# NumPy

NumPy is an actively used library created in 1995. NumPy (pronounced (NUM-py) or sometimes (NUM-pee)) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. Read more on Wikipedia...

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- NumPy ranks in the top 10% of entities I track
- the NumPy website
- the NumPy wikipedia page
- NumPy first appeared in 1995
- file extensions for NumPy include numpy, numpyw and numsc
- See also: python, c, mathematical-notation, jython, scipy, matlab, simulink, matplotlib, cython
- I have 47 facts about NumPy. what would you like to know? email me and let me know how I can help.

### Example code from Wikipedia:

>>> # # # Pure iterative Python # # # >>> points = [[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]] >>> qPoint = [4,5,3] >>> minIdx = -1 >>> minDist = -1 >>> for idx, point in enumerate(points): # iterate over all points dist = sum([(dp-dq)**2 for dp,dq in zip(point,qPoint)])**0.5 # compute the euclidean distance for each point to q if dist < minDist or minDist < 0: # if necessary, update minimum distance and index of the corresponding point minDist = dist minIdx = idx >>> print 'Nearest point to q: ', points[minIdx] Nearest point to q: [3, 4, 4] >>> # # # Equivalent NumPy vectorization # # # >>> import numpy as np >>> points = np.array([[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]]) >>> qPoint = np.array([4,5,3]) >>> minIdx = np.argmin(np.linalg.norm(points-qPoint,axis=1)) # compute all euclidean distances at once and return the index of the smallest one >>> print 'Nearest point to q: ', points[minIdx] Nearest point to q: [3 4 4]

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Last updated December 10th, 2019