NumPy is Python's fundamental package for scientific computing. It is a library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

NumPy is used at the core of many popular packages in the world of Data Science and machine learning.

Learning NumPy

• The official NumPy documentation offers multiple thorough manuals including a getting started manual.

• Python Land offers a short and free getting started with NumPy tutorial and a comprehensive paid NumPy course called 'a gentle, hands-on introduction to NumPy'

• There's a complete but outdated (2006) manual by the principal author of Numpy, Travis Oliphant, is available for free (although donations are accepted).

NumPy examples

Many examples of NumPy usage can be found at http://wiki.scipy.org/Numpy_Example_List

• numpy Example

from numpy import *

from PIL import Image


ar = ones((100,100),float32)

ar = ar * 100

for i in range(0,100):
    ar[i,:] = 100 + (i * 1.5)

im = Image.fromarray(ar,"F")