Strike - NBA Source Code

# strike.place

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/four-factors',

'https://www.nba.com/stats/teams/isolation?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/transition?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/roll-man?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/playtype-post-up?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/spot-up?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/hand-off?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/cut?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/off-screen?TypeGrouping=offensive',

'https://www.nba.com/stats/teams/putbacks?TypeGrouping=offensive'

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/isolation?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/transition?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/roll-man?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/playtype-post-up?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/spot-up?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/hand-off?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/cut?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/off-screen?TypeGrouping=defensive',

'https://www.nba.com/stats/teams/putbacks?TypeGrouping=defensive'

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/drives',

'https://www.nba.com/stats/teams/defensive-impact',

'https://www.nba.com/stats/teams/catch-shoot',

'https://www.nba.com/stats/teams/passing',

'https://www.nba.com/stats/teams/touches',

'https://www.nba.com/stats/teams/pullup',

'https://www.nba.com/stats/teams/rebounding',

'https://www.nba.com/stats/teams/offensive-rebounding',

'https://www.nba.com/stats/teams/defensive-rebounding',

'https://www.nba.com/stats/teams/shooting-efficiency'

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/speed-distance',

'https://www.nba.com/stats/teams/elbow-touch',

'https://www.nba.com/stats/teams/tracking-post-ups',

'https://www.nba.com/stats/teams/paint-touch',

'https://www.nba.com/stats/teams/boxscores-traditional',

'https://www.nba.com/stats/teams/boxscores-advanced',

'https://www.nba.com/stats/teams/boxscores-four-factors',

'https://www.nba.com/stats/teams/opponent-shooting',

'https://www.nba.com/stats/teams/opponent-shots-general'

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=24-22',

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=22-18+Very+Early',

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=18-15+Early',

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=15-7+Average',

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=7-4+Late'

'https://www.nba.com/stats/teams/opponent-shots-shotclock?ShotClockRange=4-0+Very+Late'

'https://www.nba.com/stats/teams/opponent-shots-dribbles?DribbleRange=0+Dribbles',

'https://www.nba.com/stats/teams/opponent-shots-dribbles?DribbleRange=1+Dribbles',

'https://www.nba.com/stats/teams/opponent-shots-dribbles?DribbleRange=2+Dribbles',

'https://www.nba.com/stats/teams/opponent-shots-dribbles?DribbleRange=3-6+Dribbles',

'https://www.nba.com/stats/teams/opponent-shots-dribbles?DribbleRange=7%2B+Dribbles',

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/opponent-shots-closest-defender?CloseDefDistRange=0-2+Feet+-+Very+Tight',

'https://www.nba.com/stats/teams/opponent-shots-closest-defender?CloseDefDistRange=2-4+Feet+-+Tight',

'https://www.nba.com/stats/teams/opponent-shots-closest-defender?CloseDefDistRange=4-6+Feet+-+Open',

'https://www.nba.com/stats/teams/opponent-shots-closest-defender?CloseDefDistRange=6%2B+Feet+-+Wide+Open',

'https://www.nba.com/stats/teams/shooting',

'https://www.nba.com/stats/teams/shots-general',

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=24-22',

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=22-18+Very+Early',

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=18-15+Early',

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=15-7+Average',

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=7-4+Late'

'https://www.nba.com/stats/teams/shots-shotclock?ShotClockRange=4-0+Very+Late'

'https://www.nba.com/stats/teams/shots-dribbles?DribbleRange=0+Dribbles',

'https://www.nba.com/stats/teams/shots-dribbles?DribbleRange=1+Dribbles',

'https://www.nba.com/stats/teams/shots-dribbles?DribbleRange=2+Dribbles',

'https://www.nba.com/stats/teams/shots-dribbles?DribbleRange=3-6+Dribbles',

'https://www.nba.com/stats/teams/shots-dribbles?DribbleRange=7%2B+Dribbles'

]

# Dictionary to hold data frames

data_frames = {}

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

data_frames[url] = df

# Now you have a dictionary where each URL is a key and the corresponding DataFrame is the value

from scrapingant_client import ScrapingAntClient

from bs4 import BeautifulSoup

import pandas as pd

import os

token = 'get-ur-own-4-free-at-scrapingant.com'

client = ScrapingAntClient(token=token)

urls = [

'https://www.nba.com/stats/teams/shots-closest-defender?CloseDefDistRange=0-2+Feet+-+Very+Tight',

'https://www.nba.com/stats/teams/shots-closest-defender?CloseDefDistRange=2-4+Feet+-+Tight',

'https://www.nba.com/stats/teams/shots-closest-defender?CloseDefDistRange=4-6+Feet+-+Open',

'https://www.nba.com/stats/teams/shots-closest-defender?CloseDefDistRange=6%2B+Feet+-+Wide+Open',

'https://www.nba.com/stats/lineups/four-factors?slug=four-factors&GroupQuantity=5',

'https://www.nba.com/stats/lineups/four-factors?slug=four-factors&GroupQuantity=4',

'https://www.nba.com/stats/lineups/four-factors?slug=four-factors&GroupQuantity=3',

'https://www.nba.com/stats/lineups/four-factors?slug=four-factors&GroupQuantity=2',

]

# Create directory if it doesn't exist

directory = 'users/lougoat/desktop/strike/nbabase/stats/'

if not os.path.exists(directory):

os.makedirs(directory)

for url in urls:

response = client.general_request(url)

content = response.content

soup = BeautifulSoup(content, 'html.parser')

table = soup.find('table', {'class': 'Crom_table__p1iZz'})

df = pd.read_html(str(table))[0]

# Create a valid filename from the URL

filename = url.split('/')[-1].replace('?', '&') + '.csv'

filepath = os.path.join(directory, filename)

# Save to CSV

df.to_csv(filepath, index=False)

### NBA Stake

import re

import csv

from bs4 import BeautifulSoup

from scrapingant_client import ScrapingAntClient

# Add your ScrapingAnt API Token

token = 'geturown'

# Initialize the client

client = ScrapingAntClient(token=token)

# The URL you want to scrape

url = 'https://stake.com/sports/basketball/usa/nba'

# Use the client to get the content of the page

result = client.general_request(

url,

headers={

"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36",

proxy_country='BR', # Change this as needed

)

# Your HTML content

content = result.content

# We'll use regex to match the format of the URLs

pattern = re.compile(r'https://stake\.com/sports/basketball/usa/nba/\d{8}-[\w-]+-[\w-]+-[\w-]+-[\w-]+')

matches = re.findall(pattern, content)

# Open the CSV file in write mode

with open('/Users/LouGoat/Desktop/stake.csv', 'w', newline='') as file: # change path

writer = csv.writer(file)

# This will give us a list of all matching URLs in the content

for match in matches:

# If we need just the labels after 'https://stake.com/sports/baseball/usa/mlb/', we can split by '/' and print them

labels = match.split('/')[-1]

# Write the labels to the CSV file

writer.writerow([labels])

import os

import requests

# Prepare CSV file to write

results_path = os.path.join('/Users/LouGoat/Desktop/', 'results.csv')

with open(results_path, 'w', newline='') as file:

writer = csv.writer(file)

writer.writerow(["URL", "Aria-label", "Odds"])

# This will give us a list of all matching URLs in the content

for url in matches:

# Use the client to get the content of the page

result = client.general_request(

url,

headers={

"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36",

proxy_country='BR', # Change this as needed

)

# Your HTML content

content = result.content

# Initialize BeautifulSoup with the content you got

soup = BeautifulSoup(content, 'html.parser')

# Find all buttons and print aria-label

buttons = soup.find_all('button')

for button in buttons:

aria_label = button.get('aria-label')

odds = None

odds_div = button.find('div', {'class': 'odds'})

if odds_div:

odds_span = odds_div.find('span', {'class': 'weight-bold'})

if odds_span:

odds = odds_span.text.strip()

if aria_label is not None and odds is not None:

writer.writerow([url, aria_label, odds]) # write data to CSV file

import gspread

from oauth2client.service_account import ServiceAccountCredentials

import csv

import time

# Use the JSON key you downloaded when you set up the Google Sheets API

scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']

creds = ServiceAccountCredentials.from_json_keyfile_name('/users/lougoat/desktop/ontobb-efe3bad476ab.json', scope)

client = gspread.authorize(creds)

# Find the workbook by name and open the first sheet

# Make sure you use the right name here.

sheet = client.open('OntoBasketball').worksheet('Stake')

# Clear all existing content from the sheet

sheet.clear()

# Read the CSV file

with open('/users/lougoat/desktop/results.csv', 'r') as file:

csv_data = csv.reader(file)

for row in csv_data:

sheet.append_row(row) # append the row data to Google Sheet

time.sleep(1) # Add a delay of 1 second

### Webscraping to Local Database

# East and West Standings

import pandas as pd

from datetime import datetime

import requests

def fetch_and_save_standings(url):

try:

# Fetch data from the web page

dfs = pd.read_html(url)

# In this example, I assume the first table is for Eastern Conference

# and the second table is for Western Conference.

# You might need to adjust these indices based on the actual web page.

df_east = dfs[0]

df_west = dfs[1]

# Remove asterisks from team names in both DataFrames

df_east = df_east.applymap(lambda x: x.replace('*', '') if isinstance(x, str) else x)

df_west = df_west.applymap(lambda x: x.replace('*', '') if isinstance(x, str) else x)

# Save as CSV

current_date = datetime.now().strftime("%m-%d")

east_csv_path = f"/users/lougoat/desktop/strike/database/BasketballReference/east_standings_{current_date}.csv"

west_csv_path = f"/users/lougoat/desktop/strike/database/BasketballReference/west_standings_{current_date}.csv"

df_east.to_csv(east_csv_path, index=False)

df_west.to_csv(west_csv_path, index=False)

print(f"DataFrames created and CSV files saved as {east_csv_path} and {west_csv_path}.")

except Exception as e:

print(f"An error occurred: {e}")

if __name__ == '__main__':

url = 'https://www.basketball-reference.com/leagues/NBA_2024_standings.html'

fetch_and_save_standings(url)

# BR Per Possession

from urllib.request import urlopen

from bs4 import BeautifulSoup

import pandas as pd

from datetime import datetime

# Define the URL for 2023 statistics per possession

url = "https://www.basketball-reference.com/leagues/NBA_2024_per_poss.html"

# Fetch the HTML data

html = urlopen(url)

# Create a BeautifulSoup object

soup = BeautifulSoup(html, features="lxml")

# Find the column headers and store them in a list

headers = [header.getText() for header in soup.findAll('th', limit=32)]

headers = headers[1:]

# Remove empty columns

if '' in headers:

headers.remove('')

# Get the rows from the table

rows = soup.findAll('tr')[1:]

player_stats = [[td.getText() for td in rows[i].findAll('td')] for i in range(len(rows))]

# Remove empty rows

player_stats = [e for e in player_stats if e != []]

# Remove the extra column from each row (assuming it's at index 28)

player_stats = [row[:28] + row[29:] for row in player_stats]

# Create the DataFrame

nba_stats_2023 = pd.DataFrame(player_stats, columns=headers)

# Team ID Mapping

team_mapping = {

'ATL': 1, 'BOS': 2, 'BRK': 3, 'CHA': 4, 'CHI': 5,

'CLE': 6, 'DAL': 7, 'DEN': 8, 'DET': 9, 'GSW': 10,

'HOU': 11, 'IND': 12, 'LAC': 13, 'LAL': 14, 'MEM': 15,

'MIA': 16, 'MIL': 17, 'MIN': 18, 'NOP': 19, 'NYK': 20,

'OKC': 21, 'ORL': 22, 'PHI': 23, 'PHO': 24, 'POR': 25,

'SAC': 26, 'SAS': 27, 'TOR': 28, 'UTA': 29, 'WAS': 30, 'TOT': 31,

}

nba_stats_2023['Team_ID'] = nba_stats_2023['Tm'].map(team_mapping)

# Unique ID

nba_stats_2023['Unique_ID'] = nba_stats_2023.apply(lambda row: f"{row['Player']}_{row['Pos']}_{row['Age']}_{row['Tm']}", axis=1)

nba_stats_2023['Unique_ID'] = nba_stats_2023['Unique_ID'].astype('category').cat.codes.apply(lambda x: str(x).zfill(4))

# Reorder columns

nba_stats_2023 = nba_stats_2023[['Unique_ID', 'Team_ID'] + [col for col in nba_stats_2023.columns if col not in ['Unique_ID', 'Team_ID']]]

# Data manipulation

columns_to_divide = ['FG', 'FGA', '3P', '3PA', '2P', '2PA', 'FT', 'FTA', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS']

nba_stats_2023[columns_to_divide] = nba_stats_2023[columns_to_divide].astype(float) / 100

nba_stats_2023['True Shooting'] = nba_stats_2023['PTS'] / (2 * (nba_stats_2023['FGA'] + 0.44 * nba_stats_2023['FTA']))

# Get the current date

current_date = datetime.now().strftime('%m-%d')

# Save CSV

file_path = f'/users/lougoat/desktop/strike/database/BasketballReference/br_{current_date}.csv'

try:

nba_stats_2023.to_csv(file_path, index=False)

print(f"DataFrame created and CSV file saved as {file_path}.")

except Exception as e:

print(f"An error occurred: {e}")

### Inpredictable NBA Rankings

import pandas as pd

import requests

from bs4 import BeautifulSoup

# The URL you want to scrape

url = "https://stats.inpredictable.com/rankings/nba.php"

# Fetch the page content using the requests library

response = requests.get(url)

# Parse the HTML content using BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

# Find the table you're interested in scraping

table = soup.find('table') # Adjust this line if there are multiple tables

# Convert the table to a DataFrame

df = pd.read_html(str(table))[0]

# Save the DataFrame as a CSV file

save_path = "/users/lougoat/desktop/strike/database/Inpredictable/nba_rankings.csv"

df.to_csv(save_path, index=False)

print(f"DataFrame created and CSV file saved as {save_path}")

### Inpredictable Team Poss

import pandas as pd

import requests

from bs4 import BeautifulSoup

# The URL you want to scrape

url = "https://stats.inpredictable.com/nba/ssnTeamPoss.php?season=2023&po=0&frdt=2023-10-24&todt=2024-04-16&view=off"

# Fetch the page content using the requests library

response = requests.get(url)

# Parse the HTML content using BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

# Find the table you're interested in scraping

table = soup.find('table') # Adjust this line if there are multiple tables

# Convert the table to a DataFrame

df = pd.read_html(str(table))[0]

# Save the DataFrame as a CSV file

save_path = "/users/lougoat/desktop/strike/database/Inpredictable/nba_perpossesion.csv"

df.to_csv(save_path, index=False)

print(f"DataFrame created and CSV file saved as {save_path}")

### Inpredictable On/Off

import pandas as pd

import requests

from bs4 import BeautifulSoup

# The URL you want to scrape

url = "https://stats.inpredictable.com/nba/onoff.php?season=2023&pos=ALL&team=BKN&po=0&frdt=2023-10-24&todt=2024-06-12&mpos=100&rate=tot&sort=son_wpa&order=DESC"

# Fetch the page content using the requests library

response = requests.get(url)

# Parse the HTML content using BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

# Find the table you're interested in scraping

table = soup.find('table') # Adjust this line if there are multiple tables

# Convert the table to a DataFrame

df = pd.read_html(str(table))[0]

# Save the DataFrame as a CSV file

save_path = "/users/lougoat/desktop/strike/database/Inpredictable/OnOff/BKN.csv"

df.to_csv(save_path, index=False)

print(f"DataFrame created and CSV file saved as {save_path}")

### Inpredictable Player

import pandas as pd

import requests

from bs4 import BeautifulSoup

# # The URL you want to scrape

# url = "https://stats.inpredictable.com/nba/ssnPlayer.php"

# # Fetch the page content using the requests library

# response = requests.get(url)

# # Parse the HTML content using BeautifulSoup

# soup = BeautifulSoup(response.content, 'html.parser')

# # Find the table you're interested in scraping

# table = soup.find('table') # Adjust this line if there are multiple tables

# # Convert the table to a DataFrame

# df = pd.read_html(str(table))[0]

# # Save the DataFrame as a CSV file

# save_path = "/users/lougoat/desktop/strike/database/Inpredictable/nba_wp.csv"

# df.to_csv(save_path, index=False)

# print(f"DataFrame created and CSV file saved as {save_path}")

import requests

from bs4 import BeautifulSoup

import pandas as pd

# Base URL and query parameters

base_url = "https://stats.inpredictable.com/nba/ssnPlayer.php"

query_params = {

'season': '2023',

'team': 'ALL',

'pos': 'ALL',

'po': '0',

'frdt': '2023-10-24',

'todt': '2023-11-04',

'rate': 'tot',

'sort': 'sWPA',

'order': 'DESC'

}

# Iterate from grp 1 to grp 11

for grp in range(1, 12):

# Update the 'grp' parameter

query_params['grp'] = grp

# Send a GET request

response = requests.get(base_url, params=query_params)

# Check if the request was successful

if response.status_code == 200:

# Parse the HTML content using BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

# Find the table you're interested in scraping

table = soup.find('table') # Adjust this if there are multiple tables

# Convert the table to a DataFrame

df = pd.read_html(str(table))[0]

# Define the save path

save_path = f"/users/lougoat/desktop/strike/database/Inpredictable/nba_wp_grp{grp}.csv"

# Save the DataFrame as a CSV file

df.to_csv(save_path, index=False)

print(f"DataFrame for grp {grp} created and CSV file saved as {save_path}")

else:

print(f"Failed to retrieve data for grp {grp}. Status code: {response.status_code}")

### NBAStuffer Player Stats

import requests

from bs4 import BeautifulSoup

import csv

import os

# Define the URL

url = 'https://www.nbastuffer.com/2023-2024-nba-player-stats/'

# Fetch the page content

response = requests.get(url)

# Check if the request was successful

if response.status_code == 200:

# Parse the HTML content

soup = BeautifulSoup(response.text, 'html.parser')

# Locate tables in the HTML (assuming class or id attributes identify them)

tables = soup.find_all('table') # Replace with actual class or ID if needed

# Create the output directory if it doesn't exist

output_dir = '/Users/lougoat/Desktop/Strike/Database/NBAStuffer'

os.makedirs(output_dir, exist_ok=True)

# Open the CSV file for writing

with open(os.path.join(output_dir, 'AdvancedStats.csv'), 'w', newline='') as csvfile:

csvwriter = csv.writer(csvfile)

# Assuming the first table is what you want

table = tables[0]

# Write each row to the CSV

for row in table.find_all('tr'):

columns = row.find_all(['td', 'th']) # Both table data and header

column_texts = [col.text for col in columns]

csvwriter.writerow(column_texts)

print(f"Data saved to {os.path.join(output_dir, 'AdvancedStats.csv')}")

else:

print("Failed to retrieve the webpage.")

### NBAStuffer Rest Day Analysis

import requests

from bs4 import BeautifulSoup

import csv

import os

# Define the URL

url = 'https://www.nbastuffer.com/2023-2024-nba-schedule-rest-days-analysis/'

# Fetch the page content

response = requests.get(url)

# Check if the request was successful

if response.status_code == 200:

# Parse the HTML content

soup = BeautifulSoup(response.text, 'html.parser')

# Locate tables in the HTML (assuming class or id attributes identify them)

tables = soup.find_all('table') # Replace with actual class or ID if needed

# Create the output directory if it doesn't exist

output_dir = '/Users/lougoat/Desktop/Strike/Database/NBAStuffer'

os.makedirs(output_dir, exist_ok=True)

# Open the CSV file for writing

with open(os.path.join(output_dir, 'RestStats.csv'), 'w', newline='') as csvfile:

csvwriter = csv.writer(csvfile)

# Assuming the first table is what you want

table = tables[0]

# Write each row to the CSV

for row in table.find_all('tr'):

columns = row.find_all(['td', 'th']) # Both table data and header

column_texts = [col.text for col in columns]

csvwriter.writerow(column_texts)

print(f"Data saved to {os.path.join(output_dir, 'AdvancedStats.csv')}")

else:

print("Failed to retrieve the webpage.")

import gspread

import csv

import datetime

import os

import time

from oauth2client.service_account import ServiceAccountCredentials

import pandas as pd

import os

# Define the path where your CSV files are stored

folder_path = '/Users/Lougoat/desktop/Strike/Database/Inpredictable'

# Generate the list of file names based on the pattern grp 1 -11 al

# This is just an example pattern, adjust it to match your actual file names

file_names = [f'nba_wp_grp{i}.csv' for i in range(1, 12)]

# Initialize an empty list to store DataFrames

df_list = []

# Loop through each file name and read the CSV into a DataFrame

for file_name in file_names:

file_path = os.path.join(folder_path, file_name)

if os.path.exists(file_path):

df = pd.read_csv(file_path)

df_list.append(df)

else:

print(f"File not found: {file_path}")

# Concatenate all the DataFrames into a single DataFrame

combined_df = pd.concat(df_list, ignore_index=True)

# Define the path for the combined CSV file

output_file = '/Users/Lougoat/desktop/Strike/Database/Inpredictable/nba_wp.csv'

# Save the combined DataFrame to a CSV file

combined_df.to_csv(output_file, index=False)

print(f"Combined CSV file created at {output_file}")

# Initialize Google Sheets API client

scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']

creds = ServiceAccountCredentials.from_json_keyfile_name('/users/lougoat/desktop/ontobb-efe3bad476ab.json', scope)

client = gspread.authorize(creds)

def archive_sheets():

# Get yesterday's date for archive directory

yesterday = (datetime.datetime.today() - datetime.timedelta(days=1)).strftime('%m-%d') # Format: MM-DD

# Open the Google Sheet

spreadsheet = client.open('OntoBasketball')

# Iterate through each worksheet in the spreadsheet

for sheet in spreadsheet.worksheets():

sheet_name = sheet.title

# Create the archive directory if it doesn't exist

archive_dir = f'/users/lougoat/desktop/strike/database/archive/{yesterday}/{sheet_name}'

os.makedirs(archive_dir, exist_ok=True)

# Archive current sheet content

rows = sheet.get_all_values()

archive_file_path = os.path.join(archive_dir, f'{sheet_name}.csv')

with open(archive_file_path, 'w', newline='', encoding='utf-8') as csvfile:

csvwriter = csv.writer(csvfile)

csvwriter.writerows(rows)

def update_sheet(csv_path, sheet_name):

# Open the Google Sheet

sheet = client.open('OntoBasketball').worksheet(sheet_name)

# Clear existing content in the sheet

sheet.clear()

# Read CSV file

with open(csv_path, newline='', encoding='utf-8') as csvfile:

csvreader = csv.reader(csvfile)

# Collect rows to be added

rows_to_add = [row for row in csvreader]

# Add a time delay to avoid hitting API rate limits

time.sleep(2)

# Append all rows at once to Google Sheet

sheet.append_rows(rows_to_add)

# Archive all sheets

archive_sheets()

# Today's date for file paths

today = datetime.datetime.today().strftime('%m-%d') # Format: MM-DD

# Define file paths and sheet names

files_and_sheets = [

(f'/Users/Lougoat/desktop/Strike/Database/BasketballReference/br_{today}.csv', 'BasketballReference'),

(f'/Users/Lougoat/desktop/Strike/Database/BasketballReference/DailySchedule/{today[:2]}/{today[3:]}.csv', 'DailySchedule'),

(f'/Users/Lougoat/desktop/Strike/Database/BasketballReference/east_standings_{today}.csv', 'East'),

(f'/Users/Lougoat/desktop/Strike/Database/BasketballReference/west_standings_{today}.csv', 'West'),

('/Users/Lougoat/desktop/Strike/Database/Inpredictable/nba_perpossesion.csv', 'PerPossession'),

('/Users/Lougoat/desktop/Strike/Database/Inpredictable/nba_rankings.csv', 'Rankings'),

('/Users/Lougoat/desktop/Strike/Database/Inpredictable/nba_wp.csv', 'WP'),

('/Users/Lougoat/desktop/Strike/Database/NBAStuffer/AdvancedStats.csv', 'AdvancedStats'),

('/Users/Lougoat/desktop/Strike/Database/NBAStuffer/RestStats.csv', 'RestStats')

]

# Process each file

for csv_path, sheet_name in files_and_sheets:

update_sheet(csv_path, sheet_name)