All,
I am trying to Parse table from this link http://web1.ncaa.org/stats/StatsSrv/careersearch.
Please Note: For searching  under "School/Sport Search"  select All for School, Year -2005-2006, Sport -Football, Division I. The column I am trying to Parse is the School Names, and if you click on School Name.More information will output. From that link/Table I would like to Parse "Stadium Capacity" for each and every School. My question is Is something like this possible ? If yes,how ? I am new to python and BeautifulSoup, if you can provide explanation that will be Great! 
Note: There are 239 results, 
To Summarize: so Basically I would like to parse School Names along with their Stadium Capacity and convert it into Pandas Data-frame
import requests 
from bs4 import BeautifulSoup
URL = "http://web1.ncaa.org/stats/StatsSrv/careerteam"
r = requests.get(URL) 

soup = BeautifulSoup(r.content, 'html5lib') 
print(soup.prettify()) 

A:

My question is Is something like this possible ?

Yes.

If yes,how ?

There is a lot going in the code below. But the main point is to figure out the post requests being made by the browser and then emulate that using Requests. We can find out the request being made through the  "network" tab in the inspect tool.
First we make the 'search' post request. This gives a left and right table. Clicking on the left table gives us the schools in that area. But if we observe carefully clicking on the area link also is a post request (which we have to do using requests)
Eg. Clicking on 'Air Force - Eastern Ill.' gives us a table containing the links of schools in that area. Then we have to go to that school link and figure out the capacity.
Since clicking on each of the school link is also a post request we have to emulate and this returns the school page. From here we scrap the school name and capacity.
You can read Advanced Usage of requests to know about Session objects, Making a request to read about making request with Requests.
import requests
from bs4 import BeautifulSoup
import pandas as pd
end_list=[]
s = requests.Session()
URL = "http://web1.ncaa.org/stats/StatsSrv/careersearch"
data={'doWhat': 'teamSearch','searchOrg': 'X', 'academicYear': 2006, 'searchSport':'MFB','searchDiv': 1}
r = s.post(URL,data=data)
soup=BeautifulSoup(r.text,'html.parser')
area_list=soup.find_all('table')[8].find_all('tr')
area_count=len(area_list)#has no of areas + 1  tr 'Total Results of Search:  239'
for idx in range(0,area_count):
    data={
    'sortOn': 0,
    'doWhat': 'showIdx',
    'playerId':'' ,'coachId': '',
    'orgId':'' ,
    'academicYear':'' ,
    'division':'' ,
    'sportCode':'' ,
    'idx': idx
    }
    r = s.post(URL,data=data)
    soup=BeautifulSoup(r.text,'html.parser')
    last_table=soup.find_all('table')[-1]#last table
    for tr in last_table.find_all('tr'):
        link_td=tr.find('td',class_="text")
        try:
            link_a=link_td.find('a')['href']
            data_params=link_a.split('(')[1][:-2].split(',')
            try:
                #print(data_params)
                sports_code=data_params[2].replace("'","").strip()
                division=int(data_params[3])
                player_coach_id=int(data_params[0])
                academic_year=int(data_params[1])
                org_id=int(data_params[4])
                #print(sports_code,division,player_coach_id,academic_year,org_id)
                data={
                'sortOn': 0,
                'doWhat': 'display',
                'playerId': player_coach_id,
                'coachId': player_coach_id,
                'orgId': org_id,
                'academicYear': academic_year,
                'division':division,
                'sportCode':sports_code,
                'idx':''
                }
                url='http://web1.ncaa.org/stats/StatsSrv/careerteam'
                r = s.post(url,data=data)
                soup2=BeautifulSoup(r.text,'html.parser')
                institution_name=soup2.find_all('table')[1].find_all('tr')[2].find_all('td')[1].text.strip()
                capacity=soup2.find_all('table')[4].find_all('tr')[2].find_all('td')[1].text.strip()
                #print([institution_name, capacity])
                end_list.append([institution_name, capacity])

            except IndexError:
                pass

        except AttributeError:
            pass
#print(end_list)
headers=['School','Capacity']
df=pd.DataFrame(end_list, columns=headers)
print(df)

Output
                School Capacity
0            Air Force   46,692
1                Akron   30,000
2              Alabama  101,821
3         Alabama A&M;   21,000
4          Alabama St.   26,500
5          Albany (NY)    8,500
6               Alcorn   22,500
7      Appalachian St.   30,000
8              Arizona   55,675
9          Arizona St.   64,248
10     Ark.-Pine Bluff   14,500
11            Arkansas   72,000
12        Arkansas St.   30,708
13     Army West Point   38,000
14              Auburn   87,451
15         Austin Peay   10,000
16                 BYU   63,470
17            Ball St.   22,500
18              Baylor   45,140
19     Bethune-Cookman    9,601
20           Boise St.   36,387
21      Boston College   44,500
22       Bowling Green   24,000
23               Brown   20,000
24            Bucknell   13,100
25             Buffalo   29,013
26              Butler    5,647
27            Cal Poly   11,075
28          California   62,467
29   Central Conn. St.    5,500
..                 ...      ...
209               UCLA   91,136
210              UConn   40,000
211                UNI   16,324
212               UNLV   36,800
213          UT Martin    7,500
214               UTEP   52,000
215               Utah   45,807
216           Utah St.   25,100
217                VMI   10,000
218         Valparaiso    5,000
219         Vanderbilt   40,350
220          Villanova   12,000
221           Virginia   61,500
222      Virginia Tech   65,632
223             Wagner    3,300
224        Wake Forest   31,500
225         Washington   70,138
226     Washington St.   32,740
227          Weber St.   17,500
228      West Virginia   60,000
229      Western Caro.   13,742
230       Western Ill.   16,368
231        Western Ky.   22,113
232      Western Mich.   30,200
233     William & Mary   12,400
234          Wisconsin   80,321
235            Wofford   13,000
236            Wyoming   29,181
237               Yale   64,269
238     Youngstown St.   20,630

[239 rows x 2 columns]

Note:
This will take a long time. We are scrapping >239 pages. So be patient. Might take 15 mins or longer.

