Metadata-Version: 2.4
Name: arctic-fox-movies
Version: 0.1.1
Summary: A lighthearted Python package for exploring and discovering movies from the IMDB Top 250 list with fun utilities for movie selection and analysis.
Author-email: Sanjay Chunduru <sc9635@nyu.edu>, ZhiHui Chen <zc3716@nyu.edu>, Rohit Dayanand <rohitdayanand8@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Sanjay Chunduru
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
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Project-URL: Homepage, https://github.com/swe-students-spring2026/3-package-arctic_fox
Project-URL: Repository, https://github.com/swe-students-spring2026/3-package-arctic_fox
Project-URL: Bug Tracker, https://github.com/swe-students-spring2026/3-package-arctic_fox/issues
Keywords: python,package,build,tutorial
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Dynamic: license-file

[![Python package](https://github.com/swe-students-spring2026/3-package-arctic_fox/actions/workflows/python-package.yml/badge.svg)](https://github.com/swe-students-spring2026/3-package-arctic_fox/actions/workflows/python-package.yml)

# 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)](https://www.kaggle.com/datasets/rajugc/imdb-top-250-movies-dataset)

## PyPI

- Package page: [https://pypi.org/project/arctic-fox-movies/](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](examples/example_program.py)

Run it locally:

```bash
pipenv run python examples/example_program.py
```

Run with interactive quiz enabled:

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

Run with dataframe preview enabled:

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

## Function Reference

Import path:

```python
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!"`.

```python
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"`.

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

### `play_quiz(attributes)`

Interactive wrapper around `quiz(...)`.

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

### `lead_actor(actor)`

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

```python
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.

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

### `find_movie_by_director(director)`

Returns all dataset movies directed by `director`.

```python
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.

```python
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.

```python
find_shape_of_dataframe()
```

## CLI Usage

The package also exposes a CLI entry point.

```bash
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:

```bash
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:

```bash
python -m pip install --user pipenv
```

### 2. Clone and install dependencies

```bash
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

```bash
pipenv run pytest
```

### 4. Build package artifacts

```bash
pipenv run python -m build
```

Artifacts are produced in `dist/`.

### 5. Validate artifacts and upload to PyPI

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

## Continuous Integration

GitHub Actions workflow: [python-package.yml](.github/workflows/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](https://github.com/zc3716)
-
-
-


## License

Licensed under the MIT License. See [LICENSE](LICENSE).

## Course Exercise

This repository is part of the package engineering exercise documented in [instructions.md](instructions.md).
