Canada Basketball Analytics

Web Scraping Canadians in the WNBA

We’d like to cover off how to pull a list of all currently active Canadians in the WNBA using Python and some web scraping. This list is from the combination of the Wikipedia page for foreign players in the WNBA and basketball reference.

Some prerequisites before continuing are to have Anaconda or some sort of way to develop Python code.


Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing that aims to simplify package management and deployment. To download and install please visit the Anaconda homepage.

The Code

Also available via GitHub.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Thu Oct  1 20:39:02 2020

@author: Andrew

import pandas as pd
import wikipedia as wp
import requests
from bs4 import BeautifulSoup
from datetime import datetime

filename = str( + '-Canadians_WNBA_Active.csv'

#Wikipedia link with foreign players
url = ''

#Get the html source
html ="List_of_foreign_WNBA_players").html().encode("UTF-8")
df = pd.read_html(html)[1]

#Find only players which have a Canadian nationality 
df_Canada = df.loc[df['Nationality[A]']=='Canada']

#Find only active players
df_Canada_Active = df_Canada[df_Canada['Player'].str.contains('*',regex=False)==True]

df_Canada_Active = df_Canada_Active['Player'].str.replace('*','',regex=False)

#Blank dataframe to capture career stats
df_player_careers_total = pd.DataFrame(columns=['Year','Tm','Age','G','GS', 'MP', 'FG', 'FGA','FG%','3P', 
                                 '3PA','3P%','2P', '2PA','2P%','FT', 'FTA','FT%','ORB',

#Loop through all the players we just grabbed from Wikipedia
for i in df_Canada_Active: 
    strUrl = ''
    strPlayer = i
    #Add to url first initial of lastname
    strUrl = strUrl + strPlayer.split()[1][0].lower() + '%2F'
    #First five characters of last name
    strUrl = strUrl + strPlayer.split()[1][:5].lower() 
    #First two characters of first name
    strUrl = strUrl + strPlayer.split()[0][:2].lower() 
    #Plus 01w.html
    strUrl = strUrl + '01w.html&div=div_per_game0'
    page = requests.get(strUrl)
    soup = BeautifulSoup(page.content, 'html.parser')
    table = soup.find('table')
    df = pd.read_html(str(table))[0]
    df = df[df.Year != 'Career']
    df['Player'] = strPlayer

    df['Year'] = df['Year'].str[:4]
    df_player_careers_total = df_player_careers_total.append(df)