{"id":430,"date":"2021-11-27T21:49:47","date_gmt":"2021-11-27T13:49:47","guid":{"rendered":"http:\/\/101.43.40.65\/?p=430"},"modified":"2021-11-27T22:01:34","modified_gmt":"2021-11-27T14:01:34","slug":"python-numpy","status":"publish","type":"post","link":"https:\/\/zhang.mba\/index.php\/2021\/11\/27\/21\/49\/47\/430\/python-numpy\/python\/zhangzhiqi\/","title":{"rendered":"Python NumPy"},"content":{"rendered":"\n<h1>NumPy \u4ecb\u7ecd<\/h1>\n\n\n\n<p>NumPy\u662f\u4e00\u4e2aPython\u5305\u3002 \u5b83\u4ee3\u8868&#8217;Numerical Python&#8217;\u3002 \u5b83\u662f\u4e00\u4e2a\u7531\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u548c\u4e00\u7ec4\u5904\u7406\u6570\u7ec4\u7684\u4f8b\u7a0b\u7ec4\u6210\u7684\u5e93\u3002<\/p>\n\n\n\n<p>Numeric\uff0cNumPy\u7684\u7956\u5148\uff0c\u7531Jim Hugunin\u5f00\u53d1\u3002 Numarray\u7684\u53e6\u4e00\u4e2a\u8f6f\u4ef6\u5305\u4e5f\u88ab\u5f00\u53d1\u51fa\u6765\uff0c\u5177\u6709\u4e00\u4e9b\u989d\u5916\u7684\u529f\u80fd\u3002 2005\u5e74\uff0cTravis Oliphant\u521b\u5efa\u4e86NumPy\u5305\uff0c\u5c06Numarray\u7684\u7279\u6027\u5408\u5e76\u5230\u6570\u5b57\u5305\u4e2d\u3002 \u8fd9\u4e2a\u5f00\u6e90\u9879\u76ee\u6709\u8bb8\u591a\u8d21\u732e\u8005\u3002<\/p>\n\n\n\n<h3>\u4f7f\u7528NumPy\u7684\u64cd\u4f5c<\/h3>\n\n\n\n<p>\u4f7f\u7528NumPy\uff0c\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u6267\u884c\u4ee5\u4e0b\u64cd\u4f5c &#8211;<\/p>\n\n\n\n<ul><li>\u6570\u7ec4\u7684\u6570\u5b66\u548c\u903b\u8f91\u8fd0\u7b97\u3002<\/li><li>\u5085\u7acb\u53f6\u53d8\u6362\u548c\u5f62\u72b6\u64cd\u4f5c\u7684\u4f8b\u7a0b\u3002<\/li><li>\u4e0e\u7ebf\u6027\u4ee3\u6570\u6709\u5173\u7684\u64cd\u4f5c\u3002 NumPy\u5177\u6709\u7528\u4e8e\u7ebf\u6027\u4ee3\u6570\u548c\u968f\u673a\u6570\u751f\u6210\u7684\u5185\u7f6e\u51fd\u6570\u3002<\/li><\/ul>\n\n\n\n<h3>NumPy &#8211; MatLab\u7684\u66ff\u4ee3\u54c1<\/h3>\n\n\n\n<p>NumPy\u901a\u5e38\u4e0eSciPy\uff08Scientific Python\uff09\u548cMat-plotlib\uff08\u7ed8\u56fe\u5e93\uff09\u7b49\u8f6f\u4ef6\u5305\u4e00\u8d77\u4f7f\u7528\u3002 \u8fd9\u79cd\u7ec4\u5408\u5e7f\u6cdb\u7528\u4e8e\u66ff\u4ee3\u6280\u672f\u8ba1\u7b97\u7684\u6d41\u884c\u5e73\u53f0MatLab\u3002 \u7136\u800c\uff0cMatLab\u7684Python\u66ff\u4ee3\u54c1\u73b0\u5728\u88ab\u89c6\u4e3a\u66f4\u73b0\u4ee3\u548c\u5b8c\u6574\u7684\u7f16\u7a0b\u8bed\u8a00\u3002<\/p>\n\n\n\n<p>\u5b83\u662f\u5f00\u6e90\u7684\uff0c\u8fd9\u662fNumPy\u7684\u4e00\u4e2a\u9644\u52a0\u4f18\u52bf\u3002<\/p>\n\n\n\n<h1>NumPy \u73af\u5883\u5b89\u88c5<\/h1>\n\n\n\n<p>\u6807\u51c6Python\u53d1\u884c\u7248\u4e0d\u6346\u7ed1NumPy\u6a21\u5757\u3002 \u4e00\u79cd\u8f7b\u91cf\u7ea7\u7684\u9009\u62e9\u662f\u4f7f\u7528\u6d41\u884c\u7684Python\u5305\u5b89\u88c5\u7a0b\u5e8fpip\u6765\u5b89\u88c5NumPy\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">pip install numpy\n<\/pre>\n\n\n\n<p>\u542f\u7528NumPy\u7684\u6700\u4f73\u65b9\u5f0f\u662f\u4f7f\u7528\u7279\u5b9a\u4e8e\u60a8\u7684\u64cd\u4f5c\u7cfb\u7edf\u7684\u53ef\u5b89\u88c5\u4e8c\u8fdb\u5236\u5305\u3002 \u8fd9\u4e9b\u4e8c\u8fdb\u5236\u6587\u4ef6\u5305\u542b\u5b8c\u6574\u7684SciPy\u5806\u6808\uff08\u5305\u62ecNumPy\uff0cSciPy\uff0cmatplotlib\uff0cIPython\uff0cSymPy\u548cnose\u5305\u4ee5\u53ca\u6838\u5fc3Python\uff09\u3002<\/p>\n\n\n\n<h3>Windows<\/h3>\n\n\n\n<p>Anaconda\uff08\u6765\u81eawww.continuum.io\uff09\u662fSciPy\u5806\u6808\u7684\u514d\u8d39Python\u53d1\u884c\u7248\u3002 \u5b83\u4e5f\u9002\u7528\u4e8eLinux\u548cMac\u3002<\/p>\n\n\n\n<p>Canopy\uff08www.enthought.com\/products\/canopy\/\uff09\u514d\u8d39\u63d0\u4f9b\uff0c\u5e76\u63d0\u4f9b\u5b8c\u6574\u7684\u7528\u4e8eWindows\uff0cLinux\u548cMac\u7684SciPy\u5806\u6808\u7684\u5546\u4e1a\u5206\u53d1\u3002<\/p>\n\n\n\n<p>Python\uff08x\uff0cy\uff09\uff1a\u8fd9\u662f\u4e00\u4e2a\u514d\u8d39\u7684Python\u53d1\u884c\u7248\uff0c\u5176\u4e2d\u5305\u542b\u7528\u4e8eWindows\u64cd\u4f5c\u7cfb\u7edf\u7684SciPy\u5806\u6808\u548cSpyder IDE\u3002 \uff08\u53ef\u4ecewww.python-xy.github.io\/\u4e0b\u8f7d\uff09<\/p>\n\n\n\n<h3>Linux<\/h3>\n\n\n\n<p>\u5404\u4e2aLinux\u53d1\u884c\u7248\u7684\u8f6f\u4ef6\u5305\u7ba1\u7406\u5668\u7528\u4e8e\u5728SciPy\u5806\u6808\u4e2d\u5b89\u88c5\u4e00\u4e2a\u6216\u591a\u4e2a\u8f6f\u4ef6\u5305\u3002<\/p>\n\n\n\n<p>Ubuntu<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">sudo apt-get install python-numpy\npython-scipy python-matplotlibipythonipythonnotebook python-pandas\npython-sympy python-nose\n<\/pre>\n\n\n\n<h3>Fedora<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">sudo yum install numpyscipy python-matplotlibipython\npython-pandas sympy python-nose atlas-devel\n<\/pre>\n\n\n\n<h3>\u4ece\u6e90\u4ee3\u7801\u6784\u5efa<\/h3>\n\n\n\n<p>Core Python\uff082.6.x\uff0c2.7.x\u548c3.2.x\u4ee5\u4e0a\uff09\u5fc5\u987b\u4e0edistutils\u4e00\u8d77\u5b89\u88c5\uff0c\u5e76\u4e14\u5e94\u542f\u7528zlib\u6a21\u5757\u3002<\/p>\n\n\n\n<p>GNU gcc\uff084.2\u53ca\u66f4\u9ad8\u7248\u672c\uff09C\u7f16\u8bd1\u5668\u5fc5\u987b\u53ef\u7528\u3002<\/p>\n\n\n\n<p>\u8981\u5b89\u88c5NumPy\uff0c\u8bf7\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Python setup.py install\n<\/pre>\n\n\n\n<p>\u8981\u6d4b\u8bd5NumPy\u6a21\u5757\u662f\u5426\u6b63\u786e\u5b89\u88c5\uff0c\u8bf7\u5c1d\u8bd5\u4ecePython\u63d0\u793a\u7b26\u5bfc\u5165\u5b83\u3002<\/p>\n\n\n\n<p>\u5982\u679c\u672a\u5b89\u88c5\uff0c\u5219\u4f1a\u663e\u793a\u4ee5\u4e0b\u9519\u8bef\u6d88\u606f\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Traceback (most recent call last):\n\tFile \"&lt;pyshell#0&gt;\", line 1, in &lt;module&gt;\n\t\timport numpy\nImportError: No module named 'numpy'<\/pre>\n\n\n\n<h1>NumPy Ndarray\u5bf9\u8c61<\/h1>\n\n\n\n<p>NumPy\u4e2d\u5b9a\u4e49\u7684\u6700\u91cd\u8981\u7684\u5bf9\u8c61\u662f\u540d\u4e3andarray\u7684N\u7ef4\u6570\u7ec4\u7c7b\u578b\u3002 \u5b83\u63cf\u8ff0\u4e86\u76f8\u540c\u7c7b\u578b\u7684\u9879\u76ee\u7684\u96c6\u5408\u3002 \u53ef\u4ee5\u4f7f\u7528\u4ece\u96f6\u5f00\u59cb\u7684\u7d22\u5f15\u6765\u8bbf\u95ee\u96c6\u5408\u4e2d\u7684\u9879\u76ee\u3002<\/p>\n\n\n\n<p>ndarray\u4e2d\u7684\u6bcf\u4e2a\u9879\u76ee\u5728\u5185\u5b58\u4e2d\u5360\u7528\u76f8\u540c\u7684\u5757\u5927\u5c0f\u3002 ndarray\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u90fd\u662f\u6570\u636e\u7c7b\u578b\u5bf9\u8c61\uff08\u79f0\u4e3adtype\uff09\u7684\u5bf9\u8c61\u3002<\/p>\n\n\n\n<p>\u4ecendarray\u5bf9\u8c61\u4e2d\u63d0\u53d6\u7684\u4efb\u4f55\u9879\u76ee\uff08\u901a\u8fc7\u5207\u7247\uff09\u7531\u6570\u7ec4\u6807\u91cf\u7c7b\u578b\u4e4b\u4e00\u7684Python\u5bf9\u8c61\u8868\u793a\u3002 \u4e0b\u56fe\u663e\u793a\u4e86ndarray\uff0c\u6570\u636e\u7c7b\u578b\u5bf9\u8c61\uff08dtype\uff09\u548c\u6570\u7ec4\u6807\u91cf\u7c7b\u578b\u4e4b\u95f4\u7684\u5173\u7cfb &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' href='http:\/\/101.43.40.65\/wp-content\/uploads\/2021\/11\/image-199.jpeg'><img class=\"lazyload lazyload-style-2\" src=\"data:image\/svg+xml;base64,PCEtLUFyZ29uTG9hZGluZy0tPgo8c3ZnIHdpZHRoPSIxIiBoZWlnaHQ9IjEiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgc3Ryb2tlPSIjZmZmZmZmMDAiPjxnPjwvZz4KPC9zdmc+\"  loading=\"lazy\" width=\"438\" height=\"162\" data-original=\"http:\/\/101.43.40.65\/wp-content\/uploads\/2021\/11\/image-199.jpeg\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\" class=\"wp-image-443\"  sizes=\"(max-width: 438px) 100vw, 438px\" \/><\/div><\/figure>\n\n\n\n<p>ndarray\u7c7b\u7684\u4e00\u4e2a\u5b9e\u4f8b\u53ef\u4ee5\u901a\u8fc7\u672c\u6559\u7a0b\u7a0d\u540e\u4ecb\u7ecd\u7684\u4e0d\u540c\u9635\u5217\u521b\u5efa\u4f8b\u7a0b\u6765\u6784\u5efa\u3002 \u57fa\u672c\u7684ndarray\u662f\u4f7f\u7528NumPy\u4e2d\u7684\u6570\u7ec4\u51fd\u6570\u521b\u5efa\u7684\uff0c\u5982\u4e0b\u6240\u793a &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.array\n<\/pre>\n\n\n\n<p>\u5b83\u4ece\u66b4\u9732\u6570\u7ec4\u63a5\u53e3\u7684\u4efb\u4f55\u5bf9\u8c61\u6216\u4efb\u4f55\u8fd4\u56de\u6570\u7ec4\u7684\u65b9\u6cd5\u521b\u5efa\u4e00\u4e2andarray\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)\n<\/pre>\n\n\n\n<p>\u4e0a\u9762\u7684\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570 &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>object<\/b><br>\n\u4efb\u4f55\u66b4\u9732\u6570\u7ec4\u63a5\u53e3\u65b9\u6cd5\u7684\u5bf9\u8c61\u90fd\u4f1a\u8fd4\u56de\u4e00\u4e2a\u6570\u7ec4\u6216\u4efb\u4f55\uff08\u5d4c\u5957\uff09\u5e8f\u5217\u3002<\/td><\/tr><tr><td>2<\/td><td><b>dtype<\/b><br>\n\u6570\u7ec4\u7684\u671f\u671b\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u9009<\/td><\/tr><tr><td>3<\/td><td><b>copy<\/b><br>\n\u53ef\u9009\u7684\u3002 \u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff08true\uff09\uff0c\u5bf9\u8c61\u88ab\u590d\u5236<\/td><\/tr><tr><td>4<\/td><td><b>order<\/b><br>\nC\uff08\u884c\u4e3b\uff09\u6216F\uff08\u5217\u4e3b\uff09\u6216A\uff08\u4efb\u4f55\uff09\uff08\u9ed8\u8ba4\uff09<\/td><\/tr><tr><td>5<\/td><td><b>subok<\/b><br>\n\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u8fd4\u56de\u7684\u6570\u7ec4\u88ab\u5f3a\u5236\u4e3a\u57fa\u7c7b\u6570\u7ec4\u3002 \u5982\u679c\u4e3atrue\uff0c\u5219\u901a\u8fc7\u5b50\u7c7b<\/td><\/tr><tr><td>6<\/td><td><b>ndmin<\/b><br>\n\u6307\u5b9a\u7ed3\u679c\u6570\u7ec4\u7684\u6700\u5c0f\u7ef4\u6570<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u770b\u770b\u4e0b\u9762\u7684\u4f8b\u5b50\u6765\u66f4\u597d\u5730\u7406\u89e3\u3002<\/p>\n\n\n\n<h3>\u4f8b1<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1,2,3])\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[1, 2, 3]\n<\/pre>\n\n\n\n<h3>\u4f8b2<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># more than one dimensions\nimport numpy as np\na = np.array([[1, 2], [3, 4]])\n\tprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1, 2]\n [3, 4]]\n<\/pre>\n\n\n\n<h3>\u4f8b3<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># minimum dimensions\nimport numpy as np\na = np.array([1, 2, 3,4,5], ndmin = 2)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1, 2, 3, 4, 5]]\n<\/pre>\n\n\n\n<h3>\u4f8b4<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># dtype parameter\nimport numpy as np\na = np.array([1, 2, 3], dtype = complex)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 1.+0.j,  2.+0.j,  3.+0.j]\n<\/pre>\n\n\n\n<p>ndarray\u5bf9\u8c61\u7531\u8ba1\u7b97\u673a\u5185\u5b58\u7684\u8fde\u7eed\u4e00\u7ef4\u7247\u6bb5\u7ec4\u6210\uff0c\u5e76\u5c06\u7d22\u5f15\u65b9\u6848\u4e0e\u6bcf\u4e2a\u9879\u76ee\u6620\u5c04\u5230\u5185\u5b58\u5757\u4e2d\u7684\u4f4d\u7f6e\u76f8\u7ed3\u5408\u3002 \u5185\u5b58\u5757\u4ee5\u884c\u4e3b\u8981\u987a\u5e8f\uff08C\u98ce\u683c\uff09\u6216\u5217\u4e3b\u8981\u987a\u5e8f\uff08FORTRAN\u6216MatLab\u98ce\u683c\uff09\u4fdd\u5b58\u5143\u7d20\u3002<\/p>\n\n\n\n<h1>NumPy \u6570\u636e\u7c7b\u578b<\/h1>\n\n\n\n<p>NumPy\u652f\u6301\u6bd4Python\u66f4\u591a\u7684\u6570\u5b57\u7c7b\u578b\u3002 \u4e0b\u8868\u663e\u793a\u4e86\u5728NumPy\u4e2d\u5b9a\u4e49\u7684\u4e0d\u540c\u6807\u91cf\u6570\u636e\u7c7b\u578b\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u6570\u636e\u7c7b\u578b &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>bool_<\/b><br>\n\u5e03\u5c14\uff08True\u6216False\uff09\u5b58\u50a8\u4e3a\u4e00\u4e2a\u5b57\u8282<\/td><\/tr><tr><td>2<\/td><td><b>int_<\/b><br>\n\u9ed8\u8ba4\u6574\u6570\u7c7b\u578b\uff08\u4e0eC long\u76f8\u540c;\u901a\u5e38\u662fint64\u6216int32\uff09<\/td><\/tr><tr><td>3<\/td><td><b>intc<\/b><br>\n\u4e0eC int\u76f8\u540c\uff08\u901a\u5e38\u4e3aint32\u6216int64\uff09<\/td><\/tr><tr><td>4<\/td><td><b>intp<\/b><br>\n\u7528\u4e8e\u7d22\u5f15\u7684\u6574\u6570\uff08\u4e0eC ssize_t\u76f8\u540c;\u901a\u5e38\u4e3aint32\u6216int64\uff09<\/td><\/tr><tr><td>5<\/td><td><b>int8<\/b><br>\n\u5b57\u8282\uff08-128\u81f3127\uff09<\/td><\/tr><tr><td>6<\/td><td><b>int16<br>\n\u6574\u6570\uff08-32768\u81f332767\uff09<\/b><\/td><\/tr><tr><td>7<\/td><td><b>int32<\/b><br>\n\u6574\u6570\uff08-2147483648\u81f32147483647\uff09<\/td><\/tr><tr><td>8<\/td><td><b>int64<\/b>&nbsp;<p><\/p>\n<p>\u6574\u6570\uff08-9223372036854775808\u81f39223372036854775807\uff09<\/p><\/td><\/tr><tr><td>9<\/td><td><b>uint8<\/b><br>\n\u65e0\u7b26\u53f7\u6574\u6570\uff080\u5230255\uff09<\/td><\/tr><tr><td>10<\/td><td><b>uint16<\/b><br>\n\u65e0\u7b26\u53f7\u6574\u6570\uff080\u523065535\uff09<\/td><\/tr><tr><td>11<\/td><td><b>uint32<\/b><br>\n\u65e0\u7b26\u53f7\u6574\u6570\uff080\u52304294967295\uff09<\/td><\/tr><tr><td>12<\/td><td><b>uint64<\/b><br>\n\u65e0\u7b26\u53f7\u6574\u6570\uff080\u523018446744073709551615\uff09<\/td><\/tr><tr><td>13<\/td><td><b>float_<\/b><br>\n\u540cfloat64<\/td><\/tr><tr><td>14<\/td><td><b>float16<\/b><br>\n\u534a\u7cbe\u5ea6\u6d6e\u70b9\uff1a\u7b26\u53f7\u4f4d\uff0c5\u4f4d\u6307\u6570\uff0c10\u4f4d\u5c3e\u6570<\/td><\/tr><tr><td>15<\/td><td><b>float32<\/b><br>\n\u5355\u7cbe\u5ea6\u6d6e\u70b9\u6570\uff1a\u7b26\u53f7\u4f4d\uff0c8\u4f4d\u6307\u6570\uff0c23\u4f4d\u5c3e\u6570<\/td><\/tr><tr><td>16<\/td><td><b>float64<\/b><br>\n\u53cc\u7cbe\u5ea6\u6d6e\u70b9\uff1a\u7b26\u53f7\u4f4d\uff0c11\u4f4d\u6307\u6570\uff0c52\u4f4d\u5c3e\u6570<\/td><\/tr><tr><td>17<\/td><td><b>complex_<\/b><br>\n\u540ccomplex128<\/td><\/tr><tr><td>18<\/td><td><b>complex64<\/b><br>\n\u590d\u6570\uff0c\u7531\u4e24\u4e2a32\u4f4d\u6d6e\u70b9\u6570\uff08\u5b9e\u90e8\u548c\u865a\u90e8\uff09<\/td><\/tr><tr><td>19<\/td><td><b>complex128<\/b><br>\n\u590d\u6570\uff0c\u7531\u4e24\u4e2a64\u4f4d\u6d6e\u70b9\u6570\uff08\u5b9e\u90e8\u548c\u865a\u90e8\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>NumPy\u6570\u5b57\u7c7b\u578b\u662fdtype\uff08\u6570\u636e\u7c7b\u578b\uff09\u5bf9\u8c61\u7684\u5b9e\u4f8b\uff0c\u6bcf\u4e2a\u5bf9\u8c61\u90fd\u6709\u72ec\u7279\u7684\u7279\u5f81\u3002 \u8be5dtypes\u53ef\u7528\u4f5cnp.bool_\uff0cnp.float32\u7b49\u3002<\/p>\n\n\n\n<h3>\u6570\u636e\u7c7b\u578b\u5bf9\u8c61\uff08dtype\uff09<\/h3>\n\n\n\n<p>\u6570\u636e\u7c7b\u578b\u5bf9\u8c61\u6839\u636e\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\u63cf\u8ff0\u5bf9\u5e94\u4e8e\u6570\u7ec4\u7684\u56fa\u5b9a\u5185\u5b58\u5757\u7684\u89e3\u91ca &#8211;<\/p>\n\n\n\n<ul><li>\u6570\u636e\u7c7b\u578b\uff08\u6574\u578b\uff0c\u6d6e\u70b9\u578b\u6216Python\u5bf9\u8c61\uff09<\/li><li>\u6570\u636e\u7684\u5927\u5c0f<\/li><li>\u5b57\u8282\u987a\u5e8f\uff08\u5c0f\u7aef\u6216\u5927\u7aef\uff09<\/li><li>\u5728\u7ed3\u6784\u5316\u7c7b\u578b\u7684\u60c5\u51b5\u4e0b\uff0c\u5b57\u6bb5\u7684\u540d\u79f0\uff0c\u6bcf\u4e2a\u5b57\u6bb5\u7684\u6570\u636e\u7c7b\u578b\u548c\u6bcf\u4e2a\u5b57\u6bb5\u5360\u7528\u7684\u5b58\u50a8\u5668\u5757\u7684\u4e00\u90e8\u5206\u3002<\/li><li>\u5982\u679c\u6570\u636e\u7c7b\u578b\u662f\u5b50\u9635\u5217\uff0c\u5219\u5b83\u7684\u5f62\u72b6\u548c\u6570\u636e\u7c7b\u578b<\/li><\/ul>\n\n\n\n<p>\u5b57\u8282\u987a\u5e8f\u7531\u524d\u7f00&#8217;\uff06lt;&#8217;\u51b3\u5b9a \u6216&#8217;\uff06gt;&#8217; \u5230\u6570\u636e\u7c7b\u578b\u3002&#8217;\uff06LT;&#8217; \u610f\u5473\u7740\u7f16\u7801\u662f\u5c0f\u7aef\uff08\u6700\u5c0f\u6709\u6548\u5b58\u50a8\u5728\u6700\u5c0f\u5730\u5740\u4e2d\uff09\u3002&#8217;\uff06GT;&#8217; \u610f\u5473\u7740\u7f16\u7801\u662fbig-endian\uff08\u6700\u91cd\u8981\u7684\u5b57\u8282\u5b58\u50a8\u5728\u6700\u5c0f\u5730\u5740\u4e2d\uff09\u3002<\/p>\n\n\n\n<p>\u4e00\u4e2adtype\u5bf9\u8c61\u4f7f\u7528\u4e0b\u9762\u7684\u8bed\u6cd5\uff06minus\u6784\u9020\u51fa\u6765;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.dtype(object, align, copy)\n<\/pre>\n\n\n\n<p>\u53c2\u6570\u662f &#8211;<\/p>\n\n\n\n<ul><li><b>Object<\/b> \u2212 \u88ab\u8f6c\u6362\u4e3a\u6570\u636e\u7c7b\u578b\u5bf9\u8c61<\/li><li><b>Align<\/b> \u2212 \u5982\u679c\u4e3atrue\uff0c\u5219\u5411\u8be5\u5b57\u6bb5\u6dfb\u52a0\u586b\u5145\u4ee5\u4f7f\u5176\u7c7b\u4f3c\u4e8eC-struct<\/li><li><b>Copy<\/b> \u2212 \u751f\u6210\u4e00\u4e2a\u65b0\u7684dtype\u5bf9\u8c61\u3002 \u5982\u679c\u4e3afalse\uff0c\u5219\u7ed3\u679c\u662f\u5bf9\u5185\u7f6e\u6570\u636e\u7c7b\u578b\u5bf9\u8c61\u7684\u5f15\u7528<\/li><\/ul>\n\n\n\n<h3>\u4f8b1<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># using array-scalar type\nimport numpy as np\ndt = np.dtype(np.int32)\nprint dt\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">int32\n<\/pre>\n\n\n\n<h3>\u4f8b2<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc.\nimport numpy as np\n\ndt = np.dtype('i4')\nprint dt\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">int32\n<\/pre>\n\n\n\n<h3>\u4f8b3<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># using endian notation\nimport numpy as np\ndt = np.dtype('&gt;i4')\nprint dt\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">&gt;i4\n<\/pre>\n\n\n\n<h3>\u4f8b4<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># first create structured data type\nimport numpy as np\ndt = np.dtype([('age',np.int8)])\nprint dt\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[('age', 'i1')]\n<\/pre>\n\n\n\n<h3>\u4f8b5<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># now apply it to ndarray object\nimport numpy as np\n\ndt = np.dtype([('age',np.int8)])\na = np.array([(10,),(20,),(30,)], dtype = dt)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[(10,) (20,) (30,)]\n<\/pre>\n\n\n\n<h3>\u4f8b6<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\"># file name can be used to access content of age column\nimport numpy as np\n\ndt = np.dtype([('age',np.int8)])\na = np.array([(10,),(20,),(30,)], dtype = dt)\nprint a['age']\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[10 20 30]\n<\/pre>\n\n\n\n<h3>\u4f8b7<\/h3>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u5b9a\u4e49\u4e86\u4e00\u4e2a\u540d\u4e3astudent\u7684\u7ed3\u6784\u5316\u6570\u636e\u7c7b\u578b\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u4e2a\u5b57\u7b26\u4e32\u5b57\u6bb5&#8217;name&#8217;\uff0c\u4e00\u4e2a\u6574\u6570\u5b57\u6bb5&#8217;age&#8217;\u548c\u4e00\u4e2a\u6d6e\u70b9\u5b57\u6bb5&#8217;marks&#8217;\u3002 \u8fd9\u4e2adtype\u88ab\u5e94\u7528\u4e8endarray\u5bf9\u8c61\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nstudent = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])\nprint student\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[('name', 'S20'), ('age', 'i1'), ('marks', '&lt;f4')])\n<\/pre>\n\n\n\n<h3>\u4f8b8<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nstudent = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])\na = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[('abc', 21, 50.0), ('xyz', 18, 75.0)]\n<\/pre>\n\n\n\n<p>\u6bcf\u4e2a\u5185\u7f6e\u6570\u636e\u7c7b\u578b\u90fd\u6709\u4e00\u4e2a\u552f\u4e00\u6807\u8bc6\u5b83\u7684\u5b57\u7b26\u4ee3\u7801\u3002<\/p>\n\n\n\n<ul><li><b>&#8216;b&#8217;<\/b> \u2212 boolean<\/li><li><b>&#8216;i&#8217;<\/b> \u2212 (signed) integer<\/li><li><b>&#8216;u&#8217;<\/b> \u2212 unsigned integer<\/li><li><b>&#8216;f&#8217;<\/b> \u2212 floating-point<\/li><li><b>&#8216;c&#8217;<\/b> \u2212 complex-floating point<\/li><li><b>&#8216;m&#8217;<\/b> \u2212 timedelta<\/li><li><b>&#8216;M&#8217;<\/b> \u2212 datetime<\/li><li><b>&#8216;O&#8217;<\/b> \u2212 (Python) objects<\/li><li><b>&#8216;S&#8217;, &#8216;a&#8217;<\/b> \u2212 (byte-)string<\/li><li><b>&#8216;U&#8217;<\/b> \u2212 Unicode<\/li><li><b>&#8216;V&#8217;<\/b> \u2212 raw data (void)<\/li><\/ul>\n\n\n\n<h1>NumPy \u6570\u7ec4\u5c5e\u6027<\/h1>\n\n\n\n<p>\u5728\u672c\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8ba8\u8bbaNumPy\u7684\u5404\u79cd\u6570\u7ec4\u5c5e\u6027\u3002<\/p>\n\n\n\n<h3>ndarray.shape<\/h3>\n\n\n\n<p>\u8fd9\u4e2a\u6570\u7ec4\u5c5e\u6027\u8fd4\u56de\u4e00\u4e2a\u7531\u6570\u7ec4\u7ef4\u5ea6\u7ec4\u6210\u7684\u5143\u7ec4\u3002 \u5b83\u4e5f\u53ef\u4ee5\u7528\u6765\u8c03\u6574\u6570\u7ec4\u7684\u5927\u5c0f\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[4,5,6]])\nprint a.shape\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">(2, 3)\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># this resizes the ndarray\nimport numpy as np\n\na = np.array([[1,2,3],[4,5,6]])\na.shape = (3,2)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1, 2]\n [3, 4]\n [5, 6]]\n<\/pre>\n\n\n\n<p>\u4f8b3<\/p>\n\n\n\n<p>NumPy\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e2a\u8c03\u6574\u6570\u7ec4\u5927\u5c0f\u7684\u6574\u5f62\u529f\u80fd\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[4,5,6]])\nb = a.reshape(3,2)\nprint b\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1, 2]\n [3, 4]\n [5, 6]]\n<\/pre>\n\n\n\n<h3>ndarray.ndim<\/h3>\n\n\n\n<p>\u8be5\u6570\u7ec4\u5c5e\u6027\u8fd4\u56de\u6570\u7ec4\u7ef4\u6570\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># an array of evenly spaced numbers\nimport numpy as np\na = np.arange(24)\nprint a\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16 17 18 19 20 21 22 23]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># this is one dimensional array\nimport numpy as np\na = np.arange(24)\na.ndim\n\n# now reshape it\nb = a.reshape(2,4,3)\nprint b\n# b is having three dimensions\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[[ 0,  1,  2]\n  [ 3,  4,  5]\n  [ 6,  7,  8]\n  [ 9, 10, 11]]\n  [[12, 13, 14]\n   [15, 16, 17]\n   [18, 19, 20]\n   [21, 22, 23]]]\n<\/pre>\n\n\n\n<h3>numpy.itemsize<\/h3>\n\n\n\n<p>\u8fd9\u4e2a\u6570\u7ec4\u5c5e\u6027\u4ee5\u5b57\u8282\u4e3a\u5355\u4f4d\u8fd4\u56de\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684\u957f\u5ea6\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># dtype of array is int8 (1 byte)\nimport numpy as np\nx = np.array([1,2,3,4,5], dtype = np.int8)\nprint x.itemsize\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">1\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># dtype of array is now float32 (4 bytes)\nimport numpy as np\nx = np.array([1,2,3,4,5], dtype = np.float32)\nprint x.itemsize\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">4\n<\/pre>\n\n\n\n<h3>numpy.flags<\/h3>\n\n\n\n<p>ndarray\u5bf9\u8c61\u5177\u6709\u4ee5\u4e0b\u5c5e\u6027\u3002 \u5b83\u7684\u5f53\u524d\u503c\u7531\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u5c5e\u6027 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>C_CONTIGUOUS (C)<\/b><br>\n\u6570\u636e\u5728\u4e00\u4e2a\u5355\u4e00\u7684\uff0cC\u98ce\u683c\u7684\u8fde\u7eed\u6bb5<\/td><\/tr><tr><td>2<\/td><td><b>F_CONTIGUOUS (F)<\/b><br>\n\u8fd9\u4e9b\u6570\u636e\u4f4d\u4e8e\u4e00\u4e2aFortran\u98ce\u683c\u7684\u8fde\u7eed\u5206\u6bb5\u4e2d<\/td><\/tr><tr><td>3<\/td><td><b>OWNDATA (O)<\/b><br>\n\u6570\u7ec4\u62e5\u6709\u5b83\u4f7f\u7528\u7684\u5185\u5b58\u6216\u4ece\u53e6\u4e00\u4e2a\u5bf9\u8c61\u4e2d\u501f\u7528\u5185\u5b58<\/td><\/tr><tr><td>4<\/td><td><b>WRITEABLE (W)<\/b><br>\n\u6570\u636e\u533a\u53ef\u4ee5\u5199\u5165\u3002 \u5c06\u5176\u8bbe\u7f6e\u4e3aFalse\u4f1a\u9501\u5b9a\u6570\u636e\uff0c\u4f7f\u5176\u6210\u4e3a\u53ea\u8bfb<\/td><\/tr><tr><td>5<\/td><td><b>ALIGNED (A)<\/b><br>\n\u6570\u636e\u548c\u6240\u6709\u5143\u7d20\u90fd\u9488\u5bf9\u786c\u4ef6\u8fdb\u884c\u4e86\u9002\u5f53\u7684\u5bf9\u9f50<\/td><\/tr><tr><td>6<\/td><td><b>UPDATEIFCOPY (U)<\/b><br>\n\u8fd9\u4e2a\u6570\u7ec4\u662f\u5176\u4ed6\u6570\u7ec4\u7684\u526f\u672c\u3002 \u5f53\u8fd9\u4e2a\u6570\u7ec4\u88ab\u89e3\u9664\u5206\u914d\u65f6\uff0c\u57fa\u6570\u7ec4\u5c06\u88ab\u66f4\u65b0\u4e3a\u8fd9\u4e2a\u6570\u7ec4\u7684\u5185\u5bb9<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u6807\u5fd7\u7684\u5f53\u524d\u503c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([1,2,3,4,5])\nprint x.flags\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">C_CONTIGUOUS : True\nF_CONTIGUOUS : True\nOWNDATA : True\nWRITEABLE : True\nALIGNED : True\nUPDATEIFCOPY : False<\/pre>\n\n\n\n<h1>NumPy \u751f\u6210\u6570\u7ec4\u51fd\u6570<\/h1>\n\n\n\n<p>\u65b0\u7684ndarray\u5bf9\u8c61\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4efb\u4f55\u751f\u6210\u6570\u7ec4\u51fd\u6570\u6216\u4f7f\u7528\u4f4e\u7ea7\u522b\u7684ndarray\u6784\u9020\u51fd\u6570\u6784\u9020\u3002<\/p>\n\n\n\n<h3>numpy.empty<\/h3>\n\n\n\n<p>\u5b83\u521b\u5efa\u4e00\u4e2a\u672a\u521d\u59cb\u5316\u7684\u6307\u5b9a\u5f62\u72b6\u548cdtype\u6570\u7ec4\u3002 \u5b83\u4f7f\u7528\u4ee5\u4e0b\u6784\u9020\u51fd\u6570 &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.empty(shape, dtype = float, order = 'C')\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>Shape<\/b><br>\n\u6574\u6570\u6216\u8005\u6574\u578b\u5143\u7ec4\uff0c\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u5f62\u72b6<\/td><\/tr><tr><td>2<\/td><td><b>Dtype<\/b><br>\n\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u9009\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u7c7b\u578b\u3002<\/td><\/tr><tr><td>3<\/td><td><b>Order<\/b><br>\n{\u2018C\u2019, \u2018F\u2019}, \u53ef\u9009\u89c4\u5b9a\u8fd4\u56de\u6570\u7ec4\u5143\u7d20\u5728\u5185\u5b58\u7684\u5b58\u50a8\u987a\u5e8f\uff1aC\uff08C\u8bed\u8a00\uff09-rowmajor\uff1bF\uff08Fortran\uff09column-major\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<p>\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u4e00\u4e2a\u7a7a\u6570\u7ec4\u7684\u793a\u4f8b\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.empty([3,2], dtype = int)\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[22649312    1701344351]\n [1818321759  1885959276]\n [16779776    156368896]]\n<\/pre>\n\n\n\n<p><b>\u6ce8<\/b> &#8211; \u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u663e\u793a\u968f\u673a\u503c\uff0c\u56e0\u4e3a\u5b83\u4eec\u672a\u88ab\u521d\u59cb\u5316\u3002<\/p>\n\n\n\n<h3>numpy.zeros<\/h3>\n\n\n\n<p>\u8fd4\u56de\u6307\u5b9a\u5927\u5c0f\u7684\u65b0\u6570\u7ec4\uff0c\u586b\u5145\u96f6\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.zeros(shape, dtype = float, order = 'C')\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>Shape<\/b><br>\n\u6574\u6570\u6216\u8005\u6574\u578b\u5143\u7ec4\uff0c\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u5f62\u72b6<\/td><\/tr><tr><td>2<\/td><td><b>Dtype<\/b><br>\n\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u9009\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u7c7b\u578b\u3002<\/td><\/tr><tr><td>3<\/td><td><b>Order<\/b><br>\n{\u2018C\u2019, \u2018F\u2019}, \u53ef\u9009\u89c4\u5b9a\u8fd4\u56de\u6570\u7ec4\u5143\u7d20\u5728\u5185\u5b58\u7684\u5b58\u50a8\u987a\u5e8f\uff1aC\uff08C\u8bed\u8a00\uff09-rowmajor\uff1bF\uff08Fortran\uff09column-major\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># array of five zeros. Default dtype is float\nimport numpy as np\nx = np.zeros(5)\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 0.  0.  0.  0.  0.]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.zeros((5,), dtype = np.int)\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0  0  0  0  0]\n<\/pre>\n\n\n\n<p>\u4f8b3<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># custom type\nimport numpy as np\nx = np.zeros((2,2), dtype = [('x', 'i4'), ('y', 'i4')])\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[(0,0)(0,0)]\n [(0,0)(0,0)]]\n<\/pre>\n\n\n\n<h3>numpy.ones<\/h3>\n\n\n\n<p>\u8fd4\u56de\u6307\u5b9a\u5927\u5c0f\u548c\u7c7b\u578b\u7684\u65b0\u6570\u7ec4\uff0c\u586b\u51451\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.ones(shape, dtype = None, order = 'C')\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>Shape<\/b><br>\n\u6574\u6570\u6216\u8005\u6574\u578b\u5143\u7ec4\uff0c\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u5f62\u72b6<\/td><\/tr><tr><td>2<\/td><td><b>Dtype<\/b><br>\n\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u9009\u5b9a\u4e49\u8fd4\u56de\u6570\u7ec4\u7684\u7c7b\u578b\u3002<\/td><\/tr><tr><td>3<\/td><td><b>Order<\/b><br>\n{\u2018C\u2019, \u2018F\u2019}, \u53ef\u9009\u89c4\u5b9a\u8fd4\u56de\u6570\u7ec4\u5143\u7d20\u5728\u5185\u5b58\u7684\u5b58\u50a8\u987a\u5e8f\uff1aC\uff08C\u8bed\u8a00\uff09-rowmajor\uff1bF\uff08Fortran\uff09column-major\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># array of five ones. Default dtype is float\nimport numpy as np\nx = np.ones(5)\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 1.  1.  1.  1.  1.]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.ones([2,2], dtype = int)\nprint x\n<\/pre>\n\n\n\n<p>\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1  1]\n [1  1]]<\/pre>\n\n\n\n<h1>NumPy \u521b\u5efa\u6570\u7ec4\uff08\u4ece\u73b0\u5b58\u6570\u636e\uff09<\/h1>\n\n\n\n<p>\u5728\u672c\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8ba8\u8bba\u5982\u4f55\u4ece\u73b0\u6709\u6570\u636e\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\u3002<\/p>\n\n\n\n<h3>numpy.asarray<\/h3>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u4e0enumpy.array\u7c7b\u4f3c\uff0c\u53ea\u662f\u5b83\u7684\u53c2\u6570\u8f83\u5c11\u3002 \u6b64\u4f8b\u7a0b\u5bf9\u4e8e\u5c06Python\u5e8f\u5217\u8f6c\u6362\u4e3andarray\u5f88\u6709\u7528\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.asarray(a, dtype = None, order = None)\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>a<\/b><br>\n\u4ee5\u4efb\u4f55\u5f62\u5f0f\u8f93\u5165\u6570\u636e\uff0c\u5982\u5217\u8868\uff0c\u5143\u7ec4\u5217\u8868\uff0c\u5143\u7ec4\u5217\u8868\uff0c\u5143\u7ec4\u5143\u7ec4\u6216\u5143\u7ec4\u5143\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>dtype<\/b><br>\n\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u8f93\u5165\u6570\u636e\u7684\u6570\u636e\u7c7b\u578b\u5e94\u7528\u4e8endarray<\/td><\/tr><tr><td>3<\/td><td><b>order<\/b><br>\nC\uff08row-major \u884c\u5e8f\u4f18\u5148\uff09\u6216F\uff08column-major \u5217\u5e8f\u4f18\u5148\uff09\u3002 C\u662f\u9ed8\u8ba4\u7684<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u4f7f\u7528asarray\u51fd\u6570\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># convert list to ndarray\nimport numpy as np\n\nx = [1,2,3]\na = np.asarray(x)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[1  2  3]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># dtype is set\nimport numpy as np\n\nx = [1,2,3]\na = np.asarray(x, dtype = float)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 1.  2.  3.]\n<\/pre>\n\n\n\n<p>\u4f8b3<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># ndarray from tuple\nimport numpy as np\n\nx = (1,2,3)\na = np.asarray(x)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[1  2  3]\n<\/pre>\n\n\n\n<p>\u4f8b4<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># ndarray from list of tuples\nimport numpy as np\n\nx = [(1,2,3),(4,5)]\na = np.asarray(x)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[(1, 2, 3) (4, 5)]\n<\/pre>\n\n\n\n<h3>numpy.frombuffer<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u5c06\u7f13\u51b2\u533a\u89e3\u91ca\u4e3a\u4e00\u7ef4\u6570\u7ec4\u3002 \u516c\u5f00\u7f13\u51b2\u533a\u63a5\u53e3\u7684\u4efb\u4f55\u5bf9\u8c61\u90fd\u5c06\u7528\u4f5c\u8fd4\u56dendarray\u7684\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0)\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>buffer<\/b><br>\n\u4efb\u4f55\u516c\u5f00\u7f13\u51b2\u533a\u63a5\u53e3\u7684\u5bf9\u8c61<\/td><\/tr><tr><td>2<\/td><td><b>dtype<\/b><br>\n\u8fd4\u56de\u7684ndarray\u7684\u6570\u636e\u7c7b\u578b\u3002 \u9ed8\u8ba4\u4e3a\u6d6e\u70b9<\/td><\/tr><tr><td>3<\/td><td><b>count<\/b><br>\n\u8981\u8bfb\u53d6\u7684\u6570\u91cf\uff0c\u9ed8\u8ba4\u503c-1\u8868\u793a\u6240\u6709\u6570\u636e<\/td><\/tr><tr><td>4<\/td><td><b>offset<\/b><br>\n\u8bfb\u53d6\u7684\u8d77\u59cb\u4f4d\u7f6e\u3002 \u7f3a\u7701\u503c\u662f0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u4f7f\u7528frombuffer\u51fd\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\ns = 'Hello World'\na = np.frombuffer(s, dtype = 'S1')\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">['H'  'e'  'l'  'l'  'o'  ' '  'W'  'o'  'r'  'l'  'd']\n<\/pre>\n\n\n\n<h3>numpy.fromiter<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u4ece\u4efb\u4f55\u53ef\u8fed\u4ee3\u5bf9\u8c61\u6784\u5efa\u4e00\u4e2andarray\u5bf9\u8c61\u3002 \u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u4e00\u7ef4\u6570\u7ec4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.fromiter(iterable, dtype, count = -1)\n<\/pre>\n\n\n\n<p>\u8fd9\u91cc\uff0c\u6784\u9020\u51fd\u6570\u63a5\u53d7\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>iterable<\/b><br>\n\u4efb\u4f55\u53ef\u8fed\u4ee3\u7684\u5bf9\u8c61<\/td><\/tr><tr><td>2<\/td><td><b>dtype<\/b><br>\n\u7ed3\u679c\u6570\u7ec4\u7684\u6570\u636e\u7c7b\u578b<\/td><\/tr><tr><td>3<\/td><td><b>count<\/b><br>\n\u8981\u4ece\u8fed\u4ee3\u5668\u8bfb\u53d6\u7684\u9879\u76ee\u6570\u91cf\u3002 \u9ed8\u8ba4\u503c\u662f-1\uff0c\u8fd9\u610f\u5473\u7740\u6240\u6709\u8981\u8bfb\u53d6\u7684\u6570\u636e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u4f7f\u7528\u5185\u7f6e\u7684range\uff08\uff09\u51fd\u6570\u8fd4\u56de\u5217\u8868\u5bf9\u8c61\u3002 \u8fd9\u4e2a\u5217\u8868\u7684\u8fed\u4ee3\u5668\u7528\u4e8e\u5f62\u6210\u4e00\u4e2andarray\u5bf9\u8c61\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># create list object using range function\nimport numpy as np\nlist = range(5)\nprint list\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0,  1,  2,  3,  4]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># obtain iterator object from list\nimport numpy as np\nlist = range(5)\nit = iter(list)\n\n# use iterator to create ndarray\nx = np.fromiter(it, dtype = float)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0.   1.   2.   3.   4.]<\/pre>\n\n\n\n<h1>NumPy \u521b\u5efa\u6570\u7ec4\uff08\u4ece\u6570\u503c\u8303\u56f4\uff09<\/h1>\n\n\n\n<p>\u5728\u672c\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u770b\u5230\u5982\u4f55\u4ece\u6570\u503c\u8303\u56f4\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\u3002<\/p>\n\n\n\n<h3>numpy.arange<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u7ed9\u5b9a\u8303\u56f4\u5185\u5747\u5300\u95f4\u9694\u503c\u7684ndarray\u5bf9\u8c61\u3002 \u8be5\u529f\u80fd\u7684\u683c\u5f0f\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.arange(start, stop, step, dtype)\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>start<\/b><br>\n\u95f4\u9694\u7684\u5f00\u59cb\u3002 \u5982\u679c\u7701\u7565\uff0c\u5219\u9ed8\u8ba4\u4e3a0<\/td><\/tr><tr><td>2<\/td><td><b>stop<\/b><br>\n\u95f4\u9694\u7ed3\u675f\uff08\u4e0d\u5305\u62ec\u6b64\u6570\u5b57\uff09<\/td><\/tr><tr><td>3<\/td><td><b>step<\/b><br>\n\u503c\u4e4b\u95f4\u7684\u95f4\u8ddd\uff0c\u9ed8\u8ba4\u503c\u4e3a1<\/td><\/tr><tr><td>4<\/td><td><b>dtype<\/b><br>\n\u751f\u6210\u7684ndarray\u7684\u6570\u636e\u7c7b\u578b\u3002 \u5982\u679c\u6ca1\u6709\u7ed9\u51fa\uff0c\u5219\u4f7f\u7528\u8f93\u5165\u7684\u6570\u636e\u7c7b\u578b<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u4f7f\u7528\u6b64\u529f\u80fd\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.arange(5)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0  1  2  3  4]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n# dtype set\nx = np.arange(5, dtype = float)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[0.  1.  2.  3.  4.]\n<\/pre>\n\n\n\n<p>\u4f8b3<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># start and stop parameters set\nimport numpy as np\nx = np.arange(10,20,2)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[10  12  14  16  18]\n<\/pre>\n\n\n\n<h3>numpy.linspace<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u4e0earange\uff08\uff09\u51fd\u6570\u7c7b\u4f3c\u3002 \u5728\u6b64\u51fd\u6570\u4e2d\uff0c\u4e0d\u662f\u6b65\u957f\uff0c\u800c\u662f\u6307\u5b9a\u95f4\u9694\u4e4b\u95f4\u7684\u5747\u5300\u95f4\u9694\u503c\u7684\u6570\u91cf\u3002 \u8be5\u529f\u80fd\u7684\u7528\u6cd5\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.linspace(start, stop, num, endpoint, retstep, dtype)\n<\/pre>\n\n\n\n<p>\u6784\u9020\u51fd\u6570\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>start<\/b><br>\n\u5e8f\u5217\u7684\u8d77\u59cb\u503c<\/td><\/tr><tr><td>2<\/td><td><b>stop<\/b><br>\n\u5e8f\u5217\u7684\u7ed3\u675f\u503c\uff0c\u5305\u62ec\u5728\u7aef\u70b9\u8bbe\u7f6e\u4e3atrue\u7684\u5e8f\u5217\u4e2d<\/td><\/tr><tr><td>3<\/td><td><b>num<\/b><br>\n\u8981\u751f\u6210\u7684\u5747\u5300\u95f4\u9694\u6837\u672c\u7684\u6570\u91cf\u3002 \u9ed8\u8ba4\u503c\u662f50<\/td><\/tr><tr><td>4<\/td><td><b>endpoint<\/b><br>\n\u5982\u679c\u4e3a\u771f\uff0c\u5219\u6700\u540e\u4e00\u4e2a\u503c\uff08stop\u5bf9\u5e94\u7684\u503c\uff09\u5305\u542b\u5728\u6837\u672c\u4e2d<\/td><\/tr><tr><td>5<\/td><td><b>retstep<\/b><br>\n\u5982\u679c\u4e3a\u771f\uff0c\u8fd4\u56de\u6837\u672c\u53ca\u6b65\u957f<\/td><\/tr><tr><td>6<\/td><td><b>dtype<\/b><br>\n\u8f93\u51fandarray\u7684\u6570\u636e\u7c7b\u578b<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u4e86\u4f7f\u7528linspace\u51fd\u6570\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.linspace(10,20,5)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[10.   12.5   15.   17.5  20.]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># endpoint set to false\nimport numpy as np\nx = np.linspace(10,20, 5, endpoint = False)\nprint x\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[10.   12.   14.   16.   18.]\n<\/pre>\n\n\n\n<p>\u4f8b3<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># find retstep value\nimport numpy as np\n\nx = np.linspace(1,2,5, retstep = True)\nprint x\n# retstep here is 0.25\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">(array([ 1.  ,  1.25,  1.5 ,  1.75,  2.  ]), 0.25)\n<\/pre>\n\n\n\n<h3>numpy.logspace<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2andarray\u5bf9\u8c61\uff0c\u8be5\u5bf9\u8c61\u5305\u542b\u5728\u5bf9\u6570\u523b\u5ea6\u4e0a\u5747\u5300\u95f4\u9694\u7684\u6570\u5b57\u3002 \u89c4\u6a21\u7684\u8d77\u6b62\u70b9\u662f\u57fa\u6570\u7684\u6307\u6570\uff0c\u901a\u5e38\u4e3a10\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.logspace(start, stop, num, endpoint, base, dtype)\n<\/pre>\n\n\n\n<p>\u4ee5\u4e0b\u53c2\u6570\u51b3\u5b9a\u4e86logspace\u51fd\u6570\u7684\u8f93\u51fa\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>start<\/b><br>\n\u5e8f\u5217\u7684\u8d77\u59cb\u503c\u662fbase<sup>start<\/sup><\/td><\/tr><tr><td>2<\/td><td><b>stop<\/b><br>\n\u5e8f\u5217\u7684\u7ed3\u675f\u503c\u662fbase<sup>stop<\/sup><\/td><\/tr><tr><td>3<\/td><td><b>num<\/b><br>\n\u8981\u751f\u6210\u7684\u5747\u5300\u95f4\u9694\u6837\u672c\u7684\u6570\u91cf\u3002 \u9ed8\u8ba4\u503c\u662f50<\/td><\/tr><tr><td>4<\/td><td><b>endpoint<\/b><br>\n\u5982\u679c\u4e3a\u771f\uff0c\u5219\u6700\u540e\u4e00\u4e2a\u503c\uff08stop\u5bf9\u5e94\u7684\u503c\uff09\u5305\u542b\u5728\u6837\u672c\u4e2d<\/td><\/tr><tr><td>5<\/td><td><b>base<\/b><br>\nbase\u503c\uff0c\u9ed8\u8ba4\u4e3a10<\/td><\/tr><tr><td>6<\/td><td><b>dtype<\/b><br>\n\u8f93\u51fa\u6570\u7ec4\u7684\u6570\u636e\u7c7b\u578b\u3002 \u5982\u679c\u6ca1\u6709\u7ed9\u51fa\uff0c\u5219\u53d6\u51b3\u4e8e\u5176\u4ed6\u8f93\u5165\u53c2\u6570<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u5c06\u5e2e\u52a9\u60a8\u4e86\u89e3logspace\u529f\u80fd\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n# default base is 10\na = np.logspace(1.0, 2.0, num = 10)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 10.           12.91549665     16.68100537      21.5443469  27.82559402\n  35.93813664   46.41588834     59.94842503      77.42636827    100.    ]\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># set base of log space to 2\nimport numpy as np\na = np.logspace(1,10,num = 10, base = 2)\nprint a\n<\/pre>\n\n\n\n<p>\u6267\u884c\u7ed3\u679c\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 2.     4.     8.    16.    32.    64.   128.   256.    512.   1024.]<\/pre>\n\n\n\n<h1>NumPy \u7d22\u5f15\u548c\u5207\u7247<\/h1>\n\n\n\n<p>\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u6216\u5207\u7247\u6765\u8bbf\u95ee\u548c\u4fee\u6539ndarray\u5bf9\u8c61\u7684\u5185\u5bb9\uff0c\u5c31\u50cfPython\u7684\u5185\u7f6e\u5bb9\u5668\u5bf9\u8c61\u4e00\u6837\u3002<\/p>\n\n\n\n<p>\u5982\u524d\u6240\u8ff0\uff0cndarray\u5bf9\u8c61\u4e2d\u7684\u9879\u9075\u5faa\u4ece\u96f6\u5f00\u59cb\u7684\u7d22\u5f15\u3002 \u6709\u4e09\u79cd\u7d22\u5f15\u65b9\u6cd5\u53ef\u7528 &#8211; \u5b57\u6bb5\u8bbf\u95ee\uff0c\u57fa\u672c\u5207\u7247\u548c\u9ad8\u7ea7\u7d22\u5f15\u3002<\/p>\n\n\n\n<p>\u57fa\u672c\u5207\u7247\u662fPython\u5207\u7247\u4e3an\u7ef4\u7684\u57fa\u672c\u6982\u5ff5\u7684\u6269\u5c55\u3002 \u901a\u8fc7\u7ed9\u5185\u7f6e\u5207\u7247\u51fd\u6570\u63d0\u4f9bstart\uff0cstop\u548cstep\u53c2\u6570\u6765\u6784\u9020Python\u5207\u7247\u5bf9\u8c61\u3002 \u8be5\u5207\u7247\u5bf9\u8c61\u88ab\u4f20\u9012\u7ed9\u6570\u7ec4\u4ee5\u63d0\u53d6\u6570\u7ec4\u7684\u4e00\u90e8\u5206\u3002<\/p>\n\n\n\n<p><b>\u4f8b1<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(10)\ns = slice(2,7,2)\nprint a[s]\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[2  4  6]\n<\/pre>\n\n\n\n<p>\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u4e00\u4e2andarray\u5bf9\u8c61\u7531arange\uff08\uff09\u51fd\u6570\u51c6\u5907\u3002 \u7136\u540e\u5206\u522b\u7528start\uff0cstop\u548cstep\u503c2,7\u548c2\u5b9a\u4e49\u5207\u7247\u5bf9\u8c61\u3002 \u5f53\u8fd9\u4e2aslice\u5bf9\u8c61\u88ab\u4f20\u9012\u7ed9ndarray\u65f6\uff0c\u5b83\u7684\u4e00\u90e8\u5206\u4ee5\u7d22\u5f152\u5f00\u59cb\uff0c\u6700\u591a\u4e3a7\uff0c\u6b65\u957f\u4e3a2\u3002<\/p>\n\n\n\n<p>\u901a\u8fc7\u76f4\u63a5\u5411\u5192\u53f7\u5bf9\u8c61\u63d0\u4f9b\u7531\u5192\u53f7\u5206\u9694\u7684\u5207\u7247\u53c2\u6570\uff08\uff08start\uff1astop\uff1astep\uff09\uff09\uff0c\u4e5f\u53ef\u4ee5\u83b7\u5f97\u76f8\u540c\u7684\u7ed3\u679c\u3002<\/p>\n\n\n\n<p><b>\u4f8b2<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(10)\nb = a[2:7:2]\nprint b\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[2  4  6]\n<\/pre>\n\n\n\n<p>\u5982\u679c\u53ea\u653e\u5165\u4e00\u4e2a\u53c2\u6570\uff0c\u5219\u4f1a\u8fd4\u56de\u4e0e\u8be5\u7d22\u5f15\u5bf9\u5e94\u7684\u5355\u4e2a\u9879\u76ee\u3002 \u5982\u679c\u5c06a\uff1a\u63d2\u5165\u5176\u524d\u9762\uff0c\u5219\u5c06\u4ece\u8be5\u7d22\u5f15\u5f00\u59cb\u63d0\u53d6\u6240\u6709\u9879\u76ee\u3002 \u5982\u679c\u4f7f\u7528\u4e24\u4e2a\u53c2\u6570\uff08\u5728\u5b83\u4eec\u4e4b\u95f4\uff09\uff0c\u5219\u5c06\u4f7f\u7528\u9ed8\u8ba4\u6b65\u9aa41\u5bf9\u4e24\u4e2a\u7d22\u5f15\uff08\u4e0d\u5305\u62ec\u505c\u6b62\u7d22\u5f15\uff09\u4e4b\u95f4\u7684\u9879\u76ee\u8fdb\u884c\u5207\u7247\u3002<\/p>\n\n\n\n<p><b>\u4f8b3<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># slice single item\nimport numpy as np\n\na = np.arange(10)\nb = a[5]\nprint b\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">5\n<\/pre>\n\n\n\n<p><b>\u4f8b4<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># slice items starting from index\nimport numpy as np\na = np.arange(10)\nprint a[2:]\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[2  3  4  5  6  7  8  9]\n<\/pre>\n\n\n\n<p><b>\u4f8b5<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># slice items between indexes\nimport numpy as np\na = np.arange(10)\nprint a[2:5]\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[2  3  4]\n<\/pre>\n\n\n\n<p>\u4ee5\u4e0a\u63cf\u8ff0\u4e5f\u9002\u7528\u4e8e\u591a\u7ef4\u7684ndarray\u3002<\/p>\n\n\n\n<p><b>\u4f8b6<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[3,4,5],[4,5,6]])\nprint a\n\n# slice items starting from index\nprint 'Now we will slice the array from the index a[1:]'\nprint a[1:]\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1 2 3]\n [3 4 5]\n [4 5 6]]\n \nNow we will slice the array from the index a[1:]\n[[3 4 5]\n [4 5 6]]\n<\/pre>\n\n\n\n<p>\u5207\u7247\u8fd8\u53ef\u4ee5\u5305\u542b\u7701\u7565\u53f7\uff08&#8230;\uff09\u4ee5\u5236\u4f5c\u4e0e\u6570\u7ec4\u7ef4\u5ea6\u957f\u5ea6\u76f8\u540c\u7684\u9009\u62e9\u5143\u7ec4\u3002 \u5982\u679c\u5728\u884c\u4f4d\u7f6e\u4f7f\u7528\u7701\u7565\u53f7\uff0c\u5b83\u5c06\u8fd4\u56de\u5305\u542b\u884c\u4e2d\u9879\u76ee\u7684ndarray\u3002<\/p>\n\n\n\n<p><b>\u4f8b7<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># array to begin with\nimport numpy as np\na = np.array([[1,2,3],[3,4,5],[4,5,6]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\n# this returns array of items in the second column\nprint 'The items in the second column are:'\nprint a[...,1]\nprint '\\n'\n\n# Now we will slice all items from the second row\nprint 'The items in the second row are:'\nprint a[1,...]\nprint '\\n'\n\n# Now we will slice all items from column 1 onwards\nprint 'The items column 1 onwards are:'\nprint a[...,1:]\n<\/pre>\n\n\n\n<p>\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[1 2 3]\n [3 4 5]\n [4 5 6]]\n \nThe items in the second column are:\n[2 4 5]\n \nThe items in the second row are:\n[3 4 5]\n \nThe items column 1 onwards are:\n[[2 3]\n [4 5]\n [5 6]]<\/pre>\n\n\n\n<h1>NumPy \u9ad8\u7ea7\u7d22\u5f15<\/h1>\n\n\n\n<p>\u53ef\u4ee5\u4ecendarray\u4e2d\u9009\u62e9\u4e00\u4e2a\u975e\u5143\u7ec4\u5e8f\u5217\uff0c\u6574\u6570\u6216\u5e03\u5c14\u6570\u636e\u7c7b\u578b\u7684ndarray\u5bf9\u8c61\uff0c\u6216\u8005\u81f3\u5c11\u5305\u542b\u4e00\u4e2a\u9879\u76ee\u4f5c\u4e3a\u5e8f\u5217\u5bf9\u8c61\u7684\u5143\u7ec4\u3002 \u9ad8\u7ea7\u7d22\u5f15\u59cb\u7ec8\u8fd4\u56de\u6570\u636e\u7684\u526f\u672c\u3002 \u4e0e\u6b64\u76f8\u53cd\uff0c\u5207\u7247\u53ea\u5448\u73b0\u4e00\u4e2a\u89c6\u56fe\u3002<\/p>\n\n\n\n<p>\u6709\u4e24\u79cd\u9ad8\u7ea7\u7d22\u5f15 &#8211; \u6574\u578b\u548c\u5e03\u5c14\u578b\u3002<\/p>\n\n\n\n<h3>\u6574\u6570\u7d22\u5f15<\/h3>\n\n\n\n<p>\u8be5\u673a\u5236\u6709\u52a9\u4e8e\u6839\u636e\u5176\u65e0\u9650\u7ef4\u7d22\u5f15\u5728\u6570\u7ec4\u4e2d\u9009\u62e9\u4efb\u610f\u7684\u9879\u76ee\u3002 \u6bcf\u4e2a\u6574\u6570\u6570\u7ec4\u4ee3\u8868\u8be5\u7ef4\u5ea6\u4e2d\u7684\u7d22\u5f15\u6570\u91cf\u3002 \u5f53\u7d22\u5f15\u7531\u4e0e\u76ee\u6807ndarray\u7684\u7ef4\u5ea6\u76f8\u540c\u6570\u91cf\u7684\u6574\u6570\u6570\u7ec4\u7ec4\u6210\u65f6\uff0c\u5b83\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u3002<\/p>\n\n\n\n<p>\u5728\u4ee5\u4e0b\u793a\u4f8b\u4e2d\uff0c\u9009\u62e9\u4e86\u4ecendarray\u5bf9\u8c61\u7684\u6bcf\u4e00\u884c\u4e2d\u6307\u5b9a\u5217\u7684\u4e00\u4e2a\u5143\u7d20\u3002 \u56e0\u6b64\uff0c\u884c\u7d22\u5f15\u5305\u542b\u6240\u6709\u884c\u53f7\uff0c\u5217\u7d22\u5f15\u6307\u5b9a\u8981\u9009\u62e9\u7684\u5143\u7d20\u3002<\/p>\n\n\n\n<p><b>\u4f8b1<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nx = np.array([[1, 2], [3, 4], [5, 6]])\ny = x[[0,1,2], [0,1,0]]\nprint y\n<\/pre>\n\n\n\n<p>\u5176\u7ed3\u679c\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[1  4  5]\n<\/pre>\n\n\n\n<p>\u9009\u62e9\u5305\u62ec\u7b2c\u4e00\u4e2a\u6570\u7ec4\u4e2d\u7684\uff080,0\uff09\uff0c\uff081,1\uff09\u548c\uff082,0\uff09\u5143\u7d20\u3002<\/p>\n\n\n\n<p>\u5728\u4ee5\u4e0b\u793a\u4f8b\u4e2d\uff0c\u9009\u62e9\u653e\u7f6e\u57284X3\u9635\u5217\u89d2\u843d\u7684\u5143\u7d20\u3002 \u9009\u62e9\u7684\u884c\u7d22\u5f15\u662f[0,0]\u548c[3,3]\uff0c\u800c\u5217\u7d22\u5f15\u662f[0,2]\u548c[0,2]\u3002<\/p>\n\n\n\n<p><b>\u4f8b2<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])\n\nprint 'Our array is:'\nprint x\nprint '\\n'\n\nrows = np.array([[0,0],[3,3]])\ncols = np.array([[0,2],[0,2]])\ny = x[rows,cols]\n\nprint 'The corner elements of this array are:'\nprint y\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[ 0  1  2]\n [ 3  4  5]\n [ 6  7  8]\n [ 9 10 11]]\n\nThe corner elements of this array are:\n[[ 0  2]\n [ 9 11]]\n<\/pre>\n\n\n\n<p>\u6700\u7ec8\u7684\u9009\u62e9\u662f\u5305\u542b\u89d2\u70b9\u5143\u7d20\u7684ndarray\u5bf9\u8c61\u3002<\/p>\n\n\n\n<p>\u9ad8\u7ea7\u548c\u57fa\u672c\u7d22\u5f15\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u4e00\u4e2aslice\uff08:)\u6216\u7701\u7565\u53f7\uff08&#8230;\uff09\u4e0e\u7d22\u5f15\u6570\u7ec4\u7ed3\u5408\u4f7f\u7528\u3002 \u4ee5\u4e0b\u793a\u4f8b\u5bf9\u5217\u4f7f\u7528slice\u548c\u9ad8\u7ea7\u7d22\u5f15\u3002 \u5f53\u5207\u7247\u7528\u4e8e\u4e24\u8005\u65f6\u7ed3\u679c\u76f8\u540c\u3002 \u4f46\u9ad8\u7ea7\u7d22\u5f15\u4f1a\u5bfc\u81f4\u590d\u5236\uff0c\u5e76\u53ef\u80fd\u6709\u4e0d\u540c\u7684\u5185\u5b58\u5e03\u5c40\u3002<\/p>\n\n\n\n<p><b>\u4f8b3<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])\n\nprint 'Our array is:'\nprint x\nprint '\\n'\n\n# slicing\nz = x[1:4,1:3]\n\nprint 'After slicing, our array becomes:'\nprint z\nprint '\\n'\n\n# using advanced index for column\ny = x[1:4,[1,2]]\n\nprint 'Slicing using advanced index for column:'\nprint y\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[ 0  1  2]\n [ 3  4  5]\n [ 6  7  8]\n [ 9 10 11]]\n\nAfter slicing, our array becomes:\n[[ 4  5]\n [ 7  8]\n [10 11]]\n\nSlicing using advanced index for column:\n[[ 4  5]\n [ 7  8]\n [10 11]]\n<\/pre>\n\n\n\n<h3>\u5e03\u5c14\u6570\u7ec4\u7d22\u5f15<\/h3>\n\n\n\n<p>\u8fd9\u79cd\u7c7b\u578b\u7684\u9ad8\u7ea7\u7d22\u5f15\u662f\u5728\u7ed3\u679c\u5bf9\u8c61\u662f\u5e03\u5c14\u64cd\u4f5c\u7684\u7ed3\u679c\u65f6\u4f7f\u7528\u7684\uff0c\u4f8b\u5982\u6bd4\u8f83\u8fd0\u7b97\u7b26\u3002<\/p>\n\n\n\n<p><b>\u4f8b1<\/b><\/p>\n\n\n\n<p>\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u7531\u4e8e\u5e03\u5c14\u7d22\u5f15\u800c\u8fd4\u56de\u5927\u4e8e5\u7684\u9879\u76ee\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])\n\nprint 'Our array is:'\nprint x\nprint '\\n'\n\n# Now we will print the items greater than 5\nprint 'The items greater than 5 are:'\nprint x[x &gt; 5]\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[ 0  1  2]\n [ 3  4  5]\n [ 6  7  8]\n [ 9 10 11]]\n\nThe items greater than 5 are:\n[ 6  7  8  9 10 11]\n<\/pre>\n\n\n\n<p><b>\u4f8b2<\/b><\/p>\n\n\n\n<p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u901a\u8fc7\u4f7f\u7528\u301c\uff08\u8865\u6570\u8fd0\u7b97\u7b26\uff09\u7701\u7565NaN\uff08\u975e\u6570\u5b57\uff09\u5143\u7d20\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([np.nan, 1,2,np.nan,3,4,5])\nprint a[~np.isnan(a)]\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 1.   2.   3.   4.   5.]\n<\/pre>\n\n\n\n<p><b>\u4f8b3<\/b><\/p>\n\n\n\n<p>\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u4ece\u6570\u7ec4\u4e2d\u8fc7\u6ee4\u975e\u590d\u6570\u5143\u7d20\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1, 2+6j, 5, 3.5+5j])\nprint a[np.iscomplex(a)]\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[2.0+6.j  3.5+5.j]<\/pre>\n\n\n\n<h1>NumPy \u5e7f\u64ad<\/h1>\n\n\n\n<p>\u672f\u8bed\u5e7f\u64ad\u662f\u6307\u5728\u7b97\u672f\u8fd0\u7b97\u671f\u95f4NumPy\u5904\u7406\u4e0d\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u7684\u80fd\u529b\u3002 \u6570\u7ec4\u4e0a\u7684\u7b97\u672f\u8fd0\u7b97\u901a\u5e38\u5728\u76f8\u5e94\u7684\u5143\u7d20\u4e0a\u5b8c\u6210\u3002 \u5982\u679c\u4e24\u4e2a\u9635\u5217\u5177\u6709\u5b8c\u5168\u76f8\u540c\u7684\u5f62\u72b6\uff0c\u90a3\u4e48\u8fd9\u4e9b\u64cd\u4f5c\u5c06\u987a\u5229\u6267\u884c\u3002<\/p>\n\n\n\n<h3>\u4f8b1<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\na = np.array([1,2,3,4])\nb = np.array([10,20,30,40])\nc = a * b\nprint c\n<\/pre>\n\n\n\n<p>\u8be5\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[10   40   90   160]\n<\/pre>\n\n\n\n<p>\u5982\u679c\u4e24\u4e2a\u6570\u7ec4\u7684\u7ef4\u6570\u4e0d\u76f8\u540c\uff0c\u5219\u5143\u7d20\u5230\u5143\u7d20\u7684\u64cd\u4f5c\u662f\u4e0d\u53ef\u80fd\u7684\u3002 \u4f46\u662f\uff0c\u7531\u4e8e\u5e7f\u64ad\u80fd\u529b\u7684\u539f\u56e0\uff0c\u5728NumPy\u4e2d\u4ecd\u7136\u53ef\u4ee5\u5bf9\u975e\u76f8\u4f3c\u5f62\u72b6\u7684\u9635\u5217\u8fdb\u884c\u64cd\u4f5c\u3002 \u8f83\u5c0f\u7684\u9635\u5217\u88ab\u5e7f\u64ad\u5230\u8f83\u5927\u9635\u5217\u7684\u5927\u5c0f\uff0c\u4ee5\u4fbf\u5b83\u4eec\u5177\u6709\u517c\u5bb9\u7684\u5f62\u72b6\u3002<\/p>\n\n\n\n<p>\u5982\u679c\u6ee1\u8db3\u4ee5\u4e0b\u89c4\u5219\uff0c\u5e7f\u64ad\u662f\u53ef\u80fd\u7684 &#8211;<\/p>\n\n\n\n<ul><li>\u5177\u6709\u8f83\u5c0fndim\u7684\u6570\u7ec4\u5728\u5176\u5f62\u72b6\u4e0a\u9884\u7f6e\u4e3a&#8217;1&#8217;\u3002<\/li><li>\u8f93\u51fa\u5f62\u72b6\u7684\u6bcf\u4e2a\u7ef4\u5ea6\u4e2d\u7684\u5927\u5c0f\u662f\u8be5\u7ef4\u5ea6\u4e2d\u8f93\u5165\u5927\u5c0f\u7684\u6700\u5927\u503c\u3002<\/li><li>\u5982\u679c\u8f93\u5165\u7684\u5927\u5c0f\u4e0e\u8f93\u51fa\u5927\u5c0f\u5339\u914d\u6216\u8005\u5176\u503c\u6070\u597d\u4e3a1\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528\u8f93\u5165\u8fdb\u884c\u8ba1\u7b97\u3002<\/li><li>\u5982\u679c\u8f93\u5165\u7684\u7ef4\u5ea6\u5927\u5c0f\u4e3a1\uff0c\u5219\u8be5\u7ef4\u5ea6\u4e2d\u7684\u7b2c\u4e00\u4e2a\u6570\u636e\u6761\u76ee\u5c06\u7528\u4e8e\u6cbf\u8be5\u7ef4\u5ea6\u7684\u6240\u6709\u8ba1\u7b97\u3002<\/li><\/ul>\n\n\n\n<p>\u5982\u679c\u4e0a\u8ff0\u89c4\u5219\u4ea7\u751f\u6709\u6548\u7ed3\u679c\u5e76\u4e14\u4ee5\u4e0b\u60c5\u51b5\u4e4b\u4e00\u6210\u7acb\uff0c\u5219\u79f0\u4e00\u7ec4\u6570\u636e\u53ef\u4ee5\u5e7f\u64ad &#8211;<\/p>\n\n\n\n<ul><li>\u6570\u7ec4\u7684\u5f62\u72b6\u5b8c\u5168\u76f8\u540c\u3002<\/li><li>\u6570\u7ec4\u5177\u6709\u76f8\u540c\u7684\u7ef4\u5ea6\u6570\u91cf\uff0c\u6bcf\u4e2a\u7ef4\u5ea6\u7684\u957f\u5ea6\u53ef\u4ee5\u662f\u5e38\u7528\u957f\u5ea6\u62161\u3002<\/li><li>\u5c3a\u5bf8\u592a\u5c0f\u7684\u9635\u5217\u53ef\u80fd\u4f1a\u5c06\u5176\u5f62\u72b6\u9884\u5148\u8bbe\u7f6e\u4e3a\u957f\u5ea6\u4e3a1\u7684\u7ef4\u5ea6\uff0c\u4ee5\u4f7f\u4e0a\u8ff0\u5c5e\u6027\u4e3a\u771f\u3002<\/li><\/ul>\n\n\n\n<p>\u4ee5\u4e0b\u7a0b\u5e8f\u663e\u793a\u4e86\u5e7f\u64ad\u793a\u4f8b\u3002<\/p>\n\n\n\n<h3>\u4f8b2<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[0.0,0.0,0.0],[10.0,10.0,10.0],[20.0,20.0,20.0],[30.0,30.0,30.0]])\nb = np.array([1.0,2.0,3.0])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Second array:'\nprint b\nprint '\\n'\n\nprint 'First Array + Second Array'\nprint a + b\n<\/pre>\n\n\n\n<p>\u8be5\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[ 0. 0. 0.]\n [ 10. 10. 10.]\n [ 20. 20. 20.]\n [ 30. 30. 30.]]\n\nSecond array:\n[ 1. 2. 3.]\n\nFirst Array + Second Array\n[[ 1. 2. 3.]\n [ 11. 12. 13.]\n [ 21. 22. 23.]\n [ 31. 32. 33.]]\n<\/pre>\n\n\n\n<p>\u4e0b\u56fe\u6f14\u793a\u4e86\u9635\u5217b\u5982\u4f55\u5e7f\u64ad\u4ee5\u4e0ea\u517c\u5bb9\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h1>NumPy \u904d\u5386\u6570\u7ec4<\/h1>\n\n\n\n<p>NumPy\u5305\u4e2d\u5305\u542b\u4e00\u4e2a\u8fed\u4ee3\u5668\u5bf9\u8c61numpy.nditer\u3002 \u5b83\u662f\u4e00\u4e2a\u6709\u6548\u7684\u591a\u7ef4\u8fed\u4ee3\u5668\u5bf9\u8c61\uff0c\u53ef\u4ee5\u7528\u5b83\u8fed\u4ee3\u6570\u7ec4\u3002 \u4f7f\u7528Python\u7684\u6807\u51c6Iterator\u63a5\u53e3\u8bbf\u95ee\u6570\u7ec4\u7684\u6bcf\u4e2a\u5143\u7d20\u3002<\/p>\n\n\n\n<p>\u8ba9\u6211\u4eec\u4f7f\u7528arange\uff08\uff09\u51fd\u6570\u521b\u5efa\u4e00\u4e2a3X4\u6570\u7ec4\uff0c\u5e76\u4f7f\u7528nditer\u5bf9\u5176\u8fdb\u884c\u8fed\u4ee3\u3002<\/p>\n\n\n\n<p>\u4f8b1<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\n\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nprint 'Modified array is:'\nfor x in np.nditer(a):\n\tprint x,\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nModified array is:\n0 5 10 15 20 25 30 35 40 45 50 55\n<\/pre>\n\n\n\n<p>\u4f8b2<\/p>\n\n\n\n<p>\u8fed\u4ee3\u987a\u5e8f\u9009\u62e9\u4e3a\u5339\u914d\u6570\u7ec4\u7684\u5185\u5b58\u5e03\u5c40\uff0c\u800c\u4e0d\u8003\u8651\u7279\u5b9a\u7684\u987a\u5e8f\u3002 \u8fd9\u53ef\u4ee5\u901a\u8fc7\u8fed\u4ee3\u4e0a\u8ff0\u6570\u7ec4\u7684\u8f6c\u7f6e\u6765\u770b\u5230\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\n\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nprint 'Transpose of the original array is:'\nb = a.T\nprint b\nprint '\\n'\n\nprint 'Modified array is:'\nfor x in np.nditer(b):\n\tprint x,\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nTranspose of the original array is:\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n\nModified array is:\n0 5 10 15 20 25 30 35 40 45 50 55\n<\/pre>\n\n\n\n<h3>\u8fed\u4ee3\u6b21\u5e8f<\/h3>\n\n\n\n<p>\u5982\u679c\u4f7f\u7528F\u6837\u5f0f\u987a\u5e8f\u5b58\u50a8\u76f8\u540c\u7684\u5143\u7d20\uff0c\u5219\u8fed\u4ee3\u5668\u4f1a\u9009\u62e9\u66f4\u6709\u6548\u7684\u65b9\u5f0f\u904d\u5386\u6570\u7ec4\u3002<\/p>\n\n\n\n<p><b>\u4f8b1<\/b><\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nprint 'Transpose of the original array is:'\nb = a.T\nprint b\nprint '\\n'\n\nprint 'Sorted in C-style order:'\nc = b.copy(order='C')\nprint c\nfor x in np.nditer(c):\n\tprint x,\n\nprint '\\n'\n\nprint 'Sorted in F-style order:'\nc = b.copy(order='F')\nprint c\nfor x in np.nditer(c):\n\tprint x,\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nTranspose of the original array is:\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n\nSorted in C-style order:\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n0 20 40 5 25 45 10 30 50 15 35 55\n\nSorted in F-style order:\n[[ 0 20 40]\n [ 5 25 45]\n [10 30 50]\n [15 35 55]]\n0 5 10 15 20 25 30 35 40 45 50 55\n<\/pre>\n\n\n\n<p><b>\u4f8b2<\/b><\/p>\n\n\n\n<p>\u53ef\u4ee5\u901a\u8fc7\u660e\u786e\u63d0\u53canditer\u5bf9\u8c61\u6765\u4f7f\u7528\u7279\u5b9a\u987a\u5e8f\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\n\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nprint 'Sorted in C-style order:'\nfor x in np.nditer(a, order = 'C'):\n\tprint x,\nprint '\\n'\n\nprint 'Sorted in F-style order:'\nfor x in np.nditer(a, order = 'F'):\n\tprint x,\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nSorted in C-style order:\n0 5 10 15 20 25 30 35 40 45 50 55\n\nSorted in F-style order:\n0 20 40 5 25 45 10 30 50 15 35 55\n<\/pre>\n\n\n\n<h3>\u4fee\u6539\u6570\u7ec4\u503c<\/h3>\n\n\n\n<p>nditer\u5bf9\u8c61\u5177\u6709\u53e6\u4e00\u4e2a\u53ef\u9009\u53c2\u6570\uff0c\u79f0\u4e3aop_flags\u3002 \u5b83\u7684\u9ed8\u8ba4\u503c\u662f\u53ea\u8bfb\u7684\uff0c\u4f46\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u8bfb\u5199\u6a21\u5f0f\u6216\u53ea\u5199\u6a21\u5f0f\u3002 \u8fd9\u5c06\u542f\u7528\u4f7f\u7528\u6b64\u8fed\u4ee3\u5668\u4fee\u6539\u6570\u7ec4\u5143\u7d20\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nfor x in np.nditer(a, op_flags=['readwrite']):\n\tx[...]=2*x\nprint 'Modified array is:'\nprint a\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n[20 25 30 35]\n[40 45 50 55]]\n\nModified array is:\n[[ 0 10 20 30]\n [ 40 50 60 70]\n [ 80 90 100 110]]\n<\/pre>\n\n\n\n<h3>\u5916\u90e8\u5faa\u73af<\/h3>\n\n\n\n<p>nditer\u7c7b\u7684\u6784\u9020\u51fd\u6570\u6709\u4e00\u4e2a&#8217;flags&#8217;\u53c2\u6570\uff0c\u5b83\u53ef\u4ee5\u53d6\u4e0b\u5217\u503c &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>c_index<\/b><br>\nC_order\u7d22\u5f15<\/td><\/tr><tr><td>2<\/td><td><b>f_index<\/b><br>\nFortran_order\u7d22\u5f15<\/td><\/tr><tr><td>3<\/td><td><b>multi-index<\/b><br>\n\u53ef\u4ee5\u8ddf\u8e2a\u6bcf\u6b21\u8fed\u4ee3\u4e00\u6b21\u7684\u7d22\u5f15\u7c7b\u578b<\/td><\/tr><tr><td>4<\/td><td><b>external_loop<\/b><br>\n\u4f7f\u7ed9\u51fa\u7684\u503c\u6210\u4e3a\u5177\u6709\u591a\u4e2a\u503c\u800c\u4e0d\u662f\u96f6\u7ef4\u6570\u7ec4\u7684\u4e00\u7ef4\u6570\u7ec4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><b>\u4f8b\u5b50<\/b><\/p>\n\n\n\n<p>\u5728\u4ee5\u4e0b\u793a\u4f8b\u4e2d\uff0c\u8fed\u4ee3\u5668\u5c06\u904d\u5386\u4e0e\u6bcf\u5217\u5bf9\u5e94\u7684\u4e00\u7ef4\u6570\u7ec4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\n\nprint 'Original array is:'\nprint a\nprint '\\n'\n\nprint 'Modified array is:'\nfor x in np.nditer(a, flags = ['external_loop'], order = 'F'):\n\tprint x,\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nModified array is:\n[ 0 20 40] [ 5 25 45] [10 30 50] [15 35 55]\n<\/pre>\n\n\n\n<h3>\u5e7f\u64ad\u8fed\u4ee3<\/h3>\n\n\n\n<p>\u5982\u679c\u4e24\u4e2a\u6570\u7ec4\u53ef\u4ee5\u5e7f\u64ad\uff0c\u5219\u7ec4\u5408\u7684nditer\u5bf9\u8c61\u53ef\u4ee5\u540c\u65f6\u8fed\u4ee3\u5b83\u4eec\u3002 \u5047\u8bbe\u6570\u7ec4a\u7684\u7ef4\u6570\u4e3a3X4\uff0c\u5e76\u4e14\u5b58\u5728\u7ef4\u6570\u4e3a1X4\u7684\u53e6\u4e00\u4e2a\u6570\u7ec4b\uff0c\u5219\u4f7f\u7528\u4ee5\u4e0b\u7c7b\u578b\u7684\u8fed\u4ee3\u5668\uff08\u6570\u7ec4b\u4ee5\u5e7f\u64ad\u7684\u5927\u5c0fa\uff09\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(0,60,5)\na = a.reshape(3,4)\n\nprint 'First array is:'\nprint a\nprint '\\n'\n\nprint 'Second array is:'\nb = np.array([1, 2, 3, 4], dtype = int)\nprint b\nprint '\\n'\n\nprint 'Modified array is:'\nfor x,y in np.nditer([a,b]):\n\tprint \"%d:%d\" % (x,y),\n<\/pre>\n\n\n\n<p>\u8fd9\u4e2a\u7a0b\u5e8f\u7684\u8f93\u51fa\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array is:\n[[ 0 5 10 15]\n [20 25 30 35]\n [40 45 50 55]]\n\nSecond array is:\n[1 2 3 4]\n\nModified array is:\n0:1 5:2 10:3 15:4 20:1 25:2 30:3 35:4 40:1 45:2 50:3 55:4<\/pre>\n\n\n\n<h1>NumPy \u6570\u7ec4\u64cd\u4f5c<\/h1>\n\n\n\n<p>NumPy\u5305\u4e2d\u6709\u51e0\u4e2a\u4f8b\u7a0b\u53ef\u7528\u4e8e\u5904\u7406ndarray\u5bf9\u8c61\u4e2d\u7684\u5143\u7d20\u3002 \u5b83\u4eec\u53ef\u4ee5\u5206\u4e3a\u4ee5\u4e0b\u51e0\u7c7b &#8211;<\/p>\n\n\n\n<h3>\u6539\u53d8\u5f62\u72b6<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u5f62\u72b6 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>reshape<\/b><br>\n\u7ed9\u6570\u7ec4\u8d4b\u4e88\u65b0\u7684\u5f62\u72b6\u800c\u4e0d\u66f4\u6539\u5176\u6570\u636e<\/td><\/tr><tr><td>2<\/td><td><b>flat<\/b><br>\n\u6570\u7ec4\u4e0a\u7684\u4e00\u7ef4\u8fed\u4ee3\u5668<\/td><\/tr><tr><td>3<\/td><td><b>flatten<\/b><br>\n\u8fd4\u56de\u4e00\u7ef4\u6570\u7ec4\u7684\u526f\u672c<\/td><\/tr><tr><td>4<\/td><td><b>ravel<\/b><br>\n\u8fd4\u56de\u4e00\u4e2a\u8fde\u7eed\u7684\u6241\u5e73\u6570\u7ec4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>numpy.reshape<\/h4>\n\n\n\n<p>\u6b64\u529f\u80fd\u5728\u4e0d\u66f4\u6539\u6570\u636e\u7684\u60c5\u51b5\u4e0b\u4e3a\u9635\u5217\u63d0\u4f9b\u65b0\u7684\u5f62\u72b6\u3002 \u5b83\u63a5\u53d7\u4ee5\u4e0b\u53c2\u6570 &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.reshape(arr, newshape, order')\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u6570\u7ec4\u88ab\u91cd\u6784<\/td><\/tr><tr><td>2<\/td><td><b>newshape<\/b><br>\nint\u6216int\u7684\u5143\u7ec4\u3002 \u65b0\u5f62\u72b6\u5e94\u8be5\u4e0e\u539f\u59cb\u5f62\u72b6\u517c\u5bb9<\/td><\/tr><tr><td>3<\/td><td><b>order<\/b><br>\n&#8216;C&#8217;\u8868\u793aC\u8bed\u8a00\u98ce\u683c\uff0c&#8217;F&#8217;\u8868\u793aFortran\u98ce\u683c\uff0c&#8217;A&#8217;\u8868\u793a\u5982\u679c\u6570\u7ec4\u5b58\u50a8\u5728\u7c7b\u4f3cFortran\u7684\u8fde\u7eed\u5185\u5b58\u4e2d\uff0c\u5219\u8868\u793aFortran\uff0c\u5426\u5219\u4e3aC\u98ce\u683c<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(8)\nprint 'The original array:'\nprint a\nprint '\\n'\n\nb = a.reshape(4,2)\nprint 'The modified array:'\nprint b\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array:\n[0 1 2 3 4 5 6 7]\n\nThe modified array:\n[[0 1]\n [2 3]\n [4 5]\n [6 7]]\n<\/pre>\n\n\n\n<h4>numpy.ndarray.flat<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u6570\u7ec4\u4e0a\u7684\u4e00\u7ef4\u8fed\u4ee3\u5668\u3002 \u5b83\u7684\u884c\u4e3a\u4e0ePython\u7684\u5185\u7f6e\u8fed\u4ee3\u5668\u76f8\u4f3c\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(8).reshape(2,4)\nprint 'The original array:'\nprint a\nprint '\\n'\n\nprint 'After applying the flat function:'\n# returns element corresponding to index in flattened array\nprint a.flat[5]\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array:\n[[0 1 2 3]\n [4 5 6 7]]\n\nAfter applying the flat function:\n5\n<\/pre>\n\n\n\n<h4>numpy.ndarray.flatten<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u6298\u53e0\u6210\u4e00\u7ef4\u7684\u6570\u7ec4\u7684\u526f\u672c\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">ndarray.flatten(order)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>order<\/b><br>\n&#8216;C&#8217;\u884c\u4e3b\uff08\u9ed8\u8ba4\u503c\uff1a&#8217;F&#8217;\uff1a\u5217\u4e3b\u8981&#8217;A&#8217;\uff1a\u6309\u5217\u4e3b\u8981\u987a\u5e8f\u5c55\u5e73\uff0c\u5982\u679ca\u662fFortran\u5728\u5185\u5b58\u4e2d\u8fde\u7eed\u7684\u884c\uff0c\u5426\u5219\u6309\u884c\u6392\u5e8f&#8217;K&#8217;\uff1a\u6309\u5143\u7d20\u7684\u987a\u5e8f\u5c55\u5e73a \u53d1\u751f\u5728\u8bb0\u5fc6\u4e2d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(8).reshape(2,4)\n\nprint 'The original array is:'\nprint a\nprint '\\n'\n# default is column-major\n\nprint 'The flattened array is:'\nprint a.flatten()\nprint '\\n'\n\nprint 'The flattened array in F-style ordering:'\nprint a.flatten(order = 'F')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array is:\n[[0 1 2 3]\n [4 5 6 7]]\n\nThe flattened array is:\n[0 1 2 3 4 5 6 7]\n\nThe flattened array in F-style ordering:\n[0 4 1 5 2 6 3 7]\n<\/pre>\n\n\n\n<h4>numpy.ravel<\/h4>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5e73\u5766\u7684\u4e00\u7ef4\u6570\u7ec4\u3002 \u53ea\u6709\u5728\u9700\u8981\u65f6\u624d\u8fdb\u884c\u590d\u5236\u3002 \u8fd4\u56de\u7684\u6570\u7ec4\u7684\u7c7b\u578b\u4e0e\u8f93\u5165\u6570\u7ec4\u7684\u7c7b\u578b\u76f8\u540c\u3002 \u8be5\u529f\u80fd\u9700\u8981\u4e00\u4e2a\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.ravel(a, order)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>order<\/b><br>\n&#8216;C&#8217;\u884c\u4e3b\uff08\u9ed8\u8ba4\u503c\uff1a&#8217;F&#8217;\uff1a\u5217\u4e3b\u8981&#8217;A&#8217;\uff1a\u6309\u5217\u4e3b\u8981\u987a\u5e8f\u5c55\u5e73\uff0c\u5982\u679ca\u662fFortran\u5728\u5185\u5b58\u4e2d\u8fde\u7eed\u7684\u884c\uff0c\u5426\u5219\u6309\u884c\u6392\u5e8f&#8217;K&#8217;\uff1a\u6309\u5143\u7d20\u7684\u987a\u5e8f\u5c55\u5e73a \u53d1\u751f\u5728\u8bb0\u5fc6\u4e2d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(8).reshape(2,4)\n\nprint 'The original array is:'\nprint a\nprint '\\n'\n\nprint 'After applying ravel function:'\nprint a.ravel()\nprint '\\n'\n\nprint 'Applying ravel function in F-style ordering:'\nprint a.ravel(order = 'F')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array is:\n[[0 1 2 3]\n [4 5 6 7]]\n\nAfter applying ravel function:\n[0 1 2 3 4 5 6 7]\n\nApplying ravel function in F-style ordering:\n[0 4 1 5 2 6 3 7]\n<\/pre>\n\n\n\n<h3>\u8f6c\u7f6e\u64cd\u4f5c<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u64cd\u4f5c &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>transpose<\/b><br>\n\u7f6e\u6362\u6570\u7ec4\u7684\u7ef4\u6570<\/td><\/tr><tr><td>2<\/td><td><b>ndarray.T<\/b><br>\n\u540cself.transpose()<\/td><\/tr><tr><td>3<\/td><td><b>rollaxis<\/b><br>\n\u5411\u540e\u6eda\u52a8\u6307\u5b9a\u7684\u8f74<\/td><\/tr><tr><td>4<\/td><td><b>swapaxes<\/b><br>\n\u4ea4\u6362\u6570\u7ec4\u7684\u4e24\u4e2a\u8f74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>transpose<\/h4>\n\n\n\n<p>\u6b64\u51fd\u6570\u53ef\u4ee5\u5bf9\u7ed9\u5b9a\u6570\u7ec4\u7684\u7ef4\u5ea6\u8fdb\u884c\u7f6e\u6362\u3002 \u5b83\u5c3d\u53ef\u80fd\u8fd4\u56de\u4e00\u4e2a\u89c6\u56fe\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.transpose(arr, axes)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8be5\u6570\u7ec4\u5c06\u88ab\u8c03\u6362<\/td><\/tr><tr><td>2<\/td><td><b>axes<\/b><br>\n\u8f93\u5165\u7684\u5217\u8868\uff0c\u5bf9\u5e94\u4e8e\u7ef4\u5ea6\u3002 \u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u7ef4\u5ea6\u98a0\u5012<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(12).reshape(3,4)\n\nprint 'The original array is:'\nprint a\nprint '\\n'\n\nprint 'The transposed array is:'\nprint np.transpose(a)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array is:\n[[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]]\n\nThe transposed array is:\n[[ 0 4 8]\n[ 1 5 9]\n[ 2 6 10]\n[ 3 7 11]]\n<\/pre>\n\n\n\n<h4>numpy.ndarray.T<\/h4>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u5c5e\u4e8endarray\u7c7b\u3002 \u5b83\u7684\u884c\u4e3a\u4e0enumpy.transpose\u76f8\u4f3c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"><\/pre>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(12).reshape(3,4)\n\nprint 'The original array is:'\nprint a\nprint '\\n'\n\nprint 'Array after applying the function:'\nprint a.T\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array is:\n[[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]]\n\nArray after applying the function:\n[[ 0 4 8]\n [ 1 5 9]\n [ 2 6 10]\n [ 3 7 11]]\n<\/pre>\n\n\n\n<h4>numpy.rollaxis<\/h4>\n\n\n\n<p>\u8be5\u529f\u80fd\u5411\u540e\u6eda\u52a8\u6307\u5b9a\u7684\u8f74\uff0c\u76f4\u5230\u5b83\u4f4d\u4e8e\u6307\u5b9a\u7684\u4f4d\u7f6e\u3002 \u8be5\u529f\u80fd\u9700\u8981\u4e09\u4e2a\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.rollaxis(arr, axis, start)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>axes<\/b><br>\n\u8f74\u5411\u540e\u6eda\u52a8\u3002 \u5176\u4ed6\u8f74\u7684\u4f4d\u7f6e\u76f8\u5bf9\u4e8e\u5f7c\u6b64\u4e0d\u53d8<\/td><\/tr><tr><td>3<\/td><td><b>start<\/b><br>\n\u9ed8\u8ba4\u4e3a\u96f6\uff0c\u5bfc\u81f4\u5b8c\u6574\u7684\u6eda\u52a8\u3002 \u6eda\u52a8\uff0c\u76f4\u5230\u5230\u8fbe\u6307\u5b9a\u7684\u4f4d\u7f6e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># It creates 3 dimensional ndarray\nimport numpy as np\na = np.arange(8).reshape(2,2,2)\n\nprint 'The original array:'\nprint a\nprint '\\n'\n# to roll axis-2 to axis-0 (along width to along depth)\n\nprint 'After applying rollaxis function:'\nprint np.rollaxis(a,2)\n# to roll axis 0 to 1 (along width to height)\nprint '\\n'\n\nprint 'After applying rollaxis function:'\nprint np.rollaxis(a,2,1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array:\n[[[0 1]\n [2 3]]\n [[4 5]\n [6 7]]]\n\nAfter applying rollaxis function:\n[[[0 2]\n [4 6]]\n [[1 3]\n [5 7]]]\n\nAfter applying rollaxis function:\n[[[0 2]\n [1 3]]\n [[4 6]\n [5 7]]]\n<\/pre>\n\n\n\n<h4>numpy.swapaxes<\/h4>\n\n\n\n<p>\u8be5\u529f\u80fd\u4ea4\u6362\u6570\u7ec4\u7684\u4e24\u4e2a\u8f74\u3002 \u5bf9\u4e8e1.10\u4e4b\u540e\u7684NumPy\u7248\u672c\uff0c\u8fd4\u56de\u4ea4\u6362\u6570\u7ec4\u7684\u89c6\u56fe\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.swapaxes(arr, axis1, axis2)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4\uff0c\u5176\u8f74\u8981\u4ea4\u6362<\/td><\/tr><tr><td>2<\/td><td><b>axis1<\/b><br>\nint\u503c\u5bf9\u5e94\u7b2c\u4e00\u4e2a\u8f74<\/td><\/tr><tr><td>3<\/td><td><b>axis2<\/b><br>\nint\u503c\u5bf9\u5e94\u7b2c\u4e8c\u4e2a\u8f74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># It creates a 3 dimensional ndarray\nimport numpy as np\na = np.arange(8).reshape(2,2,2)\n\nprint 'The original array:'\nprint a\nprint '\\n'\n# now swap numbers between axis 0 (along depth) and axis 2 (along width)\n\nprint 'The array after applying the swapaxes function:'\nprint np.swapaxes(a, 2, 0)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The original array:\n[[[0 1]\n [2 3]]\n\n[[4 5]\n [6 7]]]\n\nThe array after applying the swapaxes function:\n[[[0 4]\n [2 6]]\n\n[[1 5]\n [3 7]]]\n<\/pre>\n\n\n\n<h3>\u66f4\u6539\u7ef4\u5ea6<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u7ef4\u5ea6 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>broadcast<\/b><br>\n\u4ea7\u751f\u4e00\u4e2a\u6a21\u4eff\u5e7f\u64ad\u7684\u5bf9\u8c61<\/td><\/tr><tr><td>2<\/td><td><b>broadcast_to<\/b><br>\n\u5c06\u6570\u7ec4\u5e7f\u64ad\u5230\u65b0\u7684\u5f62\u72b6<\/td><\/tr><tr><td>3<\/td><td><b>expand_dims<\/b><br>\n\u6269\u5c55\u6570\u7ec4\u7684\u5f62\u72b6<\/td><\/tr><tr><td>4<\/td><td><b>squeeze<\/b><br>\n\u4ece\u6570\u7ec4\u5f62\u72b6\u4e2d\u79fb\u9664\u4e00\u7ef4\u6761\u76ee<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>numpy.broadcast<\/h4>\n\n\n\n<p>\u5982\u524d\u6240\u8ff0\uff0cNumPy\u5df2\u7ecf\u5185\u7f6e\u4e86\u5bf9\u5e7f\u64ad\u7684\u652f\u6301\u3002 \u6b64\u529f\u80fd\u6a21\u4eff\u5e7f\u64ad\u673a\u5236\u3002 \u5b83\u8fd4\u56de\u4e00\u4e2a\u5c01\u88c5\u4e00\u4e2a\u6570\u7ec4\u4e0e\u53e6\u4e00\u4e2a\u6570\u7ec4\u7684\u5e7f\u64ad\u7ed3\u679c\u7684\u5bf9\u8c61\u3002<br>\n\u8be5\u51fd\u6570\u5c06\u4e24\u4e2a\u6570\u7ec4\u4f5c\u4e3a\u8f93\u5165\u53c2\u6570\u3002 \u4ee5\u4e0b\u793a\u4f8b\u8bf4\u660e\u4e86\u5b83\u7684\u7528\u6cd5\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([[1], [2], [3]])\ny = np.array([4, 5, 6])\n\n# tobroadcast x against y\nb = np.broadcast(x,y)\n# it has an iterator property, a tuple of iterators along self's \"components.\"\n\nprint 'Broadcast x against y:'\nr,c = b.iters\nprint r.next(), c.next()\nprint r.next(), c.next()\nprint '\\n'\n# shape attribute returns the shape of broadcast object\n\nprint 'The shape of the broadcast object:'\nprint b.shape\nprint '\\n'\n# to add x and y manually using broadcast\nb = np.broadcast(x,y)\nc = np.empty(b.shape)\n\nprint 'Add x and y manually using broadcast:'\nprint c.shape\nprint '\\n'\nc.flat = [u + v for (u,v) in b]\n\nprint 'After applying the flat function:'\nprint c\nprint '\\n'\n# same result obtained by NumPy's built-in broadcasting support\n\nprint 'The summation of x and y:'\nprint x + y\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Broadcast x against y:\n1 4\n1 5\n\nThe shape of the broadcast object:\n(3, 3)\n\nAdd x and y manually using broadcast:\n(3, 3)\n\nAfter applying the flat function:\n[[ 5. 6. 7.]\n [ 6. 7. 8.]\n [ 7. 8. 9.]]\n\nThe summation of x and y:\n[[5 6 7]\n [6 7 8]\n [7 8 9]]\n<\/pre>\n\n\n\n<h4>numpy.broadcast_to<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u5c06\u6570\u7ec4\u5e7f\u64ad\u5230\u65b0\u7684\u5f62\u72b6\u3002 \u5b83\u5728\u539f\u59cb\u6570\u7ec4\u4e0a\u8fd4\u56de\u4e00\u4e2a\u53ea\u8bfb\u89c6\u56fe\u3002 \u5b83\u901a\u5e38\u4e0d\u662f\u8fde\u7eed\u7684\u3002 \u5982\u679c\u65b0\u5f62\u72b6\u4e0d\u7b26\u5408NumPy\u7684\u5e7f\u64ad\u89c4\u5219\uff0c\u8be5\u51fd\u6570\u53ef\u80fd\u4f1a\u629b\u51faValueError\u3002<br>\n\u6ce8 &#8211; \u6b64\u529f\u80fd\u53ef\u7528\u4e8e1.10.0\u53ca\u66f4\u9ad8\u7248\u672c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.broadcast_to(array, shape, subok)\n<\/pre>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(4).reshape(1,4)\n\nprint 'The original array:'\nprint a\nprint '\\n'\n\nprint 'After applying the broadcast_to function:'\nprint np.broadcast_to(a,(4,4))\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[0  1  2  3]\n [0  1  2  3]\n [0  1  2  3]\n [0  1  2  3]]\n<\/pre>\n\n\n\n<h4>numpy.expand_dims<\/h4>\n\n\n\n<p>\u8be5\u529f\u80fd\u901a\u8fc7\u5728\u6307\u5b9a\u4f4d\u7f6e\u63d2\u5165\u65b0\u8f74\u6765\u6269\u5c55\u6570\u7ec4\u3002 \u8be5\u529f\u80fd\u9700\u8981\u4e24\u4e2a\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.expand_dims(arr, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>axis<\/b><br>\n\u8981\u63d2\u5165\u5728\u65b0\u8f74\u7684\u4f4d\u7f6e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array(([1,2],[3,4]))\n\nprint 'Array x:'\nprint x\nprint '\\n'\ny = np.expand_dims(x, axis = 0)\n\nprint 'Array y:'\nprint y\nprint '\\n'\n\nprint 'The shape of X and Y array:'\nprint x.shape, y.shape\nprint '\\n'\n# insert axis at position 1\ny = np.expand_dims(x, axis = 1)\n\nprint 'Array Y after inserting axis at position 1:'\nprint y\nprint '\\n'\n\nprint 'x.ndim and y.ndim:'\nprint x.ndim,y.ndim\nprint '\\n'\n\nprint 'x.shape and y.shape:'\nprint x.shape, y.shape\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array x:\n[[1 2]\n [3 4]]\n\nArray y:\n[[[1 2]\n [3 4]]]\n\nThe shape of X and Y array:\n(2, 2) (1, 2, 2)\n\nArray Y after inserting axis at position 1:\n[[[1 2]]\n [[3 4]]]\n\nx.ndim and y.ndim:\n2 3\n\nx.shape and y.shape:\n(2, 2) (2, 1, 2)\n<\/pre>\n\n\n\n<h4>numpy.squeeze<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u4ece\u7ed9\u5b9a\u6570\u7ec4\u7684\u5f62\u72b6\u4e2d\u79fb\u9664\u4e00\u7ef4\u6761\u76ee\u3002 \u8be5\u529f\u80fd\u9700\u8981\u4e24\u4e2a\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.squeeze(arr, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>axis<\/b><br>\nint\u6216int\u7684\u5143\u7ec4\u3002 \u9009\u62e9\u5f62\u72b6\u4e2d\u7684\u5355\u4e2a\u7ef4\u6761\u76ee\u7684\u5b50\u96c6<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.arange(9).reshape(1,3,3)\n\nprint 'Array X:'\nprint x\nprint '\\n'\ny = np.squeeze(x)\n\nprint 'Array Y:'\nprint y\nprint '\\n'\n\nprint 'The shapes of X and Y array:'\nprint x.shape, y.shape\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array X:\n[[[0 1 2]\n [3 4 5]\n [6 7 8]]]\n\nArray Y:\n[[0 1 2]\n [3 4 5]\n [6 7 8]]\n\nThe shapes of X and Y array:\n(1, 3, 3) (3, 3)\n<\/pre>\n\n\n\n<h3>\u52a0\u5165\u6570\u7ec4<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u6570\u7ec4 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>concatenate<\/b><br>\n\u6cbf\u7740\u73b0\u6709\u8f74\u52a0\u5165\u4e00\u7cfb\u5217\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>stack<\/b><br>\n\u6cbf\u7740\u4e00\u4e2a\u65b0\u7684\u8f74\u52a0\u5165\u4e00\u7cfb\u5217\u6570\u7ec4<\/td><\/tr><tr><td>3<\/td><td><b>hstack<\/b><br>\n\u6309\u6c34\u5e73\u987a\u5e8f\u5806\u53e0\u6570\u7ec4\uff08\u6309\u5217\uff09<\/td><\/tr><tr><td>4<\/td><td><b>vstack<\/b><br>\n\u6309\u5782\u76f4\u987a\u5e8f\u5806\u53e0\u6570\u7ec4\uff08\u6309\u884c\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>numpy.concatenate<\/h4>\n\n\n\n<p>\u8fde\u63a5\u662f\u6307\u52a0\u5165\u3002 \u8be5\u51fd\u6570\u7528\u4e8e\u6cbf\u6307\u5b9a\u7684\u8f74\u8fde\u63a5\u4e24\u4e2a\u6216\u591a\u4e2a\u76f8\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.concatenate((a1, a2, ...), axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>a1,a2..<\/b><br>\n\u76f8\u540c\u7c7b\u578b\u7684\u6570\u7ec4\u7684\u5e8f\u5217<\/td><\/tr><tr><td>2<\/td><td><b>axis<\/b><br>\n\u5fc5\u987b\u8fde\u63a5\u6570\u7ec4\u7684\u8f74\u3002 \u7f3a\u7701\u503c\u662f0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4]])\n\nprint 'First array:'\nprint a\nprint '\\n'\nb = np.array([[5,6],[7,8]])\n\nprint 'Second array:'\nprint b\nprint '\\n'\n# both the arrays are of same dimensions\n\nprint 'Joining the two arrays along axis 0:'\nprint np.concatenate((a,b))\nprint '\\n'\n\nprint 'Joining the two arrays along axis 1:'\nprint np.concatenate((a,b),axis = 1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2]\n [3 4]]\n\nSecond array:\n[[5 6]\n [7 8]]\n\nJoining the two arrays along axis 0:\n[[1 2]\n [3 4]\n [5 6]\n [7 8]]\n\nJoining the two arrays along axis 1:\n[[1 2 5 6]\n [3 4 7 8]]\n<\/pre>\n\n\n\n<h4>numpy.stack<\/h4>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u6cbf\u7740\u4e00\u4e2a\u65b0\u7684\u8f74\u52a0\u5165\u6570\u7ec4\u5e8f\u5217\u3002 \u81eaNumPy\u7248\u672c1.10.0\u5f00\u59cb\uff0c\u6b64\u529f\u80fd\u5df2\u6dfb\u52a0\u3002 \u9700\u8981\u63d0\u4f9b\u4ee5\u4e0b\u53c2\u6570\u3002<br>\n\u6ce8 &#8211; \u6b64\u529f\u80fd\u5728\u7248\u672c1.10.0\u53ca\u66f4\u9ad8\u7248\u672c\u4e2d\u53ef\u7528\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.stack(arrays, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arrays<\/b><br>\n\u8f93\u76f8\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u5e8f\u5217<\/td><\/tr><tr><td>2<\/td><td><b>axis<\/b><br>\n\u8f93\u5165\u6570\u7ec4\u6cbf\u5176\u5806\u53e0\u7684\u7ed3\u679c\u6570\u7ec4\u4e2d\u7684\u8f74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4]])\n\nprint 'First Array:'\nprint a\nprint '\\n'\nb = np.array([[5,6],[7,8]])\n\nprint 'Second Array:'\nprint b\nprint '\\n'\n\nprint 'Stack the two arrays along axis 0:'\nprint np.stack((a,b),0)\nprint '\\n'\n\nprint 'Stack the two arrays along axis 1:'\nprint np.stack((a,b),1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2]\n [3 4]]\n\nSecond array:\n[[5 6]\n [7 8]]\n\nStack the two arrays along axis 0:\n[[[1 2]\n [3 4]]\n [[5 6]\n [7 8]]]\n\nStack the two arrays along axis 1:\n[[[1 2]\n [5 6]]\n [[3 4]\n [7 8]]]\n<\/pre>\n\n\n\n<h4>numpy.hstack<\/h4>\n\n\n\n<p>numpy.stack\u51fd\u6570\u7684\u53d8\u4f53\u8fdb\u884c\u5806\u6808\u4ee5\u4fbf\u6c34\u5e73\u5730\u5236\u4f5c\u5355\u4e2a\u6570\u7ec4\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4]])\n\nprint 'First array:'\nprint a\nprint '\\n'\nb = np.array([[5,6],[7,8]])\n\nprint 'Second array:'\nprint b\nprint '\\n'\n\nprint 'Horizontal stacking:'\nc = np.hstack((a,b))\nprint c\nprint '\\n'\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2]\n [3 4]]\n\nSecond array:\n[[5 6]\n [7 8]]\n\nHorizontal stacking:\n[[1 2 5 6]\n [3 4 7 8]]\n<\/pre>\n\n\n\n<h4>numpy.vstack<\/h4>\n\n\n\n<p>numpy.stack\u51fd\u6570\u7684\u53d8\u4f53\u8fdb\u884c\u5806\u6808\u4ee5\u5782\u76f4\u5730\u521b\u5efa\u5355\u4e2a\u6570\u7ec4\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4]])\n\nprint 'First array:'\nprint a\nprint '\\n'\nb = np.array([[5,6],[7,8]])\n\nprint 'Second array:'\nprint b\nprint '\\n'\n\nprint 'Vertical stacking:'\nc = np.vstack((a,b))\nprint c\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2]\n [3 4]]\n\nSecond array:\n[[5 6]\n [7 8]]\n\nVertical stacking:\n[[1 2]\n [3 4]\n [5 6]\n [7 8]]\n<\/pre>\n\n\n\n<h3>\u5206\u88c2\u9635\u5217<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u6570\u7ec4 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>split<\/b><br>\n\u5c06\u6570\u7ec4\u62c6\u5206\u6210\u591a\u4e2a\u5b50\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>hsplit<\/b><br>\n\u5c06\u6570\u7ec4\u6c34\u5e73\u5206\u5272\u6210\u591a\u4e2a\u5b50\u6570\u7ec4\uff08\u6309\u5217\uff09<\/td><\/tr><tr><td>3<\/td><td><b>vsplit<\/b><br>\n\u5c06\u6570\u7ec4\u5782\u76f4\u5206\u5272\u6210\u591a\u4e2a\u5b50\u6570\u7ec4\uff08\u6309\u884c\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>numpy.split<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u5c06\u6570\u7ec4\u6cbf\u7740\u6307\u5b9a\u7684\u8f74\u5206\u6210\u5b50\u9635\u5217\u3002 \u8be5\u529f\u80fd\u9700\u8981\u4e09\u4e2a\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.split(ary, indices_or_sections, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>ary<\/b><br>\n\u8f93\u5165\u6570\u7ec4\uff0c\u5c06\u88ab\u62c6\u5206<\/td><\/tr><tr><td>2<\/td><td><b>indices_or_sections<\/b><br>\n\u53ef\u4ee5\u662f\u4e00\u4e2a\u6574\u6570\uff0c\u8868\u793a\u8981\u4ece\u8f93\u5165\u6570\u7ec4\u521b\u5efa\u7684\u76f8\u7b49\u5927\u5c0f\u7684\u5b50\u6570\u7ec4\u7684\u6570\u91cf\u3002 \u5982\u679c\u6b64\u53c2\u6570\u662f\u4e00\u7ef4\u6570\u7ec4\uff0c\u5219\u8fd9\u4e9b\u6761\u76ee\u5c06\u6307\u793a\u8981\u521b\u5efa\u65b0\u5b50\u6570\u7ec4\u7684\u70b9\u3002<\/td><\/tr><tr><td>3<\/td><td><b>axis<\/b><br>\n\u9ed8\u8ba4\u503c\u662f0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(9)\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Split the array in 3 equal-sized subarrays:'\nb = np.split(a,3)\nprint b\nprint '\\n'\n\nprint 'Split the array at positions indicated in 1-D array:'\nb = np.split(a,[4,7])\nprint b\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[0 1 2 3 4 5 6 7 8]\n\nSplit the array in 3 equal-sized subarrays:\n[array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8])]\n\nSplit the array at positions indicated in 1-D array:\n[array([0, 1, 2, 3]), array([4, 5, 6]), array([7, 8])]\n<\/pre>\n\n\n\n<h4>numpy.hsplit<\/h4>\n\n\n\n<p>numpy.hsplit\u662fsplit\uff08\uff09\u51fd\u6570\u7684\u4e00\u4e2a\u7279\u4f8b\uff0c\u5176\u4e2daxis\u662f1\uff0c\u8868\u793a\u6c34\u5e73\u5206\u5272\uff0c\u4e0e\u8f93\u5165\u6570\u7ec4\u7684\u7ef4\u5ea6\u65e0\u5173\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(16).reshape(4,4)\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Horizontal splitting:'\nb = np.hsplit(a,2)\nprint b\nprint '\\n'\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]\n [12 13 14 15]]\n\nHorizontal splitting:\n[array([[ 0,  1],\n\t   [ 4,  5],\n\t   [ 8,  9],\n\t   [12, 13]]), array([[ 2,  3],\n\t   [ 6,  7],\n\t   [10, 11],\n\t   [14, 15]])]\n<\/pre>\n\n\n\n<h4>numpy.vsplit<\/h4>\n\n\n\n<p>numpy.vsplit\u662fsplit\uff08\uff09\u51fd\u6570\u7684\u4e00\u4e2a\u7279\u4f8b\uff0c\u5176\u4e2daxis\u662f1\uff0c\u8868\u793a\u7eb5\u5411\u5206\u5272\uff0c\u800c\u4e0d\u7ba1\u8f93\u5165\u6570\u7ec4\u7684\u7ef4\u5ea6\u5982\u4f55\u3002 \u4ee5\u4e0b\u793a\u4f8b\u8bf4\u660e\u4e86\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(16).reshape(4,4)\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Vertical splitting:'\nb = np.vsplit(a,2)\nprint b\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]\n [12 13 14 15]]\n\nVertical splitting:\n[array([[0, 1, 2, 3],\n\t   [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],\n\t   [12, 13, 14, 15]])]\n<\/pre>\n\n\n\n<h3>\u6dfb\u52a0\/\u5220\u9664\u5143\u7d20<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u5143\u7d20 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>resize<\/b><br>\n\u8fd4\u56de\u5177\u6709\u6307\u5b9a\u5f62\u72b6\u7684\u65b0\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>append<\/b><br>\n\u5c06\u503c\u9644\u52a0\u5230\u6570\u7ec4\u7684\u672b\u5c3e<\/td><\/tr><tr><td>3<\/td><td><b>insert<\/b><br>\n\u5728\u7ed9\u5b9a\u7d22\u5f15\u4e4b\u524d\u6cbf\u7ed9\u5b9a\u8f74\u63d2\u5165\u503c<\/td><\/tr><tr><td>4<\/td><td><b>delete<\/b><br>\n\u8fd4\u56de\u4e00\u4e2a\u65b0\u6570\u7ec4\uff0c\u5176\u4e2d\u6cbf\u7740\u4e00\u4e2a\u8f74\u7684\u5b50\u6570\u7ec4\u88ab\u5220\u9664<\/td><\/tr><tr><td>5<\/td><td><b>unique<\/b><br>\n\u67e5\u627e\u6570\u7ec4\u7684\u7279\u5b9a\u5143\u7d20<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>numpy.resize<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5177\u6709\u6307\u5b9a\u5927\u5c0f\u7684\u65b0\u6570\u7ec4\u3002 \u5982\u679c\u65b0\u7ef4\u5ea6\u5927\u4e8e\u539f\u4ef6\u7ef4\u5ea6\uff0c\u5219\u5305\u542b\u539f\u4ef6\u4e2d\u91cd\u590d\u7684\u6761\u76ee\u526f\u672c\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.resize(arr, shape)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u8981\u8c03\u6574\u5927\u5c0f\u7684\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>shape<\/b><br>\n\u7ed3\u679c\u6570\u7ec4\u7684\u65b0\u5f62\u72b6<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[4,5,6]])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'The shape of first array:'\nprint a.shape\nprint '\\n'\nb = np.resize(a, (3,2))\n\nprint 'Second array:'\nprint b\nprint '\\n'\n\nprint 'The shape of second array:'\nprint b.shape\nprint '\\n'\n# Observe that first row of a is repeated in b since size is bigger\n\nprint 'Resize the second array:'\nb = np.resize(a,(3,3))\nprint b\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2 3]\n [4 5 6]]\n\nThe shape of first array:\n(2, 3)\n\nSecond array:\n[[1 2]\n [3 4]\n [5 6]]\n\nThe shape of second array:\n(3, 2)\n\nResize the second array:\n[[1 2 3]\n [4 5 6]\n [1 2 3]]\n<\/pre>\n\n\n\n<h4>numpy.append<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u5728\u8f93\u5165\u6570\u7ec4\u7684\u672b\u5c3e\u6dfb\u52a0\u503c\u3002 \u8ffd\u52a0\u64cd\u4f5c\u4e0d\u5728\u4f4d\uff0c\u5206\u914d\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4\u3002 \u6b64\u5916\uff0c\u8f93\u5165\u6570\u7ec4\u7684\u7ef4\u5ea6\u5fc5\u987b\u5339\u914d\uff0c\u5426\u5219\u4f1a\u751f\u6210ValueError\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.append(arr, values, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>values<\/b><br>\n\u9644\u52a0\u5230arr\u3002 \u5b83\u7684\u5f62\u72b6\u5fc5\u987b\u4e0earr\u76f8\u540c\uff08\u4e0d\u5305\u62ec\u9644\u52a0\u8f74\uff09<\/td><\/tr><tr><td>3<\/td><td><b>axis<\/b><br>\n\u8ffd\u52a0\u64cd\u4f5c\u6240\u6cbf\u7684\u8f74\u3002 \u5982\u679c\u6ca1\u6709\u7ed9\u51fa\uff0c\u5219\u4e24\u4e2a\u53c2\u6570\u90fd\u662f\u5e73\u5766\u7684<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[4,5,6]])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Append elements to array:'\nprint np.append(a, [7,8,9])\nprint '\\n'\n\nprint 'Append elements along axis 0:'\nprint np.append(a, [[7,8,9]],axis = 0)\nprint '\\n'\n\nprint 'Append elements along axis 1:'\nprint np.append(a, [[5,5,5],[7,8,9]],axis = 1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2 3]\n [4 5 6]]\n\nAppend elements to array:\n[1 2 3 4 5 6 7 8 9]\n\nAppend elements along axis 0:\n[[1 2 3]\n [4 5 6]\n [7 8 9]]\n\nAppend elements along axis 1:\n[[1 2 3 5 5 5]\n [4 5 6 7 8 9]]\n<\/pre>\n\n\n\n<h4>numpy.insert<\/h4>\n\n\n\n<p>\u6b64\u51fd\u6570\u5c06\u503c\u63d2\u5165\u8f93\u5165\u6570\u7ec4\u4e2d\u6cbf\u7ed9\u5b9a\u8f74\u548c\u7ed9\u5b9a\u7d22\u5f15\u4e4b\u524d\u3002 \u5982\u679c\u5c06\u503c\u7684\u7c7b\u578b\u8f6c\u6362\u4e3a\u63d2\u5165\uff0c\u5219\u5b83\u4e0e\u8f93\u5165\u6570\u7ec4\u4e0d\u540c\u3002 \u63d2\u5165\u6ca1\u6709\u5b8c\u6210\uff0c\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4\u3002 \u53e6\u5916\uff0c\u5982\u679c\u6ca1\u6709\u63d0\u5230\u8f74\uff0c\u5219\u8f93\u5165\u6570\u7ec4\u53d8\u5e73\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.insert(arr, obj, values, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>obj<\/b><br>\n\u63d2\u5165\u4e4b\u524d\u7684\u7d22\u5f15\/td&gt;<\/td><\/tr><tr><td>3<\/td><td><b>values<\/b><br>\n\u8981\u63d2\u5165\u7684\u503c\u7684\u6570\u7ec4<\/td><\/tr><tr><td>4<\/td><td><b>axis<\/b><br>\n\u63d2\u5165\u7684\u8f74\u7ebf\u3002 \u5982\u679c\u6ca1\u6709\u7ed9\u51fa\uff0c\u8f93\u5165\u6570\u7ec4\u53d8\u5e73<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4],[5,6]])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Axis parameter not passed. The input array is flattened before insertion.'\nprint np.insert(a,3,[11,12])\nprint '\\n'\nprint 'Axis parameter passed. The values array is broadcast to match input array.'\n\nprint 'Broadcast along axis 0:'\nprint np.insert(a,1,[11],axis = 0)\nprint '\\n'\n\nprint 'Broadcast along axis 1:'\nprint np.insert(a,1,11,axis = 1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[1 2]\n [3 4]\n [5 6]]\n\nAxis parameter not passed. The input array is flattened before insertion.\n[ 1 2 3 11 12 4 5 6]\n\nAxis parameter passed. The values array is broadcast to match input array.\nBroadcast along axis 0:\n[[ 1 2]\n [11 11]\n [ 3 4]\n [ 5 6]]\n\nBroadcast along axis 1:\n[[ 1 11 2]\n [ 3 11 4]\n [ 5 11 6]]\n<\/pre>\n\n\n\n<h4>numpy.delete<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4\uff0c\u5176\u4e2d\u4ece\u8f93\u5165\u6570\u7ec4\u4e2d\u5220\u9664\u6307\u5b9a\u7684\u5b50\u6570\u7ec4\u3002 \u4e0einsert\uff08\uff09\u51fd\u6570\u4e00\u6837\uff0c\u5982\u679c\u4e0d\u4f7f\u7528axis\u53c2\u6570\uff0c\u5219\u8f93\u5165\u6570\u7ec4\u5c06\u53d8\u5e73\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570 &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Numpy.delete(arr, obj, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>obj<\/b><br>\n\u53ef\u4ee5\u662f\u5207\u7247\uff0c\u6574\u6570\u6216\u6574\u6570\u6570\u7ec4\uff0c\u6307\u793a\u8981\u4ece\u8f93\u5165\u6570\u7ec4\u4e2d\u5220\u9664\u7684\u5b50\u6570\u7ec4<\/td><\/tr><tr><td>3<\/td><td><b>axis<\/b><br>\n\u5220\u9664\u7ed9\u5b9a\u5b50\u6570\u7ec4\u7684\u8f74\u3002 \u5982\u679c\u6ca1\u6709\u7ed9\u51fa\uff0carr\u662f\u6241\u5e73\u7684<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(12).reshape(3,4)\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Array flattened before delete operation as axis not used:'\nprint np.delete(a,5)\nprint '\\n'\n\nprint 'Column 2 deleted:'\nprint np.delete(a,1,axis = 1)\nprint '\\n'\n\nprint 'A slice containing alternate values from array deleted:'\na = np.array([1,2,3,4,5,6,7,8,9,10])\nprint np.delete(a, np.s_[::2])\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]]\n\nArray flattened before delete operation as axis not used:\n[ 0 1 2 3 4 6 7 8 9 10 11]\n\nColumn 2 deleted:\n[[ 0 2 3]\n [ 4 6 7]\n [ 8 10 11]]\n\nA slice containing alternate values from array deleted:\n[ 2 4 6 8 10]\n<\/pre>\n\n\n\n<h4>numpy.unique<\/h4>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u4e2d\u552f\u4e00\u5143\u7d20\u7684\u6570\u7ec4\u3002 \u8be5\u51fd\u6570\u53ef\u4ee5\u8fd4\u56de\u552f\u4e00\u7684vales\u6570\u7ec4\u548c\u76f8\u5173\u7d22\u5f15\u6570\u7ec4\u7684\u5143\u7ec4\u3002 \u7d22\u5f15\u7684\u6027\u8d28\u53d6\u51b3\u4e8e\u51fd\u6570\u8c03\u7528\u4e2d\u8fd4\u56de\u53c2\u6570\u7684\u7c7b\u578b\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.unique(arr, return_index, return_inverse, return_counts)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>arr<\/b><br>\n\u8f93\u5165\u6570\u7ec4\uff0c\u5982\u679c\u4e0d\u662f\u4e00\u7ef4\u9635\u5217\uff0c\u5c06\u4f1a\u53d8\u5e73<\/td><\/tr><tr><td>2<\/td><td><b>return_index<\/b><br>\n\u5982\u679c\u4e3aTrue\uff0c\u5219\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u4e2d\u5143\u7d20\u7684\u7d22\u5f15<\/td><\/tr><tr><td>3<\/td><td><b>return_inverse<\/b><br>\n\u5982\u679c\u4e3aTrue\uff0c\u5219\u8fd4\u56de\u552f\u4e00\u6570\u7ec4\u7684\u7d22\u5f15\uff0c\u8be5\u6570\u7ec4\u53ef\u7528\u4e8e\u91cd\u6784\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>4<\/td><td><b>return_counts<\/b><br>\n\u5982\u679c\u4e3aTrue\uff0c\u5219\u8fd4\u56de\u552f\u4e00\u6570\u7ec4\u4e2d\u5143\u7d20\u51fa\u73b0\u5728\u539f\u59cb\u6570\u7ec4\u4e2d\u7684\u6b21\u6570<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([5,2,6,2,7,5,6,8,2,9])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Unique values of first array:'\nu = np.unique(a)\nprint u\nprint '\\n'\n\nprint 'Unique array and Indices array:'\nu,indices = np.unique(a, return_index = True)\nprint indices\nprint '\\n'\n\nprint 'We can see each number corresponds to index in original array:'\nprint a\nprint '\\n'\n\nprint 'Indices of unique array:'\nu,indices = np.unique(a,return_inverse = True)\nprint u\nprint '\\n'\n\nprint 'Indices are:'\nprint indices\nprint '\\n'\n\nprint 'Reconstruct the original array using indices:'\nprint u[indices]\nprint '\\n'\n\nprint 'Return the count of repetitions of unique elements:'\nu,indices = np.unique(a,return_counts = True)\nprint u\nprint indices\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[5 2 6 2 7 5 6 8 2 9]\n\nUnique values of first array:\n[2 5 6 7 8 9]\n\nUnique array and Indices array:\n[1 0 2 4 7 9]\n\nWe can see each number corresponds to index in original array:\n[5 2 6 2 7 5 6 8 2 9]\n\nIndices of unique array:\n[2 5 6 7 8 9]\n\nIndices are:\n[1 0 2 0 3 1 2 4 0 5]\n\nReconstruct the original array using indices:\n[5 2 6 2 7 5 6 8 2 9]\n\nReturn the count of repetitions of unique elements:\n[2 5 6 7 8 9]\n[3 2 2 1 1 1]<\/pre>\n\n\n\n<h1>NumPy \u4f4d\u8fd0\u7b97\u7b26<\/h1>\n\n\n\n<p>\u4ee5\u4e0b\u662fNumPy\u5305\u4e2d\u53ef\u7528\u7684\u6309\u4f4d\u8fd0\u7b97\u529f\u80fd\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u64cd\u4f5c &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>bitwise_and<\/b><br>\n\u8ba1\u7b97\u6570\u7ec4\u5143\u7d20\u7684\u6309\u4f4d\u4e0e\u64cd\u4f5c<\/td><\/tr><tr><td>2<\/td><td><b>bitwise_or<\/b><br>\n\u8ba1\u7b97\u6570\u7ec4\u5143\u7d20\u7684\u6309\u4f4d\u6216\u8fd0\u7b97<\/td><\/tr><tr><td>3<\/td><td><b>invert<\/b><br>\n\u8ba1\u7b97\u6309\u4f4dNOT<\/td><\/tr><tr><td>4<\/td><td><b>left_shift<\/b><br>\n\u5c06\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u4f4d\u5411\u5de6\u79fb\u4f4d<\/td><\/tr><tr><td>5<\/td><td><b>right_shift<\/b><br>\n\u5c06\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u4f4d\u5411\u53f3\u79fb\u4f4d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>NumPy &#8211; bitwise_and<\/h3>\n\n\n\n<p>\u8f93\u5165\u6570\u7ec4\u4e2d\u6574\u6570\u7684\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u76f8\u5e94\u4f4d\u4e0a\u7684\u6309\u4f4d\u201c\u4e0e\u201d\u64cd\u4f5c\u7531np.bitwise_and\uff08\uff09\u51fd\u6570\u8ba1\u7b97\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint 'Binary equivalents of 13 and 17:'\na,b = 13,17\nprint bin(a), bin(b)\nprint '\\n'\n\nprint 'Bitwise AND of 13 and 17:'\nprint np.bitwise_and(13, 17)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Binary equivalents of 13 and 17:\n0b1101 0b10001\n\nBitwise AND of 13 and 17:\n1\n<\/pre>\n\n\n\n<h3>NumPy &#8211; bitwise_or<\/h3>\n\n\n\n<p>\u8f93\u5165\u6570\u7ec4\u4e2d\u6574\u6570\u7684\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u76f8\u5e94\u4f4d\u4e0a\u7684\u6309\u4f4d\u6216\u8fd0\u7b97\u7531np.bitwise_or\uff08\uff09\u51fd\u6570\u8ba1\u7b97\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na,b = 13,17\nprint 'Binary equivalents of 13 and 17:'\nprint bin(a), bin(b)\n\nprint 'Bitwise OR of 13 and 17:'\nprint np.bitwise_or(13, 17)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Binary equivalents of 13 and 17:\n0b1101 0b10001\n\nBitwise OR of 13 and 17:\n29\n<\/pre>\n\n\n\n<h3>numpy.invert()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8ba1\u7b97\u8f93\u5165\u6570\u7ec4\u4e2d\u6574\u6570\u7684\u6309\u4f4dNOT\u7ed3\u679c\u3002 \u5bf9\u4e8e\u6709\u7b26\u53f7\u6574\u6570\uff0c\u8fd4\u56de2\u7684\u8865\u7801\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nprint 'Invert of 13 where dtype of ndarray is uint8:'\nprint np.invert(np.array([13], dtype = np.uint8))\nprint '\\n'\n# Comparing binary representation of 13 and 242, we find the inversion of bits\n\nprint 'Binary representation of 13:'\nprint np.binary_repr(13, width = 8)\nprint '\\n'\n\nprint 'Binary representation of 242:'\nprint np.binary_repr(242, width = 8)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Invert of 13 where dtype of ndarray is uint8:\n[242]\n\nBinary representation of 13:\n00001101\n\nBinary representation of 242:\n11110010\n<\/pre>\n\n\n\n<h3>NumPy &#8211; left_shift<\/h3>\n\n\n\n<p>numpy.left_shift\uff08\uff09\u51fd\u6570\u5c06\u6570\u7ec4\u5143\u7d20\u7684\u4e8c\u8fdb\u5236\u8868\u793a\u4e2d\u7684\u4f4d\u79fb\u52a8\u5230\u6307\u5b9a\u4f4d\u7f6e\u7684\u5de6\u4fa7\u3002 \u4ece\u53f3\u8fb9\u8ffd\u52a0\u76f8\u7b49\u6570\u91cf\u76840\u3002<\/p>\n\n\n\n<p>\u4f8b\u5b50<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nprint 'Left shift of 10 by two positions:'\nprint np.left_shift(10,2)\nprint '\\n'\n\nprint 'Binary representation of 10:'\nprint np.binary_repr(10, width = 8)\nprint '\\n'\n\nprint 'Binary representation of 40:'\nprint np.binary_repr(40, width = 8)\n# Two bits in '00001010' are shifted to left and two 0s appended from right.\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Left shift of 10 by two positions:\n40\n\nBinary representation of 10:\n00001010\n\nBinary representation of 40:\n00101000\n<\/pre>\n\n\n\n<h3>NumPy &#8211; right_shift<\/h3>\n\n\n\n<p>numpy.right_shift\uff08\uff09\u51fd\u6570\u5c06\u6570\u7ec4\u5143\u7d20\u7684\u4e8c\u8fdb\u5236\u8868\u793a\u4e2d\u7684\u4f4d\u5411\u53f3\u79fb\u52a8\u6307\u5b9a\u4f4d\u7f6e\uff0c\u5e76\u4ece\u5de6\u4fa7\u8ffd\u52a0\u76f8\u540c\u6570\u91cf\u76840\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nprint 'Right shift 40 by two positions:'\nprint np.right_shift(40,2)\nprint '\\n'\n\nprint 'Binary representation of 40:'\nprint np.binary_repr(40, width = 8)\nprint '\\n'\n\nprint 'Binary representation of 10'\nprint np.binary_repr(10, width = 8)\n# Two bits in '00001010' are shifted to right and two 0s appended from left.\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Right shift 40 by two positions:\n10\n\nBinary representation of 40:\n00101000\n\nBinary representation of 10\n00001010<\/pre>\n\n\n\n<h1>NumPy \u5b57\u7b26\u4e32\u51fd\u6570<\/h1>\n\n\n\n<p>\u4ee5\u4e0b\u51fd\u6570\u7528\u4e8e\u5bf9dtype numpy.string_\u6216numpy.unicode_\u7684\u6570\u7ec4\u6267\u884c\u5411\u91cf\u5316\u5b57\u7b26\u4e32\u64cd\u4f5c\u3002 \u5b83\u4eec\u57fa\u4e8ePython\u5185\u7f6e\u5e93\u4e2d\u7684\u6807\u51c6\u5b57\u7b26\u4e32\u51fd\u6570\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u51fd\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>add()<\/b><br>\n\u8fd4\u56de\u4e24\u4e2astr\u6216Unicode\u6570\u7ec4\u7684\u5143\u7d20\u65b9\u5f0f\u5b57\u7b26\u4e32\u8fde\u63a5<\/td><\/tr><tr><td>2<\/td><td><b>multiply()<\/b><br>\n\u8fd4\u56de\u5177\u6709\u591a\u4e2a\u7ea7\u8054\u7684\u5b57\u7b26\u4e32<\/td><\/tr><tr><td>3<\/td><td><b>center()<\/b><br>\n\u8fd4\u56de\u7ed9\u5b9a\u5b57\u7b26\u4e32\u7684\u526f\u672c\uff0c\u5176\u5143\u7d20\u4ee5\u6307\u5b9a\u957f\u5ea6\u7684\u5b57\u7b26\u4e32\u4e3a\u4e2d\u5fc3<\/td><\/tr><tr><td>4<\/td><td><b>capitalize()<\/b><br>\n\u4ec5\u8fd4\u56de\u5927\u5199\u5b57\u6bcd\u7684\u7b2c\u4e00\u4e2a\u5b57\u7b26\u7684\u526f\u672c<\/td><\/tr><tr><td>5<\/td><td><b>title()<\/b><br>\n\u8fd4\u56de\u5b57\u7b26\u4e32\u6216unicode\u7684\u57fa\u4e8e\u5143\u7d20\u7684\u6807\u9898<\/td><\/tr><tr><td>6<\/td><td><b>lower()<\/b><br>\n\u8f6c\u6362\u4e3a\u5c0f\u5199<\/td><\/tr><tr><td>7<\/td><td><b>upper()<\/b><br>\n\u8f6c\u6362\u4e3a\u5927\u5199<\/td><\/tr><tr><td>8<\/td><td><b>split()<\/b><br>\n\u8fd4\u56de\u5b57\u7b26\u4e32\u4e2d\u7684\u5355\u8bcd\u5217\u8868<\/td><\/tr><tr><td>9<\/td><td><b>splitlines()<\/b><br>\n\u8fd4\u56de\u5143\u7d20\u4e2d\u7684\u884c\u7684\u5217\u8868\uff0c\u5728\u884c\u8fb9\u754c\u5904\u7a81\u7834<\/td><\/tr><tr><td>10<\/td><td><b>strip()<\/b><br>\n\u8fd4\u56de\u5220\u9664\u524d\u5bfc\u5b57\u7b26\u548c\u5c3e\u968f\u5b57\u7b26\u7684\u526f\u672c<\/td><\/tr><tr><td>11<\/td><td><b>join()<\/b><br>\n\u8fd4\u56de\u5e8f\u5217\u4e2d\u5b57\u7b26\u4e32\u8fde\u63a5\u7684\u5b57\u7b26\u4e32<\/td><\/tr><tr><td>12<\/td><td><b>replace()<\/b>\u8fd4\u56de\u6240\u6709\u51fa\u73b0\u7684\u5b50\u5b57\u7b26\u4e32\u88ab\u65b0\u5b57\u7b26\u4e32\u66ff\u6362\u7684\u5b57\u7b26\u4e32\u526f\u672c<\/td><\/tr><tr><td>13<\/td><td><b>decode()<\/b><br>\n\u8c03\u7528str.decode<\/td><\/tr><tr><td>14<\/td><td><b>encode()<\/b><br>\n\u8c03\u7528str.encode<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u8fd9\u4e9b\u51fd\u6570\u5728\u5b57\u7b26\u6570\u7ec4\u7c7b\uff08numpy.char\uff09\u4e2d\u5b9a\u4e49\u3002 \u8f83\u65e7\u7684Numarray\u8f6f\u4ef6\u5305\u5305\u542bchararray\u7c7b\u3002 numpy.char\u7c7b\u4e2d\u7684\u4e0a\u8ff0\u51fd\u6570\u5728\u6267\u884c\u77e2\u91cf\u5316\u5b57\u7b26\u4e32\u64cd\u4f5c\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n\n\n\n<p><strong>numpy.char.add()<\/strong><\/p>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u6267\u884c\u5143\u7d20\u5b57\u7b26\u4e32\u8fde\u63a5\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint 'Concatenate two strings:'\nprint np.char.add(['hello'],[' xyz'])\nprint '\\n'\n\nprint 'Concatenation example:'\nprint np.char.add(['hello', 'hi'],[' abc', ' xyz'])\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Concatenate two strings:\n['hello xyz']\n\nConcatenation example:\n['hello abc' 'hi xyz']\n<\/pre>\n\n\n\n<p><strong>numpy.char.multiply()<\/strong><\/p>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u6267\u884c\u591a\u4e2a\u7ea7\u8054\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.multiply('Hello ',3)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Hello Hello Hello\n<\/pre>\n\n\n\n<p><strong>numpy.char.center()<\/strong><\/p>\n\n\n\n<p>\u6b64\u51fd\u6570\u8fd4\u56de\u6240\u9700\u5bbd\u5ea6\u7684\u6570\u7ec4\uff0c\u4ee5\u4fbf\u8f93\u5165\u5b57\u7b26\u4e32\u5c45\u4e2d\u5e76\u7528fillchar\u586b\u5145\u5de6\u4fa7\u548c\u53f3\u4fa7\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n# np.char.center(arr, width,fillchar)\nprint np.char.center('hello', 20,fillchar = '*')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">*******hello********\n<\/pre>\n\n\n\n<p><strong>numpy.char.capitalize()<\/strong><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u5927\u5199\u5b57\u6bcd\u7684\u7b2c\u4e00\u4e2a\u5b57\u6bcd\u7684\u526f\u672c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.capitalize('hello world')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Hello world\n<\/pre>\n\n\n\n<p><strong>numpy.char.title()<\/strong><\/p>\n\n\n\n<p>\u6b64\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u5b57\u7b26\u4e32\u7684\u6807\u9898\u5c01\u88c5\u7248\u672c\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5355\u8bcd\u7684\u9996\u5b57\u6bcd\u5927\u5199\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.title('hello how are you?')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Hello How Are You?\n<\/pre>\n\n\n\n<p><strong>numpy.char.lower()<\/strong><\/p>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5143\u7d20\u8f6c\u6362\u4e3a\u5c0f\u5199\u7684\u6570\u7ec4\u3002 \u5b83\u4e3a\u6bcf\u4e2a\u5143\u7d20\u8c03\u7528str.lower\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.lower(['HELLO','WORLD'])\nprint np.char.lower('HELLO')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">['hello' 'world']\nhello\n<\/pre>\n\n\n\n<p><strong>numpy.char.upper()<\/strong><\/p>\n\n\n\n<p>\u6b64\u51fd\u6570\u8c03\u7528\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684str.upper\u51fd\u6570\u4ee5\u8fd4\u56de\u5927\u5199\u6570\u7ec4\u5143\u7d20\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.upper('hello')\nprint np.char.upper(['hello','world'])\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">HELLO\n['HELLO' 'WORLD']\n<\/pre>\n\n\n\n<p><strong>numpy.char.split()<\/strong><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u5b57\u7b26\u4e32\u4e2d\u7684\u5355\u8bcd\u5217\u8868\u3002 \u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u7a7a\u683c\u4f5c\u4e3a\u5206\u9694\u7b26\u3002 \u5426\u5219\uff0c\u4f7f\u7528\u6307\u5b9a\u7684\u5206\u9694\u7b26\u6765\u6ea2\u51fa\u5b57\u7b26\u4e32\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.split ('hello how are you?')\nprint np.char.split ('TutorialsPoint,Hyderabad,Telangana', sep = ',')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">['hello', 'how', 'are', 'you?']\n['TutorialsPoint', 'Hyderabad', 'Telangana']\n<\/pre>\n\n\n\n<p><strong>numpy.char.splitlines()<\/strong><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u6570\u7ec4\u4e2d\u5143\u7d20\u7684\u5217\u8868\uff0c\u5e76\u5728\u884c\u8fb9\u754c\u5904\u7a81\u7834\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.splitlines('hello\\nhow are you?')\nprint np.char.splitlines('hello\\rhow are you?')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">['hello', 'how are you?']\n['hello', 'how are you?']\n<\/pre>\n\n\n\n<p><strong>numpy.char.strip()<\/strong><\/p>\n\n\n\n<p>\u6b64\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u6570\u7ec4\u7684\u526f\u672c\uff0c\u5176\u4e2d\u7684\u5143\u7d20\u88ab\u5265\u593a\u4e86\u6307\u5b9a\u5b57\u7b26\u7684\u524d\u5bfc\u548c\/\u6216\u5c3e\u968f\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.strip('ashok arora','a')\nprint np.char.strip(['arora','admin','java'],'a')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">shok aror\n['ror' 'dmin' 'jav']\n<\/pre>\n\n\n\n<p><strong>numpy.char.join()<\/strong><\/p>\n\n\n\n<p>\u6b64\u65b9\u6cd5\u8fd4\u56de\u4e00\u4e2a\u5b57\u7b26\u4e32\uff0c\u5176\u4e2d\u5404\u4e2a\u5b57\u7b26\u901a\u8fc7\u6307\u5b9a\u7684\u5206\u9694\u7b26\u8fde\u63a5\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.join(':','dmy')\nprint np.char.join([':','-'],['dmy','ymd'])\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">d:m:y\n['d:m:y' 'y-m-d']\n<\/pre>\n\n\n\n<p><strong>numpy.char.replace()<\/strong><\/p>\n\n\n\n<p>\u6b64\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u5b57\u7b26\u4e32\u7684\u65b0\u526f\u672c\uff0c\u5176\u4e2d\u6240\u6709\u51fa\u73b0\u7684\u5b57\u7b26\u5e8f\u5217\u90fd\u88ab\u53e6\u4e00\u4e2a\u7ed9\u5b9a\u5e8f\u5217\u66ff\u6362\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.char.replace ('He is a good boy', 'is', 'was')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">He was a good boy\n<\/pre>\n\n\n\n<p><strong>numpy.char.decode()<\/strong><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u8c03\u7528numpy.char.decode\uff08\uff09\u4f7f\u7528\u6307\u5b9a\u7684\u7f16\u89e3\u7801\u5668\u89e3\u7801\u7ed9\u5b9a\u7684\u5b57\u7b26\u4e32\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\na = np.char.encode('hello', 'cp500')\nprint a\nprint np.char.decode(a,'cp500')\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">\ufffd\ufffd\ufffd\ufffd\ufffd\nhello\n<\/pre>\n\n\n\n<p><strong>numpy.char.encode()<\/strong><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u4e3a\u6570\u7ec4\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u8c03\u7528str.encode\u51fd\u6570\u3002 \u9ed8\u8ba4\u7f16\u7801\u662futf_8\uff0c\u53ef\u4ee5\u4f7f\u7528\u6807\u51c6Python\u5e93\u4e2d\u7684\u7f16\u89e3\u7801\u5668\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.char.encode('hello', 'cp500')\nprint a\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u5982\u4e0b-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">\ufffd\ufffd\ufffd\ufffd\ufffd<\/pre>\n\n\n\n<h1>NumPy \u6570\u5b66\u51fd\u6570<\/h1>\n\n\n\n<p>\u5f88\u53ef\u4ee5\u7406\u89e3\uff0cNumPy\u5305\u542b\u5927\u91cf\u7684\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\u3002 NumPy\u63d0\u4f9b\u6807\u51c6\u7684\u4e09\u89d2\u51fd\u6570\uff0c\u7528\u4e8e\u7b97\u672f\u8fd0\u7b97\u7684\u51fd\u6570\uff0c\u5904\u7406\u590d\u6570\u7684\u51fd\u6570\u7b49\u3002<\/p>\n\n\n\n<h3>\u4e09\u89d2\u51fd\u6570<\/h3>\n\n\n\n<p>NumPy\u5177\u6709\u6807\u51c6\u7684\u4e09\u89d2\u51fd\u6570\uff0c\u53ef\u4ee5\u4ee5\u5f27\u5ea6\u8fd4\u56de\u7ed9\u5b9a\u89d2\u5ea6\u7684\u4e09\u89d2\u6bd4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([0,30,45,60,90])\n\nprint 'Sine of different angles:'\n# Convert to radians by multiplying with pi\/180\nprint np.sin(a*np.pi\/180)\nprint '\\n'\n\nprint 'Cosine values for angles in array:'\nprint np.cos(a*np.pi\/180)\nprint '\\n'\n\nprint 'Tangent values for given angles:'\nprint np.tan(a*np.pi\/180)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u4e3a-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Sine of different angles:\n[ 0.          0.5         0.70710678  0.8660254   1.        ]\n\n\nCosine values for angles in array:\n[  1.00000000e+00   8.66025404e-01   7.07106781e-01   5.00000000e-01\n6.12323400e-17]\n\n\nTangent values for given angles:\n[  0.00000000e+00   5.77350269e-01   1.00000000e+00   1.73205081e+00\n1.63312394e+16]\n<\/pre>\n\n\n\n<p>arcsin\uff0carcos\u548carctan\u51fd\u6570\u8fd4\u56de\u7ed9\u5b9a\u89d2\u5ea6\u7684sin\uff0ccos\u548ctan\u7684\u4e09\u89d2\u51fd\u6570\u7684\u5012\u6570\u3002 \u8fd9\u4e9b\u51fd\u6570\u7684\u7ed3\u679c\u53ef\u4ee5\u901a\u8fc7numpy.degrees\uff08\uff09\u51fd\u6570\u901a\u8fc7\u5c06\u5f27\u5ea6\u8f6c\u6362\u4e3a\u5ea6\u6765\u9a8c\u8bc1\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([0,30,45,60,90])\n\nprint 'Array containing sine values:'\nsin = np.sin(a*np.pi\/180)\nprint sin\nprint '\\n'\n\nprint 'Compute sine inverse of angles. Returned values are in radians.'\ninv = np.arcsin(sin)\nprint inv\nprint '\\n'\n\nprint 'Check result by converting to degrees:'\nprint np.degrees(inv)\nprint '\\n'\n\nprint 'arccos and arctan functions behave similarly:'\ncos = np.cos(a*np.pi\/180)\nprint cos\nprint '\\n'\n\nprint 'Inverse of cos:'\ninv = np.arccos(cos)\nprint inv\nprint '\\n'\n\nprint 'In degrees:'\nprint np.degrees(inv)\nprint '\\n'\n\nprint 'Tan function:'\ntan = np.tan(a*np.pi\/180)\nprint tan\nprint '\\n'\n\nprint 'Inverse of tan:'\ninv = np.arctan(tan)\nprint inv\nprint '\\n'\n\nprint 'In degrees:'\nprint np.degrees(inv)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u4e3a-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array containing sine values:\n[ 0.          0.5         0.70710678  0.8660254   1.        ]\n\nCompute sine inverse of angles. Returned values are in radians.\n[ 0.          0.52359878  0.78539816  1.04719755  1.57079633]\n\nCheck result by converting to degrees:\n[  0.  30.  45.  60.  90.]\n\narccos and arctan functions behave similarly:\n[  1.00000000e+00   8.66025404e-01   7.07106781e-01   5.00000000e-01\n6.12323400e-17]\n\nInverse of cos:\n[ 0.          0.52359878  0.78539816  1.04719755  1.57079633]\n\nIn degrees:\n[  0.  30.  45.  60.  90.]\n\nTan function:\n[  0.00000000e+00   5.77350269e-01   1.00000000e+00   1.73205081e+00\n1.63312394e+16]\n\nInverse of tan:\n[ 0.          0.52359878  0.78539816  1.04719755  1.57079633]\n\nIn degrees:\n[  0.  30.  45.  60.  90.]\n<\/pre>\n\n\n\n<h3>\u56db\u820d\u4e94\u5165\u7684\u529f\u80fd<\/h3>\n\n\n\n<p><b>numpy.around()<\/b><\/p>\n\n\n\n<p>\u8fd9\u662f\u4e00\u4e2a\u8fd4\u56de\u56db\u820d\u4e94\u5165\u5230\u6240\u9700\u7cbe\u5ea6\u7684\u51fd\u6570\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.around(a,decimals)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>a<\/b><br>\n\u8f93\u5165\u6570\u636e<\/td><\/tr><tr><td>2<\/td><td><b>decimals<\/b><br>\n\u8981\u820d\u5165\u7684\u5c0f\u6570\u4f4d\u6570\u3002 \u9ed8\u8ba4\u503c\u4e3a0.\u5982\u679c\u4e3a\u8d1f\u503c\uff0c\u5219\u5c06\u6574\u6570\u820d\u5165\u5230\u4f4d\u4e8e\u5c0f\u6570\u70b9\u5de6\u4fa7\u7684\u4f4d\u7f6e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1.0,5.55, 123, 0.567, 25.532])\n\nprint 'Original array:'\nprint a\nprint '\\n'\n\nprint 'After rounding:'\nprint np.around(a)\nprint np.around(a, decimals = 1)\nprint np.around(a, decimals = -1)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u4e3a-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Original array:\n[   1.       5.55   123.       0.567   25.532]\n\nAfter rounding:\n[   1.    6.   123.    1.   26. ]\n[   1.    5.6  123.    0.6  25.5]\n[   0.    10.  120.    0.   30. ]\n<\/pre>\n\n\n\n<p><b>numpy.floor()<\/b><\/p>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e0d\u5927\u4e8e\u8f93\u5165\u53c2\u6570\u7684\u6700\u5927\u6574\u6570\u3002 \u6807\u91cfx\u7684\u5e95\u90e8\u662f\u6700\u5927\u7684\u6574\u6570i\uff0c\u4f7f\u5f97i &lt;= x\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([-1.7, 1.5, -0.2, 0.6, 10])\n\nprint 'The given array:'\nprint a\nprint '\\n'\n\nprint 'The modified array:'\nprint np.floor(a)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u4e3a-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The given array:\n[ -1.7   1.5  -0.2   0.6  10. ]\n\nThe modified array:\n[ -2.   1.  -1.   0.  10.]\n<\/pre>\n\n\n\n<p><b>numpy.ceil()<\/b><\/p>\n\n\n\n<p>ceil\uff08\uff09\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u503c\u7684\u4e0a\u9650\uff0c\u5373\u6807\u91cfx\u7684\u6700\u5c0f\u503c\u662f\u6700\u5c0f\u7684\u6574\u6570i\uff0c\u4f7f\u5f97i&gt; = x\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([-1.7, 1.5, -0.2, 0.6, 10])\n\nprint 'The given array:'\nprint a\nprint '\\n'\n\nprint 'The modified array:'\nprint np.ceil(a)\n<\/pre>\n\n\n\n<p>\u7a0b\u5e8f\u8f93\u51fa\u4e3a-<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">The given array:\n[ -1.7   1.5  -0.2   0.6  10. ]\n\nThe modified array:\n[ -1.   2.  -0.   1.  10.]<\/pre>\n\n\n\n<h1>NumPy \u7b97\u672f\u8fd0\u7b97\u7b26<\/h1>\n\n\n\n<p>\u7528\u4e8e\u6267\u884c\u8bf8\u5982add\uff08\uff09\uff0csubtract\uff08\uff09\uff0cmultiply\uff08\uff09\u548cdivide\uff08\uff09\u7b49\u7b97\u672f\u8fd0\u7b97\u7684\u8f93\u5165\u6570\u7ec4\u5fc5\u987b\u662f\u76f8\u540c\u7684\u5f62\u72b6\uff0c\u6216\u8005\u5e94\u7b26\u5408\u6570\u7ec4\u5e7f\u64ad\u89c4\u5219\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(9, dtype = np.float_).reshape(3,3)\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Second array:'\nb = np.array([10,10,10])\nprint b\nprint '\\n'\n\nprint 'Add the two arrays:'\nprint np.add(a,b)\nprint '\\n'\n\nprint 'Subtract the two arrays:'\nprint np.subtract(a,b)\nprint '\\n'\n\nprint 'Multiply the two arrays:'\nprint np.multiply(a,b)\nprint '\\n'\n\nprint 'Divide the two arrays:'\nprint np.divide(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[[ 0. 1. 2.]\n [ 3. 4. 5.]\n [ 6. 7. 8.]]\n\nSecond array:\n[10 10 10]\n\nAdd the two arrays:\n[[ 10. 11. 12.]\n [ 13. 14. 15.]\n [ 16. 17. 18.]]\n\nSubtract the two arrays:\n[[-10. -9. -8.]\n [ -7. -6. -5.]\n [ -4. -3. -2.]]\n\nMultiply the two arrays:\n[[ 0. 10. 20.]\n [ 30. 40. 50.]\n [ 60. 70. 80.]]\n\nDivide the two arrays:\n[[ 0. 0.1 0.2]\n [ 0.3 0.4 0.5]\n [ 0.6 0.7 0.8]]\n<\/pre>\n\n\n\n<p>\u73b0\u5728\u8ba9\u6211\u4eec\u6765\u8ba8\u8bba\u4e00\u4e0bNumPy\u4e2d\u53ef\u7528\u7684\u5176\u4ed6\u4e00\u4e9b\u91cd\u8981\u7684\u7b97\u672f\u51fd\u6570\u3002<\/p>\n\n\n\n<h3>numpy.reciprocal()<\/h3>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u53c2\u6570\u7684\u5012\u6570\u3002 \u5bf9\u4e8e\u7edd\u5bf9\u503c\u5927\u4e8e1\u7684\u5143\u7d20\uff0c\u7531\u4e8ePython\u5904\u7406\u6574\u6570\u9664\u6cd5\u7684\u65b9\u5f0f\uff0c\u7ed3\u679c\u59cb\u7ec8\u4e3a0\u3002 \u5bf9\u4e8e\u6574\u65700\uff0c\u53d1\u51fa\u6ea2\u51fa\u8b66\u544a\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([0.25, 1.33, 1, 0, 100])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'After applying reciprocal function:'\nprint np.reciprocal(a)\nprint '\\n'\n\nb = np.array([100], dtype = int)\nprint 'The second array is:'\nprint b\nprint '\\n'\n\nprint 'After applying reciprocal function:'\nprint np.reciprocal(b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[   0.25    1.33    1.      0.    100.  ]\n\n\nAfter applying reciprocal function:\nmain.py:9: RuntimeWarning: divide by zero encountered in reciprocal\nprint np.reciprocal(a)\n[ 4.         0.7518797  1.               inf  0.01     ]\n\n\nThe second array is:\n[100]\n\n\nAfter applying reciprocal function:\n[0]\n<\/pre>\n\n\n\n<h3>numpy.power()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u5c06\u7b2c\u4e00\u4e2a\u8f93\u5165\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u4f5c\u4e3a\u57fa\u7840\u5904\u7406\uff0c\u5e76\u5c06\u5176\u8fd4\u56de\u4e3a\u7b2c\u4e8c\u4e2a\u8f93\u5165\u6570\u7ec4\u4e2d\u76f8\u5e94\u5143\u7d20\u7684\u6743\u529b\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([10,100,1000])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying power function:'\nprint np.power(a,2)\nprint '\\n'\n\nprint 'Second array:'\nb = np.array([1,2,3])\nprint b\nprint '\\n'\n\nprint 'Applying power function again:'\nprint np.power(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[  10  100 1000]\n\n\nApplying power function:\n[    100   10000 1000000]\n\n\nSecond array:\n[1 2 3]\n\n\nApplying power function again:\n[        10      10000 1000000000]\n<\/pre>\n\n\n\n<h3>numpy.mod()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u4e2d\u76f8\u5e94\u5143\u7d20\u7684\u9664\u6cd5\u4f59\u6570\u3002 \u51fd\u6570numpy.remainder\uff08\uff09\u4e5f\u4f1a\u4ea7\u751f\u76f8\u540c\u7684\u7ed3\u679c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([10,20,30])\nb = np.array([3,5,7])\n\nprint 'First array:'\nprint a\nprint '\\n'\n\nprint 'Second array:'\nprint b\nprint '\\n'\n\nprint 'Applying mod() function:'\nprint np.mod(a,b)\nprint '\\n'\n\nprint 'Applying remainder() function:'\nprint np.remainder(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">First array:\n[10 20 30]\n\nSecond array:\n[3 5 7]\n\nApplying mod() function:\n[1 0 2]\n\nApplying remainder() function:\n[1 0 2]\n<\/pre>\n\n\n\n<p>\u4ee5\u4e0b\u51fd\u6570\u7528\u4e8e\u5bf9\u5177\u6709\u590d\u6570\u7684\u6570\u7ec4\u6267\u884c\u64cd\u4f5c\u3002<\/p>\n\n\n\n<ul><li>numpy.real\uff08\uff09 &#8211; \u8fd4\u56de\u590d\u6742\u6570\u636e\u7c7b\u578b\u53c2\u6570\u7684\u5b9e\u90e8\u3002<\/li><li>numpy.imag\uff08\uff09 &#8211; \u8fd4\u56de\u590d\u6570\u6570\u636e\u7c7b\u578b\u53c2\u6570\u7684\u865a\u90e8\u3002<\/li><li>numpy.conj\uff08\uff09 &#8211; \u8fd4\u56de\u901a\u8fc7\u6539\u53d8\u865a\u90e8\u7b26\u53f7\u5f97\u5230\u7684\u590d\u5171\u8f6d\u3002<\/li><li>numpy.angle\uff08\uff09 &#8211; \u8fd4\u56de\u590d\u6742\u53c2\u6570\u7684\u89d2\u5ea6\u3002 \u8be5\u529f\u80fd\u5177\u6709\u5ea6\u6570\u53c2\u6570\u3002 \u5982\u679c\u4e3atrue\uff0c\u5219\u8fd4\u56de\u89d2\u5ea6\uff0c\u5426\u5219\u89d2\u5ea6\u4ee5\u5f27\u5ea6\u8868\u793a\u3002<\/li><\/ul>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([-5.6j, 0.2j, 11. , 1+1j])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying real() function:'\nprint np.real(a)\nprint '\\n'\n\nprint 'Applying imag() function:'\nprint np.imag(a)\nprint '\\n'\n\nprint 'Applying conj() function:'\nprint np.conj(a)\nprint '\\n'\n\nprint 'Applying angle() function:'\nprint np.angle(a)\nprint '\\n'\n\nprint 'Applying angle() function again (result in degrees)'\nprint np.angle(a, deg = True)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[ 0.-5.6j 0.+0.2j 11.+0.j 1.+1.j ]\n\nApplying real() function:\n[ 0. 0. 11. 1.]\n\nApplying imag() function:\n[-5.6 0.2 0. 1. ]\n\nApplying conj() function:\n[ 0.+5.6j 0.-0.2j 11.-0.j 1.-1.j ]\n\nApplying angle() function:\n[-1.57079633 1.57079633 0. 0.78539816]\n\nApplying angle() function again (result in degrees)\n[-90. 90. 0. 45.]<\/pre>\n\n\n\n<h1>NumPy \u7edf\u8ba1\u51fd\u6570<\/h1>\n\n\n\n<p>NumPy\u5177\u6709\u76f8\u5f53\u591a\u7684\u6709\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u7528\u4e8e\u4ece\u6570\u7ec4\u4e2d\u7684\u7ed9\u5b9a\u5143\u7d20\u4e2d\u627e\u51fa\u6700\u5c0f\u503c\uff0c\u6700\u5927\u503c\uff0c\u767e\u5206\u4f4d\u6807\u51c6\u504f\u5dee\u548c\u65b9\u5dee\u7b49\u3002 \u529f\u80fd\u89e3\u91ca\u5982\u4e0b &#8211;<\/p>\n\n\n\n<h3>numpy.amin() and numpy.amax()<\/h3>\n\n\n\n<p>\u8fd9\u4e9b\u51fd\u6570\u8fd4\u56de\u7ed9\u5b9a\u6570\u7ec4\u4e2d\u6cbf\u6307\u5b9a\u8f74\u7684\u5143\u7d20\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[3,7,5],[8,4,3],[2,4,9]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying amin() function:'\nprint np.amin(a,1)\nprint '\\n'\n\nprint 'Applying amin() function again:'\nprint np.amin(a,0)\nprint '\\n'\n\nprint 'Applying amax() function:'\nprint np.amax(a)\nprint '\\n'\n\nprint 'Applying amax() function again:'\nprint np.amax(a, axis = 0)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[3 7 5]\n[8 4 3]\n[2 4 9]]\n\nApplying amin() function:\n[3 3 2]\n\nApplying amin() function again:\n[2 4 3]\n\nApplying amax() function:\n9\n\nApplying amax() function again:\n[8 7 9]\n<\/pre>\n\n\n\n<h3>numpy.ptp()<\/h3>\n\n\n\n<p>numpy.ptp\uff08\uff09\u51fd\u6570\u8fd4\u56de\u6cbf\u8f74\u7684\u503c\u8303\u56f4\uff08\u6700\u5927\u503c &#8211; \u6700\u5c0f\u503c\uff09\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[3,7,5],[8,4,3],[2,4,9]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying ptp() function:'\nprint np.ptp(a)\nprint '\\n'\n\nprint 'Applying ptp() function along axis 1:'\nprint np.ptp(a, axis = 1)\nprint '\\n'\n\nprint 'Applying ptp() function along axis 0:'\nprint np.ptp(a, axis = 0)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[3 7 5]\n[8 4 3]\n[2 4 9]]\n\nApplying ptp() function:\n7\n\nApplying ptp() function along axis 1:\n[4 5 7]\n\nApplying ptp() function along axis 0:\n[6 3 6]\n<\/pre>\n\n\n\n<h3>numpy.percentile()<\/h3>\n\n\n\n<p>\u767e\u5206\u4f4d\u6570\uff08\u6216\u767e\u5206\u4f4d\u6570\uff09\u662f\u7edf\u8ba1\u6570\u636e\u4e2d\u4f7f\u7528\u7684\u5ea6\u91cf\uff0c\u8868\u793a\u4e00\u7ec4\u89c2\u6d4b\u503c\u4e2d\u7ed9\u5b9a\u767e\u5206\u6bd4\u7684\u89c2\u6d4b\u503c\u4e0b\u964d\u7684\u503c\u3002 \u51fd\u6570numpy.percentile\uff08\uff09\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.percentile(a, q, axis)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>a<\/b><br>\n\u8f93\u5165\u6570\u7ec4<\/td><\/tr><tr><td>2<\/td><td><b>q<\/b><br>\n\u8ba1\u7b97\u7684\u767e\u5206\u4f4d\u6570\u5fc5\u987b\u57280-100\u4e4b\u95f4<\/td><\/tr><tr><td>3<\/td><td><b>axis<\/b><br>\n\u8981\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u7684\u8f74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[30,40,70],[80,20,10],[50,90,60]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying percentile() function:'\nprint np.percentile(a,50)\nprint '\\n'\n\nprint 'Applying percentile() function along axis 1:'\nprint np.percentile(a,50, axis = 1)\nprint '\\n'\n\nprint 'Applying percentile() function along axis 0:'\nprint np.percentile(a,50, axis = 0)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[30 40 70]\n[80 20 10]\n[50 90 60]]\n\nApplying percentile() function:\n50.0\n\nApplying percentile() function along axis 1:\n[ 40. 20. 60.]\n\nApplying percentile() function along axis 0:\n[ 50. 40. 60.]\n<\/pre>\n\n\n\n<h3>numpy.median()<\/h3>\n\n\n\n<p>\u4e2d\u4f4d\u6570\u88ab\u5b9a\u4e49\u4e3a\u5c06\u6570\u636e\u6837\u672c\u7684\u9ad8\u534a\u90e8\u5206\u4e0e\u4e0b\u534a\u90e8\u5206\u5206\u5f00\u7684\u503c\u3002 \u4f7f\u7528numpy.median\uff08\uff09\u51fd\u6570\uff0c\u5982\u4e0b\u9762\u7684\u7a0b\u5e8f\u6240\u793a\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[30,65,70],[80,95,10],[50,90,60]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying median() function:'\nprint np.median(a)\nprint '\\n'\n\nprint 'Applying median() function along axis 0:'\nprint np.median(a, axis = 0)\nprint '\\n'\n\nprint 'Applying median() function along axis 1:'\nprint np.median(a, axis = 1)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[30 65 70]\n[80 95 10]\n[50 90 60]]\n\nApplying median() function:\n65.0\n\nApplying median() function along axis 0:\n[ 50. 90. 60.]\n\nApplying median() function along axis 1:\n[ 65. 80. 60.]\n<\/pre>\n\n\n\n<h3>numpy.mean()<\/h3>\n\n\n\n<p>\u7b97\u672f\u5e73\u5747\u503c\u662f\u6cbf\u8f74\u7684\u5143\u7d20\u603b\u548c\u9664\u4ee5\u5143\u7d20\u7684\u6570\u91cf\u3002 numpy.mean\uff08\uff09\u51fd\u6570\u8fd4\u56de\u6570\u7ec4\u4e2d\u5143\u7d20\u7684\u7b97\u672f\u5e73\u5747\u503c\u3002 \u5982\u679c\u63d0\u5230\u8be5\u8f74\uff0c\u5219\u4f1a\u6cbf\u7740\u5b83\u8fdb\u884c\u8ba1\u7b97\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2,3],[3,4,5],[4,5,6]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying mean() function:'\nprint np.mean(a)\nprint '\\n'\n\nprint 'Applying mean() function along axis 0:'\nprint np.mean(a, axis = 0)\nprint '\\n'\n\nprint 'Applying mean() function along axis 1:'\nprint np.mean(a, axis = 1)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[1 2 3]\n[3 4 5]\n[4 5 6]]\n\nApplying mean() function:\n3.66666666667\n\nApplying mean() function along axis 0:\n[ 2.66666667 3.66666667 4.66666667]\n\nApplying mean() function along axis 1:\n[ 2. 4. 5.]\n<\/pre>\n\n\n\n<h3>numpy.average()<\/h3>\n\n\n\n<p>\u52a0\u6743\u5e73\u5747\u6570\u662f\u7531\u6bcf\u4e2a\u7ec4\u4ef6\u4e58\u4ee5\u53cd\u6620\u5176\u91cd\u8981\u6027\u7684\u56e0\u7d20\u6240\u4ea7\u751f\u7684\u5e73\u5747\u503c\u3002 numpy.average\uff08\uff09\u51fd\u6570\u6839\u636e\u53e6\u4e00\u4e2a\u6570\u7ec4\u4e2d\u7ed9\u5b9a\u7684\u6743\u91cd\u8ba1\u7b97\u6570\u7ec4\u4e2d\u5143\u7d20\u7684\u52a0\u6743\u5e73\u5747\u503c\u3002 \u8be5\u529f\u80fd\u53ef\u4ee5\u6709\u4e00\u4e2a\u8f74\u53c2\u6570\u3002 \u5982\u679c\u6ca1\u6709\u6307\u5b9a\u8f74\uff0c\u5219\u6570\u7ec4\u53d8\u5e73\u3002<br>\n\u8003\u8651\u4e00\u4e2a\u6570\u7ec4[1,2,3,4]\u548c\u76f8\u5e94\u7684\u6743\u91cd[4,3,2,1]\uff0c\u52a0\u6743\u5e73\u5747\u503c\u662f\u901a\u8fc7\u76f8\u52a0\u5143\u7d20\u7684\u4e58\u79ef\u548c\u9664\u4ee5\u6743\u91cd\u4e4b\u548c\u5f97\u51fa\u7684\u3002<br>\n\u52a0\u6743\u5e73\u5747=\uff081 * 4 + 2 * 3 + 3 * 2 + 4 * 1\uff09\/\uff084 + 3 + 2 + 1\uff09<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1,2,3,4])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying average() function:'\nprint np.average(a)\nprint '\\n'\n\n# this is same as mean when weight is not specified\nwts = np.array([4,3,2,1])\n\nprint 'Applying average() function again:'\nprint np.average(a,weights = wts)\nprint '\\n'\n\n# Returns the sum of weights, if the returned parameter is set to True.\nprint 'Sum of weights'\nprint np.average([1,2,3, 4],weights = [4,3,2,1], returned = True)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[1 2 3 4]\n\nApplying average() function:\n2.5\n\nApplying average() function again:\n2.0\n\nSum of weights\n(2.0, 10.0)\n<\/pre>\n\n\n\n<p>\u5728\u591a\u7ef4\u6570\u7ec4\u4e2d\uff0c\u53ef\u4ee5\u6307\u5b9a\u8ba1\u7b97\u7684\u8f74\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(6).reshape(3,2)\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Modified array:'\nwt = np.array([3,5])\nprint np.average(a, axis = 1, weights = wt)\nprint '\\n'\n\nprint 'Modified array:'\nprint np.average(a, axis = 1, weights = wt, returned = True)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[0 1]\n[2 3]\n[4 5]]\n\nModified array:\n[ 0.625 2.625 4.625]\n\nModified array:\n(array([ 0.625, 2.625, 4.625]), array([ 8., 8., 8.]))\n<\/pre>\n\n\n\n<h3>\u6807\u51c6\u504f\u5dee<\/h3>\n\n\n\n<p>\u6807\u51c6\u504f\u5dee\u662f\u5e73\u5747\u504f\u5dee\u5e73\u65b9\u7684\u5e73\u65b9\u6839\u3002 \u6807\u51c6\u5dee\u7684\u516c\u5f0f\u5982\u4e0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">std = sqrt(mean(abs(x - x.mean())**2))\n<\/pre>\n\n\n\n<p>\u5982\u679c\u6570\u7ec4\u662f[1,2,3,4]\uff0c\u90a3\u4e48\u5b83\u7684\u5e73\u5747\u503c\u662f2.5\u3002 \u56e0\u6b64\uff0c\u5e73\u65b9\u504f\u5dee\u4e3a[2.25,0.25,0.25,2.25]\uff0c\u5176\u5747\u503c\u7684\u5e73\u65b9\u6839\u9664\u4ee54\uff0c\u5373sqrt\uff085\/4\uff09\u4e3a1.1180339887498949\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.std([1,2,3,4])\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">1.1180339887498949\n<\/pre>\n\n\n\n<h3>\u65b9\u5dee<\/h3>\n\n\n\n<p>\u65b9\u5dee\u662f\u5e73\u65b9\u504f\u5dee\u7684\u5e73\u5747\u503c\uff0c\u5373mean(abs(x &#8211; x.mean())**2)\u3002 \u6362\u53e5\u8bdd\u8bf4\uff0c\u6807\u51c6\u504f\u5dee\u662f\u65b9\u5dee\u7684\u5e73\u65b9\u6839\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.var([1,2,3,4])\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">1.25<\/pre>\n\n\n\n<h1>NumPy \u6392\u5e8f\uff0c\u641c\u7d22\u548c\u8ba1\u6570\u529f\u80fd<\/h1>\n\n\n\n<p>NumPy\u63d0\u4f9b\u4e86\u5404\u79cd\u4e0e\u5206\u7c7b\u76f8\u5173\u7684\u529f\u80fd\u3002 \u8fd9\u4e9b\u6392\u5e8f\u529f\u80fd\u5b9e\u73b0\u4e86\u4e0d\u540c\u7684\u6392\u5e8f\u7b97\u6cd5\uff0c\u6bcf\u79cd\u6392\u5e8f\u7b97\u6cd5\u90fd\u4ee5\u6267\u884c\u901f\u5ea6\uff0c\u6700\u5dee\u60c5\u51b5\u4e0b\u7684\u6027\u80fd\uff0c\u6240\u9700\u7684\u5de5\u4f5c\u7a7a\u95f4\u4ee5\u53ca\u7b97\u6cd5\u7684\u7a33\u5b9a\u6027\u4e3a\u7279\u5f81\u3002 \u4e0b\u8868\u663e\u793a\u4e86\u4e09\u79cd\u6392\u5e8f\u7b97\u6cd5\u7684\u6bd4\u8f83\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5206\u7c7b<\/th><th>\u65f6\u95f4\u590d\u6742\u5ea6<\/th><th>\u6700\u574f\u7684\u60c5\u51b5<\/th><th>\u7a7a\u95f4\u590d\u6742\u5ea6<\/th><th>\u7a33\u5b9a\u6027<\/th><\/tr><tr><td>\u5feb\u901f\u6392\u5e8f<\/td><td>1<\/td><td>O(n^2)<\/td><td>0<\/td><td>no<\/td><\/tr><tr><td>\u5f52\u5e76\u6392\u5e8f<\/td><td>2<\/td><td>O(n*log(n))<\/td><td>~n\/2<\/td><td>yes<\/td><\/tr><tr><td>\u5806\u6392\u5e8f<\/td><td>3<\/td><td>O(n*log(n))<\/td><td>0<\/td><td>no<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>numpy.sort()<\/h3>\n\n\n\n<p>sort\uff08\uff09\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u7684\u6709\u5e8f\u526f\u672c\u3002 \u5b83\u6709\u4ee5\u4e0b\u53c2\u6570 &#8211;<\/p>\n\n\n\n<p>numpy.sort(a, axis, kind, order)<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>a<\/b><br>\n\u6570\u7ec4\u8fdb\u884c\u6392\u5e8f<\/td><\/tr><tr><td>2<\/td><td><b>axis<\/b><br>\n\u8981\u6392\u5e8f\u6570\u7ec4\u7684\u8f74\u3002 \u5982\u679c\u6ca1\u6709\uff0c\u5219\u6570\u7ec4\u5c06\u53d8\u5e73\uff0c\u5e76\u5728\u6700\u540e\u4e00\u4e2a\u8f74\u4e0a\u6392\u5e8f<\/td><\/tr><tr><td>3<\/td><td><b>kind<\/b><br>\n\u9ed8\u8ba4\u662f\u5feb\u901f\u6392\u5e8f<\/td><\/tr><tr><td>4<\/td><td><b>order<\/b><br>\n\u5982\u679c\u6570\u7ec4\u5305\u542b\u5b57\u6bb5\uff0c\u5219\u8981\u6392\u5e8f\u5b57\u6bb5\u7684\u987a\u5e8f<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[3,7],[9,1]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying sort() function:'\nprint np.sort(a)\nprint '\\n'\n\nprint 'Sort along axis 0:'\nprint np.sort(a, axis = 0)\nprint '\\n'\n\n# Order parameter in sort function\ndt = np.dtype([('name', 'S10'),('age', int)])\na = np.array([(\"raju\",21),(\"anil\",25),(\"ravi\", 17), (\"amar\",27)], dtype = dt)\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Order by name:'\nprint np.sort(a, order = 'name')\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[3 7]\n[9 1]]\n\nApplying sort() function:\n[[3 7]\n[1 9]]\n\nSort along axis 0:\n[[3 1]\n[9 7]]\n\nOur array is:\n[('raju', 21) ('anil', 25) ('ravi', 17) ('amar', 27)]\n\nOrder by name:\n[('amar', 27) ('anil', 25) ('raju', 21) ('ravi', 17)]\n<\/pre>\n\n\n\n<h3>numpy.argsort()<\/h3>\n\n\n\n<p>\u51fd\u6570\u4f7f\u7528\u4e00\u7cfb\u5217\u952e\u6267\u884c\u95f4\u63a5\u6392\u5e8f\u3002 \u8fd9\u4e9b\u952e\u53ef\u4ee5\u770b\u4f5c\u7535\u5b50\u8868\u683c\u4e2d\u7684\u4e00\u5217\u3002 \u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u7d22\u5f15\u6570\u7ec4\uff0c\u4f7f\u7528\u8be5\u7d22\u5f15\u53ef\u4ee5\u83b7\u53d6\u6392\u5e8f\u540e\u7684\u6570\u636e\u3002 \u8bf7\u6ce8\u610f\uff0c\u6700\u540e\u4e00\u4e2a\u952e\u6070\u597d\u662f\u6392\u5e8f\u7684\u4e3b\u952e\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.array([3, 1, 2])\n\nprint 'Our array is:'\nprint x\nprint '\\n'\n\nprint 'Applying argsort() to x:'\ny = np.argsort(x)\nprint y\nprint '\\n'\n\nprint 'Reconstruct original array in sorted order:'\nprint x[y]\nprint '\\n'\n\nprint 'Reconstruct the original array using loop:'\nfor i in y:\nprint x[i],\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[3 1 2]\n\nApplying argsort() to x:\n[1 2 0]\n\nReconstruct original array in sorted order:\n[1 2 3]\n\nReconstruct the original array using loop:\n1 2 3\n<\/pre>\n\n\n\n<h3>numpy.lexsort()<\/h3>\n\n\n\n<p>\u51fd\u6570\u4f7f\u7528\u4e00\u7cfb\u5217\u952e\u6267\u884c\u95f4\u63a5\u6392\u5e8f\u3002 \u8fd9\u4e9b\u952e\u53ef\u4ee5\u770b\u4f5c\u7535\u5b50\u8868\u683c\u4e2d\u7684\u4e00\u5217\u3002 \u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u7d22\u5f15\u6570\u7ec4\uff0c\u4f7f\u7528\u8be5\u7d22\u5f15\u53ef\u4ee5\u83b7\u53d6\u6392\u5e8f\u540e\u7684\u6570\u636e\u3002 \u8bf7\u6ce8\u610f\uff0c\u6700\u540e\u4e00\u4e2a\u952e\u6070\u597d\u662f\u6392\u5e8f\u7684\u4e3b\u952e\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nnm = ('raju','anil','ravi','amar')\ndv = ('f.y.', 's.y.', 's.y.', 'f.y.')\nind = np.lexsort((dv,nm))\n\nprint 'Applying lexsort() function:'\nprint ind\nprint '\\n'\n\nprint 'Use this index to get sorted data:'\nprint [nm[i] + \", \" + dv[i] for i in ind]\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Applying lexsort() function:\n[3 1 0 2]\n\nUse this index to get sorted data:\n['amar, f.y.', 'anil, s.y.', 'raju, f.y.', 'ravi, s.y.']\n<\/pre>\n\n\n\n<p>NumPy\u6a21\u5757\u6709\u4e00\u4e9b\u7528\u4e8e\u5728\u6570\u7ec4\u5185\u641c\u7d22\u7684\u51fd\u6570\u3002 \u7528\u4e8e\u67e5\u627e\u6700\u5927\u503c\uff0c\u6700\u5c0f\u503c\u4ee5\u53ca\u6ee1\u8db3\u7ed9\u5b9a\u6761\u4ef6\u7684\u5143\u7d20\u7684\u51fd\u6570\u662f\u53ef\u7528\u7684\u3002<\/p>\n\n\n\n<h3>numpy.argmax()\u548cnumpy.argmin()<\/h3>\n\n\n\n<p>\u8fd9\u4e24\u4e2a\u51fd\u6570\u5206\u522b\u6cbf\u7ed9\u5b9a\u7684\u8f74\u8fd4\u56de\u6700\u5927\u548c\u6700\u5c0f\u5143\u7d20\u7684\u7d22\u5f15\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[30,40,70],[80,20,10],[50,90,60]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying argmax() function:'\nprint np.argmax(a)\nprint '\\n'\n\nprint 'Index of maximum number in flattened array'\nprint a.flatten()\nprint '\\n'\n\nprint 'Array containing indices of maximum along axis 0:'\nmaxindex = np.argmax(a, axis = 0)\nprint maxindex\nprint '\\n'\n\nprint 'Array containing indices of maximum along axis 1:'\nmaxindex = np.argmax(a, axis = 1)\nprint maxindex\nprint '\\n'\n\nprint 'Applying argmin() function:'\nminindex = np.argmin(a)\nprint minindex\nprint '\\n'\n\nprint 'Flattened array:'\nprint a.flatten()[minindex]\nprint '\\n'\n\nprint 'Flattened array along axis 0:'\nminindex = np.argmin(a, axis = 0)\nprint minindex\nprint '\\n'\n\nprint 'Flattened array along axis 1:'\nminindex = np.argmin(a, axis = 1)\nprint minindex\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[30 40 70]\n[80 20 10]\n[50 90 60]]\n\nApplying argmax() function:\n7\n\nIndex of maximum number in flattened array\n[30 40 70 80 20 10 50 90 60]\n\nArray containing indices of maximum along axis 0:\n[1 2 0]\n\nArray containing indices of maximum along axis 1:\n[2 0 1]\n\nApplying argmin() function:\n5\n\nFlattened array:\n10\n\nFlattened array along axis 0:\n[0 1 1]\n\nFlattened array along axis 1:\n[0 2 0]\n<\/pre>\n\n\n\n<h3>numpy.nonzero()<\/h3>\n\n\n\n<p>numpy.nonzero\uff08\uff09\u51fd\u6570\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u4e2d\u975e\u96f6\u5143\u7d20\u7684\u7d22\u5f15\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[30,40,0],[0,20,10],[50,0,60]])\n\nprint 'Our array is:'\nprint a\nprint '\\n'\n\nprint 'Applying nonzero() function:'\nprint np.nonzero (a)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[30 40 0]\n [ 0 20 10]\n [50 0 60]]\n\nApplying nonzero() function:\n(array([0, 0, 1, 1, 2, 2]), array([0, 1, 1, 2, 0, 2]))\n<\/pre>\n\n\n\n<h3>numpy.where()<\/h3>\n\n\n\n<p>where\uff08\uff09\u51fd\u6570\u8fd4\u56de\u6ee1\u8db3\u7ed9\u5b9a\u6761\u4ef6\u7684\u8f93\u5165\u6570\u7ec4\u4e2d\u5143\u7d20\u7684\u7d22\u5f15\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.arange(9.).reshape(3, 3)\n\nprint 'Our array is:'\nprint x\n\nprint 'Indices of elements &gt; 3'\ny = np.where(x &gt; 3)\nprint y\n\nprint 'Use these indices to get elements satisfying the condition'\nprint x[y]\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[ 0. 1. 2.]\n[ 3. 4. 5.]\n[ 6. 7. 8.]]\n\nIndices of elements &gt; 3\n(array([1, 1, 2, 2, 2]), array([1, 2, 0, 1, 2]))\n\nUse these indices to get elements satisfying the condition\n[ 4. 5. 6. 7. 8.]\n<\/pre>\n\n\n\n<h3>numpy.extract()<\/h3>\n\n\n\n<p>extract\uff08\uff09\u51fd\u6570\u8fd4\u56de\u6ee1\u8db3\u4efb\u4f55\u6761\u4ef6\u7684\u5143\u7d20\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nx = np.arange(9.).reshape(3, 3)\n\nprint 'Our array is:'\nprint x\n\n# define a condition\ncondition = np.mod(x,2) == 0\n\nprint 'Element-wise value of condition'\nprint condition\n\nprint 'Extract elements using condition'\nprint np.extract(condition, x)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[ 0. 1. 2.]\n[ 3. 4. 5.]\n[ 6. 7. 8.]]\n\nElement-wise value of condition\n[[ True False True]\n[False True False]\n[ True False True]]\n\nExtract elements using condition\n[ 0. 2. 4. 6. 8.]<\/pre>\n\n\n\n<h1>NumPy \u5b57\u8282\u4ea4\u6362<\/h1>\n\n\n\n<p>\u6211\u4eec\u5df2\u7ecf\u770b\u5230\uff0c\u5b58\u50a8\u5728\u8ba1\u7b97\u673a\u5185\u5b58\u4e2d\u7684\u6570\u636e\u53d6\u51b3\u4e8eCPU\u4f7f\u7528\u7684\u4f53\u7cfb\u7ed3\u6784\u3002 \u5b83\u53ef\u80fd\u662f\u5c0f\u7aef\uff08\u6700\u4f4e\u6709\u6548\u4f4d\u7f6e\u5b58\u50a8\u5728\u6700\u5c0f\u5730\u5740\u4e2d\uff09\u6216\u5927\u7aef\uff08\u6700\u5c0f\u5730\u5740\u4e2d\u6700\u9ad8\u6709\u6548\u5b57\u8282\uff09\u3002<\/p>\n\n\n\n<h3>numpy.ndarray.byteswap()<\/h3>\n\n\n\n<p>numpy.ndarray.byteswap\uff08\uff09\u51fd\u6570\u5728\u4e24\u4e2a\u8868\u793a\u4e4b\u95f4\u5207\u6362\uff1abigendian\u548clittle-endian\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1, 256, 8755], dtype = np.int16)\n\nprint 'Our array is:'\nprint a\n\nprint 'Representation of data in memory in hexadecimal form:'\nprint map(hex,a)\n# byteswap() function swaps in place by passing True parameter\n\nprint 'Applying byteswap() function:'\nprint a.byteswap(True)\n\nprint 'In hexadecimal form:'\nprint map(hex,a)\n# We can see the bytes being swapped\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[1 256 8755]\n\nRepresentation of data in memory in hexadecimal form:\n['0x1', '0x100', '0x2233']\n\nApplying byteswap() function:\n[256 1 13090]\n\nIn hexadecimal form:\n['0x100', '0x1', '0x3322']<\/pre>\n\n\n\n<h1>NumPy \u526f\u672c\u548c\u89c6\u56fe<\/h1>\n\n\n\n<p>\u5728\u6267\u884c\u51fd\u6570\u65f6\uff0c\u5176\u4e2d\u4e00\u4e9b\u8fd4\u56de\u8f93\u5165\u6570\u7ec4\u7684\u526f\u672c\uff0c\u800c\u53e6\u4e00\u4e9b\u5219\u8fd4\u56de\u89c6\u56fe\u3002 \u5f53\u5185\u5bb9\u7269\u7406\u5730\u5b58\u50a8\u5728\u53e6\u4e00\u4e2a\u4f4d\u7f6e\u65f6\uff0c\u5b83\u88ab\u79f0\u4e3a\u590d\u5236\u3002 \u53e6\u4e00\u65b9\u9762\uff0c\u5982\u679c\u63d0\u4f9b\u4e86\u76f8\u540c\u5185\u5b58\u5185\u5bb9\u7684\u4e0d\u540c\u89c6\u56fe\uff0c\u6211\u4eec\u5c06\u5176\u79f0\u4e3aView\u3002<\/p>\n\n\n\n<h3>\u6ca1\u6709\u526f\u672c<\/h3>\n\n\n\n<p>\u7b80\u5355\u7684\u8d4b\u503c\u4e0d\u4f1a\u521b\u5efa\u6570\u7ec4\u5bf9\u8c61\u7684\u526f\u672c\u3002 \u76f8\u53cd\uff0c\u5b83\u4f7f\u7528\u539f\u59cb\u6570\u7ec4\u7684\u76f8\u540cid\uff08\uff09\u6765\u8bbf\u95ee\u5b83\u3002 id\uff08\uff09\u8fd4\u56dePython\u5bf9\u8c61\u7684\u901a\u7528\u6807\u8bc6\u7b26\uff0c\u7c7b\u4f3c\u4e8eC\u4e2d\u7684\u6307\u9488\u3002<\/p>\n\n\n\n<p>\u800c\u4e14\uff0c\u4efb\u4f55\u4e00\u65b9\u7684\u53d8\u5316\u90fd\u4f1a\u53cd\u6620\u5728\u53e6\u4e00\u65b9\u9762\u3002 \u4f8b\u5982\uff0c\u4e00\u4e2a\u53d8\u5316\u7684\u5f62\u72b6\u4e5f\u4f1a\u6539\u53d8\u53e6\u4e00\u4e2a\u7684\u5f62\u72b6\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.arange(6)\n\nprint 'Our array is:'\nprint a\n\nprint 'Applying id() function:'\nprint id(a)\n\nprint 'a is assigned to b:'\nb = a\nprint b\n\nprint 'b has same id():'\nprint id(b)\n\nprint 'Change shape of b:'\nb.shape = 3,2\nprint b\n\nprint 'Shape of a also gets changed:'\nprint a\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[0 1 2 3 4 5]\n\nApplying id() function:\n139747815479536\n\na is assigned to b:\n[0 1 2 3 4 5]\nb has same id():\n139747815479536\n\nChange shape of b:\n[[0 1]\n[2 3]\n[4 5]]\n\nShape of a also gets changed:\n[[0 1]\n[2 3]\n[4 5]]\n<\/pre>\n\n\n\n<h3>\u89c6\u56fe\u6216\u6d45\u62f7\u8d1d<\/h3>\n\n\n\n<p>NumPy\u6709ndarray.view\uff08\uff09\u65b9\u6cd5\uff0c\u5b83\u662f\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4\u5bf9\u8c61\uff0c\u5b83\u67e5\u770b\u539f\u59cb\u6570\u7ec4\u7684\u76f8\u540c\u6570\u636e\u3002 \u4e0e\u4e4b\u524d\u7684\u60c5\u51b5\u4e0d\u540c\uff0c\u65b0\u9635\u5217\u7684\u5c3a\u5bf8\u66f4\u6539\u4e0d\u4f1a\u66f4\u6539\u539f\u59cb\u5c3a\u5bf8\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n# To begin with, a is 3X2 array\na = np.arange(6).reshape(3,2)\n\nprint 'Array a:'\nprint a\n\nprint 'Create view of a:'\nb = a.view()\nprint b\n\nprint 'id() for both the arrays are different:'\nprint 'id() of a:'\nprint id(a)\nprint 'id() of b:'\nprint id(b)\n\n# Change the shape of b. It does not change the shape of a\nb.shape = 2,3\n\nprint 'Shape of b:'\nprint b\n\nprint 'Shape of a:'\nprint a\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array a:\n[[0 1]\n[2 3]\n[4 5]]\n\nCreate view of a:\n[[0 1]\n[2 3]\n[4 5]]\n\nid() for both the arrays are different:\nid() of a:\n140424307227264\nid() of b:\n140424151696288\n\nShape of b:\n[[0 1 2]\n[3 4 5]]\n\nShape of a:\n[[0 1]\n[2 3]\n[4 5]]\n<\/pre>\n\n\n\n<p>\u6570\u7ec4\u7684\u5207\u7247\u521b\u5efa\u4e00\u4e2a\u89c6\u56fe\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[10,10], [2,3], [4,5]])\n\nprint 'Our array is:'\nprint a\n\nprint 'Create a slice:'\ns = a[:, :2]\nprint s\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Our array is:\n[[10 10]\n [ 2 3]\n [ 4 5]]\n\nCreate a slice:\n[[10 10]\n [ 2 3]\n [ 4 5]]\n<\/pre>\n\n\n\n<h3>\u6df1\u5ea6\u590d\u5236<\/h3>\n\n\n\n<p>ndarray.copy\uff08\uff09\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u6df1\u5ea6\u526f\u672c\u3002 \u5b83\u662f\u6570\u7ec4\u53ca\u5176\u6570\u636e\u7684\u5b8c\u6574\u526f\u672c\uff0c\u5e76\u4e0d\u4e0e\u539f\u59cb\u6570\u7ec4\u5171\u4eab\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[10,10], [2,3], [4,5]])\n\nprint 'Array a is:'\nprint a\n\nprint 'Create a deep copy of a:'\nb = a.copy()\nprint 'Array b is:'\nprint b\n\n#b does not share any memory of a\nprint 'Can we write b is a'\nprint b is a\n\nprint 'Change the contents of b:'\nb[0,0] = 100\n\nprint 'Modified array b:'\nprint b\n\nprint 'a remains unchanged:'\nprint a\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array a is:\n[[10 10]\n [ 2 3]\n [ 4 5]]\n\nCreate a deep copy of a:\nArray b is:\n[[10 10]\n [ 2 3]\n [ 4 5]]\nCan we write b is a\nFalse\n\nChange the contents of b:\nModified array b:\n[[100 10]\n [ 2 3]\n [ 4 5]]\n\na remains unchanged:\n[[10 10]\n [ 2 3]\n [ 4 5]]<\/pre>\n\n\n\n<h1>NumPy \u77e9\u9635\u5e93<\/h1>\n\n\n\n<p>NumPy\u5305\u4e2d\u5305\u542b\u4e00\u4e2aMatrix\u5e93numpy.matlib\u3002 \u8be5\u6a21\u5757\u5177\u6709\u8fd4\u56de\u77e9\u9635\u800c\u4e0d\u662fndarray\u5bf9\u8c61\u7684\u51fd\u6570\u3002<\/p>\n\n\n\n<h3>matlib.empty()<\/h3>\n\n\n\n<p>matlib.empty\uff08\uff09\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u77e9\u9635\uff0c\u800c\u4e0d\u521d\u59cb\u5316\u6761\u76ee\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.matlib.empty(shape, dtype, order)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>shape<\/b><br>\nint\u6216int\u7684\u5143\u7ec4\u5b9a\u4e49\u65b0\u77e9\u9635\u7684\u5f62\u72b6<\/td><\/tr><tr><td>2<\/td><td><b>Dtype<\/b><br>\n\u53ef\u9009\u7684\u3002 \u8f93\u51fa\u7684\u6570\u636e\u7c7b\u578b<\/td><\/tr><tr><td>3<\/td><td><b>order<\/b><br>\nC \u6216 F<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\n\nprint np.matlib.empty((2,2))\n# filled with random data\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 2.12199579e-314,   4.24399158e-314]\n[ 4.24399158e-314,   2.12199579e-314]]\n<\/pre>\n\n\n\n<h3>numpy.matlib.zeros()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u586b\u5145\u96f6\u7684\u77e9\u9635\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\nprint np.matlib.zeros((2,2))\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 0.  0.]\n [ 0.  0.]])\n<\/pre>\n\n\n\n<h3>numpy.matlib.ones()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u586b\u51451\u7684\u77e9\u9635\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\nprint np.matlib.ones((2,2))\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 1.  1.]\n [ 1.  1.]]\n<\/pre>\n\n\n\n<h3>numpy.matlib.eye()<\/h3>\n\n\n\n<p>\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u77e9\u9635\uff0c\u5176\u4e2d\u5bf9\u89d2\u7ebf\u5143\u7d20\u4e3a1\uff0c\u5176\u4ed6\u5730\u65b9\u4e3a0\u3002 \u8be5\u529f\u80fd\u91c7\u7528\u4ee5\u4e0b\u53c2\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">numpy.matlib.eye(n, M,k, dtype)\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u53c2\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>n<\/b><br>\n\u7ed3\u679c\u77e9\u9635\u4e2d\u7684\u884c\u6570<\/td><\/tr><tr><td>2<\/td><td><b>M<\/b><br>\n\u5217\u6570\uff0c\u9ed8\u8ba4\u4e3an<\/td><\/tr><tr><td>3<\/td><td><b>k<\/b><br>\n\u5bf9\u89d2\u7ebf\u7d22\u5f15<\/td><\/tr><tr><td>4<\/td><td><b>dtype<\/b><br>\n\u8f93\u51fa\u7684\u6570\u636e\u7c7b\u578b<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\nprint np.matlib.eye(n = 3, M = 4, k = 0, dtype = float)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 1.  0.  0.  0.]\n [ 0.  1.  0.  0.]\n [ 0.  0.  1.  0.]])\n<\/pre>\n\n\n\n<h3>numpy.matlib.identity()<\/h3>\n\n\n\n<p>numpy.matlib.identity\uff08\uff09\u51fd\u6570\u8fd4\u56de\u7ed9\u5b9a\u5927\u5c0f\u7684\u6807\u8bc6\u77e9\u9635\u3002 \u5355\u4f4d\u77e9\u9635\u662f\u4e00\u4e2a\u6240\u6709\u5bf9\u89d2\u5143\u7d20\u5747\u4e3a1\u7684\u65b9\u9635\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\nprint np.matlib.identity(5, dtype = float)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 1.  0.  0.  0.  0.]\n [ 0.  1.  0.  0.  0.]\n [ 0.  0.  1.  0.  0.]\n [ 0.  0.  0.  1.  0.]\n [ 0.  0.  0.  0.  1.]]\n<\/pre>\n\n\n\n<h3>numpy.matlib.rand()<\/h3>\n\n\n\n<p>numpy.matlib.rand\uff08\uff09\u51fd\u6570\u8fd4\u56de\u586b\u5145\u4e86\u968f\u673a\u503c\u7684\u7ed9\u5b9a\u5927\u5c0f\u7684\u77e9\u9635\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\nprint np.matlib.rand(3,3)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 0.82674464  0.57206837  0.15497519]\n [ 0.33857374  0.35742401  0.90895076]\n [ 0.03968467  0.13962089  0.39665201]]\n<\/pre>\n\n\n\n<p>\u8bf7\u6ce8\u610f\uff0c\u77e9\u9635\u603b\u662f\u4e8c\u7ef4\u7684\uff0c\u800cndarray\u662f\u4e00\u4e2an\u7ef4\u6570\u7ec4\u3002 \u4e24\u4e2a\u5bf9\u8c61\u90fd\u662f\u53ef\u4ee5\u76f8\u4e92\u8f6c\u6362\u7684\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\n\ni = np.matrix('1,2;3,4')\nprint i\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1  2]\n [3  4]]\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\n\nj = np.asarray(i)\nprint j\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1  2]\n [3  4]]\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\n\nk = np.asmatrix (j)\nprint k\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1  2]\n [3  4]]<\/pre>\n\n\n\n<h1>NumPy \u7ebf\u6027\u4ee3\u6570<\/h1>\n\n\n\n<p>NumPy\u8f6f\u4ef6\u5305\u5305\u542bnumpy.linalg\u6a21\u5757\uff0c\u63d0\u4f9b\u7ebf\u6027\u4ee3\u6570\u6240\u9700\u7684\u5168\u90e8\u529f\u80fd\u3002 \u4e0b\u8868\u63cf\u8ff0\u4e86\u8be5\u6a21\u5757\u4e2d\u7684\u4e00\u4e9b\u91cd\u8981\u529f\u80fd\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u51fd\u6570 &amp; \u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>dot<\/b><br>\n\u4e24\u4e2a\u6570\u7ec4\u7684\u70b9\u79ef<\/td><\/tr><tr><td>2<\/td><td><b>vdot<\/b><br>\n\u4e24\u4e2a\u5411\u91cf\u7684\u70b9\u79ef<\/td><\/tr><tr><td>3<\/td><td><b>inner<\/b><br>\n\u4e24\u4e2a\u9635\u5217\u7684\u5185\u79ef<\/td><\/tr><tr><td>4<\/td><td><b>matmul<\/b><br>\n\u4e24\u4e2a\u9635\u5217\u7684\u77e9\u9635\u4e58\u79ef<\/td><\/tr><tr><td>5<\/td><td><b>determinant<\/b><br>\n\u8ba1\u7b97\u6570\u7ec4\u7684\u884c\u5217\u5f0f<\/td><\/tr><tr><td>6<\/td><td><b>solve<\/b><br>\n\u6c42\u89e3\u7ebf\u6027\u77e9\u9635\u65b9\u7a0b<\/td><\/tr><tr><td>7<\/td><td><b>inv<\/b><br>\n\u627e\u51fa\u77e9\u9635\u7684\u4e58\u6cd5\u9006<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>numpy.dot()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e24\u4e2a\u6570\u7ec4\u7684\u70b9\u79ef\u3002 \u5bf9\u4e8e\u4e8c\u7ef4\u5411\u91cf\uff0c\u5b83\u7b49\u4ef7\u4e8e\u77e9\u9635\u4e58\u6cd5\u3002 \u5bf9\u4e8e\u4e00\u7ef4\u6570\u7ec4\uff0c\u5b83\u662f\u77e2\u91cf\u7684\u5185\u79ef\u3002 \u5bf9\u4e8e\u65e0\u5411\u6570\u7ec4\uff0c\u5b83\u662fa\u7684\u6700\u540e\u4e00\u4e2a\u8f74\u548cb\u7684\u5012\u6570\u7b2c\u4e8c\u4e2a\u8f74\u7684\u548c\u79ef\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy.matlib\nimport numpy as np\n\na = np.array([[1,2],[3,4]])\nb = np.array([[11,12],[13,14]])\nnp.dot(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[37  40]\n [85  92]]\n<\/pre>\n\n\n\n<p>\u8bf7\u6ce8\u610f\uff0c\u70b9\u79ef\u8ba1\u7b97\u4e3a &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1*11+2*13, 1*12+2*14],[3*11+4*13, 3*12+4*14]]\n<\/pre>\n\n\n\n<h3>numpy.vdot()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e24\u4e2a\u5411\u91cf\u7684\u70b9\u79ef\u3002 \u5982\u679c\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u590d\u6570\uff0c\u90a3\u4e48\u5b83\u7684\u5171\u8f6d\u7528\u4e8e\u8ba1\u7b97\u3002 \u5982\u679c\u53c2\u6570id\u662f\u591a\u7ef4\u6570\u7ec4\uff0c\u5219\u5b83\u53d8\u5e73\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2],[3,4]])\nb = np.array([[11,12],[13,14]])\nprint np.vdot(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">130\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\">\u6ce8 \u2212 1*11 + 2*12 + 3*13 + 4*14 = 130\n<\/pre>\n\n\n\n<h3>numpy.inner()<\/h3>\n\n\n\n<p>\u8be5\u51fd\u6570\u8fd4\u56de\u4e00\u7ef4\u6570\u7ec4\u7684\u5411\u91cf\u7684\u5185\u79ef\u3002 \u5bf9\u4e8e\u66f4\u9ad8\u7684\u5c3a\u5bf8\uff0c\u5b83\u5c06\u8fd4\u56de\u6700\u540e\u4e00\u4e2a\u8f74\u4e0a\u7684\u548c\u79ef\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nprint np.inner(np.array([1,2,3]),np.array([0,1,0]))\n# Equates to 1*0+2*1+3*0\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">2\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\"># Multi-dimensional array example\nimport numpy as np\na = np.array([[1,2], [3,4]])\n\nprint 'Array a:'\nprint a\nb = np.array([[11, 12], [13, 14]])\n\nprint 'Array b:'\nprint b\n\nprint 'Inner product:'\nprint np.inner(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array a:\n[[1 2]\n [3 4]]\n\nArray b:\n[[11 12]\n [13 14]]\n\nInner product:\n[[35 41]\n [81 95]]\n<\/pre>\n\n\n\n<p>\u5728\u4e0a\u8ff0\u60c5\u51b5\u4e0b\uff0c\u5185\u90e8\u4ea7\u54c1\u8ba1\u7b97\u4e3a &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">1*11+2*12, 1*13+2*14\n3*11+4*12, 3*13+4*14\n<\/pre>\n\n\n\n<h3>numpy.matmul()<\/h3>\n\n\n\n<p>numpy.matmul\uff08\uff09\u51fd\u6570\u8fd4\u56de\u4e24\u4e2a\u6570\u7ec4\u7684\u77e9\u9635\u4e58\u79ef\u3002 \u867d\u7136\u5b83\u8fd4\u56de\u4e8c\u7ef4\u6570\u7ec4\u7684\u6b63\u5e38\u4e58\u79ef\uff0c\u4f46\u5982\u679c\u4efb\u4e00\u53c2\u6570\u7684\u7ef4\u6570\u5927\u4e8e2\uff0c\u5219\u5b83\u5c06\u88ab\u89c6\u4e3a\u4f4d\u4e8e\u6700\u540e\u4e24\u4e2a\u7d22\u5f15\u4e2d\u7684\u77e9\u9635\u5806\u6808\uff0c\u5e76\u76f8\u5e94\u5730\u8fdb\u884c\u5e7f\u64ad\u3002<br>\n\u53e6\u4e00\u65b9\u9762\uff0c\u5982\u679c\u5176\u4e2d\u4e00\u4e2a\u53c2\u6570\u662f\u4e00\u7ef4\u6570\u7ec4\uff0c\u5219\u901a\u8fc7\u5728\u5b83\u7684\u7ef4\u4e0a\u9644\u52a01\u6765\u5c06\u5176\u63d0\u5347\u4e3a\u77e9\u9635\uff0c\u5176\u5728\u4e58\u6cd5\u4e4b\u540e\u88ab\u53bb\u9664\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\"># For 2-D array, it is matrix multiplication\nimport numpy.matlib\nimport numpy as np\n\na = [[1,0],[0,1]]\nb = [[4,1],[2,2]]\nprint np.matmul(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[4  1]\n [2  2]]\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\"># 2-D mixed with 1-D\nimport numpy.matlib\nimport numpy as np\n\na = [[1,0],[0,1]]\nb = [1,2]\nprint np.matmul(a,b)\nprint np.matmul(b,a)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[1  2]\n[1  2]\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\"># one array having dimensions &gt; 2\nimport numpy.matlib\nimport numpy as np\n\na = np.arange(8).reshape(2,2,2)\nb = np.arange(4).reshape(2,2)\nprint np.matmul(a,b)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[[2   3]\n  [6   11]]\n [[10  19]\n  [14  27]]]\n<\/pre>\n\n\n\n<h3>NumPy &#8211; Determinant<\/h3>\n\n\n\n<p>\u884c\u5217\u5f0f\u662f\u7ebf\u6027\u4ee3\u6570\u4e2d\u975e\u5e38\u6709\u7528\u7684\u503c\u3002 \u5b83\u6839\u636e\u65b9\u9635\u7684\u5bf9\u89d2\u7ebf\u5143\u7d20\u8fdb\u884c\u8ba1\u7b97\u3002 \u5bf9\u4e8e\u4e00\u4e2a2&#215;2\u77e9\u9635\uff0c\u5b83\u4ec5\u4ec5\u662f\u5de6\u4e0a\u89d2\u548c\u53f3\u4e0b\u89d2\u5143\u7d20\u4e0e\u5176\u4ed6\u4e24\u4e2a\u5143\u7d20\u4e58\u79ef\u7684\u51cf\u6cd5\u3002<br>\n\u6362\u53e5\u8bdd\u8bf4\uff0c\u5bf9\u4e8e\u77e9\u9635[[a\uff0cb]\uff0c[c\uff0cd]]\uff0c\u884c\u5217\u5f0f\u88ab\u8ba1\u7b97\u4e3a&#8217;ad-bc&#8217;\u3002 \u8f83\u5927\u7684\u5e73\u65b9\u77e9\u9635\u88ab\u8ba4\u4e3a\u662f2\u00d72\u77e9\u9635\u7684\u7ec4\u5408\u3002<br>\nnumpy.linalg.det\uff08\uff09\u51fd\u6570\u8ba1\u7b97\u8f93\u5165\u77e9\u9635\u7684\u884c\u5217\u5f0f\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,2], [3,4]])\nprint np.linalg.det(a)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">-2.0\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nb = np.array([[6,1,1], [4, -2, 5], [2,8,7]])\nprint b\nprint np.linalg.det(b)\nprint 6*(-2*7 - 5*8) - 1*(4*7 - 5*2) + 1*(4*8 - -2*2)\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[ 6 1 1]\n [ 4 -2 5]\n [ 2 8 7]]\n\n-306.0\n\n-306\n<\/pre>\n\n\n\n<h3>numpy.linalg.solve()<\/h3>\n\n\n\n<p>numpy.linalg.solve\uff08\uff09\u51fd\u6570\u4ee5\u77e9\u9635\u5f62\u5f0f\u7ed9\u51fa\u4e86\u7ebf\u6027\u65b9\u7a0b\u7ec4\u7684\u89e3\u3002<br>\n\u8003\u8651\u4ee5\u4e0b\u7ebf\u6027\u65b9\u7a0b &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">x + y + z = 6\n2y + 5z = -4\n2x + 5y - z = 27\n<\/pre>\n\n\n\n<p>\u5b83\u4eec\u53ef\u4ee5\u7528\u77e9\u9635\u5f62\u5f0f\u8868\u793a\u4e3a &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<p>\u5982\u679c\u8fd9\u4e09\u4e2a\u77e9\u9635\u88ab\u79f0\u4e3aA\uff0cX\u548cB\uff0c\u5219\u65b9\u7a0b\u53d8\u4e3a &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">A*X = B\n\nOr\n\nX = A-1B\n<\/pre>\n\n\n\n<h3>numpy.linalg.inv()<\/h3>\n\n\n\n<p>\u6211\u4eec\u4f7f\u7528numpy.linalg.inv\uff08\uff09\u51fd\u6570\u6765\u8ba1\u7b97\u77e9\u9635\u7684\u9006\u3002 \u77e9\u9635\u7684\u9006\u77e9\u9635\u662f\u8fd9\u6837\u7684\uff0c\u5373\u5982\u679c\u5b83\u4e58\u4ee5\u539f\u59cb\u77e9\u9635\uff0c\u5b83\u5c06\u5bfc\u81f4\u5355\u4f4d\u77e9\u9635\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\nx = np.array([[1,2],[3,4]])\ny = np.linalg.inv(x)\nprint x\nprint y\nprint np.dot(x,y)\n<\/pre>\n\n\n\n<p>\u5b83\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[[1 2]\n [3 4]]\n[[-2.   1. ]\n [ 1.5 -0.5]]\n[[  1.00000000e+00   1.11022302e-16]\n [  0.00000000e+00   1.00000000e+00]]\n<\/pre>\n\n\n\n<p>\u73b0\u5728\u8ba9\u6211\u4eec\u5728\u6211\u4eec\u7684\u4f8b\u5b50\u4e2d\u521b\u5efa\u4e00\u4e2a\u77e9\u9635A\u7684\u9006\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([[1,1,1],[0,2,5],[2,5,-1]])\n\nprint 'Array a:\u201d\nprint a\nainv = np.linalg.inv(a)\n\nprint 'Inverse of a:'\nprint ainv\n\nprint 'Matrix B is:'\nb = np.array([[6],[-4],[27]])\nprint b\n\nprint 'Compute A-1B:'\nx = np.linalg.solve(a,b)\nprint x\n# this is the solution to linear equations x = 5, y = 3, z = -2\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">Array a:\n[[ 1 1 1]\n [ 0 2 5]\n [ 2 5 -1]]\n\nInverse of a:\n[[ 1.28571429 -0.28571429 -0.14285714]\n [-0.47619048 0.14285714 0.23809524]\n [ 0.19047619 0.14285714 -0.0952381 ]]\n\nMatrix B is:\n[[ 6]\n [-4]\n [27]]\n\nCompute A-1B:\n[[ 5.]\n [ 3.]\n [-2.]]\n<\/pre>\n\n\n\n<p>\u4f7f\u7528\u51fd\u6570\u53ef\u4ee5\u83b7\u5f97\u76f8\u540c\u7684\u7ed3\u679c &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">x = np.dot(ainv,b)<\/pre>\n\n\n\n<h1>NumPy &#8211; Matplotlib<\/h1>\n\n\n\n<p>Matplotlib\u662fPython\u7684\u7ed8\u56fe\u5e93\u3002 \u5b83\u4e0eNumPy\u4e00\u8d77\u4f7f\u7528\uff0c\u4e3aMatLab\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6709\u6548\u7684\u5f00\u6e90\u66ff\u4ee3\u65b9\u6848\u3002 \u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u50cfPyQt\u548cwxPython\u8fd9\u6837\u7684\u56fe\u5f62\u5de5\u5177\u5305\u3002<\/p>\n\n\n\n<p>Matplotlib\u6a21\u5757\u9996\u5148\u7531John D. Hunter\u7f16\u5199\u3002 \u81ea2012\u5e74\u4ee5\u6765\uff0cMichael Droettboom\u662f\u4e3b\u8981\u5f00\u53d1\u4eba\u5458\u3002 \u76ee\u524d\uff0cMatplotlib ver\u3002 1.5.1\u662f\u53ef\u7528\u7684\u7a33\u5b9a\u7248\u672c\u3002 \u8be5\u8f6f\u4ef6\u5305\u4ee5\u4e8c\u8fdb\u5236\u53d1\u884c\u7248\u4ee5\u53cawww.matplotlib.org\u4e0a\u7684\u6e90\u4ee3\u7801\u683c\u5f0f\u63d0\u4f9b\u3002<\/p>\n\n\n\n<p>\u901a\u5e38\uff0c\u901a\u8fc7\u6dfb\u52a0\u4ee5\u4e0b\u8bed\u53e5\u5c06\u5305\u5bfc\u5165\u5230Python\u811a\u672c\u4e2d &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">from matplotlib import pyplot as plt\n<\/pre>\n\n\n\n<p>\u8fd9\u91ccpyplot\uff08\uff09\u662fmatplotlib\u5e93\u4e2d\u6700\u91cd\u8981\u7684\u51fd\u6570\uff0c\u7528\u4e8e\u7ed8\u5236\u4e8c\u7ef4\u6570\u636e\u3002 \u4ee5\u4e0b\u811a\u672c\u7ed8\u5236\u65b9\u7a0by = 2x + 5<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nfrom matplotlib import pyplot as plt\n\nx = np.arange(1,11)\ny = 2 * x + 5\nplt.title(\"Matplotlib demo\")\nplt.xlabel(\"x axis caption\")\nplt.ylabel(\"y axis caption\")\nplt.plot(x,y)\nplt.show()\n<\/pre>\n\n\n\n<p>ndarray\u5bf9\u8c61x\u662f\u7531np.arange\uff08\uff09\u51fd\u6570\u521b\u5efa\u7684\uff0c\u4f5c\u4e3ax\u8f74\u4e0a\u7684\u503c\u3002 y\u8f74\u4e0a\u7684\u5bf9\u5e94\u503c\u5b58\u50a8\u5728\u53e6\u4e00\u4e2andarray\u5bf9\u8c61y\u4e2d\u3002 \u8fd9\u4e9b\u503c\u4f7f\u7528matplotlib\u8f6f\u4ef6\u5305\u7684pyplot\u5b50\u6a21\u5757\u7684plot\uff08\uff09\u51fd\u6570\u7ed8\u5236\u3002<br>\n\u56fe\u5f62\u8868\u793a\u7531show\uff08\uff09\u51fd\u6570\u663e\u793a\u3002<br>\n\u4e0a\u9762\u7684\u4ee3\u7801\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<p>\u901a\u8fc7\u5411plot\uff08\uff09\u51fd\u6570\u6dfb\u52a0\u683c\u5f0f\u5b57\u7b26\u4e32\uff0c\u53ef\u4ee5\u79bb\u6563\u663e\u793a\u503c\u800c\u4e0d\u662f\u7ebf\u6027\u56fe\u3002 \u53ef\u4ee5\u4f7f\u7528\u683c\u5f0f\u5316\u5b57\u7b26\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5e8f\u53f7<\/th><th>\u5b57\u7b26 &amp;\u63cf\u8ff0<\/th><\/tr><tr><td>1<\/td><td><b>&#8216;-&#8216;<\/b><br>\n\u5b9e\u7ebf\u6837\u5f0f<\/td><\/tr><tr><td>2<\/td><td><b>&#8216;&#8211;&#8216;<\/b><br>\n\u865a\u7ebf\u6837\u5f0f<\/td><\/tr><tr><td>3<\/td><td><b>&#8216;-.&#8217;<\/b><br>\n\u70b9\u5212\u7ebf\u6837\u5f0f<\/td><\/tr><tr><td>4<\/td><td><b>&#8216;:&#8217;<\/b><br>\n\u865a\u7ebf\u6837\u5f0f<\/td><\/tr><tr><td>5<\/td><td><b>&#8216;.&#8217;<\/b><br>\n\u70b9\u6807\u8bb0<\/td><\/tr><tr><td>6<\/td><td><b>&#8216;,&#8217;<\/b><br>\n\u50cf\u7d20\u6807\u8bb0<\/td><\/tr><tr><td>7<\/td><td><b>&#8216;o&#8217;<\/b><br>\n\u5706\u6807\u8bb0<\/td><\/tr><tr><td>8<\/td><td><b>&#8216;v&#8217;<\/b><br>\n\u4e09\u89d2\u5411\u4e0b\u6807\u8bb0<\/td><\/tr><tr><td>9<\/td><td><b>&#8216;^&#8217;<\/b><br>\n\u4e09\u89d2\u5411\u4e0a\u6807\u8bb0<\/td><\/tr><tr><td>10<\/td><td><b>&#8216;\uff06lt;&#8217;<\/b><br>\n\u4e09\u89d2\u5411\u5de6\u6807\u8bb0<\/td><\/tr><tr><td>11<\/td><td><b>&#8216;\uff06gt;&#8217;<\/b><br>\n\u4e09\u89d2\u5411\u53f3\u6807\u8bb0<\/td><\/tr><tr><td>12<\/td><td><b>&#8216;1&#8217;<\/b><br>\nTri_down\u6807\u8bb0<\/td><\/tr><tr><td>13<\/td><td><b>&#8216;2&#8217;<\/b><br>\nTri_up\u6807\u8bb0<\/td><\/tr><tr><td>14<\/td><td><b>&#8216;3&#8217;<\/b><br>\nTri_left\u6807\u8bb0<\/td><\/tr><tr><td>15<\/td><td><b>&#8216;4&#8217;<\/b><br>\nTri_right\u6807\u8bb0<\/td><\/tr><tr><td>16<\/td><td><b>&#8216;s&#8217;<\/b><br>\n\u65b9\u5f62\u6807\u8bb0<\/td><\/tr><tr><td>17<\/td><td><b>&#8216;p&#8217;<\/b><br>\n\u4e94\u89d2\u6807\u8bb0<\/td><\/tr><tr><td>18<\/td><td><b>&#8216;*&#8217;<\/b><br>\n\u661f\u6807<\/td><\/tr><tr><td>19<\/td><td><b>&#8216;h&#8217;<\/b><br>\nHexagon1\u6807\u8bb0<\/td><\/tr><tr><td>20<\/td><td><b>&#8216;H&#8217;<\/b><br>\nHexagon2\u6807\u8bb0<\/td><\/tr><tr><td>21<\/td><td><b>&#8216;+&#8217;<\/b><br>\n\u52a0\u53f7\u6807\u8bb0<\/td><\/tr><tr><td>22<\/td><td><b>&#8216;x&#8217;<\/b><br>\nX\u6807\u8bb0<\/td><\/tr><tr><td>23<\/td><td><b>&#8216;D&#8217;<\/b><br>\n\u94bb\u77f3\u6807\u8bb0<\/td><\/tr><tr><td>24<\/td><td><b>&#8216;d&#8217;<\/b><br>\n\u7ec6\u94bb\u77f3\u6807\u8bb0<\/td><\/tr><tr><td>25<\/td><td><b>&#8216;|&#8217;<\/b><br>\nVline\u6807\u8bb0<\/td><\/tr><tr><td>26<\/td><td><b>&#8216;_&#8217;<\/b><br>\nHline\u6807\u8bb0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u4ee5\u4e0b\u989c\u8272\u7f29\u5199\u4e5f\u88ab\u5b9a\u4e49\u3002<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>\u5b57\u7b26<\/th><th>\u989c\u8272<\/th><\/tr><tr><td>&#8216;b&#8217;<\/td><td>\u84dd<\/td><\/tr><tr><td>&#8216;g&#8217;<\/td><td>\u7eff\u8272<\/td><\/tr><tr><td>&#8216;r&#8217;<\/td><td>\u7ea2\u8272<\/td><\/tr><tr><td>&#8216;c&#8217;<\/td><td>\u9752\u8272<\/td><\/tr><tr><td>&#8216;m&#8217;<\/td><td>\u54c1\u7ea2<\/td><\/tr><tr><td>&#8216;y&#8217;<\/td><td>\u9ec4\u8272<\/td><\/tr><tr><td>&#8216;k&#8217;<\/td><td>\u9ed1<\/td><\/tr><tr><td>&#8216;w&#8217;<\/td><td>\u767d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u8981\u663e\u793a\u4ee3\u8868\u70b9\u7684\u5706\u5708\uff0c\u800c\u4e0d\u662f\u4e0a\u9762\u793a\u4f8b\u4e2d\u7684\u76f4\u7ebf\uff0c\u8bf7\u5728plot\uff08\uff09\u51fd\u6570\u4e2d\u4f7f\u7528\u201cob\u201d\u4f5c\u4e3a\u683c\u5f0f\u5b57\u7b26\u4e32\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nfrom matplotlib import pyplot as plt\n\nx = np.arange(1,11)\ny = 2 * x + 5\nplt.title(\"Matplotlib demo\")\nplt.xlabel(\"x axis caption\")\nplt.ylabel(\"y axis caption\")\nplt.plot(x,y,\"ob\")\nplt.show()\n<\/pre>\n\n\n\n<p>\u4e0a\u9762\u7684\u4ee3\u7801\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h3>\u6b63\u5f26\u6ce2\u66f2\u7ebf<\/h3>\n\n\n\n<p>\u4ee5\u4e0b\u811a\u672c\u4f7f\u7528matplotlib\u751f\u6210\u6b63\u5f26\u6ce2\u56fe\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nimport matplotlib.pyplot as plt\n\n# Compute the x and y coordinates for points on a sine curve\nx = np.arange(0, 3 * np.pi, 0.1)\ny = np.sin(x)\nplt.title(\"sine wave form\")\n\n# Plot the points using matplotlib\nplt.plot(x, y)\nplt.show()\n<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h3>subplot()<\/h3>\n\n\n\n<p>subplot\uff08\uff09\u51fd\u6570\u5141\u8bb8\u4f60\u5728\u540c\u4e00\u4e2a\u56fe\u4e2d\u7ed8\u5236\u4e0d\u540c\u7684\u4e1c\u897f\u3002 \u5728\u4e0b\u9762\u7684\u811a\u672c\u4e2d\uff0c\u7ed8\u5236\u4e86\u6b63\u5f26\u548c\u4f59\u5f26\u503c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nimport matplotlib.pyplot as plt\n\n# Compute the x and y coordinates for points on sine and cosine curves\nx = np.arange(0, 3 * np.pi, 0.1)\ny_sin = np.sin(x)\ny_cos = np.cos(x)\n\n# Set up a subplot grid that has height 2 and width 1,\n# and set the first such subplot as active.\nplt.subplot(2, 1, 1)\n\n# Make the first plot\nplt.plot(x, y_sin)\nplt.title('Sine')\n\n# Set the second subplot as active, and make the second plot.\nplt.subplot(2, 1, 2)\nplt.plot(x, y_cos)\nplt.title('Cosine')\n\n# Show the figure.\nplt.show()\n<\/pre>\n\n\n\n<p>\u4e0a\u9762\u7684\u4ee3\u7801\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h3>bar()<\/h3>\n\n\n\n<p>pyplot\u5b50\u6a21\u5757\u63d0\u4f9bbar\uff08\uff09\u51fd\u6570\u6765\u751f\u6210\u6761\u5f62\u56fe\u3002 \u4ee5\u4e0b\u793a\u4f8b\u751f\u6210\u4e24\u7ec4x\u548cy\u6570\u7ec4\u7684\u6761\u5f62\u56fe\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">from matplotlib import pyplot as plt\nx = [5,8,10]\ny = [12,16,6]\n\nx2 = [6,9,11]\ny2 = [6,15,7]\nplt.bar(x, y, align = 'center')\nplt.bar(x2, y2, color = 'g', align = 'center')\nplt.title('Bar graph')\nplt.ylabel('Y axis')\nplt.xlabel('X axis')\n\nplt.show()\n<\/pre>\n\n\n\n<p>\u6b64\u4ee3\u7801\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h1>NumPy \u4f7f\u7528Matplotlib\u7684\u76f4\u65b9\u56fe<\/h1>\n\n\n\n<p>NumPy\u6709\u4e00\u4e2anumpy.histogram\uff08\uff09\u51fd\u6570\uff0c\u5b83\u662f\u6570\u636e\u9891\u7387\u5206\u5e03\u7684\u56fe\u5f62\u8868\u793a\u3002 \u76f8\u7b49\u7684\u6c34\u5e73\u5c3a\u5bf8\u7684\u957f\u65b9\u5f62\u5bf9\u5e94\u4e8e\u7c7b\u95f4\u9694\u79f0\u4e3abin\u548c\u53ef\u53d8\u9ad8\u5ea6\uff0c\u5bf9\u5e94\u4e8e\u9891\u7387\u3002<\/p>\n\n\n\n<h3>numpy.histogram()<\/h3>\n\n\n\n<p>numpy.histogram\uff08\uff09\u51fd\u6570\u5c06\u8f93\u5165\u6570\u7ec4\u548c\u6570\u7ec4\u4f5c\u4e3a\u4e24\u4e2a\u53c2\u6570\u3002 \u7bb1\u9635\u5217\u4e2d\u7684\u8fde\u7eed\u5143\u7d20\u5145\u5f53\u6bcf\u4e2a\u7bb1\u7684\u8fb9\u754c\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\na = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])\nnp.histogram(a,bins = [0,20,40,60,80,100])\nhist,bins = np.histogram(a,bins = [0,20,40,60,80,100])\nprint hist\nprint bins\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[3 4 5 2 1]\n[0 20 40 60 80 100]\n<\/pre>\n\n\n\n<h3>plt()<\/h3>\n\n\n\n<p>Matplotlib\u53ef\u4ee5\u5c06\u8fd9\u4e2a\u76f4\u65b9\u56fe\u7684\u6570\u5b57\u8868\u793a\u8f6c\u6362\u4e3a\u56fe\u5f62\u3002 pyplot\u5b50\u6a21\u5757\u7684plt\uff08\uff09\u51fd\u6570\u5c06\u5305\u542b\u6570\u636e\u548cbin\u6570\u7ec4\u7684\u6570\u7ec4\u4f5c\u4e3a\u53c2\u6570\u5e76\u8f6c\u6362\u4e3a\u76f4\u65b9\u56fe\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">from matplotlib import pyplot as plt\nimport numpy as np\n\na = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])\nplt.hist(a, bins = [0,20,40,60,80,100])\nplt.title(\"histogram\")\nplt.show()\n<\/pre>\n\n\n\n<p>\u5b83\u5e94\u8be5\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><div class='fancybox-wrapper lazyload-container-unload' data-fancybox='post-images' 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\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAANSURBVBhXYzh8+PB\/AAffA0nNPuCLAAAAAElFTkSuQmCC\" alt=\"\"\/><\/div><\/figure>\n\n\n\n<h1>NumPy \u8f93\u5165\u8f93\u51fa<\/h1>\n\n\n\n<p>ndarray\u5bf9\u8c61\u53ef\u4ee5\u4fdd\u5b58\u5230\u78c1\u76d8\u6587\u4ef6\u5e76\u4ece\u78c1\u76d8\u6587\u4ef6\u52a0\u8f7d\u3002 \u53ef\u7528\u7684IO\u529f\u80fd\u662f &#8211;<\/p>\n\n\n\n<ul><li>load\uff08\uff09\u548csave\uff08\uff09\u51fd\u6570\u5904\u7406\/ numPy\u4e8c\u8fdb\u5236\u6587\u4ef6\uff08\u4f7f\u7528npy\u6269\u5c55\u540d\uff09<\/li><li>loadtxt\uff08\uff09\u548csavetxt\uff08\uff09\u51fd\u6570\u5904\u7406\u666e\u901a\u7684\u6587\u672c\u6587\u4ef6<\/li><\/ul>\n\n\n\n<p>NumPy\u4e3andarray\u5bf9\u8c61\u5f15\u5165\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6587\u4ef6\u683c\u5f0f\u3002 \u6b64.npy\u6587\u4ef6\u5b58\u50a8\u91cd\u5efa\u78c1\u76d8\u6587\u4ef6\u4e2d\u7684ndarray\u6240\u9700\u7684\u6570\u636e\uff0c\u5f62\u72b6\uff0cdtype\u548c\u5176\u4ed6\u4fe1\u606f\uff0c\u4ee5\u4fbf\u5373\u4f7f\u6587\u4ef6\u4f4d\u4e8e\u5177\u6709\u4e0d\u540c\u4f53\u7cfb\u7ed3\u6784\u7684\u53e6\u4e00\u53f0\u8ba1\u7b97\u673a\u4e0a\uff0c\u4e5f\u80fd\u6b63\u786e\u68c0\u7d22\u8be5\u9635\u5217\u3002<\/p>\n\n\n\n<h3>numpy.save()<\/h3>\n\n\n\n<p>numpy.save\uff08\uff09\u6587\u4ef6\u5c06\u8f93\u5165\u6570\u7ec4\u5b58\u50a8\u5728\u5e26\u6709npy\u6269\u5c55\u540d\u7684\u78c1\u76d8\u6587\u4ef6\u4e2d\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\na = np.array([1,2,3,4,5])\nnp.save('outfile',a)\n<\/pre>\n\n\n\n<p>\u8981\u4eceoutfile.npy\u91cd\u5efa\u6570\u7ec4\uff0c\u8bf7\u4f7f\u7528load\uff08\uff09\u51fd\u6570\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\nb = np.load('outfile.npy')\nprint b\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">array([1, 2, 3, 4, 5])\n<\/pre>\n\n\n\n<pre class=\"wp-block-preformatted code\">save\uff08\uff09\u548cload\uff08\uff09\u51fd\u6570\u63a5\u53d7\u4e00\u4e2a\u989d\u5916\u7684\u5e03\u5c14\u53c2\u6570allow_pickles\u3002 \u5728\u4fdd\u5b58\u5230\u78c1\u76d8\u6587\u4ef6\u6216\u4ece\u78c1\u76d8\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\u4e4b\u524d\uff0cPython\u4e2d\u7684pickle\u7528\u4e8e\u5e8f\u5217\u5316\u548c\u53cd\u5e8f\u5217\u5316\u5bf9\u8c61\u3002\n<\/pre>\n\n\n\n<h3>savetxt()<\/h3>\n\n\n\n<p>\u4ee5\u7b80\u5355\u6587\u672c\u6587\u4ef6\u683c\u5f0f\u5b58\u50a8\u548c\u68c0\u7d22\u6570\u7ec4\u6570\u636e\u662f\u901a\u8fc7savetxt\uff08\uff09\u548cloadtxt\uff08\uff09\u51fd\u6570\u5b8c\u6210\u7684\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">import numpy as np\n\na = np.array([1,2,3,4,5])\nnp.savetxt('out.txt',a)\nb = np.loadtxt('out.txt')\nprint b\n<\/pre>\n\n\n\n<p>\u5b83\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u8f93\u51fa &#8211;<\/p>\n\n\n\n<pre class=\"wp-block-preformatted code\">[ 1.  2.  3.  4.  5.]\n<\/pre>\n\n\n\n<p>savetxt\uff08\uff09\u548cloadtxt\uff08\uff09\u51fd\u6570\u63a5\u53d7\u9644\u52a0\u7684\u53ef\u9009\u53c2\u6570\uff0c\u4f8b\u5982\u9875\u7709\uff0c\u9875\u811a\u548c\u5206\u9694\u7b26\u3002<\/p>\n<!--CusAds0-->\n<div style=\"font-size: 0px; height: 0px; line-height: 0px; margin: 0; padding: 0; clear: both;\"><\/div>","protected":false},"excerpt":{"rendered":"<p>NumPy \u4ecb\u7ecd NumPy\u662f\u4e00\u4e2aPython\u5305\u3002 \u5b83\u4ee3\u8868&#8217;Numerical Python&#8217;\u3002 \u5b83\u662f\u4e00\u4e2a\u7531\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u548c\u4e00\u7ec4\u5904\u7406\u6570\u7ec4\u7684\u4f8b\u7a0b\u7ec4\u6210\u7684\u5e93\u3002 Numeric\uff0cNumPy\u7684\u7956\u5148\uff0c\u7531Jim Hugunin\u5f00\u53d1\u3002 Numarray\u7684\u53e6\u4e00\u4e2a\u8f6f\u4ef6\u5305\u4e5f\u88ab\u5f00\u53d1\u51fa\u6765\uff0c\u5177\u6709\u4e00\u4e9b\u989d\u5916\u7684\u529f\u80fd\u3002 2005\u5e74\uff0cTravis Oli &#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"categories":[12],"tags":[],"_links":{"self":[{"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/posts\/430"}],"collection":[{"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/comments?post=430"}],"version-history":[{"count":0,"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/posts\/430\/revisions"}],"wp:attachment":[{"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/media?parent=430"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/categories?post=430"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhang.mba\/index.php\/wp-json\/wp\/v2\/tags?post=430"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}