Visual Studio Code and Anaconda Collaboration

Not so long ago, the inclusion of Visual Studio Code in the Anaconda distribution was announced , which is undoubtedly a big step in the development of open source data analysis tools.

Anaconda , founded by Travis Oliphant, author of NumPy , has become an integral tool in the field of working with data, which has in its arsenal a large number of libraries and plugins that cover most analytical cases. Since Python is an interpreted language with REPL support , you can test code snippets from the command line, work with data sources before running more complex scripts.

Anaconda is an assembly designed for developers who use Python to analyze data. It includes a GUI, many scientifically-oriented work environments and tools to simplify the processing of data. It can also be used as a general replacement for the standard Python distribution if the differences between them are not significant to you.

When installing Anaconda for the first time, users will have the opportunity to install Visual Studio Code, including the Python extension for Visual Studio Code. This gives Anaconda users a powerful IDE to make the most of Python packages.



Anaconda users can then easily install and run VS Code from the Anaconda Navigator main screen.



Microsoft, in turn, uses Anaconda as a data analysis tool in SQL Server.
Implementing an interactive analytics tool at the heart of the database is a smart and informed move. At the same time, if you do not need such a tool in a productive database, you can not install it, leaving the combination of SQL Server / Anaconda for the development environment.

Azure also has access to Anaconda as part of Azure Machine Learning. Indeed, to get the most out of the machine learning platform, you should be able to create and test your statistical models before deploying them on a large scale. Using Anaconda to create analytic models in Python and R, you can test them on simple data in Visual Studio Code before embedding them in the Azure ML pipeline.

What is included in Anaconda

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Anaconda Python interpreter includes by default the latest version of the Python interpreter. This is not a standard assembly of CPython, but a custom assembly created by Anaconda Inc. specially for the distribution. And according to team members, Anaconda has “more efficient performance optimization.” However, the Anaconda Python interpreter is fully compatible with CPython.

Anaconda Navigator
The most noteworthy that Anaconda adds to the Python experience is the Anaconda Navigator GUI. This is not an IDE, and it does not try to be one of them, because most integrated IDEs with Python can themselves use the Anaconda Python environment. Instead, Navigator is an organizational system for Anaconda.

Using Navigator, you can add and run high-level applications such as R Studio or Jupyterlab, manage virtual environments and packages, and perform various administrative functions.

Navigator provides the convenience of a graphical interface, but it does not replace the command line functions in Anaconda or Python. For example, you can manage packages through a graphical interface, and you can also use the command line to do this.

CPython, by contrast, does not have a formal GUI. It comes with IDLE, a mini IDE suitable for quick, one-time tasks. Microsoft Visual Studio has a graphical interface for the Python Pip package manager, Anaconda, in turn, provides its own Conda package manager.

Conda
Python comes with a package managerPip , for installing and managing third-party Python packages. Although Python developers have been expanding the power of Pip over the years, it is still limited. It only manages packages for Python itself, and not for the entire system.

Anaconda developers struggled with this limitation, but eventually decided to develop their own solution: Conda, a package management solution that handles not only Python packages, but also dependencies outside the Python ecosystem.

For example: if you have several Conda packages that depend on the compiler, such as GCC or LLVM, Conda can resolve external dependencies for all of these packages. It can install one instance of a specific version of GCC for all Conda packages that need it.

Therefore, Conda is not interchangeable with Pip. It doesn't even use the same package format - packages created for Pip must be recreated for Conda. But almost every package used in the Python ecosystem is available through Conda.