44:
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was written as a more flexible replacement for
Numeric. Like Numeric, it too is now deprecated. Numarray had faster operations for large arrays, but was slower than Numeric on small ones, so for a time both packages were used in parallel for different use cases. The last version of Numeric (v24.2)
603:
extensions to the CPython interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries. This functionality is exploited by the SciPy package, which wraps a number of such libraries (notably BLAS and LAPACK). NumPy has built-in support for
642:
of some operations from constant to linear, because temporary arrays must be created that are as large as the inputs. Runtime compilation of numerical code has been implemented by several groups to avoid these problems; open source solutions that interoperate with NumPy include numexpr and
470:
equivalents due to the absence of compiler optimization. NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays; using these requires rewriting some code, mostly
506:
package that provides MATLAB-like plotting functionality. Although matlab can perform sparse matrix operations, numpy alone cannot perform such operations and requires the use of the scipy.sparse library. Internally, both MATLAB and NumPy rely on
428:
In early 2005, NumPy developer Travis
Oliphant wanted to unify the community around a single array package and ported Numarray's features to Numeric, releasing the result as NumPy 1.0 in 2006. This new project was part of
3544:
3487:
433:. To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0.
328:
The Python programming language was not originally designed for numerical computing, but attracted the attention of the scientific and engineering community early on. In 1995 the
624:
operation does not actually link the two arrays but returns a new one, filled with the entries from both given arrays in sequence. Reshaping the dimensionality of an array with
628:
is only possible as long as the number of elements in the array does not change. These circumstances originate from the fact that NumPy's arrays must be views on contiguous
3534:
584:
views on memory. In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type.
658:
scientific computing applications have requirements that exceed the capabilities of the NumPy arrays. For example, NumPy arrays are usually loaded into a computer's
3529:
3524:
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that are not expressible as a vectorized operation will typically run slowly because they must be implemented in "pure Python", while vectorization may increase
389:
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routine to extend arrays actually creates new arrays of the desired shape and padding values, copies the given array into the new one and returns it. NumPy's
542:
with other arrays are very efficient ways to access specific pixels of an image. The NumPy array as universal data structure in OpenCV for images, extracted
686:
applications rely on. As a result, several alternative array implementations have arisen in the scientific python ecosystem over the recent years, such as
420:
There was a desire to get
Numeric into the Python standard library, but Guido van Rossum decided that the code was not maintainable in its state then.
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utilize NumPy arrays to store and operate on data. Since images with multiple channels are simply represented as three-dimensional arrays, indexing,
3539:
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397:
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385:
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1767:# OpenCV images are interpreted as BGR, the depth-stacked array will be written to an 8bit RGB PNG-file called 'gradients.png'
2866:
van der Walt, Stéfan; Colbert, S. Chris; Varoquaux, Gaël (2011). "The NumPy array: a structure for efficient numerical computation".
269:
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NumPy also provides a C API, which allows Python code to interoperate with external libraries written in low-level languages.
294:
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or mimic it, so that users can change their array implementation with minimal changes to their code required. A library named
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started development on an implementation of the NumPy API for PyPy. As of 2023, it is not yet fully compatible with NumPy.
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596:
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498:. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and
482:
since they are both interpreted, and they both allow the user to write fast programs as long as most operations work on
133:
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created NumPy by incorporating features of the competing
Numarray into Numeric, with extensive modifications. NumPy is
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was released on 11 November 2005, while the last version of numarray (v1.5.2) was released on 24 August 2006.
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An implementation of a matrix package was completed by Jim Fulton, then generalized by Jim
Hugunin and called
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667:
558:
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Inserting or appending entries to an array is not as trivially possible as it is with Python's lists. The
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400:(LLNL) to take over as maintainer. Other early contributors include David Ascher, Konrad Hinsen and
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364:(also variously known as the "Numerical Python extensions" or "NumPy"), with influences from the
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Charles R Harris; K. Jarrod
Millman; Stéfan J. van der Walt; et al. (16 September 2020).
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to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by
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1680:# 256x256 pixel array with a horizontal gradient from 0 to 255 for the red color channel
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183:
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873:# create an array with four equally spaced points starting with 0 and ending with 2.
494:, whereas NumPy is intrinsically integrated with Python, a more modern and complete
3476:
3471:
207:
2841:
David Ascher; Paul F. Dubois; Konrad Hinsen; Jim
Hugunin; Travis Oliphant (1999).
2127:# compute all euclidean distances at once and return the index of the smallest one
670:. However, many linear algebra operations can be accelerated by executing them on
1710:# array of same size and type as r but filled with 0s for the green color channel
3408:
629:
304:
297:
490:. In comparison, MATLAB boasts a large number of additional toolboxes, notably
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Python for Data
Analysis : Data Wrangling with Pandas, NumPy, and IPython
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58:
49:
34:
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for computations on GPUs. Because of its popularity, these often implement a
1929:# if necessary, update minimum distance and index of the corresponding point
635:
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315:
and has many contributors. NumPy is a NumFOCUS fiscally sponsored project.
2698:
2684:
3119:
McKinney, Wes (2014). "NumPy Basics: Arrays and
Vectorized Computation".
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663:
655:
551:
491:
467:
456:
2940:
2196:! Fortran subroutines only not functions--easier than JNI with C wrapper
340:
computing package; among its members was Python designer and maintainer
2546:
1731:# transposed r will give a vertical gradient for the blue color channel
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3366:
Python Data
Science Handbook: Essential Tools for Working with Data
3195:
Shohei Hido - CuPy: A NumPy-compatible Library for GPU - PyCon 2018
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framework, has also shown potential for faster computing, being a '
662:, which might have insufficient capacity for the analysis of large
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2193:! Compile Fortran into python named module using intent statements
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3412:
3123:(First Edition, Third release ed.). O'Reilly. p. 79.
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Such arrays can also be views into memory buffers allocated by
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written for this version of Python often run much slower than
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and many more vastly simplifies the programming workflow and
3172:
1317:# create a 3x3 random matrix of values within scaled by 20
1047:# Functions can take both numbers and arrays as parameters.
307:
with contributions from several other developers. In 2005,
261:
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Iterative Python algorithm and vectorized NumPy version.
252:
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Using NumPy in Python gives functionality comparable to
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The core functionality of NumPy is its "ndarray", for
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666:. Further, NumPy operations are executed on a single
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1896:# compute the euclidean distance for each point to q
258:
3044:"PyPy Status Blog: NumPy funding and status update"
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3545:Python (programming language) scientific libraries
3364:VanderPlas, Jake (2016). "Introduction to NumPy".
48:Plot of y=sin(x) function, created with NumPy and
384:, and others. Hugunin, a graduate student at the
557:Importantly, many NumPy operations release the
285:, adding support for large, multi-dimensional
3424:
2748:Millman, K. Jarrod; Aivazis, Michael (2011).
2182:Quickly wrap native code for faster scripts.
561:, which allows for multithreaded processing.
390:Corporation for National Research Initiatives
8:
2963:Travis Oliphant and other SciPy developers.
674:of CPUs or of specialized hardware, such as
651:are static-compiling alternatives to these.
348:(in particular the indexing syntax) to make
3328:Scipy and Numpy: An Overview for Developers
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2834:
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3295:"Writing fast Fortran routines for Python"
2004:# # # Equivalent NumPy vectorization # # #
226:
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42:
33:
29:
3304:. University of California, Santa Barbara
2879:
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2741:
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2187:! Python Fortran native code call example
1392:# Starting with Python 3.5 and NumPy 1.10
2613:
2611:
336:was founded with the aim of defining an
27:Python library for numerical programming
3535:Numerical analysis software for Windows
2567:
3349:(2nd ed.). Sebastopol: O'Reilly.
3219:Entschev, Peter Andreas (2019-07-23).
2939:Travis E. Oliphant (7 December 2006).
2861:
2859:
2857:
2855:
398:Lawrence Livermore National Laboratory
3530:Numerical analysis software for macOS
3525:Numerical analysis software for Linux
2750:"Python for Scientists and Engineers"
455:of Python, which is a non-optimizing
386:Massachusetts Institute of Technology
7:
3006:"History_of_SciPy - SciPy wiki dump"
2868:Computing in Science and Engineering
2807:Computing in Science and Engineering
2754:Computing in Science and Engineering
2532:List of numerical-analysis software
293:, along with a large collection of
25:
2800:"Python for Scientific Computing"
2479:# or c,d = instead of a.c and a.d
1788:# # # Pure iterative Python # # #
3268:"A python vs. Fortran smackdown"
242:
3540:Numerical programming languages
3266:Worthey, Guy (3 January 2022).
3202:from the original on 2021-12-21
2720:"Indexing — NumPy v1.20 Manual"
3550:Software using the BSD license
2620:"Array programming with NumPy"
1:
2699:"NumFOCUS Sponsored Projects"
3368:. O'Reilly. pp. 33–96.
2199:! requires gfortran and make
368:family of languages, Basis,
3510:Array programming languages
3302:UCSB Engineering Department
3024:"NumPy 1.5.0 Release Notes"
2552:Row- and column-major order
1275:# solve the equation ax = b
690:for distributed arrays and
283:Python programming language
113:2.1.1 / 3 September 2024
3566:
3221:"Single-GPU CuPy Speedups"
568:The ndarray data structure
392:(CNRI) in 1997 to work on
3515:Free mathematics software
3447:
2986:"NumPy Sourceforge Files"
2651:10.1038/S41586-020-2649-2
2142:'Nearest point to q:
1968:'Nearest point to q:
1872:# iterate over all points
1584:Incorporation with OpenCV
396:, leaving Paul Dubois of
128:
102:
41:
32:
3121:Python for Data Analysis
3085:"NumPy for Matlab users"
2798:Travis Oliphant (2007).
2413:
2184:
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1587:
1413:
1113:
930:
810:
729:
453:reference implementation
3439:Scientific software in
1776:Nearest-neighbor search
1749:'gradients.png'
1410:Multidimensional arrays
1365:# matrix multiplication
559:global interpreter lock
486:or matrices instead of
464:Mathematical algorithms
3345:McKinney, Wes (2017).
3326:Bressert, Eli (2012).
3245:"F2PY docs from NumPy"
2190:! f2py -c -m foo *.f90
330:special interest group
3520:Free science software
3106:"numpy release notes"
3083:The SciPy Community.
412:A new package called
115:; 19 days ago
2965:" Status of Numeric"
2898:10.1109/MCSE.2011.37
2774:10.1109/MCSE.2011.36
576:-dimensional array,
496:programming language
313:open-source software
2920:"Numarray Homepage"
2890:2011CSE....13b..22V
2766:2011CSE....13b...9M
2604:. NumPy developers.
2515:# foo.ftest.__doc__
2509:'foo.ftest'
927:Universal functions
720:drop-in replacement
580:. These arrays are
526:of the widely used
2843:"Numerical Python"
2578:. 3 September 2024
1071:0.8414709848078965
448:NumPy targets the
388:(MIT), joined the
196:Numerical analysis
59:Original author(s)
3497:
3496:
3375:978-1-4919-1205-8
3356:978-1-4919-5766-0
3337:978-1-4493-0546-8
3130:978-1-449-31979-3
2635:(7825): 357–362.
2537:Theano (software)
2527:Array programming
710:, accelerated by
640:memory complexity
235:
234:
75:Community project
16:(Redirected from
3557:
3433:
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3404:History of NumPy
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3392:Official website
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3144:Francesc Alted.
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626:np.reshape(...)
614:
570:
528:computer vision
475:, using NumPy.
446:
426:
410:
402:Travis Oliphant
358:
350:array computing
346:Python's syntax
344:, who extended
326:
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309:Travis Oliphant
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80:Initial release
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3384:External links
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3293:Shell, Scott.
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2942:Guide to NumPy
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630:memory buffers
613:
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578:data structure
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548:filter kernels
544:feature points
519:computations.
517:linear algebra
515:for efficient
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108:Stable release
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18:Numeric Python
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3274:. Guy Worthey
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2816:on 2013-10-14
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2780:on 2019-02-19
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2500:>>>
2482:>>>
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2431:>>>
2416:>>>
2241:implicit none
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684:deep learning
682:, which many
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647:. Cython and
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606:memory-mapped
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3477:scikit-image
3472:scikit-learn
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3330:. O'Reilly.
3327:
3306:. Retrieved
3301:
3288:
3276:. Retrieved
3271:
3261:
3249:. Retrieved
3239:
3228:. Retrieved
3224:
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3204:, retrieved
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3149:
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3088:. Retrieved
3078:
3067:. Retrieved
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3047:. Retrieved
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3027:. Retrieved
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3000:
2989:. Retrieved
2980:
2968:. Retrieved
2958:
2946:. Retrieved
2941:
2934:
2923:. Retrieved
2914:
2871:
2867:
2818:. Retrieved
2811:the original
2806:
2793:
2782:. Retrieved
2778:the original
2757:
2753:
2727:. Retrieved
2723:
2714:
2703:. Retrieved
2693:
2632:
2626:
2601:
2592:
2582:22 September
2580:. Retrieved
2570:
2181:
2130:>>>
2064:>>>
2043:>>>
2040:(,,,,,,,,,])
2022:>>>
2007:>>>
2001:>>>
1956:>>>
1842:>>>
1827:>>>
1812:>>>
1803:>>>
1791:>>>
1785:>>>
1779:
1734:>>>
1713:>>>
1683:>>>
1614:>>>
1605:>>>
1590:>>>
1536:>>>
1476:>>>
1416:>>>
1380:>>>
1338:>>>
1320:>>>
1284:>>>
1254:>>>
1233:>>>
1206:>>>
1179:>>>
1152:>>>
1137:numpy.linalg
1131:>>>
1122:numpy.random
1116:>>>
1074:>>>
1050:>>>
1044:>>>
1041:>>>
1014:>>>
987:>>>
933:>>>
906:>>>
894:>>>
876:>>>
834:>>>
813:>>>
759:numpy.linalg
747:numpy.random
722:' of NumPy.
654:Many modern
653:
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521:
477:
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419:
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361:
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298:mathematical
240:(pronounced
237:
236:
71:Developer(s)
3272:Guy Worthey
2760:(2): 9–12.
2202:subroutine
702:of NumPy's
656:large-scale
618:np.pad(...)
612:Limitations
473:inner loops
460:interpreter
305:Jim Hugunin
3504:Categories
3462:matplotlib
3230:2021-05-11
3206:2021-05-11
3090:2 February
3069:2023-12-19
3049:2011-12-22
3029:2011-04-29
2991:2008-03-24
2970:2 February
2948:2 February
2925:2006-06-24
2820:2013-10-12
2784:2014-07-07
2729:2021-04-06
2705:2021-10-25
2701:. NumFOCUS
2642:2006.10256
2563:References
2542:Matplotlib
1800:,,,,,,,,,]
1698:zeros_like
692:TensorFlow
636:Algorithms
608:ndarrays.
500:Matplotlib
334:matrix-sig
324:matrix-sig
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