2023-11-26 10:30:42 +00:00

76 lines
2.5 KiB
Python

import pandas as pd
from icalendar import Calendar, Event
from datetime import datetime, timedelta
import uuid
import os
def store_df_as_csv(df:pd.DataFrame)->None:
"""_summary_
Args:
df (pd.DataFrame): _description_
"""
df.to_csv('./fixtures.csv', index=False)
def compare_dfs(df:pd.DataFrame)->bool:
"""_summary_
Args:
df (pd.DataFrame): _description_
Returns:
bool: _description_
"""
df2 = pd.read_csv('./fixtures.csv')
return df.equals(df2)
def write_calendar(cal:Calendar)->None:
"""_summary_
Args:
cal (Calendar): _description_
"""
f = open(os.path.join('./', 'fixtures.ics'), 'wb')
f.write(cal.to_ical())
f.close()
def does_csv_exist()->bool:
"""_summary_
Returns:
bool: _description_
"""
return os.path.isfile('./fixtures.csv')
def create_ical_file(df, cal)->None:
for index, row in df.iterrows():
event = Event()
print(row['Date / Time'], row['Home Team'], row['Away Team.1'], row['Venue'])
start_date_time = datetime.strptime(row['Date / Time'], '%d/%m/%y %H:%M')
if start_date_time.hour == 8:
start_date_time = start_date_time + timedelta(hours=1, minutes=30)
arrival_time = start_date_time + timedelta(minutes=-30)
event.add('summary', str(row['Home Team']) + " vs " + str(row['Away Team.1']))
event.add('description', f'Arrive by - {arrival_time}')
event.add('dtstart', start_date_time)
event.add('dtend', start_date_time + timedelta(hours=2))
event.add('dtstamp', start_date_time)
event.add('uid', str(uuid.uuid4()))
event.add('location', str(row['Venue']))
cal.add_component(event)
write_calendar(cal)
cal = Calendar()
cal.add('prodid', 'Down Grange Pumas Fixtures')
cal.add('version', '2.0')
url = "https://fulltime.thefa.com/fixtures.html?selectedSeason=19010414&selectedFixtureGroupAgeGroup=11&selectedFixtureGroupKey=1_579285719&selectedDateCode=all&selectedClub=&selectedTeam=466317969&selectedRelatedFixtureOption=3&selectedFixtureDateStatus=&selectedFixtureStatus=&previousSelectedFixtureGroupAgeGroup=11&previousSelectedFixtureGroupKey=1_579285719&previousSelectedClub=&itemsPerPage=25"
df = pd.read_html(url)[0]
df.head()
exists = does_csv_exist()
if exists:
no_change = compare_dfs(df)
if not no_change:
store_df_as_csv(df)
create_ical_file(df, cal)
else:
store_df_as_csv(df)
create_ical_file(df, cal)