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Conda

Conda is an open-source package and environment management system that is designed for data science and machine learning projects. It is cross-platform, meaning it works on Windows, macOS, and Linux, and it allows users to manage dependencies and isolate projects effectively. This is particularly useful for users who need to work with different software versions or conflicting requirements across various projects 2.

One of the key features of Conda is its ability to manage environments, which are similar to Python’s virtual environments but are language-agnostic. This means that Conda can support not only Python but also other programming languages, making it versatile for a wide range of applications 3. Users can create, activate, and deactivate environments easily, allowing for a clean and organized workflow 5.

Additionally, Conda provides a robust package management system that can install not just Python packages but also libraries and applications from other languages, which sets it apart from pip, a package manager specifically for Python 1. Overall, Conda is a powerful tool for anyone involved in data science or software development.

Conda: essential concepts and tricks

 Towards Data Science

In this blog post, I will describe what conda is, and how to use it effectively, whether it is the first time you look at it or you are a seasoned user. While in the latter case many things will be…

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Getting Started with Conda

 Towards Data Science

Conda is an open source package and environment management system for data science and machine learning projects that runs on Windows, Mac OS and Linux.

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The Definitive Guide to Conda Environments

 Towards Data Science

Conda environments are like cousins of Python’s virtual environments. Both serve to help manage dependencies and isolate projects, and they function in a similar way, with one key distinction: conda…

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Conda + Google Colab

 Towards Data Science

Conda is the recommended environment and package management solution for a number of popular data science tools including Pandas, Scikit-Learn, PyTorch, NVIDIA Rapids and many others. Conda also…

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How to deal with Conda Environments

 Analytics Vidhya

Anaconda Navigator & Anaconda Prompt conda create --name conda activate conda deactivate conda create --clone --name conda env remove --name

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Introducing Conda Environments

 Python in Plain English

Anaconda is a free distribution of the Python programming language. As the most popular Python distribution platform, it has over 30 million users worldwide. Anaconda makes it easy to install Python, ...

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Introduction to Conda virtual environments

 Towards Data Science

Reproducibility is an important characteristic of a good data science project. Many factors from setting random seeds, data versioning to using virtual environments can help improve the…

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Conda Development Environment for Python

 Python in Plain English

Conda is an open-source package management and environment management system that can be used to create different, isolated coding environments. With Conda, you can create separate environments for sp...

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Using conda on an M1 Mac

 Towards Data Science

If you recently bought or got a new M1 Mac from work and you are using Python to develop or work on data science projects you probably already wasted some hours trying to get some packages to run. I…

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From soup to nuts guide for setting up a conda environment

 Towards Data Science

Hello! Conda is one of the most popular tools at data science community, and yet, it can be confusing to understand the steps and the cost of implementing that step, as there is hardly a single place…...

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13 Conda Commands for Data Scientists

 Towards Data Science

Conda is the most common tool to create a virtual environment and manage packages for data scientists using Python. Unfortunately, it's not easy to find the most useful commands for using conda and pi...

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Understand Conda and Pip

 Analytics Vidhya

Conda and pip are too identical yet are too different. Although, some of the functionality of these two tools overlap, but they were designed for different purposes. Pip is the Python Packaging…

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