For decades, Base SAS software has been the “gold standard” for data manipulation and analysis. The software can read any data source and is superb at transforming and shaping data for analysis. It has been the beneficiary of enormous resource investments over its lifetime. The company has one of the industry’s most innovative R&D staff, and its products are well supported by an outstanding technical support and well documented by very capable technical writers. SAS Institute Inc. has remained focused on gathering customer input and building desired features. All of these characteristics help explain its popularity.
Since the beginning of this millennium, the accelerated growth of open source software has produced outstanding projects offering data scientists enormous capabilities to tackle problems that were previously considered outside the realm of feasibility. Chief among these is Python. Python has its heritage in scientific and technical computing domains and has a very compact syntax. It is a full-featured language that is relatively easy to learn and is able to scale offering good performance with large data volumes. This is one of the reasons why firms like Netflix1 use it so extensively.
By nature, SAS users are intrepid and are constantly trying to find new ways to expand the use of the software in pursuit of meeting business objectives. And given the extensive role of SAS within organizations, it only makes sense to find ways to combine the capabilities of these two languages to complement one another.
We have four main goals for our readers. The first is to provide a quick start to learning Python for users already familiar with the SAS language.
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