About
Developing in python is fast. Computation, however, can often be another story. Or at least that is how it may seem. When working with arrays and numerical datasets one can subvert many of python's computational limitations by utilizing numpy. Numpy is python's standard matrix computation library. Many python users only use numpy to store and generate arrays, failing to utilize one of python's most powerful computational tools. By leveraging numpy's ufuncs, aggregation, broadcasting and slicing/masking/indexing functionality one can cut back on slow python loops and increase the speed of their programs by as much as 100x. This talk aims at teaching attendees how to use these tools through toy examples..
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The talk
Credits and licence
- Institution
- CERN
- Department
- Developers@CERN Forum
- Speaker
- Thoth Kenneth Gunter
- Year
- 2016
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed