-
Notifications
You must be signed in to change notification settings - Fork 6
/
nep-0022-ndarray-duck-typing-overview.html
899 lines (710 loc) · 50.8 KB
/
nep-0022-ndarray-duck-typing-overview.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>NEP 22 — Duck typing for NumPy arrays – high level overview — NumPy Enhancement Proposals</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!--
this give us a css class that will be invisible only if js is disabled
-->
<noscript>
<style>
.pst-js-only { display: none !important; }
</style>
</noscript>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=26a4bc78f4c0ddb94549" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=26a4bc78f4c0ddb94549" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
<!-- So that users can add custom icons -->
<script src="_static/scripts/fontawesome.js?digest=26a4bc78f4c0ddb94549"></script>
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=26a4bc78f4c0ddb94549" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=26a4bc78f4c0ddb94549" />
<script src="_static/documentation_options.js?v=7f41d439"></script>
<script src="_static/doctools.js?v=888ff710"></script>
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'nep-0022-ndarray-duck-typing-overview';</script>
<link rel="icon" href="_static/favicon.ico"/>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="NEP 27 — Zero rank arrays" href="nep-0027-zero-rank-arrarys.html" />
<link rel="prev" title="NEP 20 — Expansion of generalized universal function signatures" href="nep-0020-gufunc-signature-enhancement.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docsearch:version" content="" />
<meta name="docbuild:last-update" content="Nov 17, 2024"/>
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<dialog id="pst-search-dialog">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form>
</dialog>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class="col-lg-3 navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="content.html">
<img src="_static/numpylogo.svg" class="logo__image only-light" alt="NumPy Enhancement Proposals - Home"/>
<img src="_static/numpylogo.svg" class="logo__image only-dark pst-js-only" alt="NumPy Enhancement Proposals - Home"/>
</a></div>
</div>
<div class="col-lg-9 navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item current active">
<a class="nav-link nav-internal" href="index.html">
Index
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="scope.html">
The Scope of NumPy
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="roadmap.html">
Current roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wish list
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wishlist
</a>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/numpy/numpy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<dialog id="pst-primary-sidebar-modal"></dialog>
<div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item current active">
<a class="nav-link nav-internal" href="index.html">
Index
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="scope.html">
The Scope of NumPy
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="roadmap.html">
Current roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wish list
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wishlist
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/numpy/numpy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="scope.html">The Scope of NumPy</a></li>
<li class="toctree-l1"><a class="reference internal" href="roadmap.html">Current roadmap</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">Wish list</a></li>
</ul>
<ul class="current nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="meta.html">Meta-NEPs (NEPs about NEPs or active Processes)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0000.html">NEP 0 — Purpose and process</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0023-backwards-compatibility.html">NEP 23 — Backwards compatibility and deprecation policy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0036-fair-play.html">NEP 36 — Fair play</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0045-c_style_guide.html">NEP 45 — C style guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0046-sponsorship-guidelines.html">NEP 46 — NumPy sponsorship guidelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0048-spending-project-funds.html">NEP 48 — Spending NumPy project funds</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-template.html">NEP X — Template and instructions</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="provisional.html">Provisional NEPs (provisionally accepted; interface may change)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="accepted.html">Accepted NEPs (implementation in progress)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0041-improved-dtype-support.html">NEP 41 — First step towards a new datatype system</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0042-new-dtypes.html">NEP 42 — New and extensible DTypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0044-restructuring-numpy-docs.html">NEP 44 — Restructuring the NumPy documentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0051-scalar-representation.html">NEP 51 — Changing the representation of NumPy scalars</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="open.html">Open NEPs (under consideration)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0043-extensible-ufuncs.html">NEP 43 — Enhancing the extensibility of UFuncs</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0053-c-abi-evolution.html">NEP 53 — Evolving the NumPy C-API for NumPy 2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0054-simd-cpp-highway.html">NEP 54 — SIMD infrastructure evolution: adopting Google Highway when moving to C++?</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="finished.html">Finished NEPs</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="nep-0001-npy-format.html">NEP 1 — A simple file format for NumPy arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0005-generalized-ufuncs.html">NEP 5 — Generalized universal functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0007-datetime-proposal.html">NEP 7 — A proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0010-new-iterator-ufunc.html">NEP 10 — Optimizing iterator/UFunc performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0013-ufunc-overrides.html">NEP 13 — A mechanism for overriding Ufuncs</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0014-dropping-python2.7-proposal.html">NEP 14 — Plan for dropping Python 2.7 support</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0015-merge-multiarray-umath.html">NEP 15 — Merging multiarray and umath</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0018-array-function-protocol.html">NEP 18 — A dispatch mechanism for NumPy's high level array functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0019-rng-policy.html">NEP 19 — Random number generator policy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0020-gufunc-signature-enhancement.html">NEP 20 — Expansion of generalized universal function signatures</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">NEP 22 — Duck typing for NumPy arrays – high level overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0027-zero-rank-arrarys.html">NEP 27 — Zero rank arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0028-website-redesign.html">NEP 28 — numpy.org website redesign</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0029-deprecation_policy.html">NEP 29 — Recommend Python and NumPy version support as a community policy standard</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0032-remove-financial-functions.html">NEP 32 — Remove the financial functions from NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0034-infer-dtype-is-object.html">NEP 34 — Disallow inferring ``dtype=object`` from sequences</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0035-array-creation-dispatch-with-array-function.html">NEP 35 — Array creation dispatching with __array_function__</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0038-SIMD-optimizations.html">NEP 38 — Using SIMD optimization instructions for performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0040-legacy-datatype-impl.html">NEP 40 — Legacy datatype implementation in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0049.html">NEP 49 — Data allocation strategies</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0050-scalar-promotion.html">NEP 50 — Promotion rules for Python scalars</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0052-python-api-cleanup.html">NEP 52 — Python API cleanup for NumPy 2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0055-string_dtype.html">NEP 55 — Add a UTF-8 variable-width string DType to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0056-array-api-main-namespace.html">NEP 56 — Array API standard support in NumPy's main namespace</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="deferred.html">Deferred and Superseded NEPs</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0002-warnfix.html">NEP 2 — A proposal to build numpy without warning with a big set of warning flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0003-math_config_clean.html">NEP 3 — Cleaning the math configuration of numpy.core</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0004-datetime-proposal3.html">NEP 4 — A (third) proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0006-newbugtracker.html">NEP 6 — Replacing Trac with a different bug tracker</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0008-groupby_additions.html">NEP 8 — A proposal for adding groupby functionality to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0009-structured_array_extensions.html">NEP 9 — Structured array extensions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0011-deferred-ufunc-evaluation.html">NEP 11 — Deferred UFunc evaluation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0012-missing-data.html">NEP 12 — Missing data functionality in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0021-advanced-indexing.html">NEP 21 — Simplified and explicit advanced indexing</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0024-missing-data-2.html">NEP 24 — Missing data functionality - alternative 1 to NEP 12</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0025-missing-data-3.html">NEP 25 — NA support via special dtypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0026-missing-data-summary.html">NEP 26 — Summary of missing data NEPs and discussion</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0030-duck-array-protocol.html">NEP 30 — Duck typing for NumPy arrays - implementation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0031-uarray.html">NEP 31 — Context-local and global overrides of the NumPy API</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0037-array-module.html">NEP 37 — A dispatch protocol for NumPy-like modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0047-array-api-standard.html">NEP 47 — Adopting the array API standard</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="rejected.html">Rejected and Withdrawn NEPs</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0016-abstract-array.html">NEP 16 — An abstract base class for identifying "duck arrays"</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0017-split-out-maskedarray.html">NEP 17 — Split out masked arrays</a></li>
</ul>
</details></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
<div id="rtd-footer-container"></div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="content.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item"><a href="index.html" class="nav-link">Roadmap & NumPy enhancement proposals</a></li>
<li class="breadcrumb-item"><a href="finished.html" class="nav-link">Finished NEPs</a></li>
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">NEP 22 — Duck typing for NumPy arrays – high level overview</span></li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="nep-22-duck-typing-for-numpy-arrays-high-level-overview">
<span id="nep22"></span><h1>NEP 22 — Duck typing for NumPy arrays – high level overview<a class="headerlink" href="#nep-22-duck-typing-for-numpy-arrays-high-level-overview" title="Link to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Stephan Hoyer <<a class="reference external" href="mailto:shoyer%40google.com">shoyer<span>@</span>google<span>.</span>com</a>>, Nathaniel J. Smith <<a class="reference external" href="mailto:njs%40pobox.com">njs<span>@</span>pobox<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Final</p>
</dd>
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Informational</p>
</dd>
<dt class="field-even">Created<span class="colon">:</span></dt>
<dd class="field-even"><p>2018-03-22</p>
</dd>
<dt class="field-odd">Resolution<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://mail.python.org/pipermail/numpy-discussion/2018-September/078752.html">https://mail.python.org/pipermail/numpy-discussion/2018-September/078752.html</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>We outline a high-level vision for how NumPy will approach handling
“duck arrays”. This is an Informational-class NEP; it doesn’t
prescribe full details for any particular implementation. In brief, we
propose developing a number of new protocols for defining
implementations of multi-dimensional arrays with high-level APIs
matching NumPy.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<p>Traditionally, NumPy’s <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> objects have provided two things: a
high level API for expression operations on homogeneously-typed,
arbitrary-dimensional, array-structured data, and a concrete
implementation of the API based on strided in-RAM storage. The API is
powerful, fairly general, and used ubiquitously across the scientific
Python stack. The concrete implementation, on the other hand, is
suitable for a wide range of uses, but has limitations: as data sets
grow and NumPy becomes used in a variety of new environments, there
are increasingly cases where the strided in-RAM storage strategy is
inappropriate, and users find they need sparse arrays, lazily
evaluated arrays (as in dask), compressed arrays (as in blosc), arrays
stored in GPU memory, arrays stored in alternative formats such as
Arrow, and so forth – yet users still want to work with these arrays
using the familiar NumPy APIs, and reuse existing code with minimal
(ideally zero) porting overhead. As a working shorthand, we call these
“duck arrays”, by analogy with Python’s “duck typing”: a “duck array”
is a Python object which “quacks like” a numpy array in the sense that
it has the same or similar Python API, but doesn’t share the C-level
implementation.</p>
<p>This NEP doesn’t propose any specific changes to NumPy or other
projects; instead, it gives an overview of how we hope to extend NumPy
to support a robust ecosystem of projects implementing and relying
upon its high level API.</p>
<section id="terminology">
<h3>Terminology<a class="headerlink" href="#terminology" title="Link to this heading">#</a></h3>
<p>“Duck array” works fine as a placeholder for now, but it’s pretty
jargony and may confuse new users, so we may want to pick something
else for the actual API functions. Unfortunately, “array-like” is
already taken for the concept of “anything that can be coerced into an
array” (including e.g. list objects), and “anyarray” is already taken
for the concept of “something that shares ndarray’s implementation,
but has different semantics”, which is the opposite of a duck array
(e.g., np.matrix is an “anyarray”, but is not a “duck array”). This is
a classic bike-shed so for now we’re just using “duck array”. Some
possible options though include: arrayish, pseudoarray, nominalarray,
ersatzarray, arraymimic, …</p>
</section>
<section id="general-approach">
<h3>General approach<a class="headerlink" href="#general-approach" title="Link to this heading">#</a></h3>
<p>At a high level, duck array support requires working through each of
the API functions provided by NumPy, and figuring out how it can be
extended to work with duck array objects. In some cases this is easy
(e.g., methods/attributes on ndarray itself); in other cases it’s more
difficult. Here are some principles we’ve found useful so far:</p>
<section id="principle-1-focus-on-full-duck-arrays-but-dont-rule-out-partial-duck-arrays">
<h4>Principle 1: focus on “full” duck arrays, but don’t rule out “partial” duck arrays<a class="headerlink" href="#principle-1-focus-on-full-duck-arrays-but-dont-rule-out-partial-duck-arrays" title="Link to this heading">#</a></h4>
<p>We can distinguish between two classes:</p>
<ul class="simple">
<li><p>“full” duck arrays, which aspire to fully implement np.ndarray’s
Python-level APIs and work essentially anywhere that np.ndarray
works</p></li>
<li><p>“partial” duck arrays, which intentionally implement only a subset
of np.ndarray’s API.</p></li>
</ul>
<p>Full duck arrays are, well, kind of boring. They have exactly the same
semantics as ndarray, with differences being restricted to
under-the-hood decisions about how the data is actually stored. The
kind of people that are excited about making numpy more extensible are
also, unsurprisingly, excited about changing or extending numpy’s
semantics. So there’s been a lot of discussion of how to best support
partial duck arrays. We’ve been guilty of this ourself.</p>
<p>At this point though, we think the best general strategy is to focus
our efforts primarily on supporting full duck arrays, and only worry
about partial duck arrays as much as we need to make sure we don’t
accidentally rule them out for no reason.</p>
<p>Why focus on full duck arrays? Several reasons:</p>
<p>First, there are lots of very clear use cases. Potential consumers of
the full duck array interface include almost every package that uses
numpy (scipy, sklearn, astropy, …), and in particular packages that
provide array-wrapping-classes that handle multiple types of arrays,
such as xarray and dask.array. Potential implementers of the full duck
array interface include: distributed arrays, sparse arrays, masked
arrays, arrays with units (unless they switch to using dtypes),
labeled arrays, and so forth. Clear use cases lead to good and
relevant APIs.</p>
<p>Second, the Anna Karenina principle applies here: full duck arrays are
all alike, but every partial duck array is partial in its own way:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> is mostly a duck array, but has incompatible
broadcasting semantics.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">xarray.Dataset</span></code> wraps multiple arrays in one object; it still
implements some array interfaces like <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code>, but
certainly not all of them.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">pandas.Series</span></code> has methods with similar behavior to numpy, but
unique null-skipping behavior.</p></li>
<li><p>scipy’s <code class="docutils literal notranslate"><span class="pre">LinearOperator</span></code>s support matrix multiplication and nothing else</p></li>
<li><p>h5py and similar libraries for accessing array storage have objects
that support numpy-like slicing and conversion into a full array,
but not computation.</p></li>
<li><p>Some classes may be similar to ndarray, but without supporting the
full indexing semantics.</p></li>
</ul>
<p>And so forth.</p>
<p>Despite our best attempts, we haven’t found any clear, unique way of
slicing up the ndarray API into a hierarchy of related types that
captures these distinctions; in fact, it’s unlikely that any single
person even understands all the distinctions. And this is important,
because we have a <em>lot</em> of APIs that we need to add duck array support
to (both in numpy and in all the projects that depend on numpy!). By
definition, these already work for <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, so hopefully getting
them to work for full duck arrays shouldn’t be so hard, since by
definition full duck arrays act like <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>. It’d be very
cumbersome to have to go through each function and identify the exact
subset of the ndarray API that it needs, then figure out which partial
array types can/should support it. Once we have things working for
full duck arrays, we can go back later and refine the APIs needed
further as needed. Focusing on full duck arrays allows us to start
making progress immediately.</p>
<p>In the future, it might be useful to identify specific use cases for
duck arrays and standardize narrower interfaces targeted just at those
use cases. For example, it might make sense to have a standard “array
loader” interface that file access libraries like h5py, netcdf, pydap,
zarr, … all implement, to make it easy to switch between these
libraries. But that’s something that we can do as we go, and it
doesn’t necessarily have to involve the NumPy devs at all. For an
example of what this might look like, see the documentation for
<a class="reference external" href="http://dask.pydata.org/en/latest/array-api.html#dask.array.from_array">dask.array.from_array</a>.</p>
</section>
<section id="principle-2-take-advantage-of-duck-typing">
<h4>Principle 2: take advantage of duck typing<a class="headerlink" href="#principle-2-take-advantage-of-duck-typing" title="Link to this heading">#</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">ndarray</span></code> has a very large API surface area:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">dir</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="nb">dir</span><span class="p">(</span><span class="nb">object</span><span class="p">)))</span>
<span class="n">Out</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="mi">138</span>
</pre></div>
</div>
<p>And this is a huge <strong>under</strong>estimate, because there are also many
free-standing functions in NumPy and other libraries which currently
use the NumPy C API and thus only work on <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> objects. In type
theory, a type is defined by the operations you can perform on an
object; thus, the actual type of <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> includes not just its
methods and attributes, but <em>all</em> of these functions. For duck arrays
to be successful, they’ll need to implement a large proportion of the
<code class="docutils literal notranslate"><span class="pre">ndarray</span></code> API – but not all of it. (For example,
<code class="docutils literal notranslate"><span class="pre">dask.array.Array</span></code> does not provide an equivalent to the
<code class="docutils literal notranslate"><span class="pre">ndarray.ptp</span></code> method, presumably because no-one has ever noticed or
cared about its absence. But this doesn’t seem to have stopped people
from using dask.)</p>
<p>This means that realistically, we can’t hope to define the whole duck
array API up front, or that anyone will be able to implement it all in
one go; this will be an incremental process. It also means that even
the so-called “full” duck array interface is somewhat fuzzily defined
at the borders; there are parts of the <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code> API that duck
arrays won’t have to implement, but we aren’t entirely sure what those
are.</p>
<p>And ultimately, it isn’t really up to the NumPy developers to define
what does or doesn’t qualify as a duck array. If we want scikit-learn
functions to work on dask arrays (for example), then that’s going to
require negotiation between those two projects to discover
incompatibilities, and when an incompatibility is discovered it will
be up to them to negotiate who should change and how. The NumPy
project can provide technical tools and general advice to help resolve
these disagreements, but we can’t force one group or another to take
responsibility for any given bug.</p>
<p>Therefore, even though we’re focusing on “full” duck arrays, we
<em>don’t</em> attempt to define a normative “array ABC” – maybe this will be
useful someday, but right now, it’s not. And as a convenient
side-effect, the lack of a normative definition leaves partial duck
arrays room to experiment.</p>
<p>But, we do provide some more detailed advice for duck array
implementers and consumers below.</p>
</section>
<section id="principle-3-focus-on-protocols">
<h4>Principle 3: focus on protocols<a class="headerlink" href="#principle-3-focus-on-protocols" title="Link to this heading">#</a></h4>
<p>Historically, numpy has had lots of success at interoperating with
third-party objects by defining <em>protocols</em>, like <code class="docutils literal notranslate"><span class="pre">__array__</span></code> (asks
an arbitrary object to convert itself into an array),
<code class="docutils literal notranslate"><span class="pre">__array_interface__</span></code> (a precursor to Python’s buffer protocol), and
<code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> (allows third-party objects to support ufuncs like
<code class="docutils literal notranslate"><span class="pre">np.exp</span></code>).</p>
<p><a class="reference external" href="https://github.com/numpy/numpy/pull/10706">NEP 16</a> took a
different approach: we need a duck-array equivalent of
<code class="docutils literal notranslate"><span class="pre">asarray</span></code>, and it proposed to do this by defining a version of
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> that would let through objects which implemented a new
AbstractArray ABC. As noted above, we now think that trying to define
an ABC is a bad idea for other reasons. But when this NEP was
discussed on the mailing list, we realized that even on its own
merits, this idea is not so great. A better approach is to define a
<em>method</em> that can be called on an arbitrary object to ask it to
convert itself into a duck array, and then define a version of
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> that calls this method.</p>
<p>This is strictly more powerful: if an object is already a duck array,
it can simply <code class="docutils literal notranslate"><span class="pre">return</span> <span class="pre">self</span></code>. It allows more correct semantics: NEP
16 assumed that <code class="docutils literal notranslate"><span class="pre">asarray(obj,</span> <span class="pre">dtype=X)</span></code> is the same as
<code class="docutils literal notranslate"><span class="pre">asarray(obj).astype(X)</span></code>, but this isn’t true. And it supports more
use cases: if h5py supported sparse arrays, it might want to provide
an object which is not itself a sparse array, but which can be
automatically converted into a sparse array. See NEP <XX, to be
written> for full details.</p>
<p>The protocol approach is also more consistent with core Python
conventions: for example, see the <code class="docutils literal notranslate"><span class="pre">__iter__</span></code> method for coercing
objects to iterators, or the <code class="docutils literal notranslate"><span class="pre">__index__</span></code> protocol for safe integer
coercion. And finally, focusing on protocols leaves the door open for
partial duck arrays, which can pick and choose which subset of the
protocols they want to participate in, each of which have well-defined
semantics.</p>
<p>Conclusion: protocols are one honking great idea – let’s do more of
those.</p>
</section>
<section id="principle-4-reuse-existing-methods-when-possible">
<h4>Principle 4: reuse existing methods when possible<a class="headerlink" href="#principle-4-reuse-existing-methods-when-possible" title="Link to this heading">#</a></h4>
<p>It’s tempting to try to define cleaned up versions of ndarray methods
with a more minimal interface to allow for easier implementation. For
example, <code class="docutils literal notranslate"><span class="pre">__array_reshape__</span></code> could drop some of the strange
arguments accepted by <code class="docutils literal notranslate"><span class="pre">reshape</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_basic_getitem__</span></code>
could drop all the <a class="reference external" href="http://www.numpy.org/neps/nep-0021-advanced-indexing.html">strange edge cases</a> of
NumPy’s advanced indexing.</p>
<p>But as discussed above, we don’t really know what APIs we need for
duck-typing ndarray. We would inevitably end up with a very long list
of new special methods. In contrast, existing methods like <code class="docutils literal notranslate"><span class="pre">reshape</span></code>
and <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> have the advantage of already being widely
used/exercised by libraries that use duck arrays, and in practice, any
serious duck array type is going to have to implement them anyway.</p>
</section>
<section id="principle-5-make-it-easy-to-do-the-right-thing">
<h4>Principle 5: make it easy to do the right thing<a class="headerlink" href="#principle-5-make-it-easy-to-do-the-right-thing" title="Link to this heading">#</a></h4>
<p>Making duck arrays work well is going to be a community effort.
Documentation helps, but only goes so far. We want to make it easy to
implement duck arrays that do the right thing.</p>
<p>One way NumPy can help is by providing mixin classes for implementing
large groups of related functionality at once.
<code class="docutils literal notranslate"><span class="pre">NDArrayOperatorsMixin</span></code> is a good example: it allows for
implementing arithmetic operators implicitly via the
<code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> method. It’s not complete, and we’ll want more
helpers like that (e.g. for reductions).</p>
<p>(We initially thought that the importance of these mixins might be an
argument for providing an array ABC, since that’s the standard way to
do mixins in modern Python. But in discussion around NEP 16 we
realized that partial duck arrays also wanted to take advantage of
these mixins in some cases, so even if we did have an array ABC then
the mixins would still need some sort of separate existence. So never
mind that argument.)</p>
</section>
</section>
<section id="tentative-duck-array-guidelines">
<h3>Tentative duck array guidelines<a class="headerlink" href="#tentative-duck-array-guidelines" title="Link to this heading">#</a></h3>
<p>As a general rule, libraries using duck arrays should insist upon the
minimum possible requirements, and libraries implementing duck arrays
should provide as complete of an API as possible. This will ensure
maximum compatibility. For example, users should prefer to rely on
<code class="docutils literal notranslate"><span class="pre">.transpose()</span></code> rather than <code class="docutils literal notranslate"><span class="pre">.swapaxes()</span></code> (which can be implemented
in terms of transpose), but duck array authors should ideally
implement both.</p>
<p>If you are trying to implement a duck array, then you should strive to
implement everything. You certainly need <code class="docutils literal notranslate"><span class="pre">.shape</span></code>, <code class="docutils literal notranslate"><span class="pre">.ndim</span></code> and
<code class="docutils literal notranslate"><span class="pre">.dtype</span></code>, but also your dtype attribute should actually be a
<code class="docutils literal notranslate"><span class="pre">numpy.dtype</span></code> object, weird fancy indexing edge cases should ideally
work, etc. Only details related to NumPy’s specific <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>
implementation (e.g., <code class="docutils literal notranslate"><span class="pre">strides</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code>, <code class="docutils literal notranslate"><span class="pre">view</span></code>) are explicitly
out of scope.</p>
</section>
<section id="a-very-rough-sketch-of-future-plans">
<h3>A (very) rough sketch of future plans<a class="headerlink" href="#a-very-rough-sketch-of-future-plans" title="Link to this heading">#</a></h3>
<p>The proposals discussed so far – <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> and some kind of
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> protocol – are clearly necessary but not sufficient for
full duck typing support. We expect the need for additional protocols
to support (at least) these features:</p>
<ul class="simple">
<li><p><strong>Concatenating</strong> duck arrays, which would be used internally by other
array combining methods like stack/vstack/hstack. The implementation
of concatenate will need to be negotiated among the list of array
arguments. We expect to use an <code class="docutils literal notranslate"><span class="pre">__array_concatenate__</span></code> protocol
like <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> instead of multiple dispatch.</p></li>
<li><p><strong>Ufunc-like functions</strong> that currently aren’t ufuncs. Many NumPy
functions like median, percentile, sort, where and clip could be
written as generalized ufuncs but currently aren’t. Either these
functions should be written as ufuncs, or we should consider adding
another generic wrapper mechanism that works similarly to ufuncs but
makes fewer guarantees about how the implementation is done.</p></li>
<li><p><strong>Random number generation</strong> with duck arrays, e.g.,
<code class="docutils literal notranslate"><span class="pre">np.random.randn()</span></code>. For example, we might want to add new APIs
like <code class="docutils literal notranslate"><span class="pre">random_like()</span></code> for generating new arrays with a matching
shape <em>and</em> type – though we’ll need to look at some real examples
of how these functions are used to figure out what would be helpful.</p></li>
<li><p><strong>Miscellaneous other functions</strong> such as <code class="docutils literal notranslate"><span class="pre">np.einsum</span></code>,
<code class="docutils literal notranslate"><span class="pre">np.zeros_like</span></code>, and <code class="docutils literal notranslate"><span class="pre">np.broadcast_to</span></code> that don’t fall into any
of the above categories.</p></li>
<li><p><strong>Checking mutability</strong> on duck arrays, which would imply that they
support assignment with <code class="docutils literal notranslate"><span class="pre">__setitem__</span></code> and the out argument to
ufuncs. Many otherwise fine duck arrays are not easily mutable (for
example, because they use some kinds of sparse or compressed
storage, or are in read-only shared memory), and it turns out that
frequently-used code like the default implementation of <code class="docutils literal notranslate"><span class="pre">np.mean</span></code>
needs to check this (to decide whether it can reuse temporary
arrays).</p></li>
</ul>
<p>We intentionally do not describe exactly how to add support for these
types of duck arrays here. These will be the subject of future NEPs.</p>
</section>
</section>
<section id="copyright">
<h2>Copyright<a class="headerlink" href="#copyright" title="Link to this heading">#</a></h2>
<p>This document has been placed in the public domain.</p>
</section>
</section>
</article>
</div>
<dialog id="pst-secondary-sidebar-modal"></dialog>
<div id="pst-secondary-sidebar" class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
<div class="sidebar-secondary-item">
<div
id="pst-page-navigation-heading-2"
class="page-toc tocsection onthispage">
<i class="fa-solid fa-list"></i> On this page
</div>
<nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#abstract">Abstract</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#detailed-description">Detailed description</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#terminology">Terminology</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#general-approach">General approach</a><ul class="nav section-nav flex-column">
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#principle-1-focus-on-full-duck-arrays-but-dont-rule-out-partial-duck-arrays">Principle 1: focus on “full” duck arrays, but don’t rule out “partial” duck arrays</a></li>
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#principle-2-take-advantage-of-duck-typing">Principle 2: take advantage of duck typing</a></li>
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#principle-3-focus-on-protocols">Principle 3: focus on protocols</a></li>
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#principle-4-reuse-existing-methods-when-possible">Principle 4: reuse existing methods when possible</a></li>
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#principle-5-make-it-easy-to-do-the-right-thing">Principle 5: make it easy to do the right thing</a></li>
</ul>
</li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tentative-duck-array-guidelines">Tentative duck array guidelines</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#a-very-rough-sketch-of-future-plans">A (very) rough sketch of future plans</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#copyright">Copyright</a></li>
</ul>
</nav></div>
</div></div>
</div>
<footer class="bd-footer-content">
</footer>
</main>
</div>
</div>
<!-- Scripts loaded after <body> so the DOM is not blocked -->
<script defer src="_static/scripts/bootstrap.js?digest=26a4bc78f4c0ddb94549"></script>
<script defer src="_static/scripts/pydata-sphinx-theme.js?digest=26a4bc78f4c0ddb94549"></script>
<footer class="bd-footer">
<div class="bd-footer__inner bd-page-width">
<div class="footer-items__start">
<div class="footer-item">
<p class="copyright">
© Copyright 2017-2024, NumPy Developers.
<br/>
</p>
</div>
<div class="footer-item">
<p class="sphinx-version">
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 7.2.6.
<br/>
</p>
</div>
</div>
<div class="footer-items__end">
<div class="footer-item">
<p class="theme-version">
Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.16.0.
</p></div>
</div>
</div>
</footer>
</body>
</html>