{"id":1175795,"date":"2025-01-15T17:36:52","date_gmt":"2025-01-15T09:36:52","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1175795.html"},"modified":"2025-01-15T17:36:55","modified_gmt":"2025-01-15T09:36:55","slug":"mac%e7%9a%84python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8numpy","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1175795.html","title":{"rendered":"mac\u7684python\u5982\u4f55\u8c03\u7528numpy"},"content":{"rendered":"

\"mac\u7684python\u5982\u4f55\u8c03\u7528numpy\"<\/p>\n

\u5728Mac\u4e0a\u4f7f\u7528Python\u8c03\u7528NumPy\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5NumPy\u5e93\u3001\u5bfc\u5165NumPy\u6a21\u5757\u3001\u4f7f\u7528NumPy\u7684\u529f\u80fd<\/strong>\u3002\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5NumPy\u5e93<\/h3>\n<\/p>\n
    \n
  1. \n

    \u4f7f\u7528pip\u5b89\u88c5<\/strong><\/p>\n<\/p>\n

    NumPy\u662fPython\u7684\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u5e93\uff0c\u901a\u5e38\u4f7f\u7528Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b89\u88c5\u3002\u6253\u5f00\u7ec8\u7aef\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5NumPy\uff1a<\/p>\n<\/p>\n

    pip install numpy<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u4f7f\u7528Anaconda<\/strong><\/p>\n<\/p>\n
    \u5982\u679c\u4f60\u4f7f\u7528\u7684\u662fAnaconda\u53d1\u884c\u7248\u7684Python\uff0c\u53ef\u4ee5\u901a\u8fc7conda\u6765\u5b89\u88c5NumPy\u3002\u6253\u5f00\u7ec8\u7aef\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n
    conda install numpy<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u5bfc\u5165NumPy\u6a21\u5757<\/h3>\n<\/p>\n
    \u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u89e3\u91ca\u5668\u4e2d\uff0c\u5bfc\u5165NumPy\u6a21\u5757\u901a\u5e38\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4f7f\u7528as np<\/code>\u662f\u4e3a\u4e86\u7b80\u5316\u540e\u7eed\u4ee3\u7801\u4e2d\u5bf9NumPy\u51fd\u6570\u548c\u5c5e\u6027\u7684\u8c03\u7528\u3002<\/p>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528NumPy\u7684\u529f\u80fd<\/h3>\n<\/p>\n
    NumPy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u6570\u7ec4\u64cd\u4f5c\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u64cd\u4f5c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      \n
    1. \n
      \u521b\u5efa\u6570\u7ec4<\/strong><\/p>\n<\/p>\n
      NumPy\u7684\u6838\u5fc3\u662fndarray\u5bf9\u8c61\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u521b\u5efa\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
      a = np.array([1, 2, 3, 4, 5])<\/p>\n
      print(a)<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
      b = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
      print(b)<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u5168\u96f6\u6570\u7ec4<\/strong><\/h2>\n
      c = np.zeros((3, 3))<\/p>\n
      print(c)<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u5168\u4e00\u6570\u7ec4<\/strong><\/h2>\n
      d = np.ones((2, 2))<\/p>\n
      print(d)<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u5355\u4f4d\u77e9\u9635<\/strong><\/h2>\n
      e = np.eye(3)<\/p>\n
      print(e)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u6570\u7ec4\u8fd0\u7b97<\/strong><\/p>\n<\/p>\n
      NumPy\u6570\u7ec4\u652f\u6301\u591a\u79cd\u8fd0\u7b97\uff0c\u5305\u62ec\u52a0\u3001\u51cf\u3001\u4e58\u3001\u9664\u7b49\uff1a<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      a = np.array([1, 2, 3])<\/p>\n
      b = np.array([4, 5, 6])<\/p>\n
      \u6570\u7ec4\u52a0\u6cd5<\/strong><\/h2>\n
      print(a + b)<\/p>\n
      \u6570\u7ec4\u51cf\u6cd5<\/strong><\/h2>\n
      print(a - b)<\/p>\n
      \u6570\u7ec4\u4e58\u6cd5<\/strong><\/h2>\n
      print(a * b)<\/p>\n
      \u6570\u7ec4\u9664\u6cd5<\/strong><\/h2>\n
      print(a \/ b)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u6570\u7ec4\u64cd\u4f5c<\/strong><\/p>\n<\/p>\n
      NumPy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u5305\u62ec\u5f62\u72b6\u53d8\u6362\u3001\u62fc\u63a5\u3001\u5206\u5272\u7b49\uff1a<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      a = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
      \u53d8\u6362\u6570\u7ec4\u5f62\u72b6<\/strong><\/h2>\n
      b = a.reshape(3, 2)<\/p>\n
      print(b)<\/p>\n
      \u62fc\u63a5\u6570\u7ec4<\/strong><\/h2>\n
      c = np.concatenate((a, b), axis=0)<\/p>\n
      print(c)<\/p>\n
      \u5206\u5272\u6570\u7ec4<\/strong><\/h2>\n
      d = np.split(a, 2, axis=0)<\/p>\n
      print(d)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u56db\u3001NumPy\u7684\u9ad8\u7ea7\u529f\u80fd<\/h3>\n<\/p>\n
      NumPy\u4e0d\u4ec5\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u8fd0\u7b97\uff0c\u8fd8\u5305\u62ec\u4e00\u4e9b\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u7ebf\u6027\u4ee3\u6570\u3001\u968f\u673a\u6570\u751f\u6210\u3001\u5085\u91cc\u53f6\u53d8\u6362\u7b49\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u7ebf\u6027\u4ee3\u6570<\/strong><\/p>\n<\/p>\n
        NumPy\u7684\u7ebf\u6027\u4ee3\u6570\u6a21\u5757\u5305\u542b\u77e9\u9635\u4e58\u6cd5\u3001\u9006\u77e9\u9635\u3001\u7279\u5f81\u503c\u7b49\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        a = np.array([[1, 2], [3, 4]])<\/p>\n
        \u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n
        b = np.dot(a, a)<\/p>\n
        print(b)<\/p>\n
        \u8ba1\u7b97\u9006\u77e9\u9635<\/strong><\/h2>\n
        c = np.linalg.inv(a)<\/p>\n
        print(c)<\/p>\n
        \u8ba1\u7b97\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/strong><\/h2>\n
        d, e = np.linalg.eig(a)<\/p>\n
        print(d)<\/p>\n
        print(e)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u968f\u673a\u6570\u751f\u6210<\/strong><\/p>\n<\/p>\n
        NumPy\u63d0\u4f9b\u4e86\u968f\u673a\u6570\u751f\u6210\u6a21\u5757\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u5206\u5e03\u7684\u968f\u673a\u6570\uff1a<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        \u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u6570<\/strong><\/h2>\n
        a = np.random.rand(3, 3)<\/p>\n
        print(a)<\/p>\n
        \u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u6570<\/strong><\/h2>\n
        b = np.random.randn(3, 3)<\/p>\n
        print(b)<\/p>\n
        \u751f\u6210\u6574\u6570\u968f\u673a\u6570<\/strong><\/h2>\n
        c = np.random.randint(0, 10, (3, 3))<\/p>\n
        print(c)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/p>\n<\/p>\n
        NumPy\u7684\u5085\u91cc\u53f6\u53d8\u6362\u6a21\u5757\u53ef\u4ee5\u8fdb\u884c\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff1a<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        a = np.array([1, 2, 3, 4])<\/p>\n
        \u8ba1\u7b97\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
        b = np.fft.fft(a)<\/p>\n
        print(b)<\/p>\n
        \u8ba1\u7b97\u9006\u5085\u91cc\u53f6\u53d8\u6362<\/strong><\/h2>\n
        c = np.fft.ifft(b)<\/p>\n
        print(c)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u4e94\u3001NumPy\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u7684\u7ed3\u5408<\/h3>\n<\/p>\n
        NumPy\u53ef\u4ee5\u4e0e\u8bb8\u591a\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u5982SciPy\u3001Pandas\u3001Matplotlib\u7b49\uff0c\u4ee5\u8fdb\u884c\u66f4\u52a0\u590d\u6742\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          SciPy<\/strong><\/p>\n<\/p>\n
          SciPy\u662f\u5efa\u7acb\u5728NumPy\u57fa\u7840\u4e0a\u7684\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97\u529f\u80fd\uff1a<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          from scipy import linalg<\/p>\n
          a = np.array([[1, 2], [3, 4]])<\/p>\n
          \u4f7f\u7528SciPy\u8ba1\u7b97\u9006\u77e9\u9635<\/strong><\/h2>\n
          b = linalg.inv(a)<\/p>\n
          print(b)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          Pandas<\/strong><\/p>\n<\/p>\n
          Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u5904\u7406\u5e93\uff0c\u80fd\u591f\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff1a<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          import pandas as pd<\/p>\n
          a = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
          \u5c06NumPy\u6570\u7ec4\u8f6c\u6362\u4e3aPandas DataFrame<\/strong><\/h2>\n
          df = pd.DataFrame(a, columns=['A', 'B', 'C'])<\/p>\n
          print(df)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        3. \n
          Matplotlib<\/strong><\/p>\n<\/p>\n
          Matplotlib\u662f\u4e00\u4e2a\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u56fe\u8868\uff1a<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          x = np.linspace(0, 2 * np.pi, 100)<\/p>\n
          y = np.sin(x)<\/p>\n
          \u7ed8\u5236\u6b63\u5f26\u66f2\u7ebf<\/strong><\/h2>\n
          plt.plot(x, y)<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u516d\u3001NumPy\u6027\u80fd\u4f18\u5316<\/h3>\n<\/p>\n
          \u7531\u4e8eNumPy\u662f\u7528C\u8bed\u8a00\u7f16\u5199\u7684\uff0c\u5176\u6027\u80fd\u901a\u5e38\u6bd4\u7eafPython\u4ee3\u7801\u8981\u9ad8\u3002\u4e0d\u8fc7\uff0c\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u4ecd\u7136\u9700\u8981\u6ce8\u610f\u4e00\u4e9b\u6027\u80fd\u4f18\u5316\u6280\u5de7\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u907f\u514d\u4f7f\u7528Python\u5faa\u73af<\/strong><\/p>\n<\/p>\n
            \u5c3d\u91cf\u4f7f\u7528NumPy\u7684\u5411\u91cf\u5316\u64cd\u4f5c\uff0c\u907f\u514d\u4f7f\u7528Python\u7684for\u5faa\u73af\uff1a<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            a = np.arange(1000000)<\/p>\n
            \u4f7f\u7528\u5411\u91cf\u5316\u64cd\u4f5c<\/strong><\/h2>\n
            b = a + 1<\/p>\n
            \u907f\u514d\u4f7f\u7528Python\u5faa\u73af<\/strong><\/h2>\n
            for i in range(len(a)):<\/strong><\/h2>\n
                a[i] += 1<\/strong><\/h2>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u4f7f\u7528NumPy\u7684\u5e7f\u64ad\u673a\u5236<\/strong><\/p>\n<\/p>\n
            NumPy\u7684\u5e7f\u64ad\u673a\u5236\u5141\u8bb8\u5bf9\u4e0d\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\uff0c\u4ece\u800c\u907f\u514d\u4e86\u4e0d\u5fc5\u8981\u7684\u6570\u636e\u590d\u5236\uff1a<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            a = np.array([1, 2, 3])<\/p>\n
            b = np.array([[1], [2], [3]])<\/p>\n
            \u4f7f\u7528\u5e7f\u64ad\u673a\u5236<\/strong><\/h2>\n
            c = a + b<\/p>\n
            print(c)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          3. \n
            \u4f7f\u7528\u5185\u5b58\u6620\u5c04<\/strong><\/p>\n<\/p>\n
            \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u6280\u672f\u6765\u8282\u7701\u5185\u5b58\uff1a<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            \u521b\u5efa\u4e00\u4e2a\u5185\u5b58\u6620\u5c04\u6587\u4ef6<\/strong><\/h2>\n
            a = np.memmap('data.dat', dtype='float32', mode='w+', shape=(1000, 1000))<\/p>\n
            \u5bf9\u5185\u5b58\u6620\u5c04\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c<\/strong><\/h2>\n
            a[:] = np.random.rand(1000, 1000)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u4e03\u3001NumPy\u7684\u5e38\u89c1\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6cd5<\/h3>\n<\/p>\n
            \u5728\u4f7f\u7528NumPy\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u5b89\u88c5\u95ee\u9898<\/strong><\/p>\n<\/p>\n
              \u5982\u679c\u5728\u5b89\u88c5NumPy\u65f6\u9047\u5230\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
                \n
              • \u786e\u4fddpip\u6216conda\u5df2\u7ecf\u66f4\u65b0\u5230\u6700\u65b0\u7248\u672c\u3002<\/li>\n
              • \u4f7f\u7528pip install --upgrade pip<\/code>\u6765\u66f4\u65b0pip\u3002<\/li>\n
              • \u4f7f\u7528pip install numpy --user<\/code>\u6765\u8fdb\u884c\u7528\u6237\u7ea7\u5b89\u88c5\u3002<\/li>\n<\/ul>\n<\/li>\n
              • \n
                \u517c\u5bb9\u6027\u95ee\u9898<\/strong><\/p>\n<\/p>\n
                \u5982\u679c\u5728\u4f7f\u7528NumPy\u65f6\u9047\u5230\u517c\u5bb9\u6027\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
                  \n
                • \u786e\u4fdd\u6240\u6709\u76f8\u5173\u5e93\uff08\u5982SciPy\u3001Pandas\u7b49\uff09\u90fd\u66f4\u65b0\u5230\u6700\u65b0\u7248\u672c\u3002<\/li>\n
                • \u68c0\u67e5Python\u7248\u672c\u662f\u5426\u4e0eNumPy\u7248\u672c\u517c\u5bb9\u3002<\/li>\n<\/ul>\n<\/li>\n
                • \n
                  \u6027\u80fd\u95ee\u9898<\/strong><\/p>\n<\/p>\n
                  \u5982\u679c\u5728\u4f7f\u7528NumPy\u65f6\u9047\u5230\u6027\u80fd\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
                    \n
                  • \u4f18\u5316\u4ee3\u7801\uff0c\u5c3d\u91cf\u4f7f\u7528NumPy\u7684\u5411\u91cf\u5316\u64cd\u4f5c\u548c\u5e7f\u64ad\u673a\u5236\u3002<\/li>\n
                  • \u4f7f\u7528\u5185\u5b58\u6620\u5c04\u6280\u672f\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/li>\n
                  • \u8003\u8651\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6280\u672f\u6765\u5e76\u884c\u5904\u7406\u6570\u636e\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
                    \u516b\u3001NumPy\u7684\u672a\u6765\u53d1\u5c55\u8d8b\u52bf<\/h3>\n<\/p>\n
                    \u968f\u7740\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u9700\u6c42\u7684\u4e0d\u65ad\u589e\u52a0\uff0cNumPy\u7684\u672a\u6765\u53d1\u5c55\u8d8b\u52bf\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n<\/p>\n
                      \n
                    1. \n
                      \u6027\u80fd\u4f18\u5316<\/strong><\/p>\n<\/p>\n
                      \u4e0d\u65ad\u4f18\u5316NumPy\u7684\u6027\u80fd\uff0c\u4f7f\u5176\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/p>\n<\/li>\n
                    2. \n
                      \u529f\u80fd\u6269\u5c55<\/strong><\/p>\n<\/p>\n
                      \u4e0d\u65ad\u6269\u5c55NumPy\u7684\u529f\u80fd\uff0c\u589e\u52a0\u66f4\u591a\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n
                    3. \n
                      \u4e0e\u5176\u4ed6\u5e93\u7684\u96c6\u6210<\/strong><\/p>\n<\/p>\n
                      \u589e\u5f3aNumPy\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u5e93\u7684\u96c6\u6210\uff0c\u63d0\u4f9b\u66f4\u52a0\u5168\u9762\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n<\/li>\n
                    4. \n
                      \u793e\u533a\u652f\u6301<\/strong><\/p>\n<\/p>\n
                      \u4e0d\u65ad\u58ee\u5927NumPy\u793e\u533a\uff0c\u589e\u52a0\u66f4\u591a\u7684\u7528\u6237\u548c\u5f00\u53d1\u8005\uff0c\u5171\u540c\u63a8\u52a8NumPy\u7684\u53d1\u5c55\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                      \u603b\u4e4b\uff0cNumPy\u4f5c\u4e3a\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u638c\u63e1NumPy\u7684\u4f7f\u7528\u65b9\u6cd5\uff0c\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Mac\u4e0a\u5b89\u88c5numpy\u5e93\u4ee5\u4fbf\u5728Python\u4e2d\u4f7f\u7528\uff1f<\/strong>
                      \u5728Mac\u4e0a\u4f7f\u7528Python\u8c03\u7528numpy\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Python\u3002\u53ef\u4ee5\u901a\u8fc7Homebrew\u6216\u8005\u76f4\u63a5\u4ecePython\u5b98\u7f51\u4e0b\u8f7d\u5e76\u5b89\u88c5Python\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5numpy\u3002\u5728\u7ec8\u7aef\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1apip install numpy<\/code>\u3002\u5982\u679c\u60a8\u4f7f\u7528\u7684\u662fPython 3.x\uff0c\u53ef\u80fd\u9700\u8981\u4f7f\u7528pip3 install numpy<\/code>\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5c31\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165numpy\u5e93\u3002<\/p>\n
                      \u4f7f\u7528numpy\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\u7684\u57fa\u672c\u793a\u4f8b\u662f\u4ec0\u4e48\uff1f<\/strong>
                      numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u5b66\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u3002\u4f7f\u7528numpy\u65f6\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u5e93\uff1aimport numpy as np<\/code>\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\uff0c\u4f8b\u5982\uff1aarray = np.array([1, 2, 3, 4])<\/code>\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528numpy\u63d0\u4f9b\u7684\u591a\u79cd\u529f\u80fd\uff0c\u5982np.mean(array)<\/code>\u6765\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff0c\u6216\u8005np.sum(array)<\/code>\u6765\u8ba1\u7b97\u6570\u7ec4\u7684\u603b\u548c\u3002\u8fd9\u4e9b\u529f\u80fd\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5feb\u901f\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n
                      \u9047\u5230\u201cModuleNotFoundError: No module named 'numpy'\u201d\u8be5\u5982\u4f55\u89e3\u51b3\uff1f<\/strong>
                      \u5982\u679c\u5728\u8fd0\u884cPython\u4ee3\u7801\u65f6\u51fa\u73b0\u201cModuleNotFoundError: No module named 'numpy'\u201d\u7684\u9519\u8bef\uff0c\u901a\u5e38\u8868\u793anumpy\u5e93\u5c1a\u672a\u5b89\u88c5\u3002\u60a8\u53ef\u4ee5\u518d\u6b21\u5728\u7ec8\u7aef\u4e2d\u8fd0\u884cpip install numpy<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff0c\u786e\u4fdd\u5b89\u88c5\u6210\u529f\u540e\uff0c\u91cd\u65b0\u542f\u52a8Python\u73af\u5883\u3002\u5982\u679c\u4ecd\u7136\u65e0\u6cd5\u89e3\u51b3\u95ee\u9898\uff0c\u53ef\u4ee5\u68c0\u67e5Python\u548cpip\u7684\u7248\u672c\uff0c\u786e\u4fdd\u5b83\u4eec\u662f\u517c\u5bb9\u7684\uff0c\u5e76\u4e14numpy\u5e93\u5df2\u7ecf\u6b63\u786e\u5b89\u88c5\u5728\u60a8\u6b63\u5728\u4f7f\u7528\u7684Python\u7248\u672c\u4e2d\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Mac\u4e0a\u4f7f\u7528Python\u8c03\u7528NumPy\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5NumPy\u5e93\u3001\u5bfc\u5165NumPy\u6a21\u5757\u3001\u4f7f\u7528NumPy\u7684\u529f\u80fd\u3002 […]","protected":false},"author":3,"featured_media":1175798,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175795"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1175795"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175795\/revisions"}],"predecessor-version":[{"id":1175799,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175795\/revisions\/1175799"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1175798"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1175795"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1175795"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1175795"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}