dataauto 1.0.3
An open-source tool for automating data analysis tasks.
Requires Python: >=3.6

# DataAuto v1.0.3

**DataAuto** is an open-source tool designed to automate common data analysis tasks. Whether you're a beginner or a seasoned data scientist, DataAuto simplifies the process of loading, summarizing, visualizing your data, training machine learning models, generating reports, and much more.

## Features

### 1. Data Loading & Saving
- **Load Data:** Easily load data from CSV, JSON, Excel files, or SQL databases.
- **Save Data:** Save processed data to various formats or export to SQL databases.

### 2. Data Cleaning & Preprocessing
- **Handle Missing Values:** Fill missing data using mean, median, mode, or constant values.
- **Remove Outliers:** Detect and remove outliers using IQR or Z-score methods.
- **Scale Data:** Normalize or standardize numerical features using Min-Max or Standard scaling.

### 3. Data Visualization
- **Static Plots:** Generate histograms, scatter plots, box plots, heatmaps, and line charts using Matplotlib and Seaborn.
- **Interactive Plots:** Create interactive visualizations with Plotly and Bokeh.
- **Dashboards:** Launch interactive dashboards using Streamlit for real-time data exploration.

### 4. Exploratory Data Analysis (EDA)
- **Summary Statistics:** Quickly obtain descriptive statistics of your dataset.
- **Automated Reports:** Generate comprehensive PDF reports summarizing your data analysis.

### 5. Machine Learning Integration
- **Model Training:** Train machine learning models (regression and classification) with ease.
- **Model Evaluation:** Evaluate model performance with detailed reports.
- **Hyperparameter Tuning:** Optimize model parameters using Hyperopt.

### 6. Scheduling & Automation
- **Task Scheduling:** Automate recurring data analysis tasks using APScheduler or Celery.
- **CI/CD Integration:** Ensure code quality and automate deployments with GitHub Actions.

### 7. External Integrations
- **Cloud Services:** Interact with AWS and Google Cloud services for scalable data storage and processing.
- **Communication Platforms:** Send notifications and alerts via Slack or Microsoft Teams upon task completion or failures.

### 8. Testing & Quality Assurance
- **Automated Testing:** Maintain code reliability with pytest, unittest, and coverage tools.
- **Code Quality:** Enforce coding standards using flake8, pylint, and black.

### 9. Documentation & Community
- **Comprehensive Docs:** Access detailed documentation, tutorials, and API references.
- **Community Support:** Engage with other users and contributors through GitHub Discussions and dedicated Slack/Discord channels.

### 10. Advanced Data Filtering *(New Feature)*
- **Dynamic Filters:** Apply complex filters to your datasets using multiple conditions and logical operators.
- **User-Friendly Interface:** Intuitive commands for specifying filter criteria without deep technical knowledge.

## Installation

**DataAuto** can be installed via `pip`. Ensure you have Python 3.8 or higher installed.

```bash
pip install dataauto==1.0.3
```
Alternatively, you can install directly from the GitHub repository:
```bash
pip install git+https://github.com/r4mp4g3r/dataauto.git@v1.0.3
```

## Quick Start
1. Load Data

Load a CSV file:
```bash
dataauto load path/to/data.csv --format csv
```

Load data from a PostgreSQL database:
```bash
dataauto load --format sql --db-type postgresql --host localhost --port 5432 --dbname mydb --user myuser --password mypass --query "SELECT * FROM mytable"
```

2. Summarize data

Generate summary statistics:
```bash
dataauto summarize path/to/data.csv
```

3. Plot Data

Generate a histogram:
```bash
dataauto plot path/to/data.csv --plot-type histogram --column Age --output-dir plots
```

Generate an interactive scatter plot:
```bash
dataauto plot path/to/data.csv --plot-type scatter --x Age --y Salary --output-dir plots --interactive
```

4. Train a Machine Learning Model

Train a classifier:
```bash
dataauto train path/to/data.csv --target TargetColumn --model-type classifier --output-model model.joblib --output-report report.txt
```

5. Generate a Report

Create a PDF report:
```bash
dataauto report path/to/data.csv --output-report analysis_report.pdf
```

6. Launch Dashboard

Start the interactive dashboard:
```bash
dataauto dashboard
```

7. Schedule a Task

Schedule a daily data load:
```bash
dataauto schedule --cron "0 0 * * *" dataauto load path/to/data.csv --format csv
```

## Usage Examples

Detailed usage examples can be found in the Examples directory. (In Progress)

## Roadmap

Check out our [ROADMAP.md](https://github.com/r4mp4g3r/dataauto/blob/main/ROADMAP.md) for upcoming features and improvements.

## Contributing

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

## Code of Conduct

Please adhere to our CODE_OF_CONDUCT.md when interacting with the community.

## License

This project is licensed under the MIT License.

## Acknowledgements

	•	Pandas
	•	Click
	•	Seaborn
	•	Matplotlib
	•	Plotly
	•	Streamlit
	•	Scikit-learn
	•	APScheduler
	•	Celery
	•	FPDF
	•	Sphinx
	•	MkDocs
	•   Joblib