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This tool is used to scrape the team schedule from baseketballmonster.com





In [3]:



    


import requests
import bs4

import pandas as pd
from pandas import DataFrame



    











In [4]:



    


SCHED_URL = 'https://basketballmonster.com/ScheduleGrid.aspx'

with requests.Session() as s:
    r = s.get(SCHED_URL)    
   
    soup = bs4.BeautifulSoup(r.text, "html.parser")
  
    columns = soup.find('table', class_='datatable').find('tr', class_='gridHeaderTR sportGridColor')
    weeks = soup.find('table', class_='datatable').find_all('tr')



    











In [5]:



    


cols = [col.text.strip() for col in columns.find_all('td')]

# throw away rows 'gridGames', 'maxWeek', 'qualityGames' and 'gridHeaderTR' at top and bottom
weeks = weeks[4:-4] # throw away extra TR

# get value from table cell
weeks = [[col.text.strip() for col in week.find_all('td')] for week in weeks]



    











In [6]:



    


df = DataFrame(weeks, columns=cols).set_index('Week')
del(df[''])
df



    


















    
Out[6]:











  
    

      
      Date
      ATL
      BKN
      BOS
      CHA
      CHI
      CLE
      DAL
      DEN
      DET
      ...
      OKC
      ORL
      PHI
      PHO
      POR
      SAC
      SAS
      TOR
      UTA
      WAS
    

    

      Week
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      1
      10/16/2017
      3
      3
      3
      2
      2
      3
      3
      2
      3
      ...
      3
      3
      3
      3
      3
      3
      2
      2
      3
      2
    

    

      2
      10/23/2017
      4
      4
      3
      4
      3
      4
      4
      4
      4
      ...
      3
      3
      3
      3
      3
      3
      4
      3
      3
      4
    

    

      3
      10/30/2017
      3
      2
      4
      4
      3
      3
      4
      4
      3
      ...
      3
      4
      3
      4
      4
      3
      4
      4
      4
      3
    

    

      4
      11/6/2017
      3
      4
      4
      2
      3
      3
      3
      3
      3
      ...
      4
      3
      3
      4
      2
      3
      3
      3
      3
      3
    

    

      5
      11/13/2017
      3
      3
      3
      3
      3
      3
      3
      3
      3
      ...
      2
      3
      3
      4
      4
      4
      3
      4
      4
      4
    

    

      6
      11/20/2017
      4
      3
      4
      4
      4
      3
      3
      3
      2
      ...
      4
      4
      3
      3
      4
      3
      3
      3
      3
      3
    

    

      7
      11/27/2017
      2
      3
      3
      2
      3
      4
      3
      3
      4
      ...
      3
      4
      4
      3
      3
      4
      4
      2
      3
      3
    

    

      8
      12/4/2017
      4
      3
      4
      4
      4
      4
      4
      4
      4
      ...
      3
      4
      4
      4
      2
      3
      4
      3
      4
      4
    

    

      9
      12/11/2017
      3
      4
      4
      4
      3
      4
      3
      3
      4
      ...
      4
      3
      2
      3
      4
      3
      3
      4
      3
      4
    

    

      10
      12/18/2017
      4
      3
      4
      4
      4
      2
      4
      4
      2
      ...
      4
      3
      4
      4
      4
      3
      4
      3
      4
      3
    

    

      11
      12/25/2017
      3
      4
      4
      3
      4
      3
      4
      3
      3
      ...
      4
      3
      4
      3
      2
      4
      3
      3
      3
      4
    

    

      12
      1/1/2018
      3
      3
      3
      2
      4
      3
      3
      3
      3
      ...
      3
      3
      2
      4
      4
      2
      4
      3
      3
      3
    

    

      13
      1/8/2018
      3
      4
      1
      3
      3
      3
      3
      4
      3
      ...
      3
      3
      1
      2
      4
      4
      3
      4
      2
      3
    

    

      14
      1/15/2018
      3
      4
      3
      3
      3
      3
      2
      3
      4
      ...
      3
      3
      3
      2
      3
      3
      4
      4
      4
      3
    

    

      15
      1/22/2018
      4
      3
      3
      4
      4
      3
      4
      3
      3
      ...
      4
      2
      4
      4
      3
      4
      4
      3
      3
      3
    

    

      16
      1/29/2018
      4
      4
      4
      4
      2
      3
      3
      4
      3
      ...
      4
      3
      4
      4
      4
      3
      3
      4
      3
      3
    

    

      17
      2/5/2018
      4
      3
      4
      4
      3
      4
      4
      3
      4
      ...
      3
      4
      3
      3
      4
      3
      2
      3
      4
      4
    

    

      18
      2/12/2018
      2
      2
      1
      1
      2
      1
      1
      2
      2
      ...
      2
      2
      2
      2
      1
      2
      2
      2
      2
      1
    

    

      19
      2/19/2018
      1
      1
      2
      3
      2
      3
      2
      2
      2
      ...
      2
      2
      3
      2
      2
      2
      2
      1
      2
      3
    

    

      20
      2/26/2018
      4
      4
      3
      4
      3
      3
      4
      3
      4
      ...
      4
      4
      4
      4
      3
      5
      2
      4
      3
      4
    

    

      21
      3/5/2018
      3
      3
      3
      3
      4
      4
      3
      4
      3
      ...
      3
      4
      3
      3
      3
      3
      3
      4
      4
      3
    

    

      22
      3/12/2018
      3
      3
      3
      3
      3
      3
      3
      3
      3
      ...
      4
      3
      3
      3
      4
      4
      4
      4
      3
      3
    

    

      23
      3/19/2018
      4
      4
      3
      4
      4
      4
      3
      3
      4
      ...
      3
      3
      4
      3
      3
      3
      4
      4
      4
      3
    

    

      24
      3/26/2018
      3
      3
      3
      4
      4
      4
      4
      4
      4
      ...
      3
      3
      4
      4
      4
      4
      3
      2
      3
      4
    

    

      25
      4/2/2018
      4
      3
      4
      3
      3
      3
      4
      3
      3
      ...
      2
      4
      4
      3
      3
      2
      3
      4
      3
      3
    

    

      26
      4/9/2018
      1
      2
      2
      1
      2
      2
      1
      2
      2
      ...
      2
      2
      2
      1
      2
      2
      2
      2
      2
      2
    

  



26 rows × 31 columns



















In [7]:



    


df.to_json('../data/teamSched.json', orient='records', lines='TRUE')



    











In [8]:



    


df.to_csv('../data/teamSched.csv', index=False)

Content source: namiszh/fba

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