Why do we use anaconda distribution for python language?

There are several reasons why it is preferred to use Anaconda (or other of similar purpose) to manage the Python and Python packages or dependencies.

First and foremost reason of this being that you can create multiple environments for your Python development and operations. Environments enable you to create versioning, without altering, modifying or breaking the overall state of the application or other applications. You can think of this as, having Python 2 for some apps that have not yet been upgraded to Python 3, and then having state of the art Python 3.6 or 3.7 for the latest applications.

Apart from this, you can use "conda" CLI to manage the dependencies for the Python project, instead of Pip (also note that they are not similar, you cannot use Pip for some dependencies and conda for some). These are a couple of the key areas that you need to know before using Anaconda for Python development.

Oh, one more thing... Most of the software and tooling for Python development is provided by default with Anaconda and some are just a single click away! For example, the Jupyter notebooks are a single click away from installation. Not only this, you can even manage different versions of Jupyter for different environments, and sometimes even ignore the installation completely!

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Afzaal Ahmad Zeeshan7y ago
There are several reasons why it is preferred to use Anaconda (or other of similar purpose) to manage the Python and Python packages or dependencies.
First and foremost reason of this being that you can create multiple environments for your Python development and operations. Environments enable you to create versioning, without altering, modifying or breaking the overall state of the application or other applications. You can think of this as, having Python 2 for some apps that have not yet been upgraded to Python 3, and then having state of the art Python 3.6 or 3.7 for the latest applications.
Apart from this, you can use "conda" CLI to manage the dependencies for the Python project, instead of Pip (also note that they are not similar, you cannot use Pip for some dependencies and conda for some). These are a couple of the key areas that you need to know before using Anaconda for Python development.
Oh, one more thing... Most of the software and tooling for Python development is provided by default with Anaconda and some are just a single click away! For example, the Jupyter notebooks are a single click away from installation. Not only this, you can even manage different versions of Jupyter for different environments, and sometimes even ignore the installation completely!
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