This repository has been archived by the owner on May 6, 2021. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 40
/
arrays.ndarray.html
765 lines (763 loc) · 94.4 KB
/
arrays.ndarray.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
<span id="arrays-ndarray"></span><h1><span class="yiyi-st" id="yiyi-29">N维数组（<code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code>）</span></h1>
<blockquote>
<p>原文：<a href="https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html">https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html</a></p>
<p>译者：<a href="https://github.com/wizardforcel">飞龙</a> <a href="http://usyiyi.cn/">UsyiyiCN</a></p>
<p>校对：（虚位以待）</p>
</blockquote>
<p><span class="yiyi-st" id="yiyi-30"><a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>是（通常大小固定的）一个多维容器，由相同类型和大小的元素组成。</span><span class="yiyi-st" id="yiyi-31">数组中的维度和元素数量由其<a class="reference internal" href="generated/numpy.ndarray.shape.html#numpy.ndarray.shape" title="numpy.ndarray.shape"><code class="xref py py-attr docutils literal"><span class="pre">shape</span></code></a>定义，它是由<em>N</em>个正整数组成的<a class="reference external" href="https://docs.python.org/dev/library/stdtypes.html#tuple" title="(in Python v3.7)"><code class="xref py py-class docutils literal"><span class="pre">元组</span></code></a>，每个整数指定每个维度的大小。</span><span class="yiyi-st" id="yiyi-32">数组中元素的类型由单独的<a class="reference internal" href="arrays.dtypes.html#arrays-dtypes"><span class="std std-ref">数据类型对象 (dtype)</span></a>指定，每个ndarray与其中一个对象相关联。</span></p>
<p><span class="yiyi-st" id="yiyi-33">与Python中的其他容器对象一样，<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>的内容可以通过<a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">索引或切片</span></a>（例如使用<em>N</em>个整数）、以及<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>的方法和属性访问和修改数组。</span></p>
<p id="index-0"><span class="yiyi-st" id="yiyi-34">不同的<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarrays</span></code></a>可以共享相同的数据，使得在一个<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>中进行的改变在另一个中可见。</span><span class="yiyi-st" id="yiyi-35">也就是说，ndarray可以是到另一个ndarray的<em>“view”</em>，并且其引用的数据由<em>“base”</em> ndarray处理。</span><span class="yiyi-st" id="yiyi-36">ndarrays还可以是由Python <a class="reference external" href="https://docs.python.org/dev/library/stdtypes.html#str" title="(in Python v3.7)"><code class="xref py py-class docutils literal"><span class="pre">strings</span></code></a>或实现<code class="xref py py-class docutils literal"><span class="pre">buffer</span></code>或<a class="reference internal" href="arrays.interface.html#arrays-interface"><span class="std std-ref">array</span></a>接口的对象拥有的内存的视图。</span></p>
<div class="admonition-example admonition">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-37">例</span></p>
<p><span class="yiyi-st" id="yiyi-38">尺寸为2×3的二维数组，由4字节整数元素组成：</span></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go"><type 'numpy.ndarray'></span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(2, 3)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">dtype('int32')</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-39">数组可以使用类似Python容器的语法进行索引：</span></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># The element of x in the *second* row, *third* column, namely, 6.</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-40">例如，<a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">切片</span></a>可以生成数组的视图：</span></p>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">array([2, 5])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">9</span> <span class="c1"># this also changes the corresponding element in x</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">array([9, 5])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">array([[1, 9, 3],</span>
<span class="go"> [4, 5, 6]])</span>
</pre></div>
</div>
</div>
<div class="section" id="constructing-arrays">
<h2><span class="yiyi-st" id="yiyi-41">构造数组</span></h2>
<p><span class="yiyi-st" id="yiyi-42">新数组可以使用<a class="reference internal" href="routines.array-creation.html#routines-array-creation"><span class="std std-ref">数组创建例程</span></a>中详述的例程以及使用低级<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>构造函数构建：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-43"><a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal"><span class="pre">ndarray</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-44">数组对象，表示一个多维、同质、元素大小固定的数组。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="indexing-arrays">
<span id="arrays-ndarray-indexing"></span><h2><span class="yiyi-st" id="yiyi-45">索引数组</span></h2>
<p><span class="yiyi-st" id="yiyi-46">数组可以使用扩展的Python切片语法<code class="docutils literal"><span class="pre">array[selection]</span></code>来索引。</span><span class="yiyi-st" id="yiyi-47">类似的语法也用于访问<span class="xref std std-ref">结构化数组</span>中的字段。</span></p>
<div class="admonition seealso">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-48">另见</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-49"><a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">数组索引</span></a>。</span></p>
</div>
</div>
<div class="section" id="internal-memory-layout-of-an-ndarray">
<h2><span class="yiyi-st" id="yiyi-50">ndarray的内存布局</span></h2>
<p><span class="yiyi-st" id="yiyi-51">类<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>的实例由计算机存储器的一维连续段（由该数组、或一些其他对象拥有）组成，结合索引方案映射<em>N</em>个整数为块中元素的位置。</span><span class="yiyi-st" id="yiyi-52">索引可以变化的范围由数组的<a class="reference internal" href="generated/numpy.ndarray.shape.html#numpy.ndarray.shape" title="numpy.ndarray.shape"><code class="xref py py-obj docutils literal"><span class="pre">shape</span></code></a>指定。</span><span class="yiyi-st" id="yiyi-53">每个元素占用多少字节以及如何解释字节由与数组相关联的<a class="reference internal" href="arrays.dtypes.html#arrays-dtypes"><span class="std std-ref">数据类型对象</span></a>定义。</span></p>
<p id="index-1"><span class="yiyi-st" id="yiyi-54">存储器段本质上是1维的，并且将<em>N</em>维数组的元素组织为1维的内存块有许多不同方案。</span><span class="yiyi-st" id="yiyi-55">NumPy是灵活的，<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>对象可以适应任何<em>strided索引方案</em>。</span><span class="yiyi-st" id="yiyi-56">在一个strided方案中，N维索引<img alt="(n_0, n_1, ..., n_{N-1})" class="math" src="../_images/math/55e2912fdc1638596e19ae6361f2d2db30450efc.png" style="vertical-align: -4px">对应于偏移量（以字节为单位）：</span></p>
<div class="math">
<p><img alt="n_{\mathrm{offset}} = \sum_{k=0}^{N-1} s_k n_k" src="../_images/math/17fdc8233ee40cccdfeba940091cd2630850581a.png"></p>
</div><p><span class="yiyi-st" id="yiyi-57">从与数组相关联的存储器块的开始。</span><span class="yiyi-st" id="yiyi-58">这里，<img alt="s_k" class="math" src="../_images/math/d5ab8e6081db79a4afcf29b7616b48025de0e80c.png" style="vertical-align: -2px">是指定数组的<a class="reference internal" href="generated/numpy.ndarray.strides.html#numpy.ndarray.strides" title="numpy.ndarray.strides"><code class="xref py py-obj docutils literal"><span class="pre">strides</span></code></a>的整数。</span><span class="yiyi-st" id="yiyi-59"><a class="reference internal" href="../glossary.html#term-column-major"><span class="xref std std-term">column-major</span></a>顺序（例如在Fortran语言和<em>Matlab</em>中使用）和<a class="reference internal" href="../glossary.html#term-row-major"><span class="xref std std-term">row-major</span></a>方案仅仅是特定种类的跨距方案，并且对应于可以通过步幅<em>寻址的存储器</em>：</span></p>
<div class="math">
<p><img alt="s_k^{\mathrm{column}} = \mathrm{itemsize} \prod_{j=0}^{k-1} d_j ,
\quad s_k^{\mathrm{row}} = \mathrm{itemsize} \prod_{j=k+1}^{N-1} d_j ." src="../_images/math/c28fa0657966b37e188e72dbfdb5dbb3a0707d63.png"></p>
</div><p id="index-2"><span class="yiyi-st" id="yiyi-60">其中<img alt="d_j" class="math" src="../_images/math/c375e8b96fc69dad77ee104906be2a9df776ad66.png" style="vertical-align: -4px"> <em class="xref py py-obj">= self.shape [j]</em>。</span></p>
<p><span class="yiyi-st" id="yiyi-61">C和Fortran顺序都是<a class="reference external" href="https://docs.python.org/dev/glossary.html#term-contiguous" title="(in Python v3.7)"><span class="xref std std-term">contiguous</span></a>，<em>即</em> <span class="xref std std-term">单段</span>的存储器布局，其中可以访问存储器块的每个部分通过指数的一些组合。</span></p>
<p><span class="yiyi-st" id="yiyi-62">虽然具有相应标志设置的C风格和Fortran风格的连续数组可以用上述步幅来解决，但实际的步幅可能不同。</span><span class="yiyi-st" id="yiyi-63">这可能发生在两种情况下：</span></p>
<blockquote>
<div><ol class="arabic simple">
<li><span class="yiyi-st" id="yiyi-64">如果<code class="docutils literal"><span class="pre">self.shape [k]</span> <span class="pre">==</span> <span class="pre">1</span></code>，则对于任何法定索引<code class="docutils literal"><span class="pre">[k]</span> <span class="pre">==</span> <span class="pre">0</span></code>。</span><span class="yiyi-st" id="yiyi-65">这意味着在偏移<img alt="n_k = 0" class="math" src="../_images/math/9fed4bdf69e096e1239bd758fe79e8076f28d793.png" style="vertical-align: -2px">的公式中，因此<img alt="s_k n_k = 0" class="math" src="../_images/math/36e9c492db6f4ad8e065f978a3d4a33a3c6df086.png" style="vertical-align: -2px">和<img alt="s_k" class="math" src="../_images/math/d5ab8e6081db79a4afcf29b7616b48025de0e80c.png" style="vertical-align: -2px"> <em class="xref py py-obj">= self.strides [k]</em>的值是任意的。</span></li>
<li><span class="yiyi-st" id="yiyi-66">如果数组没有元素（<code class="docutils literal"><span class="pre">self.size</span> <span class="pre">==</span> <span class="pre">0</span></code>），则没有法律索引和步长从不使用。</span><span class="yiyi-st" id="yiyi-67">任何没有元素的数组都可以被认为是C风格和Fortran风格的连续。</span></li>
</ol>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-68">点1.意味着<code class="docutils literal"><span class="pre">self</span></code>和<code class="docutils literal"><span class="pre">self.squeeze()</span></code>始终具有相同的邻接和<span class="xref std std-term">对齐</span>标志值。</span><span class="yiyi-st" id="yiyi-69">这也意味着，即使一个高维的数组可以是C风格和Fortran风格的连续的同时。</span></p>
<p id="index-3"><span class="yiyi-st" id="yiyi-70">如果所有元素的内存偏移和基本偏移本身是<em class="xref py py-obj">self.itemsize</em>的倍数，则认为数组是对齐的。</span></p>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-71">注意</span></p>
<p><span class="yiyi-st" id="yiyi-72">默认情况下，点（1）和（2）尚未应用。</span><span class="yiyi-st" id="yiyi-73">从NumPy 1.8.0开始，只有在构建NumPy时定义了环境变量<code class="docutils literal"><span class="pre">NPY_RELAXED_STRIDES_CHECKING=1</span></code>，它们才会一致应用。</span><span class="yiyi-st" id="yiyi-74">最终这将成为默认值。</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-75">你可以通过查看<code class="docutils literal"><span class="pre">np.ones（（10,1），</span> <span class="pre">order ='C'）的值建立NumPy时是否启用此选项。flags .f_contiguous</span></code>。</span><span class="yiyi-st" id="yiyi-76">如果这是<code class="docutils literal"><span class="pre">True</span></code>，则您的NumPy已启用宽松检查。</span></p>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-77">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-78"><em>不</em>一般认为<code class="docutils literal"><span class="pre">self.strides [-1]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span> / t1>用于C型连续数组或<code class="docutils literal"><span class="pre">self.strides [0]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span> Fortran风格的连续数组是真的。</code></code></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-79">除非另有规定，否则新<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarrays</span></code></a>中的数据处于<a class="reference internal" href="../glossary.html#term-row-major"><span class="xref std std-term">row-major</span></a>（C）顺序，但是例如<a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">basic array slicing</span></a> <a class="reference internal" href="../glossary.html#term-view"><span class="xref std std-term">views</span></a>。</span></p>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-80">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-81">NumPy中的几个算法可以处理任意跨度的数组。</span><span class="yiyi-st" id="yiyi-82">然而，一些算法需要单段数组。</span><span class="yiyi-st" id="yiyi-83">当将不规则跨度的数组传递到这样的算法中时，自动地进行复制。</span></p>
</div>
</div>
<div class="section" id="array-attributes">
<span id="arrays-ndarray-attributes"></span><h2><span class="yiyi-st" id="yiyi-84">Array attributes</span></h2>
<p><span class="yiyi-st" id="yiyi-85">数组属性反映数组本身固有的信息。</span><span class="yiyi-st" id="yiyi-86">通常，通过其属性访问数组允许您获取并有时设置数组的固有属性，而不创建新的数组。</span><span class="yiyi-st" id="yiyi-87">暴露的属性是数组的核心部分，只有其中的一些可以有意义地重置，而不创建新的数组。</span><span class="yiyi-st" id="yiyi-88">下面给出每个属性的信息。</span></p>
<div class="section" id="memory-layout">
<h3><span class="yiyi-st" id="yiyi-89">Memory layout</span></h3>
<p><span class="yiyi-st" id="yiyi-90">以下属性包含有关数组的内存布局的信息：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-91"><a class="reference internal" href="generated/numpy.ndarray.flags.html#numpy.ndarray.flags" title="numpy.ndarray.flags"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.flags</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-92">有关数组的内存布局的信息。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-93"><a class="reference internal" href="generated/numpy.ndarray.shape.html#numpy.ndarray.shape" title="numpy.ndarray.shape"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.shape</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-94">数组维数组。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-95"><a class="reference internal" href="generated/numpy.ndarray.strides.html#numpy.ndarray.strides" title="numpy.ndarray.strides"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.strides</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-96">遍历数组时，在每个维度中步进的字节数组。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-97"><a class="reference internal" href="generated/numpy.ndarray.ndim.html#numpy.ndarray.ndim" title="numpy.ndarray.ndim"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.ndim</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-98">数组维数。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-99"><a class="reference internal" href="generated/numpy.ndarray.data.html#numpy.ndarray.data" title="numpy.ndarray.data"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.data</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-100">Python缓冲区对象指向数组的数据的开始。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-101"><a class="reference internal" href="generated/numpy.ndarray.size.html#numpy.ndarray.size" title="numpy.ndarray.size"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.size</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-102">数组中的元素数。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-103"><a class="reference internal" href="generated/numpy.ndarray.itemsize.html#numpy.ndarray.itemsize" title="numpy.ndarray.itemsize"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.itemsize</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-104">一个数组元素的长度（以字节为单位）。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-105"><a class="reference internal" href="generated/numpy.ndarray.nbytes.html#numpy.ndarray.nbytes" title="numpy.ndarray.nbytes"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.nbytes</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-106">数组的元素消耗的总字节数。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-107"><a class="reference internal" href="generated/numpy.ndarray.base.html#numpy.ndarray.base" title="numpy.ndarray.base"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.base</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-108">如果内存是来自某个其他对象的基本对象。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="data-type">
<h3><span class="yiyi-st" id="yiyi-109">Data type</span></h3>
<div class="admonition seealso">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-110">也可以看看</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-111"><a class="reference internal" href="arrays.dtypes.html#arrays-dtypes"><span class="std std-ref">Data type objects</span></a></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-112">与数组相关联的数据类型对象可以在<a class="reference internal" href="generated/numpy.ndarray.dtype.html#numpy.ndarray.dtype" title="numpy.ndarray.dtype"><code class="xref py py-attr docutils literal"><span class="pre">dtype</span></code></a>属性中找到：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-113"><a class="reference internal" href="generated/numpy.ndarray.dtype.html#numpy.ndarray.dtype" title="numpy.ndarray.dtype"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.dtype</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-114">数组元素的数据类型。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="other-attributes">
<h3><span class="yiyi-st" id="yiyi-115">Other attributes</span></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-116"><a class="reference internal" href="generated/numpy.ndarray.T.html#numpy.ndarray.T" title="numpy.ndarray.T"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.T</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-117">与self.transpose()相同，除非self是self.ndim返回</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-118"><a class="reference internal" href="generated/numpy.ndarray.real.html#numpy.ndarray.real" title="numpy.ndarray.real"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.real</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-119">数组的真实部分。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-120"><a class="reference internal" href="generated/numpy.ndarray.imag.html#numpy.ndarray.imag" title="numpy.ndarray.imag"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.imag</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-121">数组的虚部。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-122"><a class="reference internal" href="generated/numpy.ndarray.flat.html#numpy.ndarray.flat" title="numpy.ndarray.flat"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.flat</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-123">数组上的1-D迭代器。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-124"><a class="reference internal" href="generated/numpy.ndarray.ctypes.html#numpy.ndarray.ctypes" title="numpy.ndarray.ctypes"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.ctypes</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-125">一个对象，用于简化数组与ctypes模块的交互。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="array-interface">
<span id="arrays-ndarray-array-interface"></span><h3><span class="yiyi-st" id="yiyi-126">Array interface</span></h3>
<div class="admonition seealso">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-127">也可以看看</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-128"><a class="reference internal" href="arrays.interface.html#arrays-interface"><span class="std std-ref">The Array Interface</span></a>。</span></p>
</div>
<table border="1" class="docutils">
<colgroup>
<col width="43%">
<col width="57%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-129"><a class="reference internal" href="arrays.interface.html#__array_interface__" title="__array_interface__"><code class="xref py py-obj docutils literal"><span class="pre">__array_interface__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-130">数组接口的Python端</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-131"><code class="xref py py-obj docutils literal"><span class="pre">__array_struct__</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-132">C组的数组接口</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="ctypes-foreign-function-interface">
<h3><span class="yiyi-st" id="yiyi-133"><a class="reference external" href="https://docs.python.org/dev/library/ctypes.html#module-ctypes" title="(in Python v3.7)"><code class="xref py py-mod docutils literal"><span class="pre">ctypes</span></code></a> foreign function interface</span></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-134"><a class="reference internal" href="generated/numpy.ndarray.ctypes.html#numpy.ndarray.ctypes" title="numpy.ndarray.ctypes"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.ctypes</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-135">一个对象，用于简化数组与ctypes模块的交互。</span></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="array-methods">
<span id="array-ndarray-methods"></span><h2><span class="yiyi-st" id="yiyi-136">Array methods</span></h2>
<p><span class="yiyi-st" id="yiyi-137"><a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>对象有许多以某种方式对数组进行操作的方法，通常返回数组结果。</span><span class="yiyi-st" id="yiyi-138">下面简要解释这些方法。</span><span class="yiyi-st" id="yiyi-139">（每个方法的docstring有一个更完整的描述。）</span></p>
<p><span class="yiyi-st" id="yiyi-140">For the following methods there are also corresponding functions in <a class="reference internal" href="index.html#module-numpy" title="numpy"><code class="xref py py-mod docutils literal"><span class="pre">numpy</span></code></a>: <a class="reference internal" href="generated/numpy.all.html#numpy.all" title="numpy.all"><code class="xref py py-func docutils literal"><span class="pre">all</span></code></a>, <a class="reference internal" href="generated/numpy.any.html#numpy.any" title="numpy.any"><code class="xref py py-func docutils literal"><span class="pre">any</span></code></a>, <a class="reference internal" href="generated/numpy.argmax.html#numpy.argmax" title="numpy.argmax"><code class="xref py py-func docutils literal"><span class="pre">argmax</span></code></a>, <a class="reference internal" href="generated/numpy.argmin.html#numpy.argmin" title="numpy.argmin"><code class="xref py py-func docutils literal"><span class="pre">argmin</span></code></a>, <a class="reference internal" href="generated/numpy.argpartition.html#numpy.argpartition" title="numpy.argpartition"><code class="xref py py-func docutils literal"><span class="pre">argpartition</span></code></a>, <a class="reference internal" href="generated/numpy.argsort.html#numpy.argsort" title="numpy.argsort"><code class="xref py py-func docutils literal"><span class="pre">argsort</span></code></a>, <a class="reference internal" href="generated/numpy.choose.html#numpy.choose" title="numpy.choose"><code class="xref py py-func docutils literal"><span class="pre">choose</span></code></a>, <a class="reference internal" href="generated/numpy.clip.html#numpy.clip" title="numpy.clip"><code class="xref py py-func docutils literal"><span class="pre">clip</span></code></a>, <a class="reference internal" href="generated/numpy.compress.html#numpy.compress" title="numpy.compress"><code class="xref py py-func docutils literal"><span class="pre">compress</span></code></a>, <a class="reference internal" href="generated/numpy.copy.html#numpy.copy" title="numpy.copy"><code class="xref py py-func docutils literal"><span class="pre">copy</span></code></a>, <a class="reference internal" href="generated/numpy.cumprod.html#numpy.cumprod" title="numpy.cumprod"><code class="xref py py-func docutils literal"><span class="pre">cumprod</span></code></a>, <a class="reference internal" href="generated/numpy.cumsum.html#numpy.cumsum" title="numpy.cumsum"><code class="xref py py-func docutils literal"><span class="pre">cumsum</span></code></a>, <a class="reference internal" href="generated/numpy.diagonal.html#numpy.diagonal" title="numpy.diagonal"><code class="xref py py-func docutils literal"><span class="pre">diagonal</span></code></a>, <a class="reference internal" href="generated/numpy.imag.html#numpy.imag" title="numpy.imag"><code class="xref py py-func docutils literal"><span class="pre">imag</span></code></a>, <a class="reference internal" href="generated/numpy.amax.html#numpy.amax" title="numpy.amax"><code class="xref py py-func docutils literal"><span class="pre">max</span></code></a>, <a class="reference internal" href="generated/numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-func docutils literal"><span class="pre">mean</span></code></a>, <a class="reference internal" href="generated/numpy.amin.html#numpy.amin" title="numpy.amin"><code class="xref py py-func docutils literal"><span class="pre">min</span></code></a>, <a class="reference internal" href="generated/numpy.nonzero.html#numpy.nonzero" title="numpy.nonzero"><code class="xref py py-func docutils literal"><span class="pre">nonzero</span></code></a>, <a class="reference internal" href="generated/numpy.partition.html#numpy.partition" title="numpy.partition"><code class="xref py py-func docutils literal"><span class="pre">partition</span></code></a>, <a class="reference internal" href="generated/numpy.prod.html#numpy.prod" title="numpy.prod"><code class="xref py py-func docutils literal"><span class="pre">prod</span></code></a>, <a class="reference internal" href="generated/numpy.ptp.html#numpy.ptp" title="numpy.ptp"><code class="xref py py-func docutils literal"><span class="pre">ptp</span></code></a>, <a class="reference internal" href="generated/numpy.put.html#numpy.put" title="numpy.put"><code class="xref py py-func docutils literal"><span class="pre">put</span></code></a>, <a class="reference internal" href="generated/numpy.ravel.html#numpy.ravel" title="numpy.ravel"><code class="xref py py-func docutils literal"><span class="pre">ravel</span></code></a>, <a class="reference internal" href="generated/numpy.real.html#numpy.real" title="numpy.real"><code class="xref py py-func docutils literal"><span class="pre">real</span></code></a>, <a class="reference internal" href="generated/numpy.repeat.html#numpy.repeat" title="numpy.repeat"><code class="xref py py-func docutils literal"><span class="pre">repeat</span></code></a>, <a class="reference internal" href="generated/numpy.reshape.html#numpy.reshape" title="numpy.reshape"><code class="xref py py-func docutils literal"><span class="pre">reshape</span></code></a>, <a class="reference internal" href="generated/numpy.around.html#numpy.around" title="numpy.around"><code class="xref py py-func docutils literal"><span class="pre">round</span></code></a>, <a class="reference internal" href="generated/numpy.searchsorted.html#numpy.searchsorted" title="numpy.searchsorted"><code class="xref py py-func docutils literal"><span class="pre">searchsorted</span></code></a>, <a class="reference internal" href="generated/numpy.sort.html#numpy.sort" title="numpy.sort"><code class="xref py py-func docutils literal"><span class="pre">sort</span></code></a>, <a class="reference internal" href="generated/numpy.squeeze.html#numpy.squeeze" title="numpy.squeeze"><code class="xref py py-func docutils literal"><span class="pre">squeeze</span></code></a>, <a class="reference internal" href="generated/numpy.std.html#numpy.std" title="numpy.std"><code class="xref py py-func docutils literal"><span class="pre">std</span></code></a>, <a class="reference internal" href="generated/numpy.sum.html#numpy.sum" title="numpy.sum"><code class="xref py py-func docutils literal"><span class="pre">sum</span></code></a>, <a class="reference internal" href="generated/numpy.swapaxes.html#numpy.swapaxes" title="numpy.swapaxes"><code class="xref py py-func docutils literal"><span class="pre">swapaxes</span></code></a>, <a class="reference internal" href="generated/numpy.take.html#numpy.take" title="numpy.take"><code class="xref py py-func docutils literal"><span class="pre">take</span></code></a>, <a class="reference internal" href="generated/numpy.trace.html#numpy.trace" title="numpy.trace"><code class="xref py py-func docutils literal"><span class="pre">trace</span></code></a>, <a class="reference internal" href="generated/numpy.transpose.html#numpy.transpose" title="numpy.transpose"><code class="xref py py-func docutils literal"><span class="pre">transpose</span></code></a>, <a class="reference internal" href="generated/numpy.var.html#numpy.var" title="numpy.var"><code class="xref py py-func docutils literal"><span class="pre">var</span></code></a>.</span></p>
<div class="section" id="array-conversion">
<h3><span class="yiyi-st" id="yiyi-141">Array conversion</span></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-142"><a class="reference internal" href="generated/numpy.ndarray.item.html#numpy.ndarray.item" title="numpy.ndarray.item"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.item</span></code></a>（\ * args）</span></td>
<td><span class="yiyi-st" id="yiyi-143">提取数组中的元素</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-144"><a class="reference internal" href="generated/numpy.ndarray.tolist.html#numpy.ndarray.tolist" title="numpy.ndarray.tolist"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.tolist</span></code></a>()</span></td>
<td><span class="yiyi-st" id="yiyi-145">将数组返回为（可能是嵌套的）列表。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-146"><a class="reference internal" href="generated/numpy.ndarray.itemset.html#numpy.ndarray.itemset" title="numpy.ndarray.itemset"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.itemset</span></code></a>（\ * args）</span></td>
<td><span class="yiyi-st" id="yiyi-147">在指定位置插入标量值（修改原始数组）</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-148"><a class="reference internal" href="generated/numpy.ndarray.tostring.html#numpy.ndarray.tostring" title="numpy.ndarray.tostring"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.tostring</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-149">在数组中构造包含原始数据字节的Python字节。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-150"><a class="reference internal" href="generated/numpy.ndarray.tobytes.html#numpy.ndarray.tobytes" title="numpy.ndarray.tobytes"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.tobytes</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-151">在数组中构造包含原始数据字节的Python字节。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-152"><a class="reference internal" href="generated/numpy.ndarray.tofile.html#numpy.ndarray.tofile" title="numpy.ndarray.tofile"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.tofile</span></code></a>（fid [，sep，format]）</span></td>
<td><span class="yiyi-st" id="yiyi-153">将数组作为文本或二进制（默认）写入文件。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-154"><a class="reference internal" href="generated/numpy.ndarray.dump.html#numpy.ndarray.dump" title="numpy.ndarray.dump"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.dump</span></code></a>（file）</span></td>
<td><span class="yiyi-st" id="yiyi-155">将数组的pickle转储到指定的文件。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-156"><a class="reference internal" href="generated/numpy.ndarray.dumps.html#numpy.ndarray.dumps" title="numpy.ndarray.dumps"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.dumps</span></code></a>()</span></td>
<td><span class="yiyi-st" id="yiyi-157">以字符串形式返回数组的pickle。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-158"><a class="reference internal" href="generated/numpy.ndarray.astype.html#numpy.ndarray.astype" title="numpy.ndarray.astype"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.astype</span></code></a>（dtype [，order，casting，...]）</span></td>
<td><span class="yiyi-st" id="yiyi-159">数组的复制，强制转换为指定的类型。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-160"><a class="reference internal" href="generated/numpy.ndarray.byteswap.html#numpy.ndarray.byteswap" title="numpy.ndarray.byteswap"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.byteswap</span></code></a>（inplace）</span></td>
<td><span class="yiyi-st" id="yiyi-161">交换数组元素的字节</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-162"><a class="reference internal" href="generated/numpy.ndarray.copy.html#numpy.ndarray.copy" title="numpy.ndarray.copy"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.copy</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-163">返回数组的副本。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-164"><a class="reference internal" href="generated/numpy.ndarray.view.html#numpy.ndarray.view" title="numpy.ndarray.view"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.view</span></code></a>（[dtype，type]）</span></td>
<td><span class="yiyi-st" id="yiyi-165">数组的新视图与相同的数据。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-166"><a class="reference internal" href="generated/numpy.ndarray.getfield.html#numpy.ndarray.getfield" title="numpy.ndarray.getfield"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.getfield</span></code></a>（dtype [，offset]）</span></td>
<td><span class="yiyi-st" id="yiyi-167">将给定数组的字段返回为特定类型。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-168"><a class="reference internal" href="generated/numpy.ndarray.setflags.html#numpy.ndarray.setflags" title="numpy.ndarray.setflags"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.setflags</span></code></a>（[write，align，uic]）</span></td>
<td><span class="yiyi-st" id="yiyi-169">分别设置数组标志WRITEABLE，ALIGNED和UPDATEIFCOPY。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-170"><a class="reference internal" href="generated/numpy.ndarray.fill.html#numpy.ndarray.fill" title="numpy.ndarray.fill"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.fill</span></code></a>（value）</span></td>
<td><span class="yiyi-st" id="yiyi-171">使用标量值填充数组。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="shape-manipulation">
<h3><span class="yiyi-st" id="yiyi-172">Shape manipulation</span></h3>
<p><span class="yiyi-st" id="yiyi-173">对于重塑，调整大小和转置，单个元组参数可以替换为<code class="docutils literal"><span class="pre">n</span></code>整数，这将被解释为n元组。</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-174"><a class="reference internal" href="generated/numpy.ndarray.reshape.html#numpy.ndarray.reshape" title="numpy.ndarray.reshape"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.reshape</span></code></a>（shape [，order]）</span></td>
<td><span class="yiyi-st" id="yiyi-175">返回包含具有新形状的相同数据的数组。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-176"><a class="reference internal" href="generated/numpy.ndarray.resize.html#numpy.ndarray.resize" title="numpy.ndarray.resize"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.resize</span></code></a>（new_shape [，refcheck]）</span></td>
<td><span class="yiyi-st" id="yiyi-177">就地更改数组的形状和大小。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-178"><a class="reference internal" href="generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose" title="numpy.ndarray.transpose"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.transpose</span></code></a>（\ * axes）</span></td>
<td><span class="yiyi-st" id="yiyi-179">返回具有轴转置的数组的视图。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-180"><a class="reference internal" href="generated/numpy.ndarray.swapaxes.html#numpy.ndarray.swapaxes" title="numpy.ndarray.swapaxes"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.swapaxes</span></code></a>（axis1，axis2）</span></td>
<td><span class="yiyi-st" id="yiyi-181">返回数组的视图，其中<em class="xref py py-obj">axis1</em>和<em class="xref py py-obj">axis2</em>互换。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-182"><a class="reference internal" href="generated/numpy.ndarray.flatten.html#numpy.ndarray.flatten" title="numpy.ndarray.flatten"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.flatten</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-183">将折叠的数组的副本返回到一个维度。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-184"><a class="reference internal" href="generated/numpy.ndarray.ravel.html#numpy.ndarray.ravel" title="numpy.ndarray.ravel"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.ravel</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-185">返回展平的数组。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-186"><a class="reference internal" href="generated/numpy.ndarray.squeeze.html#numpy.ndarray.squeeze" title="numpy.ndarray.squeeze"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.squeeze</span></code></a>（[axis]）</span></td>
<td><span class="yiyi-st" id="yiyi-187">从<em class="xref py py-obj">a形状删除单维条目</em>。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="item-selection-and-manipulation">
<h3><span class="yiyi-st" id="yiyi-188">Item selection and manipulation</span></h3>
<p><span class="yiyi-st" id="yiyi-189">对于采用<em>轴</em>关键字的数组方法，默认为<code class="xref py py-const docutils literal"><span class="pre">None</span></code>。</span><span class="yiyi-st" id="yiyi-190">如果axis <em>None</em>，则数组被视为1-D数组。</span><span class="yiyi-st" id="yiyi-191"><em>axis</em>的任何其他值表示操作应沿其进行的维度。</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-192"><a class="reference internal" href="generated/numpy.ndarray.take.html#numpy.ndarray.take" title="numpy.ndarray.take"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.take</span></code></a>（indices [，axis，out，mode]）</span></td>
<td><span class="yiyi-st" id="yiyi-193">返回由给定索引处的<em class="xref py py-obj">a</em>元素组成的数组。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-194"><a class="reference internal" href="generated/numpy.ndarray.put.html#numpy.ndarray.put" title="numpy.ndarray.put"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.put</span></code></a>（indices，values [，mode]）</span></td>
<td><span class="yiyi-st" id="yiyi-195">对于所有<em class="xref py py-obj">n</em>，设置<code class="docutils literal"><span class="pre">a.flat [n]</span> <span class="pre">=</span> <span class="pre">在指数。</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-196"><a class="reference internal" href="generated/numpy.ndarray.repeat.html#numpy.ndarray.repeat" title="numpy.ndarray.repeat"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.repeat</span></code></a>（repeat[，axis]）</span></td>
<td><span class="yiyi-st" id="yiyi-197">重复数组的元素。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-198"><a class="reference internal" href="generated/numpy.ndarray.choose.html#numpy.ndarray.choose" title="numpy.ndarray.choose"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.choose</span></code></a>（choices [，out，mode]）</span></td>
<td><span class="yiyi-st" id="yiyi-199">使用索引数组从一组选择中构造新的数组。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-200"><a class="reference internal" href="generated/numpy.ndarray.sort.html#numpy.ndarray.sort" title="numpy.ndarray.sort"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.sort</span></code></a>（[axis，kind，order]）</span></td>
<td><span class="yiyi-st" id="yiyi-201">就地对数组进行排序。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-202"><a class="reference internal" href="generated/numpy.ndarray.argsort.html#numpy.ndarray.argsort" title="numpy.ndarray.argsort"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.argsort</span></code></a>（[axis，kind，order]）</span></td>
<td><span class="yiyi-st" id="yiyi-203">返回将此数组排序的索引。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-204"><a class="reference internal" href="generated/numpy.ndarray.partition.html#numpy.ndarray.partition" title="numpy.ndarray.partition"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.partition</span></code></a>（kth [，axis，kind，order]）</span></td>
<td><span class="yiyi-st" id="yiyi-205">重新排列数组中的元素，使得第k个位置的元素的值在排序数组中的位置。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-206"><a class="reference internal" href="generated/numpy.ndarray.argpartition.html#numpy.ndarray.argpartition" title="numpy.ndarray.argpartition"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.argpartition</span></code></a>（kth [，axis，kind，order]）</span></td>
<td><span class="yiyi-st" id="yiyi-207">返回将对此数组进行分区的索引。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-208"><a class="reference internal" href="generated/numpy.ndarray.searchsorted.html#numpy.ndarray.searchsorted" title="numpy.ndarray.searchsorted"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.searchsorted</span></code></a>（v [，side，sorter]）</span></td>
<td><span class="yiyi-st" id="yiyi-209">查找索引，其中v的元素应插入到a以维持顺序。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-210"><a class="reference internal" href="generated/numpy.ndarray.nonzero.html#numpy.ndarray.nonzero" title="numpy.ndarray.nonzero"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.nonzero</span></code></a>()</span></td>
<td><span class="yiyi-st" id="yiyi-211">返回非零元素的索引。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-212"><a class="reference internal" href="generated/numpy.ndarray.compress.html#numpy.ndarray.compress" title="numpy.ndarray.compress"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.compress</span></code></a>（condition [，axis，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-213">沿给定轴返回此数组的所选切片。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-214"><a class="reference internal" href="generated/numpy.ndarray.diagonal.html#numpy.ndarray.diagonal" title="numpy.ndarray.diagonal"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.diagonal</span></code></a>（[offset，axis1，axis2]）</span></td>
<td><span class="yiyi-st" id="yiyi-215">返回指定的对角线。</span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="calculation">
<h3><span class="yiyi-st" id="yiyi-216">Calculation</span></h3>
<p id="index-4"><span class="yiyi-st" id="yiyi-217">这些方法中的许多采用名为<em>axis</em>的参数。</span><span class="yiyi-st" id="yiyi-218">在这种情况下，</span></p>
<ul class="simple">
<li><span class="yiyi-st" id="yiyi-219">如果<em>axis</em>为<em>None</em>（默认值），则将数组视为1-D数组，并对整个数组执行该操作。</span><span class="yiyi-st" id="yiyi-220">如果self是一个0维数组或数组标量，这个行为也是默认的。</span><span class="yiyi-st" id="yiyi-221">（数组标量是类型/类float32，float64等的实例，而0维数组是一个包含正好一个数组标量的ndarray实例。</span></li>
<li><span class="yiyi-st" id="yiyi-222">如果<em>axis</em>是整数，则在给定轴上进行该操作（对于可以沿给定轴创建的每个1-D子阵列）。</span></li>
</ul>
<div class="admonition-example-of-the-axis-argument admonition">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-223"><em>轴</em>参数示例</span></p>
<p><span class="yiyi-st" id="yiyi-224">尺寸为3×3×3的三维数组，在其三个轴中的每一个上求和</span></p>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span>
<span class="go">array([[[ 0, 1, 2],</span>
<span class="go"> [ 3, 4, 5],</span>
<span class="go"> [ 6, 7, 8]],</span>
<span class="go"> [[ 9, 10, 11],</span>
<span class="go"> [12, 13, 14],</span>
<span class="go"> [15, 16, 17]],</span>
<span class="go"> [[18, 19, 20],</span>
<span class="go"> [21, 22, 23],</span>
<span class="go"> [24, 25, 26]]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([[27, 30, 33],</span>
<span class="go"> [36, 39, 42],</span>
<span class="go"> [45, 48, 51]])</span>
<span class="gp">>>> </span><span class="c1"># for sum, axis is the first keyword, so we may omit it,</span>
<span class="gp">>>> </span><span class="c1"># specifying only its value</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">(array([[27, 30, 33],</span>
<span class="go"> [36, 39, 42],</span>
<span class="go"> [45, 48, 51]]),</span>
<span class="go"> array([[ 9, 12, 15],</span>
<span class="go"> [36, 39, 42],</span>
<span class="go"> [63, 66, 69]]),</span>
<span class="go"> array([[ 3, 12, 21],</span>
<span class="go"> [30, 39, 48],</span>
<span class="go"> [57, 66, 75]]))</span>
</pre></div>
</div>
</div>
<p><span class="yiyi-st" id="yiyi-225">参数<em>dtype</em>指定应进行归约运算（如求和）的数据类型。</span><span class="yiyi-st" id="yiyi-226">默认减少数据类型与<em>self</em>的数据类型相同。</span><span class="yiyi-st" id="yiyi-227">为了避免溢出，使用较大的数据类型执行减少是有用的。</span></p>
<p><span class="yiyi-st" id="yiyi-228">对于多个方法，还可以提供可选的<em>out</em>参数，结果将放置到给定的输出数组中。</span><span class="yiyi-st" id="yiyi-229"><em>out</em>参数必须是<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>，并且具有相同数量的元素。</span><span class="yiyi-st" id="yiyi-230">它可以具有不同的数据类型，在这种情况下将执行转换。</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-231"><a class="reference internal" href="generated/numpy.ndarray.argmax.html#numpy.ndarray.argmax" title="numpy.ndarray.argmax"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.argmax</span></code></a>（[axis，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-232">沿给定轴的最大值的返回指数。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-233"><a class="reference internal" href="generated/numpy.ndarray.min.html#numpy.ndarray.min" title="numpy.ndarray.min"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.min</span></code></a>（[axis，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-234">沿给定轴返回最小值。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-235"><a class="reference internal" href="generated/numpy.ndarray.argmin.html#numpy.ndarray.argmin" title="numpy.ndarray.argmin"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.argmin</span></code></a>（[axis，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-236">沿着<em class="xref py py-obj">a</em>的给定轴的最小值的返回指数。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-237"><a class="reference internal" href="generated/numpy.ndarray.ptp.html#numpy.ndarray.ptp" title="numpy.ndarray.ptp"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.ptp</span></code></a>（[axis，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-238">沿给定轴的峰到峰（最大 - 最小）值。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-239"><a class="reference internal" href="generated/numpy.ndarray.clip.html#numpy.ndarray.clip" title="numpy.ndarray.clip"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.clip</span></code></a>（[min，max，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-240">返回值限于<code class="docutils literal"><span class="pre">[min，</span> <span class="pre">max]</span></code>的数组。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-241"><a class="reference internal" href="generated/numpy.ndarray.conj.html#numpy.ndarray.conj" title="numpy.ndarray.conj"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.conj</span></code></a>()</span></td>
<td><span class="yiyi-st" id="yiyi-242">复共轭所有元素。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-243"><a class="reference internal" href="generated/numpy.ndarray.round.html#numpy.ndarray.round" title="numpy.ndarray.round"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.round</span></code></a>（[decimals，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-244">返回<em class="xref py py-obj">a</em>，每个元素四舍五入为给定的小数位数。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-245"><a class="reference internal" href="generated/numpy.ndarray.trace.html#numpy.ndarray.trace" title="numpy.ndarray.trace"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.trace</span></code></a>（[offset，axis1，axis2，dtype，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-246">沿数组的对角线返回总和。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-247"><a class="reference internal" href="generated/numpy.ndarray.sum.html#numpy.ndarray.sum" title="numpy.ndarray.sum"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.sum</span></code></a>（[axis，dtype，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-248">返回给定轴上的数组元素的总和。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-249"><a class="reference internal" href="generated/numpy.ndarray.cumsum.html#numpy.ndarray.cumsum" title="numpy.ndarray.cumsum"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.cumsum</span></code></a>（[axis，dtype，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-250">返回沿给定轴的元素的累积和。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-251"><a class="reference internal" href="generated/numpy.ndarray.mean.html#numpy.ndarray.mean" title="numpy.ndarray.mean"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.mean</span></code></a>（[axis，dtype，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-252">返回沿给定轴的数组元素的平均值。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-253"><a class="reference internal" href="generated/numpy.ndarray.var.html#numpy.ndarray.var" title="numpy.ndarray.var"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.var</span></code></a>（[axis，dtype，out，ddof，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-254">沿给定轴返回数组元素的方差。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-255"><a class="reference internal" href="generated/numpy.ndarray.std.html#numpy.ndarray.std" title="numpy.ndarray.std"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.std</span></code></a>（[axis，dtype，out，ddof，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-256">返回给定轴上的数组元素的标准偏差。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-257"><a class="reference internal" href="generated/numpy.ndarray.prod.html#numpy.ndarray.prod" title="numpy.ndarray.prod"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.prod</span></code></a>（[axis，dtype，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-258">返回给定轴上的数组元素的乘积</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-259"><a class="reference internal" href="generated/numpy.ndarray.cumprod.html#numpy.ndarray.cumprod" title="numpy.ndarray.cumprod"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.cumprod</span></code></a>（[axis，dtype，out]）</span></td>
<td><span class="yiyi-st" id="yiyi-260">返回沿给定轴的元素的累积乘积。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-261"><a class="reference internal" href="generated/numpy.ndarray.all.html#numpy.ndarray.all" title="numpy.ndarray.all"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.all</span></code></a>（[axis，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-262">如果所有元素均为True，则返回True。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-263"><a class="reference internal" href="generated/numpy.ndarray.any.html#numpy.ndarray.any" title="numpy.ndarray.any"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.any</span></code></a>（[axis，out，keepdims]）</span></td>
<td><span class="yiyi-st" id="yiyi-264">如果<em class="xref py py-obj">a</em>的任何元素求值为True，则返回True。</span></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="arithmetic-matrix-multiplication-and-comparison-operations">
<h2><span class="yiyi-st" id="yiyi-265">Arithmetic, matrix multiplication, and comparison operations</span></h2>
<p id="index-5"><span class="yiyi-st" id="yiyi-266">对<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarrays</span></code></a>的算术和比较操作被定义为逐元素操作，并且通常产生<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>对象作为结果。</span></p>
<p><span class="yiyi-st" id="yiyi-267">Each of the arithmetic operations (<code class="docutils literal"><span class="pre">+</span></code>, <code class="docutils literal"><span class="pre">-</span></code>, <code class="docutils literal"><span class="pre">*</span></code>, <code class="docutils literal"><span class="pre">/</span></code>, <code class="docutils literal"><span class="pre">//</span></code>, <code class="docutils literal"><span class="pre">%</span></code>, <code class="docutils literal"><span class="pre">divmod()</span></code>, <code class="docutils literal"><span class="pre">**</span></code> or <code class="docutils literal"><span class="pre">pow()</span></code>, <code class="docutils literal"><span class="pre"><<</span></code>, <code class="docutils literal"><span class="pre">>></span></code>, <code class="docutils literal"><span class="pre">&</span></code>, <code class="docutils literal"><span class="pre">^</span></code>, <code class="docutils literal"><span class="pre">|</span></code>, <code class="docutils literal"><span class="pre">~</span></code>) and the comparisons (<code class="docutils literal"><span class="pre">==</span></code>, <code class="docutils literal"><span class="pre"><</span></code>, <code class="docutils literal"><span class="pre">></span></code>, <code class="docutils literal"><span class="pre"><=</span></code>, <code class="docutils literal"><span class="pre">>=</span></code>, <code class="docutils literal"><span class="pre">!=</span></code>) is equivalent to the corresponding <span class="xref std std-term">universal function</span> (or <a class="reference internal" href="../glossary.html#term-ufunc"><span class="xref std std-term">ufunc</span></a> for short) in NumPy. </span><span class="yiyi-st" id="yiyi-268">有关详细信息，请参阅<a class="reference internal" href="ufuncs.html#ufuncs"><span class="std std-ref">Universal Functions</span></a>部分。</span></p>
<p><span class="yiyi-st" id="yiyi-269">比较运算符：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-270"><a class="reference internal" href="generated/numpy.ndarray.__lt__.html#numpy.ndarray.__lt__" title="numpy.ndarray.__lt__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__lt__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-271">x.__lt__(y) <==> x<y< span=""></y<></==></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-272"><a class="reference internal" href="generated/numpy.ndarray.__le__.html#numpy.ndarray.__le__" title="numpy.ndarray.__le__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__le__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-273">x。_ le __（y）x</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-274"><a class="reference internal" href="generated/numpy.ndarray.__gt__.html#numpy.ndarray.__gt__" title="numpy.ndarray.__gt__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__gt__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-275">x .__ gt__（y）x> y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-276"><a class="reference internal" href="generated/numpy.ndarray.__ge__.html#numpy.ndarray.__ge__" title="numpy.ndarray.__ge__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ge__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-277">x ._ge__（y）x> = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-278"><a class="reference internal" href="generated/numpy.ndarray.__eq__.html#numpy.ndarray.__eq__" title="numpy.ndarray.__eq__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__eq__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-279">x .__ eq __（y）x == y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-280"><a class="reference internal" href="generated/numpy.ndarray.__ne__.html#numpy.ndarray.__ne__" title="numpy.ndarray.__ne__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ne__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-281">x .__ ne __（y）x！= y</span></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-282">数组的真值（<code class="xref py py-func docutils literal"><span class="pre">bool</span></code>）：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-283"><a class="reference internal" href="generated/numpy.ndarray.__nonzero__.html#numpy.ndarray.__nonzero__" title="numpy.ndarray.__nonzero__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__nonzero__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-284">x .__ nonzero __()x！= 0</span></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-285">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-286">数组的真值测试调用<a class="reference internal" href="generated/numpy.ndarray.__nonzero__.html#numpy.ndarray.__nonzero__" title="numpy.ndarray.__nonzero__"><code class="xref py py-meth docutils literal"><span class="pre">ndarray.__nonzero__</span></code></a>，如果数组中的元素数大于1，则会引发错误，因为此数组的真值是不确定的。</span><span class="yiyi-st" id="yiyi-287">使用<a class="reference internal" href="generated/numpy.ndarray.any.html#numpy.ndarray.any" title="numpy.ndarray.any"><code class="xref py py-meth docutils literal"><span class="pre">.any()</span></code></a>和<a class="reference internal" href="generated/numpy.ndarray.all.html#numpy.ndarray.all" title="numpy.ndarray.all"><code class="xref py py-meth docutils literal"><span class="pre">.all()</span></code></a>来清楚这些情况下的含义。</span><span class="yiyi-st" id="yiyi-288">（如果元素数为0，则数组的计算结果为<code class="docutils literal"><span class="pre">False</span></code>。）</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-289">一元操作：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-290"><a class="reference internal" href="generated/numpy.ndarray.__neg__.html#numpy.ndarray.__neg__" title="numpy.ndarray.__neg__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__neg__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-291">x .__ neg __()-x</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-292"><a class="reference internal" href="generated/numpy.ndarray.__pos__.html#numpy.ndarray.__pos__" title="numpy.ndarray.__pos__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__pos__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-293">x .__ pos __()+ x</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-294"><a class="reference internal" href="generated/numpy.ndarray.__abs__.html#numpy.ndarray.__abs__" title="numpy.ndarray.__abs__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__abs__</span></code></a>()abs（x）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-295"><a class="reference internal" href="generated/numpy.ndarray.__invert__.html#numpy.ndarray.__invert__" title="numpy.ndarray.__invert__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__invert__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-296">x .__反转__()〜x</span></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-297">算术：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-298"><a class="reference internal" href="generated/numpy.ndarray.__add__.html#numpy.ndarray.__add__" title="numpy.ndarray.__add__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__add__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-299">x .__ add __（y）x + y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-300"><a class="reference internal" href="generated/numpy.ndarray.__sub__.html#numpy.ndarray.__sub__" title="numpy.ndarray.__sub__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__sub__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-301">x .__ sub __（y）x-y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-302"><a class="reference internal" href="generated/numpy.ndarray.__mul__.html#numpy.ndarray.__mul__" title="numpy.ndarray.__mul__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__mul__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-303">x。_ mul __（y）x * y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-304"><a class="reference internal" href="generated/numpy.ndarray.__div__.html#numpy.ndarray.__div__" title="numpy.ndarray.__div__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__div__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-305">x .__ div __（y）x / y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-306"><a class="reference internal" href="generated/numpy.ndarray.__truediv__.html#numpy.ndarray.__truediv__" title="numpy.ndarray.__truediv__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__truediv__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-307">x。_ truediv __（y）x / y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-308"><a class="reference internal" href="generated/numpy.ndarray.__floordiv__.html#numpy.ndarray.__floordiv__" title="numpy.ndarray.__floordiv__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__floordiv__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-309">x .__ floordiv __（y）x // y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-310"><a class="reference internal" href="generated/numpy.ndarray.__mod__.html#numpy.ndarray.__mod__" title="numpy.ndarray.__mod__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__mod__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-311">x .__ mod __（y）x％y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-312"><a class="reference internal" href="generated/numpy.ndarray.__divmod__.html#numpy.ndarray.__divmod__" title="numpy.ndarray.__divmod__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__divmod__</span></code></a>（y）divmod（x，y）</span></td>
<td></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-313"><a class="reference internal" href="generated/numpy.ndarray.__pow__.html#numpy.ndarray.__pow__" title="numpy.ndarray.__pow__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__pow__</span></code></a>（y [，z]）pow（x，y [，z]）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-314"><a class="reference internal" href="generated/numpy.ndarray.__lshift__.html#numpy.ndarray.__lshift__" title="numpy.ndarray.__lshift__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__lshift__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-315">x .__ lshift __（y）x</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-316"><a class="reference internal" href="generated/numpy.ndarray.__rshift__.html#numpy.ndarray.__rshift__" title="numpy.ndarray.__rshift__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__rshift__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-317">x .__ rshift__（y）x >> y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-318"><a class="reference internal" href="generated/numpy.ndarray.__and__.html#numpy.ndarray.__and__" title="numpy.ndarray.__and__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__and__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-319">x。和__（y）x＆y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-320"><a class="reference internal" href="generated/numpy.ndarray.__or__.html#numpy.ndarray.__or__" title="numpy.ndarray.__or__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__or__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-321">x .__或__（y）x | y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-322"><a class="reference internal" href="generated/numpy.ndarray.__xor__.html#numpy.ndarray.__xor__" title="numpy.ndarray.__xor__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__xor__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-323">x .__ xor __（y）x ^ y</span></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-324">注意</span></p>
<ul class="last simple">
<li><span class="yiyi-st" id="yiyi-325"><a class="reference external" href="https://docs.python.org/dev/library/functions.html#pow" title="(in Python v3.7)"><code class="xref py py-func docutils literal"><span class="pre">pow</span></code></a>的任何第三个参数被默认忽略，因为底层的<a class="reference internal" href="generated/numpy.power.html#numpy.power" title="numpy.power"><code class="xref py py-func docutils literal"><span class="pre">ufunc</span></code></a>只有两个参数。</span></li>
<li><span class="yiyi-st" id="yiyi-326">三个分区运算符都被定义； <code class="xref py py-obj docutils literal"><span class="pre">div</span></code>在默认情况下处于活动状态，<code class="xref py py-obj docutils literal"><span class="pre">truediv</span></code>在<a class="reference external" href="https://docs.python.org/dev/library/__future__.html#module-__future__" title="(in Python v3.7)"><code class="xref py py-obj docutils literal"><span class="pre">__future__</span></code></a></span></li>
<li><span class="yiyi-st" id="yiyi-327">因为<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal"><span class="pre">ndarray</span></code></a>是内置类型（用C编写），所以不直接定义<code class="docutils literal"><span class="pre">__r{op}__</span></code>特殊方法。</span></li>
<li><span class="yiyi-st" id="yiyi-328">可以使用<code class="xref py py-func docutils literal"><span class="pre">set_numeric_ops</span></code>修改用于实现数组的许多算术特殊方法的函数。</span></li>
</ul>
</div>
<p><span class="yiyi-st" id="yiyi-329">算术，就地：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-330"><a class="reference internal" href="generated/numpy.ndarray.__iadd__.html#numpy.ndarray.__iadd__" title="numpy.ndarray.__iadd__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__iadd__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-331">x .__ iadd __（y）x + = y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-332"><a class="reference internal" href="generated/numpy.ndarray.__isub__.html#numpy.ndarray.__isub__" title="numpy.ndarray.__isub__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__isub__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-333">x .__ isub __（y）x- = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-334"><a class="reference internal" href="generated/numpy.ndarray.__imul__.html#numpy.ndarray.__imul__" title="numpy.ndarray.__imul__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__imul__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-335">x .__ imul __（y）x * = y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-336"><a class="reference internal" href="generated/numpy.ndarray.__idiv__.html#numpy.ndarray.__idiv__" title="numpy.ndarray.__idiv__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__idiv__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-337">x。_ idiv __（y）x / = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-338"><a class="reference internal" href="generated/numpy.ndarray.__itruediv__.html#numpy.ndarray.__itruediv__" title="numpy.ndarray.__itruediv__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__itruediv__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-339">x .__ itruediv __（y）x / = y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-340"><a class="reference internal" href="generated/numpy.ndarray.__ifloordiv__.html#numpy.ndarray.__ifloordiv__" title="numpy.ndarray.__ifloordiv__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ifloordiv__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-341">x ._ ifloordiv __（y）x // = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-342"><a class="reference internal" href="generated/numpy.ndarray.__imod__.html#numpy.ndarray.__imod__" title="numpy.ndarray.__imod__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__imod__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-343">x。_ imod __（y）x％= y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-344"><a class="reference internal" href="generated/numpy.ndarray.__ipow__.html#numpy.ndarray.__ipow__" title="numpy.ndarray.__ipow__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ipow__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-345">x。_ ipow __（y）x ** = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-346"><a class="reference internal" href="generated/numpy.ndarray.__ilshift__.html#numpy.ndarray.__ilshift__" title="numpy.ndarray.__ilshift__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ilshift__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-347">x .__ ilshift __（y）x</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-348"><a class="reference internal" href="generated/numpy.ndarray.__irshift__.html#numpy.ndarray.__irshift__" title="numpy.ndarray.__irshift__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__irshift__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-349">x .__ irshift __（y）x >> = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-350"><a class="reference internal" href="generated/numpy.ndarray.__iand__.html#numpy.ndarray.__iand__" title="numpy.ndarray.__iand__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__iand__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-351">x .__ iand __（y）x＆= y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-352"><a class="reference internal" href="generated/numpy.ndarray.__ior__.html#numpy.ndarray.__ior__" title="numpy.ndarray.__ior__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ior__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-353">x .__ ior __（y）x | = y</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-354"><a class="reference internal" href="generated/numpy.ndarray.__ixor__.html#numpy.ndarray.__ixor__" title="numpy.ndarray.__ixor__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__ixor__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-355">x。_ ixor __（y）x ^ = y</span></td>
</tr>
</tbody>
</table>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-356">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-357">就地操作将使用由两个操作数的数据类型确定的精度来执行计算，但是将静默地对结果进行向下转换（如果必要的话），以便它可以适应数组。</span><span class="yiyi-st" id="yiyi-358">因此，对于混合精度计算，<code class="docutils literal"><span class="pre">A</span> <span class="pre">{op} =</span> <span class="pre">B</span></code>可以不同于<code class="docutils literal"><span class="pre"> A</span> <span class="pre">=</span> <span class="pre">A</span> <span class="pre">{op}</span> <span class="pre">B</span></code>。</span><span class="yiyi-st" id="yiyi-359">例如，假设<code class="docutils literal"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">ones（（3,3））</span></code>。</span><span class="yiyi-st" id="yiyi-360">Then, <code class="docutils literal"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3j</span></code> is different than <code class="docutils literal"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span> <span class="pre">3j</span></code>: while they both perform the same computation, <code class="docutils literal"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3</span></code> casts the result to fit back in <code class="docutils literal"><span class="pre">a</span></code>, whereas <code class="docutils literal"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span> <span class="pre">3j</span></code> re-binds the name <code class="docutils literal"><span class="pre">a</span></code> to the result.</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-361">矩阵乘法：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-362"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__matmul__</span></code></span></td>
<td></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-363">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-364">在PEP465之后的Python 3.5中引入了矩阵运算符<code class="docutils literal"><span class="pre">@</span></code>和<code class="docutils literal"><span class="pre">@=</span></code>。</span><span class="yiyi-st" id="yiyi-365">NumPy 1.10.0具有用于测试目的的<code class="docutils literal"><span class="pre">@</span></code>的初步实现。</span><span class="yiyi-st" id="yiyi-366">更多文档可以在<a class="reference internal" href="generated/numpy.matmul.html#numpy.matmul" title="numpy.matmul"><code class="xref py py-func docutils literal"><span class="pre">matmul</span></code></a>文档中找到。</span></p>
</div>
</div>
<div class="section" id="special-methods">
<h2><span class="yiyi-st" id="yiyi-367">Special methods</span></h2>
<p><span class="yiyi-st" id="yiyi-368">对于标准库函数：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-369"><a class="reference internal" href="generated/numpy.ndarray.__copy__.html#numpy.ndarray.__copy__" title="numpy.ndarray.__copy__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__copy__</span></code></a>（[order]）</span></td>
<td><span class="yiyi-st" id="yiyi-370">返回数组的副本。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-371"><a class="reference internal" href="generated/numpy.ndarray.__deepcopy__.html#numpy.ndarray.__deepcopy__" title="numpy.ndarray.__deepcopy__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__deepcopy__</span></code></a>（() - >数组的深度副本。）</span></td>
<td><span class="yiyi-st" id="yiyi-372">在数组上调用copy.deepcopy时使用。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-373"><a class="reference internal" href="generated/numpy.ndarray.__reduce__.html#numpy.ndarray.__reduce__" title="numpy.ndarray.__reduce__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__reduce__</span></code></a>()</span></td>
<td><span class="yiyi-st" id="yiyi-374">酸洗。</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-375"><a class="reference internal" href="generated/numpy.ndarray.__setstate__.html#numpy.ndarray.__setstate__" title="numpy.ndarray.__setstate__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__setstate__</span></code></a>（version，shape，dtype，...）</span></td>
<td><span class="yiyi-st" id="yiyi-376">用于取出。</span></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-377">基本定制：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-378"><a class="reference internal" href="generated/numpy.ndarray.__new__.html#numpy.ndarray.__new__" title="numpy.ndarray.__new__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__new__</span></code></a>（（S，...）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-379"><a class="reference internal" href="generated/numpy.ndarray.__array__.html#numpy.ndarray.__array__" title="numpy.ndarray.__array__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__array__</span></code></a>（...）</span></td>
<td><span class="yiyi-st" id="yiyi-380">如果未指定dtype，则返回对self的新引用，如果dtype与数组的当前dtype不同，则返回提供的数据类型的新数组。</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-381"><a class="reference internal" href="generated/numpy.ndarray.__array_wrap__.html#numpy.ndarray.__array_wrap__" title="numpy.ndarray.__array_wrap__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__array_wrap__</span></code></a>（...）</span></td>
<td></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-382">容器自定义:(参见<a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">Indexing</span></a>）</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-383"><a class="reference internal" href="generated/numpy.ndarray.__len__.html#numpy.ndarray.__len__" title="numpy.ndarray.__len__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__len__</span></code></a>()len（x）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-384"><a class="reference internal" href="generated/numpy.ndarray.__getitem__.html#numpy.ndarray.__getitem__" title="numpy.ndarray.__getitem__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__getitem__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-385">x .__ getitem __（y）x [y]</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-386"><a class="reference internal" href="generated/numpy.ndarray.__setitem__.html#numpy.ndarray.__setitem__" title="numpy.ndarray.__setitem__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__setitem__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-387">x .__ setitem __（i，y）x [i] = y</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-388"><a class="reference internal" href="generated/numpy.ndarray.__contains__.html#numpy.ndarray.__contains__" title="numpy.ndarray.__contains__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__contains__</span></code></a></span></td>
<td><span class="yiyi-st" id="yiyi-389">x .__在x中包含__（y）y</span></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-390">转换；操作<code class="xref py py-func docutils literal"><span class="pre">complex</span></code>，<code class="xref py py-func docutils literal"><span class="pre">int</span></code>，<code class="xref py py-func docutils literal"><span class="pre">long</span></code>，<code class="xref py py-func docutils literal"><span class="pre">float</span></code>，<a class="reference external" href="https://docs.python.org/dev/library/functions.html#oct" title="(in Python v3.7)"><code class="xref py py-func docutils literal"><span class="pre">oct</span></code></a>和<a class="reference external" href="https://docs.python.org/dev/library/functions.html#hex" title="(in Python v3.7)"><code class="xref py py-func docutils literal"><span class="pre">hex</span></code></a>。</span><span class="yiyi-st" id="yiyi-391">它们只对具有一个元素的数组工作，并返回相应的标量。</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-392"><a class="reference internal" href="generated/numpy.ndarray.__int__.html#numpy.ndarray.__int__" title="numpy.ndarray.__int__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__int__</span></code></a>()int（x）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-393"><a class="reference internal" href="generated/numpy.ndarray.__long__.html#numpy.ndarray.__long__" title="numpy.ndarray.__long__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__long__</span></code></a>()long（x）</span></td>
<td></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-394"><a class="reference internal" href="generated/numpy.ndarray.__float__.html#numpy.ndarray.__float__" title="numpy.ndarray.__float__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__float__</span></code></a>()float（x）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-395"><a class="reference internal" href="generated/numpy.ndarray.__oct__.html#numpy.ndarray.__oct__" title="numpy.ndarray.__oct__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__oct__</span></code></a>()oct（x）</span></td>
<td></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-396"><a class="reference internal" href="generated/numpy.ndarray.__hex__.html#numpy.ndarray.__hex__" title="numpy.ndarray.__hex__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__hex__</span></code></a>()hex（x）</span></td>
<td></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-397">字符串表示：</span></p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%">
<col width="90%">
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-398"><a class="reference internal" href="generated/numpy.ndarray.__str__.html#numpy.ndarray.__str__" title="numpy.ndarray.__str__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__str__</span></code></a>()str（x）</span></td>
<td></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-399"><a class="reference internal" href="generated/numpy.ndarray.__repr__.html#numpy.ndarray.__repr__" title="numpy.ndarray.__repr__"><code class="xref py py-obj docutils literal"><span class="pre">ndarray.__repr__</span></code></a>()repr（x）</span></td>
<td></td>
</tr>
</tbody>
</table>
</div>