How to Create a Python Environment with Anaconda

When working on projects that require specific versions of Python or libraries, it can be challenging to manage different environments. This is where Anaconda's package manager, conda, comes in handy. In this article, we'll explore how to create an environment using conda and its various features.

To quickly create an environment, you can type the command: `conda create -name your_env_name python=3.7 -y` This guide assumes that you already have Anaconda or miniconda installed and will use the bash command line for all instructions. For reference, I run my commands on the Terminal on Mac OS X.

Conda is a popular tool in data science, and it's often used alongside virtual environments to manage dependencies. Virtual environments keep these dependencies in separate 'sandboxes' so you can switch between applications easily and get them running. This is where virtual environments become useful. If you try running both Python 2 and Python 3 at once, one of them may break because some code that runs on Python 2 doesn't run on Python 3 or vice versa.

To create a new environment using conda, simply type the command: `conda create -name your_env_name python=3.7 -y` Replace 'your_env_name' with the name you want to give your environment and 'python=3.7' with the version of Python you want to use.

Once you've created an environment, you can activate it by typing: `conda activate your_env_name` This will install all the packages specified in your requirements.txt file. If you have a project with requirements.txt to be installed with pip but would like it to have pandas and scikit-learn from conda-forge, you can add them using the following command: `conda create -name ml_api python=3.7 -c conda-forge scikit-learn pandas`

There are multiple ways to install packages using conda. You can use `conda install package_name` or `conda create -n env_name python=3.8 package_name another_package`. Both methods are correct.

In conclusion, creating an environment with Anaconda's conda package manager is a straightforward process that allows you to manage different versions of Python and libraries for your projects. By using conda, you can ensure that your projects run smoothly and efficiently.

Python anaconda create environment