# {{ display_name }}

{{ description }}

## Overview

This data pipeline automation fetches, transforms, and analyzes tabular data.
It uses pandas for data processing and generates statistical summaries.

## Pipeline Stages

1. **Fetch** - Download data from URL (CSV format)
2. **Transform** - Parse, clean, and normalize data
3. **Analyze** - Compute statistics and prepare output

## Installation

```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Usage

### Local Development

```bash
# Run the automation
torivers run --input sample_input.json

# Run tests
torivers test

# Validate before submission
torivers validate
```

### Input Schema

```json
{
  "data_url": "https://example.com/data.csv",
  "options": {
    "delimiter": ",",
    "skip_rows": 0
  }
}
```

### Output Schema

```json
{
  "row_count": 100,
  "column_count": 5,
  "statistics": {
    "summary": {...},
    "correlations": {...}
  },
  "processed_data": [...]
}
```

## Project Structure

```
{{ name }}/
├── automation.yaml    # Automation manifest
├── main.py            # Main automation class
├── state.py           # State definition
├── nodes/
│   ├── __init__.py
│   ├── fetch.py       # Data fetching
│   ├── transform.py   # Data transformation
│   └── analyze.py     # Statistical analysis
├── tests/
│   ├── __init__.py
│   └── test_automation.py
├── .gitignore
├── requirements.txt
├── README.md
└── sample_input.json
```

## Development

1. Modify the transformation logic in `nodes/transform.py`
2. Add custom analysis in `nodes/analyze.py`
3. Update the state schema in `state.py` as needed
4. Add tests in `tests/test_automation.py`

## Author

{{ author_name }} <{{ author_email }}>
