arctic-fox-movies 0.1.1

A lighthearted Python package for exploring and discovering movies from the IMDB Top 250 list with fun utilities for movie selection and analysis.

Arctic Fox Movies

Arctic Fox Movies is a lighthearted Python package for exploring the IMDB Top 250 dataset and discovering what to watch next.

It includes tools to:

• pick a movie based on your constraints,
• generate a movie quiz from random clues,
• search by lead actor,
• find collaboration movies between two people,
• list movies by a director, and
• spin a random pick by genre.

Dataset source: IMDB Top 250 Movies (Kaggle)

PyPI

• Package page: https://pypi.org/project/arctic-fox-movies/
• Install command: pip install arctic-fox-movies

Example Program

A complete example that uses all package features is available in:

• examples/example_program.py

Run it locally:

pipenv run python examples/example_program.py

Run with interactive quiz enabled:

pipenv run python examples/example_program.py --play-quiz

Run with dataframe preview enabled:

pipenv run python examples/example_program.py --show-dataframe-shape

Function Reference

Import path:

from arcticfoxmovies.movies import (
	movie_night_picker,
	quiz,
	play_quiz,
	lead_actor,
	find_collabs,
	find_movie_by_director,
	genre_roulette,
	find_shape_of_dataframe,
)

movie_night_picker(genres_to_avoid=None, runtime_max=150, minimum_rating=8.0)

Returns either a movie dictionary or the fallback string "No movies match your criteria!".

movie = movie_night_picker(
	genres_to_avoid=["Horror", "War"],
	runtime_max=180,
	minimum_rating=8.3,
)
print(movie)

quiz(attributes)

Builds a quiz question and returns quiz metadata.

Allowed values in attributes are "director", "runtime", and "year".

q = quiz(["director", "year"])
print(q["question"])
print("Answer:", q["answer"])

play_quiz(attributes)

Interactive wrapper around quiz(...).

play_quiz(["director", "runtime", "year"])

lead_actor(actor)

Returns movies where actor appears as the first listed cast member.

tom_hanks_movies = lead_actor("Tom Hanks")
print(tom_hanks_movies[:5])

find_collabs(person1, person2)

Returns movies where both people appear among directors, writers, or cast.

shared = find_collabs("Morgan Freeman", "Tim Robbins")
print(shared)

find_movie_by_director(director)

Returns all dataset movies directed by director.

nolan_movies = find_movie_by_director("Christopher Nolan")
print(nolan_movies)

genre_roulette(genre, avoid_year=None)

Returns one random movie title for genre, optionally skipping one year.

pick = genre_roulette("Drama", avoid_year=1994)
print(pick)

find_shape_of_dataframe(path=None)

Debug helper that prints a head/tail preview of the movie dataframe.

find_shape_of_dataframe()

CLI Usage

The package also exposes a CLI entry point.

python -m arcticfoxmovies --help
python -m arcticfoxmovies lead_actor "Tom Hanks"
python -m arcticfoxmovies quiz director runtime year

If installed from PyPI, you can also use the script command:

arcticfoxmovies --help

Developer Setup (Any Platform)

1. Prerequisites

• Python 3.9+ (CI currently validates 3.9, 3.10, 3.11)
• pip
• pipenv

Install pipenv if needed:

python -m pip install --user pipenv

2. Clone and install dependencies

git clone https://github.com/swe-students-spring2026/3-package-arctic_fox.git
cd 3-package-arctic_fox
pipenv install --dev
pipenv run pip install -e .

3. Run tests

pipenv run pytest

4. Build package artifacts

pipenv run python -m build

Artifacts are produced in dist/.

5. Validate artifacts and upload to PyPI

pipenv run twine check dist/*
pipenv run twine upload dist/*

Continuous Integration

GitHub Actions workflow: python-package.yml

On every push and PR to main, the workflow:

• sets up a matrix for Python 3.9, 3.10, and 3.11,
• installs dependencies with pipenv,
• runs lint checks with flake8,
• runs unit tests with pytest.

Team Workflow

Use feature branches and pull requests for all changes:

1. Create a feature branch from main.
2. Open a PR into main.
3. Request teammate review.
4. Reviewer runs tests and validates behavior.
5. Merge once approved.
6. Delete the feature branch.
7. Pull latest main locally.

Configuration and Data

• No local .env file is required to run this package.
• The dataset ships in the repository at data/IMDB Top 250 Movies.csv.
• No database setup or seed/import step is required.

Optional CI secret:

• COMMIT_LOG_API is used only by .github/workflows/event-logger.yml.
• If that workflow is enabled in your fork, configure this GitHub Actions secret in your repository settings.

Teammates

• ZhiHui Chen

License

Licensed under the MIT License. See LICENSE.

Course Exercise

This repository is part of the package engineering exercise documented in instructions.md.