Future of Python and NumPy for array-oriented computing

  • by Travis Oliphant
  • NumPy
  • SciPy
  • Array-Oriented Computed
  • Enthought is hiring!


I took Travis’ tutorial on it in 2006. I want to use this for serious number crunching. Why bridge out to another language/server if NumPy can do it for me fast and right in Python?

Python fits your brain

Thesis: Software engineering today is more about neuroscience than computer science

  • Even fits the brains of Scientists
  • Engineers say things differently than scientists
# engineering solution
from scipy.signal import lfilter, lifiltic
from numpy import zeros

# TODO get values here

def fibonocci(value):
    x = zeros(N)
    y, zf = lfilter(b,a,x,zi=zi)
  • But this is not fast enough for scientists

    • C speed
    • CPU speed
    • FASTER!!!

APL: first array oriented language

  • 1964
  • Descendants still alive: J, K, matlab
  • NumPy is a descendant of J
  • Crazy non-standard unicode characters
  • Very compact
  • Can do Conway in a single line of very dense code

Derivative Calculations

  • Complex data can be memory intensive
  • Big sets break even list generators
  • NumPy can do this for you

History of SciPy and NumPy

  • Travis started in 1997 on Python 1.4

  • Early contributors added numeric as a Python extension

    • Jim Hugenin (numeric)
    • Jim Fulton
    • Paul Dubiois
  • Fortran still exists because of complex numbers. Most languages got it wrong for a long time, including C and Java.

Travis Found Python and Numeric in 1997

  • Was good at MATLAB but it wasn’t efficient
  • Loved the expressive syntax of Python
  • Loved that slicing didn’t make copies
  • Love the multiple data types
  • Much more flexible than MATLAB
  • Loved that he could read source code a year later

1999: Early SciPy emerges

  • Wanted something more complete than numeric
  • A set of libraries and stuff
  • Lots of early contributors

NumPy started in 2006

  • Wasn’t happy with some of the directions of Numeric
  • Got it working after 18 months and the work of 6+ dedicated people

SciPy Today

  • Conferences
  • Collection of Tools (NumPy, et al)
  • Community
  • being looked at by the Financial community

What SciPy Does


  • Lots of cool data shaping tools


  • We aren’t talking about simple lists but gigantic multidimensional arrays

  • Super-duper fast

  • Terse but understandable notation

  • See Zen of NumPy:

    • strided is better than scattered
    • contiguous is better than strided
    • descriptive is better than imperative
    • TODO: finish writing this out!

Call to Action: Collaboration between Python Core and the Scientific Communication

Contention: Collaboration between Python core and scientific developers needs to be tighter

  • Index array operator (matrix multiplication is not domain specific)
  • Use of slice notation inside function calls
  • Array overloading of and and or
  • DSL blocks?

Call to Action: NumPy and PyPy

  • Stop chasing C, start chasing Fortran. Against an example:

    • Python: 202 seconds
    • PyPy: 4.71 seconds
    • NumPy: 5.56 seconds
    • Cython: 2.21 seconds
    • Fortran 90: 0.8 seconds
  • Mock Fortran if you will, but it is blazing fast for some important stuff.