Virtual Environment for Python Workspace – Part 3/3 – venv

In the earlier two posts of this series, I discussed about virtualenv  and conda  and their usage along with some examples. I this post I am going to introduce you guys with another package and environment manager called venv  which comes with standard Python3.X by default. So, nothing to worry about its installation.

Virtual Environment for Python Workspace – Part 1/3 – virtualenv
Virtual Environment for Python Workspace – Part 2/3 – conda

Creating an Environment
For example, If you have Python3.X installed in your system and you can run it’s REPL by issuing python3 in the terminal, then you can create a virtual environment based on this Python version by issuing the following command:

This will create the myvenv  directory if it doesn’t exist, and also create directories inside it containing a copy of the Python interpreter, the standard library, and various supporting files. By the way, you got the idea of using different versions of Python to use its venv  module for creating different environments, right? Just find out an exact Python 3.X (I showed how, in earlier posts) and use its binary/executable in the command <active python> -m venv <environment name> .

Activating the Environment
Its similar to the previous two workarounds and again simple as below. I assumed you are inside the newly created myvenv  directory.

Managing Packages with pip
As venv  is native with Python, you can use pip  for installing packages in your virtual environment which will pull down packages from Python’s official package repository, PyPI. For example, to install our very favorite package numpy , use the following command,

Also, for sure you can use commands like pip search <packagename> , pip list in each newly created virtual environment.

Did you know, you can use pip show <packagename>  to see detail of an installed package in an active environment?

Sharing Environment
In the earlier post, I have discussed why and how you can share your environment made using  conda  with others. If you are using venv  or virtualenv  then you can export an environment’s state/configuration to a file, issuing the following command,

Here, the file name requirements.txt , that contains environment’s package information could be anything. But people have been using this name conventionally for a long time.

So, after exporting the environment information, you can share this file to any one who can install exact same packages in his newly created virtual environment by using the following command:

By the way, sharing environment in this way is similar if you use virtualenv

Resources
https://docs.python.org/3/tutorial/venv.html

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Virtual Environment for Python Workspace – Part 2/3 – conda

Hope you already got the idea behind the necessity of virtual environment in Python ecosystem in my previous post. So, in this post I will directly go into the detail of conda  and its usage.

What is conda?
Just like virtualenv , this is also a package, dependency & environment management tool. Unlike virtualenv , conda  can work not only with Python but also with R, Javascript, Ruby, Lua, Scala etc. But, we will only focus on Python ecosystem for the sake of this series.

There are some significant benefits of conda  over virtualenv . For example – in its default configuration, conda  can install and manage thousand of packages at repo.continuum.io that are built, reviewed and maintained by Anaconda. Also, conda  can be combined with Continous Integration tools for better test & deployment.

How to install conda?
The easiest way to get conda  is to install miniconda . Yes, its sounds like Anaconda. But we don’t need to install that big fat ready-made environment. Rather we can install miniconda  that will come with conda  as package manager and its dependencies and the Python. By the way,

You do not need to uninstall other Python installations or packages in order to use conda. Even if you already have a system Python, another Python installation from a source such as the macOS Homebrew package manager and globally installed packages from pip such as pandas and NumPy, you do not need to uninstall, remove, or change any of them before using conda.

So, just download miniconda  from here and install in your system. You can choose either Python 2.X or Python 3.X version (I prefer to install miniconda  of Python 3.X version). We will talk about that very soon. The installer will add the  conda  installation of Python to your PATH environment variable. To verify that conda  is installed successfully, issue the following command.

Creating Virtual Environment
Before creating an environment, please keep in mind that, there are two variants of the installer: Miniconda is Python 2.X based and Miniconda3 is Python 3.X based. Note that the choice of which Miniconda is installed only affects the root environment. Regardless of which version of Miniconda you install, you can still install both Python 2.x and Python 3.x based environments.

Suppose you installed Python 3.X version of Miniconda and then if you issue the following command to create an environment,

the environment will be created based on Python 3.X version. On the other hand if you issue the following command, it will create a virtual environment based on Python 2.X.

Anyway, for now, forget about the mypy2env . Let’s activate the myenv  environment and start using that.

Activating an Environment 
To activate the new environment, run the appropriate command for your operating system:

  • Linux and macOS: source activate myenv
  • Windows: activate myenv

As my miniconda  version was of Python 3.X version and I created the environment myenv  with default Python, so this environment is based on Python 3.X. We can also check that by activating and running the Python REPL in it, maybe. Just check out the following Terminal activity and hope you will get it 🙂

Type exit()  in the above REPL to quit it and simply get back to the environment.

Deactivating an Environment 

Deactivating an environment is as simple as issuing the following command,

So, the above command will deactivate the myenv  environment. Oh, by the way, before deactivating, let’s check which packages are installed in this new virtual environment by the following command:

Want to install packages in this virtual environment for your next project?

Managing Packages in an Environment
If you want to install packages using the conda  package manager then it can be done simply by conda install <packagename> . For example, let’s install the package beautifulsoup4  in the environment myenv :

A good thing about conda  package manager is that, if you don’t find a package using conda install  then you can look into Anaconda.org which is another product of the same vendor of miniconda and Anaconda. For example, to install the package bottleneck , you can issue the following command:

Keep in mind that, you have to search for the desired package in the Anaconda.org site and there you will get the appropriate installation command for conda .

pip in conda
If you don’t find a package in conda or in Anaconda.org, then still you can install the package inside this conda  virtual environment by pip . Using pip  will download and install the package from Python’s official package repository PyPI . For example, let’s install numpy  in this virtual environment by the following command:

Now, let’s check the packages that are installed so far in the environment by the following command again:

which will show something like following:

In the above output, we can see that both beautifulsoup4  & numpy  exist together, though we installed beautifulsoup4  using conda  from their repository and numpy  from PyPI using pip .

Removing an Environment
Before removing any environment, you need to see all existing conda environments, right? For that, simply issue the following command which will show you all the existing virtual environments made by conda:

Lets, remove the mypy2env  which we don’t want to use.

Sharing Environment to Other
Say, you have prepared a very good Python environment with all the necessary packages installed in it and made sure that the environment is working fine for your project/app. Now, you want to share this ready-made environment with your friend who is struggling to prepare such an environment where he wants to work on the same Python project/app. Yes, you can do that by simply exporting your environment configuration in a file and sharing that file with your friend.

First, you need to activate the environment you want to share. Then, to export our myenv  environment’s configuration, issue the following command:

So, the shareable file is called environment.yml  and want to see whats in this file? It should contain something like following:

Pretty self-explanatory, right? Now, you can share this file to your friend and if your friend wants to prepare such a ready-made virtual environment, then he needs to install conda  first and the issue the following command:

conda is the package manager. Anaconda is a set of about a hundred packages including conda, numpy, scipy, ipython notebook, and so on.

Did you know, Once you have Miniconda, you can easily install Anaconda into it with conda install anaconda ?

Resources
https://conda.io/docs/user-guide/getting-started.html

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