dora-eda 3.0.1

Exploratory data analysis and presentation tool

Data Oriented Report Automator (DORA)

An interactive command-line tool to automate Exploratory Data Analysis (EDA) and generate beautiful, insightful reports in seconds.

Overview

Welcome to DORA! This isn't just a script; it's an intelligent EDA assistant. DORA empowers you to move from a raw dataset to a comprehensive HTML report with minimal effort. It is designed to be powerful and configurable for experts, yet simple enough for anyone to use thanks to its interactive wizard.

1. installation

Install DORA directly from PyPI using pip:

pip install dora-eda
# check version to validate installation
dora -v

2. Usage

DORA has two modes of operation: Interactive (for first-time runs) and Config-Driven (for reproducible automation). Run DORA without existing configuration (Fresh run)

A. Interactive Mode (Quick Start)

Simply run the command without arguments. DORA will launch a wizard to guide you through the setup.

dora

You will be prompted to:

• Select your data file (CSV, Excel, JSON, Parquet).
• Choose an output directory.
• (Optional) Select a target variable for focused analysis.
• Pick which analysis steps to perform.
• (Optional) Save your settings to a config.yaml file for future use.

B. Config-Driven Mode (Advanced)

If you already have a configuration file (e.g., from a previous run), you can skip the wizard and run the analysis immediately.

dora --config <path/to/config.yaml>

Example config.yaml:

# --- Input/Output Settings ---
input_file: 'data/insurance.csv'
output_dir: 'output/insurance_report'
report_title: 'Exploratory Data Analysis of Insurance Premiums'

# --- Dataset Settings ---
target_variable: 'charges'

# --- Analysis Pipeline ---
analysis_pipeline:
  - profile:
      enabled: true
  - univariate:
      enabled: true
      plot_types:
        numerical: ['histogram', 'boxplot']
        categorical: ['barplot']
  - bivariate:
      enabled: true
      target_centric: true
  - multivariate:
      enabled: true
      correlation_cols: ['age', 'bmi', 'children', 'charges']

3. Supported Data Formats

DORA automatically detects and reads the following file types:

• CSV (*.csv)
• Excel (*.xlsx) - Note: Analyzes the first sheet only.
• JSON (*.json)
• Parquet (*.parquet)

4. Viewing the Output

After the analysis is complete, check your output directory for:

• 📄 eda_report.html: The full, interactive report. Open this in any web browser.
• 📈 charts/: A folder containing all generated plots as high-quality images.

Developer Guide

Interested in contributing to DORA? Awesome! Follow these steps to set up your local development environment.

1. Prerequisites

You need Poetry for dependency management.

# Windows (Powershell)
(Invoke-WebRequest -Uri [https://install.python-poetry.org](https://install.python-poetry.org) -UseBasicParsing).Content | py -

# Linux/macOS
curl -sSL [https://install.python-poetry.org](https://install.python-poetry.org) | python3 -

2. Setup

Clone the repository and install dependencies (including dev tools).

git clone https://github.com/Asifdotexe/DORA.git
cd dora
poetry install --with dev

3. Code Quality

We use standard tools to keep the codebase clean. Please run these before submitting a PR.

Automated Checks (Recommended): Install the pre-commit hooks once, and they will run automatically on every commit.

poetry run pre-commit install

Manual Checks:

# Format code
poetry run black .
poetry run isort .

# Lint code
poetry run pylint src/dora

Running Tests:

poetry run pytest

4. How to Contribute

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/amazing-feature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Happy analyzing with DORA! 🎉