In this video from PyCon SG 2015, Anand S. presents: Faster Data Processing in Python. This talk covers methods to process and analyze visualize data faster in Python. The primary focus is on the technique (should you optimize? what to optimize? how to optimize?) while covering libraries that help with this (line_profiler, Pandas, Numba, etc.)
Call for Papers: Workshop on Python for High Performance & Scientific Computing
September 5, 2014 by
The 4th Workshop on Python for High Performance and Scientific Computing (PyHPC 2014) has extended its Call for Papers deadline to Sept. 12. The event takes place Nov. 17 in conjunction with SC14.
Announcing the Ceemple Tool for C++ Technical Computing
August 29, 2014 by
Programmers have a new tool for technical computing in Windows. Ceemple is an innovative solution enabling rapid C++ based scientific computing.
Python’s Role in Big Data Analytics: Past, Present, and Future
August 17, 2014 by
In this video from EuroPython 2014, Travis Oliphant from Continuum Analytics presents: Python’s Role in Big Data Analytics: Past, Present, and Future.
Online Course Breaks New Ground: Practical Numerical Methods with Python
August 7, 2014 by
George Washington University is launching a groundbreaking example of inter-institutional collaboration in open education through a Massive Open Online Course (MOOC) entitled “Practical Numerical Methods with Python.”
Lorena Barba on Why She Pushes Python
May 22, 2014 by
“Using Python has improved the effectiveness of our computer science program for all students … More students leave the course with the ability to create meaningful programs and with the positive attitude toward the experience of programming that this engenders.”
Programming GPUs Directly from Python Using NumbaPro
January 26, 2014 by
“NumbaPro is a powerful compiler that takes high-level Python code directly to the GPU producing fast-code that is the equivalent of programming in a lower-level language. It contains an implementation of CUDA Python as well as higher-level constructs that make it easy to map array-oriented code to the parallel architecture of the GPU.”
