NumPy, SciPy and SciKits

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Python provides a framework on which numerical and scientific data processing can be built. As part of our short course on Python for Physics and Astronomy we will look at the capabilities of the NumPy, SciPy and SciKits packages. This is a brief overview with a few examples drawn primarily from the excellent but short introductory book SciPy and NumPy by Eli Bressert (O'Reilly 2012).


NumPy adds arrays and linear albegra to Python, with special functions, transformations, the ability to operate on all elements of an array in one stroke.

Arrays are at the heart of NumPy. The program

import numpy as np

mylist = [1, 3, 5, 7, 11, 13]
myarray = np.array(mylist)
print myarray

creates a list and makes an array from it. You can create an array of 30 32-bit floating point zeros with

myarray = np.zeros(30, dtype=np.float32)

The dtype argument is optional and can be

  • unit8, 16, 32, 64
  • int8, 16, 32, 64
  • float32, 64, 128
  • complex64, 128