Anaconda, a distribution of the Python programming language, now includes Visual Studio Code, announced Microsoft and Anaconda, Inc.
“Visual Studio Code can easily be installed at the same time as Anaconda, providing a great editing and debugging experience for Python users, with special features tailor-made for Anaconda users. This is another example of Microsoft’s continued investment in the Python community, following our release of an official Python extension for VS Code [Visual Studio Code], strong support for Python in Azure Machine Learning Studio and SQL Server, and Azure Notebooks,” stated John Lam, principal program manager at Microsoft, in a Feb. 15 announcement.
Visual Studio Code is Microsoft’s free, lightweight and cross-platform code editor for macOS, Linux and Windows. Anaconda, originally Continuum Analytics, is an Austin, Texas software firm that distributes the popular open-source Python data science and analytics platform. The company also backs the NumPy scientific computing package for Python and SciPy, a Phython-based software library for mathematics, engineering and science.
The new integration is made possible, in large part, by Microsoft’s Python extension for Visual Studio Code, Lam noted.
The recent January 2018 release (version 2018.1.0) introduced a feature that allows users to create a Python terminal that automatically initiates a selected Conda or virtual environment, along with improved default linter rules and new linting commands. Conda is an open source package and environment management system that is included in Anaconda. A linter enables code analysis to identify potential errors in an integrated development environment like Visual Studio.
Mathew Lodge, senior vice president of products at Anaconda, Inc., said that adding Microsoft’s code editor to the Anaconda Distribution 5.1 release provides data scientists and developers who use the software with many time-saving conveniences.
“VS Code is free, fast and open source and has capabilities that other Python IDEs lack such as intelligent code completion, linting, git integration and a full debugger. Another differentiator is that it’s easily and openly extensible—for example, the Anaconda Python and YAML editing extensions make developing Python data science projects much easier,” Lodge told eWEEK in response to an email inquiry.
Looking to derive more value out of the wealth of data they generate, businesses are driving demand for both data scientists and the tools they use. IT vendors have noticed.
Mountain View, Calif.-based software startup Dremio launched a self-service analytics offering for data scientists and business analysts in July 2017. Dremio dispenses with the need for traditional data warehouses, ETL (extract, transform, load) tools and overall effort it takes to manage big data processing systems, making it possible for data scientists and line-of-business professionals to run natural language queries to gather the insights they need.
In April 2017, Amazon Web Services rolled out what may be considered “a data scientist’s dream app.” The cloud provider’s Redshift Spectrum feature allows users to run SQL queries against data stored in Amazon Simple Storage Service (Amazon S3). According to Amazon, those queries can potentially encompass exabytes wroth of data.
