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| NumPy is a package that defines a multi-dimensional array object and associated fast math functions that operate on it. It also provides simple routines for linear algebra and fft and sophisticated random-number generation. NumPy replaces both Numeric and Numarray. http://www.scipy.org and http://www.numpy.org/ | 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. |
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| A very complete manual by the principal author of Numpy, Travis Oliphant, is [[http://csc.ucdavis.edu/~chaos/courses/nlp/Software/NumPyBook.pdf|available]] for free (although donations are accepted). Note that the online documentation via docstring is rather complete and not stripped in any way. Further documentation is available from http://docs.scipy.org/doc/ | NumPy is used at the core of many popular packages in the world of Data Science and machine learning. |
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| Many examples of numpy usage can be found at http://wiki.scipy.org/Numpy_Example_List | == Learning NumPy == * The [[https://numpy.org/doc/stable/|official NumPy documentation]] offers multiple thorough manuals including a getting started manual. * Python Land offers a short and free [[https://python.land/data-science/numpy|getting started with NumPy tutorial]] and a comprehensive paid [[https://python.land/product/numpy-course|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course]] called 'a gentle, hands-on introduction to NumPy' * There's a complete but outdated (2006) manual by the principal author of Numpy, Travis Oliphant, is [[http://csc.ucdavis.edu/~chaos/courses/nlp/Software/NumPyBook.pdf|available]] for free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 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")