fppull 0.2.1

Fantasy public pull utilities

Roadmap: We are on A) Hybrid → see ROADMAP.md.
Releases: see RELEASING.md.

Roadmap: Currently on A) Hybrid. See ROADMAP.md.

Status: Continuous Integration (contracts + validation checks) passing ✅

fantasy-public-pull

Goal: Use ESPN private league endpoints only to discover league/team/roster, and use public endpoints to fetch week-by-week player stats for all players (regardless of roster), then join them when building reports.

Quickstart

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
mkdir -p data/raw data/processed
# place raw pulls in data/raw ; clean outputs go to data/processed

Local Development Safety Checks

This repo uses a shared .githooks/pre-push script to enforce style, lint, and contract tests before every push.

One-time setup per machine

# ensure hooks use the shared folder
git config core.hooksPath .githooks

# install pre-commit into your virtualenv (if not already)
pip install pre-commit

# (optional) verify everything runs cleanly
git commit --allow-empty -m "hook check"
git push

Docs

• See the full Documentation Index for developer setup, CLI usage, roadmap, and releasing.

Local guardrails (pre-push)

pip install -r requirements-dev.txt
git config core.hooksPath .githooks
# (optional) run once to build hook envs faster:
pre-commit run --all-files

The pre-push hook runs:

• pre-commit (Black + Ruff) on your changes
• ./scripts/ci.sh test (unit tests)
• ./scripts/ci.sh contracts (contract tests)

Pushes are blocked if any step fails.

Reports

Once you’ve generated a joined season file (e.g., with pull_range), you can summarize it:

# regenerate joined data (offline sample weeks 1–3)
python -m fppull.cli.pull_range --season 2025 --weeks 1-3 --out-dir data/processed

# top players by total points
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --top 15

# group by position
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --group-by position --top 10

# group by team
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --group-by team --top 10

# require a minimum number of distinct weeks (e.g., >= 2)
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --group-by player --min-weeks 2 --top 20

# optionally write the table to CSV
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --group-by team --out data/processed/top_teams.csv


## Make targets

Handy shortcuts for common tasks:

```bash
# Rebuild joined data and print a top summary (defaults: season=2025, weeks=1-3)
make report-top

# Variations:
make report-top season=2025 weeks=1-3
make report-top season=2025 group_by=team top=10
make report-top season=2025 group_by=position min_weeks=2

# Local checks
make test         # unit tests
make contracts    # contract tests
make lint         # ruff
make fmt          # black
make ci           # fmt + lint + tests + contracts

### report_top formats & PPG

```bash
# table (default) + points per game
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --top 10 --ppg

# CSV to stdout
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --format csv --top 10

# JSON to file
python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --format json --ppg --top 10 --out data/processed/top10.json

[![PyPI version](https://img.shields.io/pypi/v/fppull.svg)](https://pypi.org/project/fppull/)
[![Release](https://img.shields.io/github/v/release/Masen222/fantasy-public-pull?display_name=tag)](https://github.com/Masen222/fantasy-public-pull/releases)
[![CI](https://github.com/Masen222/fantasy-public-pull/actions/workflows/ci.yml/badge.svg)](https://github.com/Masen222/fantasy-public-pull/actions/workflows/ci.yml)

## Install

```bash
pip install fppull

Artifacts map (what gets produced)

Layer	How to run	Key outputs
Public stats	python -m fppull.cli.pull_range --season 2025 --weeks 1-3 --out-dir data/processed	player_week_stats_long.csv, player_week_stats_wide.csv, player_week_points.csv
Private league context	(your private fetch or CSVs)	teams.csv, roster_week.csv, matchups.csv
Join + reports	`python -m fppull.cli.report_top --in data/processed/season_2025_joined.csv --top 15 [--group-by position	team]`