Collaboration Visual Studio Code and Anaconda

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, the author of NumPy , has become an indispensable tool for working with data, having in its arsenal a large number of libraries and plug-ins that cover most of the analytical cases. Since Python is an interpreted language with REPL support, you can test 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 working environments, and tools to simplify data processing. It can also be used as a general replacement for the standard Python distribution, if you don’t have any significant differences between them.



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.

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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.

The introduction of an interactive analytics tool in the heart of the database is a reasonable and reasonable move. At the same time, if you do not need such a tool in a productive database, you can not install it, leaving a combination of SQL Server / Anaconda for the development environment.



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

What is included in Anaconda

Python interpreter



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



Anaconda navigator

The most remarkable thing that Anaconda adds to the experience with Python is the Anaconda Navigator GUI. This is not an IDE, and it is not trying to be one of them, because most of the integrated IDEs with Python can use Anaconda Python themselves. Instead, Navigator is the 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 for 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 Pip package manager to install and manage third-party Python packages. Although the Python developers have been extending the power of Pip over the years, it is still limited. It manages packages only for Python itself, and not for the entire system.



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



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



Thus, Conda is not interchangeable with Pip. It does not 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.