| Name | pl-viz JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Fetch and visualize data from PL. |
| upload_time | 2025-02-14 08:38:41 |
| maintainer | None |
| docs_url | None |
| author | Ben Chen |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# prairielearn-viz
`pl_viz` is a Python package designed to simplify data extraction and visualization for courses on PrairieLearn. With its object-oriented design, `pl_viz` makes it easy to fetch data for courses, students, and assessments, and generate insightful visualizations to analyze student performance and assessment outcomes.
## Features
- **Object-Oriented Design**: Includes Course, Student, and Assessment classes for modular and intuitive data handling.
- **Data Extraction**: Fetch student lists, assessment details, and submission scores directly from the PrairieLearn API.
- **Data Visualization**:
- Boxplots for score distributions across assessments.
- Histograms to analyze score frequency.
- **Summary Statistics**: Compute mean, median, min, and max scores for assessments.
## Installation
To install the pl_viz package, use the following command:
```python
pip install pl_viz
```
## Usage
You will need a PrairieLearn API token to use this package. Store the token as an environment variable for security:
```bash
export PL_API_TOKEN="your_api_token_here"
```
## Classes Overview
1. `Course`
Represents a PrairieLearn course. Use it to:
- Fetch students and assessments.
- Display summary statistics.
- Generate visualizations.
2. `Student`
Represents an individual student, providing access to their user ID, name, and UID.
3. `Assessment`
Represents an assessment within a course. Fetch submissions and analyze score distributions.
## Contributing
Contributions are welcome! If you’d like to contribute to `pl_viz`, please open an issue or submit a pull request. Ensure you follow the coding standards and add tests for new features.
## License
This project is licensed under the MIT License. See the LICENSE file for details.
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"description": "# prairielearn-viz\n\n`pl_viz` is a Python package designed to simplify data extraction and visualization for courses on PrairieLearn. With its object-oriented design, `pl_viz` makes it easy to fetch data for courses, students, and assessments, and generate insightful visualizations to analyze student performance and assessment outcomes.\n\n## Features\n- **Object-Oriented Design**: Includes Course, Student, and Assessment classes for modular and intuitive data handling.\n- **Data Extraction**: Fetch student lists, assessment details, and submission scores directly from the PrairieLearn API.\n- **Data Visualization**:\n - Boxplots for score distributions across assessments.\n - Histograms to analyze score frequency.\n- **Summary Statistics**: Compute mean, median, min, and max scores for assessments.\n\n## Installation\n\nTo install the pl_viz package, use the following command:\n\n```python\npip install pl_viz\n```\n\n## Usage\n\nYou will need a PrairieLearn API token to use this package. Store the token as an environment variable for security:\n\n```bash\nexport PL_API_TOKEN=\"your_api_token_here\"\n```\n\n## Classes Overview\n\n1. `Course`\n\nRepresents a PrairieLearn course. Use it to:\n\n- Fetch students and assessments.\n- Display summary statistics.\n- Generate visualizations.\n\n2. `Student`\n\nRepresents an individual student, providing access to their user ID, name, and UID.\n\n3. `Assessment`\n\nRepresents an assessment within a course. Fetch submissions and analyze score distributions.\n\n## Contributing\n\nContributions are welcome! If you\u2019d like to contribute to `pl_viz`, please open an issue or submit a pull request. Ensure you follow the coding standards and add tests for new features.\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n\n",
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