Taking NumPy and Python beyond Big Data
We provide tools for large-scale data-analysis and visualization that allow domain experts to understand and interact with data where it is produced instead of moving it around. We bring fast, efficient code to data and bring results back to the user.
We are improving and extending the popular NumPy package and also creating new distributed, out-of-core systems for dealing with huge-datasets with the same array-oriented ease that is NumPy’s strength.
If you are a Python or NumPy user contact us and we can prioritize our work to match your needs.
If you need training on Python, NumPy, or data-management techniques, we have experienced, skilled instructors who can provide classes.
If you would like to work with talented programmers who are creating the next-generation of data-analysis tools, send us your resume.
NumPy arrays will eventually be elevated from mere views of contiguous local memory to a universal front-end for dynamic compilation of data parallel code, which will be optimized for whatever local (CPU, GPU, MIC) or distributed multiprocessing environments. Array objects will support multiple labeled, hierarchical, and group-by indices, and the dtype mechanism will be extended to support derived fields and columns, and, eventually, nested OLAP-style pivots.