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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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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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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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Starting Python Data Science Project on conda Environment

 Analytics Vidhya

I’m new in Data Science. And on my way to find my current state, I’ve learn the hard way. This article contains several lessons that I’ve got during my first time diving into anaconda environments…

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Conda Cheat Sheets: Mastering Python Environment Management

 Python in Plain English

Conda is a powerful package and environment management system for Python. It simplifies the process of installing, running, and managing multiple Python environments and packages on your system. Wheth...

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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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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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Publish a python package to Conda

 Analytics Vidhya

Conda is a python package manager similar to pip. If you are working on building a python library then it’s highly likely you will be publishing it to Conda as well. Otherwise, Conda users won’t…

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Getting started with Python environments (using Conda)

 Towards Data Science

Whether you want one or have no idea what it is, you’ll have to deal with environments in Python eventually. If you’re a newbie to Python like myself or a newbie to generally setting up your…

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Manage your Python Virtual Environment with Conda

 Towards Data Science

Although Python 2 is officially deprecated (Python 3, your time is now!), I believe some of us still have to maintain existing Python 2 projects before fully port those projects to Python 3. In this…

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Making a new Conda environment and using it inside Jupyter Notebook.

 Analytics Vidhya

Sometimes it becomes really hard to install some python packages inside the base environment. I came across one package named Geopandas. I wanted to visualize Covid-19 data on a map and required…

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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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Python Under The Hood: Part 3 — Pip Vs Conda

 Python in Plain English

In this article, which is the third part of the “Python Under the Hood” series, I will be talking about the two important components in the Python Ecosystem, namely Pip and Conda. If you didn’t read…

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Anaconda: Start here for data science in Python!

 Towards Data Science

If you’ve been following me or have read a few of my articles, you must know that I am a big fan of Python Virtual Environments. I’ve written about this before as well which you can read here and…

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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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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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Manage Python Virtual Environments with Conda

 Level Up Coding

The main purpose of a Python virtual environment is to create separate environments for each project. Let us try to understand the need for virtual environments with an example. Assume that you are…

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Setting up an Environment for Machine Learning with Conda & Pip-Tools

 Towards Data Science

Setting up a consistent and deterministic environment for machine learning projects can sometimes be a bit confusing if we don’t set some ground rules in the beginning. In this article, I want to go…

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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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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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Comprehensive Guide to Python Virtual Environments using Conda for Data Scientists

 Towards Data Science

Guide to Virtual Environments with Conda via Terminal Continue reading on Towards Data Science

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Get your conda environment to show in Jupyter Notebooks — the “easy way”

 Towards Data Science

When I first started using Jupyter Notebooks it took me longer than I’d like to admit to figure out how to get my conda environment kernels to show in the kernels list. When I succeeded I wasn’t…

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8 Essential Commands to Get Started With Conda Environments

 Towards Data Science

Virtual environments are not the most straightforward concept to Python beginners. When we install software, such as Microsoft Office and Evernote, most of us are used to applying the default…

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Guide to Conda for TensorFlow and PyTorch

 Towards Data Science

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run……

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