Pip vs Conda: A Guide to Managing Python Packages for Data Scientists | Saturn Cloud Blog (2024)

FAQs

Should I use pip or conda to install packages? ›

Here are some guidelines: Use pip if you are working with pure Python projects and need access to the vast array of libraries available on PyPI. Use conda if you are working with projects that use multiple languages, need different versions of Python, or require complex binary dependencies.

Conda provides precompiled binary packages for popular libraries (usually), which head to better performance compared to pip , especially for scientific computing or machine learning tasks.

Pip & conda

The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only.

Both pip and conda are included in Anaconda and Miniconda, so you do not need to install them separately. Conda environments replace virtualenv, so there is no need to activate a virtualenv before using pip. It is possible to have pip installed outside a conda environment or inside a conda environment.

When you install a package with pip , it is installed globally on your system, whereas with conda , the package is installed locally in the environment you specify. If you then use pip to install the same package in the same environment, you may end up with two versions of the same package, which causes conflicts.

Slow performance: Because Anaconda comes with so many pre-installed packages, it can slow down the performance of your computer, particularly when running resource-intensive tasks. Compatibility issues: Some of the packages included in Anaconda may not be compatible with certain versions of Python or other packages.

To install the PyTorch binaries, you will need to use at least one of two supported package managers: Anaconda and pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python and pip.

While Conda is the recommended method for installing PyTorch, Pip is also a viable option. The choice of method depends on your specific needs and preferences. Regardless of the method you choose, it is important to verify the installation by running a simple Python script that imports PyTorch.

In summary, when combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software.

Anaconda comes with over 1,500 pre-installed packages, making it a comprehensive solution for data science projects. On the other hand, Miniconda allows you to install only the packages you need, reducing unnecessary clutter in your environment.

Should I use pyenv or conda? ›

Choosing the right environment management tool depends on your needs. If you need a simple, easy-to-use tool, venv might be the best choice. If you're dealing with complex dependencies, Conda env is the way to go. If you need to switch between different Python versions, consider pyenv or virtualenv.

While conda is generally great, it tends to get slow over time. Especially if you have a large environment, it can take a long time to resolve the environment when installing additional packages.

Installation. WARNING: Using pip install conda or easy_install conda will not give you conda as a standalone application. Currently supported install methods include the Anaconda installer and the miniconda installer. You can download the miniconda installer from https://conda.io/miniconda.html.

Pip is not only a program that comes with Python, but a piece of Python code that comes with Python. (The program works by starting a Python interpreter to run that code.) It's usually recommended to use Python to run Pip; that also helps with managing multiple versions of Python on the same computer.

Anaconda and Miniconda are both Python distributions. Anaconda includes hundreds of packages, whereas Miniconda includes just a few. conda is an open source tool that comes with both Anaconda and Miniconda, and it functions as both a package manager and an environment manager.

Conda „lives“ in the base environment: Conda itself is a Python program that needs dependencies. Those are installed in the base environment. If you add other things to the base environment than just Conda and its dependencies, you risk breaking your Conda installation.

If Anaconda doesn't include a package that you need, you use conda to download and install it. If Anaconda doesn't have the version of a package you need, you use conda to update it.