Metadata-Version: 2.4
Name: weather_pkg_roquel_erick
Version: 0.1.2
Summary: A weather data processor
Author: Erick Roquel
Description-Content-Type: text/markdown
Requires-Dist: pandas

# Weather Data Processor

This is a Python script to read, process, and summarize weather data.

## Project Phase: Expectations vs. Completion

**Expected:**
The main goal of this project was to create a professional Python development workflow, modularize the code into an installable package, and publish it to the Python Package Index PyPi. Requirements included managing an isolated virtual environment, sourcing a weather dataset CSV from Kaggle, processing it using `pandas`, and generating professional documentation.

**Completed:**

- **Environment Setup:** Initialized a local Git repository and created an isolated Python virtual environment (`venv`) to manage the workspace.
- **Data Acquisition:** Sourced and downloaded a weather dataset (`weather_data.csv`) from Kaggle. The dataset contains synthetic weather data generated for ten different locations, including New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, and San Jose. The data includes information about temperature, humidity, precipitation, and wind speed, with 1 million data points generated for each parameter. The program looks at the `Temperature_C` column to calculate specific descriptive statistics (mean, median, mode, and range) for the temperature metrics.
- **Modularization & Packaging:** Refactored the script into a package named `weather_pkg`. Created a `pyproject.toml` file to configure the build system, specify `pandas` as a dependency, and define the package's metadata.
- **Python Scripting:** Updated `weather_processor.py` to import and use the published custom package like a standard third-party library. It successfully reads the CSV, analyzes the data, and outputs a structured text summary.
- **Documentation:** Utilized Python's built-in `pydoc` module to generate an HTML manual (`weather_processor.html`) directly from the script's docstrings. Authored this README to document the environment setup, execution instructions, and phase outcomes.

## Kaggle Dataset
https://www.kaggle.com/datasets/prasad22/weather-data?resource=download

## Setup and Installation

1. Extract the project files.
2. Open a terminal in the project directory.
3. Create a virtual environment: `python -m venv venv`
4. Activate the virtual environment:
   - Windows: `venv\Scripts\activate`
   - Mac/Linux: `source venv/bin/activate`
5. **Install the custom published library:**
   ```bash
   pip install weather_pkg_roquel_erick
   ```
6. Ensure a dataset named `weather_data.csv` is in the root directory

## Usage

Run the main script from the terminal:

```bash
python weather_processor.py
```

## Documentation

HTML documentation was generated using `pydoc`. You can view it by opening `weather_processor.html` in any web browser, or you can regenerate it by running:

```bash
python -m pydoc -w weather_processor
```
