Python are you can use various management tools to install and manage packages (libraries) that provide additional functionality. The most common ML management tools in Python are pip and condo. Here’s an overview of each:
pip is the standard manager for Python. It allows you to install packages from the Index (PyPI) as well as from other sources.
You can use pip to install packages like this:
pip install package_name
For example, to install the popular numpy package, you would run:
pip install numpy
To list all the packages installed via pip in your Python environment, you can use:
To check if a package is installed, you can use:
pip show package_name
Conda is a package manager and environment manager bundled together, commonly used in data science and scientific computing. It’s part of the Anaconda distribution.
You can use conda to install packages as well as manage Python environments.
To install a package using conda, you can run:
conda install package_name
For instance, to install numpy with conda, you would run:
conda install numpy
To list all the packages installed in your current conda environment, you can use:
To create and manage Python environments, conda is often used like this:
conda create –name myenv python=3.7
conda activate myenv
Both pip and conda are powerful tools for managing packages in Python. Which one you use depends on your specific use case and the environment you are working in. For general management, pip is the most widely used tool. For data science and scientific computing, conda is often preferred because it also allows for easy management of environments with different package versions.