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191
src/epl.py
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191
src/epl.py
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from bs4 import BeautifulSoup
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import pandas as pd
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import re
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from datetime import date
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from lxml import etree
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import pandas as pd
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import re
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from pathlib import Path
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from datetime import datetime, date, time, timedelta, timezone
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from zoneinfo import ZoneInfo # Python 3.9+
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from great_tables import GT, md
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import os
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mlb_sched_url = os.getenv("EPL_SCHED_URL")
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import requests
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import cloudscraper
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driver = 'chrome'
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import pandas as pd
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from bs4 import BeautifulSoup
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import pytz
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# Load the HTML file
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headers = {
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'Access-Control-Allow-Origin': '*',
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'Access-Control-Allow-Methods': 'GET',
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'Access-Control-Allow-Headers': 'Content-Type',
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'Access-Control-Max-Age': '3600',
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'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'
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}
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url = f'https://fbref.com/en/comps/9/schedule/Premier-League-Scores-and-Fixtures'
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# Fetch the webpage content
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scraper = cloudscraper.create_scraper()
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response = scraper.get(url)
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# Parse the HTML content using BeautifulSoup
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soup = BeautifulSoup(response.content, 'html.parser')
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# Extract tables
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tables = pd.read_html(response.text)
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soccer_schedule = tables[0]
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# --- Clean up Date/Time ---
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# Combine Date + Time
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soccer_schedule["DateTime_UK"] = pd.to_datetime(
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soccer_schedule["Date"].astype(str) + " " + soccer_schedule["Time"].astype(str),
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errors="coerce"
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)
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# Localize to UK time
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uk = pytz.timezone("Europe/London")
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ny = pytz.timezone("America/New_York")
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soccer_schedule["DateTime_UK"] = soccer_schedule["DateTime_UK"].dt.tz_localize(uk, ambiguous="NaT", nonexistent="NaT")
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# Convert to New York time
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soccer_schedule["DateTime_NY"] = soccer_schedule["DateTime_UK"].dt.tz_convert(ny)
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# Format for display in am/pm style
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soccer_schedule["NY_Time"] = soccer_schedule["DateTime_NY"].dt.strftime("%I:%M %p")
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# Drop leading zeros from hour (optional)
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soccer_schedule["NY_Time"] = soccer_schedule["NY_Time"].str.lstrip("0")
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# Show final
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soccer_schedule = soccer_schedule.replace("Nott'ham Forest","Nottingham Forest")
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import pandas as pd
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import xml.etree.ElementTree as ET
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from datetime import date
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# Fix team naming
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soccer_schedule = soccer_schedule.replace("Nott'ham Forest", "Nottingham Forest")
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soccer_schedule = soccer_schedule.replace("Newcastle Utd", "Newcastle United")
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soccer_schedule = soccer_schedule.replace("Wolves", "Wolverhampton Wanderers")
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# Filter today's schedule & keep merge_key
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df_today = (
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soccer_schedule[
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pd.to_datetime(soccer_schedule.Date) == pd.to_datetime(date.today())
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]
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.reset_index()
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.rename({'index': 'merge_key'}, axis='columns')
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)
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df_today = df_today[['Date', 'NY_Time', 'Away', 'Home', 'Wk', 'merge_key']]
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if len(df_today)>0:
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# Load XML
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file_path = "data/epg.xml" # replace with actual path
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tree = ET.parse(file_path)
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root = tree.getroot()
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data = []
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# Iterate schedule rows
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for _, row in df_today.iterrows():
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home = str(row["Home"]).strip()
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away = str(row["Away"]).strip()
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for prog in root.findall("programme"):
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title = prog.find("title").text if prog.find("title") is not None else ""
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desc = prog.find("desc").text if prog.find("desc") is not None else ""
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# Keep only if Premier League and both team names appear
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if "Premier League" in title and home in title and away in title:
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data.append({
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"merge_key": row["merge_key"], # ✅ carry over merge_key
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"Schedule_Home": home,
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"Schedule_Away": away,
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"Start": prog.attrib.get("start"),
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"Stop": prog.attrib.get("stop"),
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"Channel": prog.attrib.get("channel"),
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"Title": title.strip() })
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# Create DataFrame
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df_matches = pd.DataFrame(data)
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# Convert start/stop to datetime
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df_matches["Start"] = pd.to_datetime(df_matches["Start"].str[:14], format="%Y%m%d%H%M%S", errors="coerce", utc=True)
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df_matches["Stop"] = pd.to_datetime(df_matches["Stop"].str[:14], format="%Y%m%d%H%M%S", errors="coerce", utc=True)
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df = pd.merge(
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df_today,
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df_matches[['merge_key','Schedule_Home','Schedule_Away','Channel']].drop_duplicates(),
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how='left',
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on='merge_key'
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)
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####
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for game in df['merge_key'].unique():
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temp = df[df['merge_key']==game].drop_duplicates().reset_index(drop=True)
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temp = temp.rename({'NY_Time':'Time'},axis='columns')
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temp['Wk'] = temp.Wk.astype('int')
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temp2 = temp[['Channel']].drop_duplicates().reset_index(drop=True)
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# Split once on "." into two columns
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split_cols = temp2['Channel'].str.split('.', n=1, expand=True)
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temp2['Channel'] = split_cols[0] # part before "."
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temp2['Country'] = split_cols[1].str.upper().fillna("") # part after ".", or "" if missing
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# Reorder
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temp2 = temp2[['Country', 'Channel']]
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# Regex pattern: 'FOX' + 4 uppercase letters (total length 7, all caps)
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temp2['Country'] = temp2['Country'].replace('US2','US')
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pattern = re.compile(r"^NBC[A-Z]{4}$")
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# Replace matching channels with 'FOX'
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temp2["Channel"] = temp2["Channel"].apply(lambda x: "NBC" if pattern.match(x) else x)
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pattern = re.compile(r"^NBC[A-Z]{5}$")
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# Replace matching channels with 'FOX'
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temp2["Channel"] = temp2["Channel"].apply(lambda x: "NBC" if pattern.match(x) else x)
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pattern = re.compile(r"^NBC[A-Z]{6}$")
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# Replace matching channels with 'FOX'
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temp2["Channel"] = temp2["Channel"].apply(lambda x: "NBC" if pattern.match(x) else x)
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pattern = re.compile(r"^NBC[A-Z]{3}$")
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# Replace matching channels with 'FOX'
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temp2["Channel"] = temp2["Channel"].apply(lambda x: "NBC" if pattern.match(x) else x)
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temp2 = temp2.drop_duplicates()
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###
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game_table = (
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GT(temp2.drop_duplicates())
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.tab_header(
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title=f"{temp['Away'][0]} @ {temp['Home'][0]}",
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subtitle=f"Matchweek {temp.Wk[0]} {temp['Time'][0]} {str(temp['Date'][0]).split(' ')[0]}",
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).tab_source_note(md(' '))
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)
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game_table_image_path = 'data/epl.png'
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game_table.save(game_table_image_path,window_size=(1000, 1000),web_driver=driver)
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with open(game_table_image_path, 'rb') as image_file:
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# Prepare the payload with the file
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payload = {
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'file': image_file
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}
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# Send the POST request to the webhook
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response = requests.post(epl_sched_url, files=payload)
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os.remove(game_table_image_path)
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