Noventis Installation Guide
Welcome! Follow the steps below to install the Noventis library and start automatically cleaning and modeling your data.
!pip install noventis
1. Prerequisites
Before installing, make sure your environment is ready:
- Python: 3.8+
- Package Manager: pip
- (Highly Recommended) Virtual Environment: use a venv to avoid conflicts.
# Create a virtual environment
python -m venv noventis-env
# Activate (Windows)
.\noventis-env\Scripts\activate
# Activate (macOS/Linux)
source noventis-env/bin/activate
2. Installation Methods
Choose one of the methods below that best suits your needs.
Option A: Standard Installation (Most Common)
This is the easiest way and will install the latest stable version of Noventis.
pip install noventis
Option B: Install a Specific Version
If you require a specific version of the library, use the following command:
pip install noventis==1.2.3
Option C: Install for Developers (from Source)
This method is perfect for those who want the latest, unreleased features or wish to contribute to the project's development. We welcome all contributions! To get started, please review our Contribution Guide for guidelines and best practices. You can find the source code at our official GitHub repository.
# 1. Clone the repository
git clone https://github.com/your-name/noventis.git
# 2. Navigate into the directory
cd noventis
# 3. Install in "editable" mode
pip install -e .
3. Verify Installation
After the installation completes, verify that everything works:
pip show noventis
The command above shows package details including version.
Check via Python: Open a Python interpreter and run this short script to ensure the library can be imported.
try:
import noventis
# print(f"Noventis v{getattr(noventis, '__version__', '?')} installed successfully!")
print("Noventis installation successful!")
except ImportError:
print("Noventis installation failed or package not found.")
4. Next Steps
Congratulations, Noventis is installed! Start here:
- Quickstart Guide: Run your first pipeline.
- AutoEDA: Generate automated EDA reports.
- Data Cleaner: Configure the cleaning pipeline.
- AutoML: Train & compare models automatically.