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6 changed files with 212 additions and 40 deletions
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2
.env-template
View file

@ -2,5 +2,3 @@ ESPN_S2 = 'S2 key here'
SWID = 'SWID key here'
LEAGUE_ID = 'League ID here'
FETCH_LEAGUE = 'true'
SPREADSHEET_ID = 'Google Sheets ID here'
WORKSHEET_NAME = 'Worksheet name here'

2
README.md
View file

@ -24,7 +24,7 @@ Copy `.env-template` as `.env` file and enter the following items:
- `ESPN_S2`: S2 key from ESPN
- `SWID`: SWID key from ESPN
- `LEAGUE_ID`: League ID for your Fantasy League from ESPN
- `FETCH_LEAGUE`: Generally, leave as `true` unless you know what you're doing
- `FETCH_LEAGE`: Generally, leave as `true` unless you know what you're doing
For more information on how to retrieve these details, head on over to GitHub and [review this discussion](https://github.com/cwendt94/espn-api/discussions/150).

45
app.py
View file

@ -1,56 +1,67 @@
from config import cfg
from config import Config as cfg
from datetime import datetime
from espn_api.football import League
from gspread_dataframe import set_with_dataframe
from huey import crontab, RedisHuey
from service import gc
from tabulate import tabulate
import pandas as pd
def aggregate_positional_points(lineup: list, position: str) -> int:
huey = RedisHuey('report-generator', host='redis')
def check_int(s):
if s is None:
return s
if s[0] in ('-', '+'):
return s[1:].isdigit()
return s.isdigit()
def extract_positional_data(lineup: list, position: str) -> int:
response = 0
for player in lineup:
if player.lineupSlot == position:
response += player.points
response = response + player.points
return response
def extract_weekly_box_scores(league: League) -> list:
def extract_matchup_box_scores(league: League) -> dict:
result = []
for week in range(1, league.current_week + 1):
matchups = league.box_scores(week=week)
for matchup in matchups:
result.append({
'WEEK #': week,
'AWAY TEAM': matchup.away_team.team_name, # pyright: ignore[reportAttributeAccessIssue]
'AWAY TEAM': matchup.away_team.team_name,
'AWAY TEAM SCORE': matchup.away_score,
'AWAY TEAM KICKER': aggregate_positional_points(matchup.away_lineup, 'K'),
'AWAY TEAM BENCH': aggregate_positional_points(matchup.away_lineup, 'BE'),
'HOME TEAM': matchup.home_team.team_name, # pyright: ignore[reportAttributeAccessIssue]
'AWAY TEAM KICKER': extract_positional_data(matchup.away_lineup, 'K'),
'AWAY TEAM BENCH': extract_positional_data(matchup.away_lineup, 'BE'),
'HOME TEAM': matchup.home_team.team_name,
'HOME TEAM SCORE': matchup.home_score,
'HOME TEAM KICKER': aggregate_positional_points(matchup.home_lineup, 'K'),
'HOME TEAM BENCH': aggregate_positional_points(matchup.home_lineup, 'BE')
'HOME TEAM KICKER': extract_positional_data(matchup.home_lineup, 'K'),
'HOME TEAM BENCH': extract_positional_data(matchup.home_lineup, 'BE')
})
return result
def write_to_google_spreadsheet(df: pd.DataFrame):
spreadsheet = gc.open_by_key(str(cfg.SPREADSHEET_ID))
spreadsheet = gc.open_by_key(cfg.SPREADSHEET_ID)
worksheet = spreadsheet.worksheet("FantasyData")
set_with_dataframe(worksheet, df)
@huey.periodic_task(crontab(hour='6'))
def process_daily_report():
league = League(league_id=cfg.LEAGUE_ID,
year=datetime.now().year,
espn_s2=cfg.ESPN_S2,
swid=cfg.SWID,
fetch_league=cfg.FETCH_LEAGUE)
matchup = extract_weekly_box_scores(league=league)
df = pd.DataFrame(matchup)
print(tabulate(df, headers='keys', tablefmt='psql', showindex=False)) # pyright: ignore[reportArgumentType]
matchup = extract_matchup_box_scores(league=league)
df = pd.DataFrame.from_dict(matchup)
print(tabulate(df, headers='keys', tablefmt='psql', showindex=False))
write_to_google_spreadsheet(df=df)
process_daily_report()

27
config.py
View file

@ -6,27 +6,16 @@ load_dotenv()
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1", "on")
return v.lower() in ("yes", "true", "t", "1")
class Config():
def __init__(self) -> None:
self.ESPN_S2 = getenv("ESPN_S2", 0)
self.LEAGUE_ID = int(getenv("LEAGUE_ID", 0))
self.SWID = getenv("SWID", 0)
self.FETCH_LEAGUE = str2bool(getenv("FETCH_LEAGUE", 0))
self.SPREADSHEET_ID = getenv("SPREADSHEET_ID", 0)
self.SHEET_NAME = getenv("WORKSHEET_NAME", 0)
self.check_values()
def check_values(self):
for attribute, value in self.__dict__.items():
if value == 0:
raise Exception("ERROR: Unproperly set environment variable {attribute}, please review".format(attribute=attribute))
ESPN_S2 = getenv("ESPN_S2")
LEAGUE_ID = getenv("LEAGUE_ID")
SWID = getenv("SWID")
FETCH_LEAGUE = str2bool(getenv("FETCH_LEAGUE"))
SPREADSHEET_ID = getenv("SPREADSHEET_ID")
WORKSHEET_NAME = getenv("WORKSHEET_NAME")
try:
cfg = Config()
except Exception as e:
print(e)
cfg = Config()

4
requirements.txt
View file

@ -1,7 +1,9 @@
-e git+https://github.com/cwendt94/espn-api#egg=espn_api
-e git+https://github.com/cwendt94/espn-api@8e131e7ccaa843abac539948c44f9d45bfcee764#egg=espn_api
google-auth-oauthlib==1.2.1
gspread==6.1.2
gspread-dataframe==4.0.0
huey==2.5.2
pandas==2.2.3
python-dotenv==1.0.1
redis==5.1.0
tabulate==0.9.0

172
wip.ipynb Normal file
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@ -0,0 +1,172 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from argparse import ArgumentParser\n",
"from config import Config as cfg\n",
"from datetime import datetime\n",
"from espn_api.football import League\n",
"from gspread_dataframe import get_as_dataframe, set_with_dataframe\n",
"from service import gc\n",
"from time import strftime\n",
"\n",
"import pandas as pd\n",
"\n",
"\n",
"def check_int(s):\n",
" if s is None:\n",
" return s\n",
" if s[0] in ('-', '+'):\n",
" return s[1:].isdigit()\n",
" return s.isdigit()\n",
"\n",
"\n",
"def extract_positional_data(lineup: list, position: str) -> int:\n",
" response = 0\n",
" for player in lineup:\n",
" if player.lineupSlot == position:\n",
" response = response + player.points\n",
" return response\n",
"\n",
"\n",
"def extract_matchup_box_scores(league: League) -> dict:\n",
" result = []\n",
" for week in range(1, league.current_week + 1):\n",
" matchups = league.box_scores(week=week)\n",
" for matchup in matchups:\n",
" result.append({\n",
" 'WEEK #': week,\n",
" 'AWAY TEAM': matchup.away_team.team_name,\n",
" 'AWAY TEAM SCORE': matchup.away_score,\n",
" 'AWAY TEAM KICKER': extract_positional_data(matchup.away_lineup, 'K'),\n",
" 'AWAY TEAM BENCH': extract_positional_data(matchup.away_lineup, 'BE'),\n",
" 'HOME TEAM': matchup.home_team.team_name,\n",
" 'HOME TEAM SCORE': matchup.home_score,\n",
" 'HOME TEAM KICKER': extract_positional_data(matchup.home_lineup, 'K'),\n",
" 'HOME TEAM BENCH': extract_positional_data(matchup.home_lineup, 'BE')\n",
" })\n",
" return result\n",
"\n",
"\n",
"def write_to_google_spreadsheet(df: pd.DataFrame):\n",
" spreadsheet = gc.open_by_key(cfg.SPREADSHEET_ID)\n",
" worksheet = spreadsheet.worksheet(\"FantasyData\")\n",
" set_with_dataframe(worksheet, df)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"league = League(league_id=cfg.LEAGUE_ID, \n",
" year=datetime.now().year, \n",
" espn_s2=cfg.ESPN_S2, \n",
" swid=cfg.SWID, \n",
" fetch_league=cfg.FETCH_LEAGUE)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"matchup = extract_matchup_box_scores(league=league)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame.from_dict(matchup) "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"#spreadsheet = gc.open_by_key(cfg.SPREADSHEET_ID)\n",
"spreadsheet = gc.open_by_key('1vBtceabKqc0QAs7MOgeg-q0FSHXIbNf31B8XEgzy6s8')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"worksheet = spreadsheet.worksheet(\"FantasyData\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"set_with_dataframe(worksheet, df)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def next_available_row(worksheet):\n",
" str_list = list(filter(None, worksheet.col_values(1)))\n",
" return str(len(str_list)+1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'7'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next_available_row(worksheet=worksheet)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}