gabecano4308

# Creating a list of dictionaries to then convert into a Pandas Dataframe
wiz_stats = []

for row in wiz_per_game.find_all('tr')[1:]:  # Excluding the first 'tr', since that's the table's title head

    player = {}
    player['Name'] = row.find('a').text.strip()
    player['Age'] = row.find('td', {'data-stat' : 'age'}).text
    player['Min PG'] = row.find('td', {'data-stat' : 'mp_per_g'}).text
    player['Field Goal %'] = row.find('td', {'data-stat' : 'fg_pct'}).text

gabecano4308

wiz_url = (f'https://www.basketball-reference.com/teams/WAS/2021.html')

# The requests library can send a GET request to the wiz_url
wiz_res = requests.get(wiz_url)

gabecano4308

# Creating a list of url endings for each NBA team's webpage

team_list = []
for i in select_team.find_all('option')[1:31]:
    
    name = i.get('value')
    team_list.append(name)
    
team_list[0:3]

gabecano4308

base_url = 'https://www.basketball-reference.com/'

# The requests library can send a GET request to the base_url
res_base = requests.get(base_url)

# BeautifulSoup library parses the content of an HTML document, in this case res_base
base_soup = BeautifulSoup(res_base.content, 'lxml')

# BeautifulSoup's .find() method searches for a tag and specified attributes, returning the first match 
select_team = base_soup.find(name = 'div', attrs = {'id' : 'teams'})

gabecano4308

import pandas as pd
import requests
from bs4 import BeautifulSoup
import re