https://github.com/fer-aguirre/cookiecutter-data-analysis-lite
A cookiecutter template for data journalism projects that offers a simplified and beginner-friendly structure.
https://github.com/fer-aguirre/cookiecutter-data-analysis-lite
cookiecutter data-analysis data-journalism project-template python
Last synced: about 1 year ago
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A cookiecutter template for data journalism projects that offers a simplified and beginner-friendly structure.
- Host: GitHub
- URL: https://github.com/fer-aguirre/cookiecutter-data-analysis-lite
- Owner: fer-aguirre
- Created: 2024-06-11T01:43:11.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-21T19:23:51.000Z (about 1 year ago)
- Last Synced: 2025-04-21T20:35:36.832Z (about 1 year ago)
- Topics: cookiecutter, data-analysis, data-journalism, project-template, python
- Language: Python
- Homepage:
- Size: 60.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Cookiecutter Data Analysis Lite 🐝
A [cookiecutter](https://github.com/cookiecutter/cookiecutter) template for data journalism projects that offers a simplified and beginner-friendly structure.
Created by: Fernanda Aguirre Ruíz
---
## Why Use This Template?
- **Consistency:** Maintains a standardized structure across all your data analysis projects
- **Efficiency:** Eliminates the repetitive setup process for new projects
- **Best Practices:** Follows established data science project organization principles
- **Flexibility:** Customizable to your specific needs while maintaining structure
---
## Requirements
- Python 3.8 or higher
- Cookiecutter 1.7.0 or higher
---
## Installation
First, make sure you have cookiecutter installed:
```
pip install cookiecutter
```
Then generate a new data project:
```
cookiecutter https://github.com/fer-aguirre/cookiecutter-data-analysis-lite
```
---
## Template parameters
During the project creation process, you will be prompted to enter values for the following parameters:
| Parameter | Description |
|--------------------|---------------------------------------------------------------|
| `project_name` | Name of your data analysis project |
| `project_slug` | URL-friendly name of the project (automatically generated) |
| `project_description` | A brief description of the project |
| `project_author` | Your name or organization name |
| `project_license` | Choose between MIT License or GNU General Public License v3 |
| `python_version` | Choose between Python 3.8 or 3.11 |
| `package_manager` | Choose between uv or poetry for dependency management |
---
## Directory Structure
```
┬
├─ .gitignore # Customized .gitignore for python projects
├─ LICENSE # Project's license
├─ pyproject.toml # Project dependencies
├─ README.md # Top-level README for this project
|
├─ assets # Resources for the project
|
├─ data # Categorized data files
| ├─ processed # Cleaned data
| └─ raw # Original data
|
├─ docs # Quarto's rendered docs
| └─ .nojekyll # Prevent Jekyll processing
|
├─ _quarto.yml # Quarto's config file
├─ custom.scss # Quarto's Sass stylesheet
├─ index.qmd # Quarto's home page
|
├─ _notebooks # Jupyter notebooks
| ├─ 0.0-collect-data.ipynb # Gathering data
| ├─ 1.0-process-data.ipynb # Data processing (fixing column types, data cleansing, etc.)
| ├─ 2.0-analyze-data.ipynb # Exploratory data analysis
| └─ 3.0-visualize-data.ipynb # Data visualization methods
|
├─ outputs # Exports generated by notebooks
| ├─ figures # Generated graphics, maps, etc. to be used in reporting
| └─ tables # Generated pivot tables to analyze data
|
├─ setup.py # Import project as a python module
┴
```
---