V2.2.1

This release has fixes for the various sasdata2dataframe methods (aliases to the one code base), and a new, second, implementation which uses an intermediary CSV file to improve performance for larger data sets. The original implementation, which is still the default, builds the dataframe in memory as the data is streamed from SAS. That method doesn't scale. Pandas performs better reading large data from disk and creating the dataframe. The original method was reworked so it will run without consuming significant system resources, like the first implementation ended up doing with larger data. saspy version 2.1.8 had this in it, but it also had a couple bugs, so 2.2.1 is basically 2.1.8 with the fixes for those couple bugs.

V2.1.7

This release has a number of fixes, enhancements, user contributed enhancements and it will have a conda install built from it too. It has support for IWA for the IOM access method. A fix to have the SAS magics share the existing connection ( or specify a connection), reverse tunneling for SSH over STDIO, better support for sascfg_personal.py, fixes to sd2df and other things.

V2.1.6

This release matches the latest version on PyPI; 2.1.6. It includes all enhancements and fixes since the v2.1.5 release. It contains a number of customer requested enhancements, as well as community pull request enhancements. The most recent is Grid HA support - High Availability support for SAS Grid using load balanced object spawners.

V2.1.5

This release matches the latest version on PyPI; 2.1.5. It includes all enhancements and fixes since the initial v2.1.0 release. Most of the enhancements have to do with the IOM access method, though you can see the commits that were made between v2.1.0 and v2.1.5. There were bug fixes and cleanups too.

the Real saspy

This release changes almost everything about saspy. In version 1 saspy only had a submit method to enable the sas_kernel. It now has much much more.

Here are the highlights:

• Transfer of SAS datasets and Pandas dataframes
• SAS Data object which provides methods for working with data in SAS. Here is a few of the methods (see the doc for the complete list)
  • Graphics (histogram, barchart, heatmap)
  • Partitioning data
  • Scoring data
  • Assessing models
  • Filtering (where, keep, drop)
• SAS Result object to store and display ODS results from modeling and data analysis
• Support for connections to SAS Grid Manager, PC SAS, and any other SAS IOM interface (even the mainframe)
• Analytical modeling methods
  • Statistics
  • Machine Learning
  • Econometric Time Series (ETS)
  • Quality Control
• Documentation using Sphinx: https://sassoftware.github.io/saspy/index.html

Initial release

This release of saspy was in support of the sas_kernel. It makes it possible to connect a Jupyter notebook to the SAS server for coding in SAS.