{"id":8956,"date":"2024-12-19T21:55:15","date_gmt":"2024-12-19T12:55:15","guid":{"rendered":"https:\/\/www.pythonic-exam.com\/?p=8956"},"modified":"2024-12-19T21:55:16","modified_gmt":"2024-12-19T12:55:16","slug":"%e7%ac%ac14%e5%9b%9e%e3%80%8cmatplotlib%e3%81%ae%e4%bd%bf%e3%81%84%e6%96%b9%e3%80%8d","status":"publish","type":"post","link":"https:\/\/www.pythonic-exam.com\/archives\/8956","title":{"rendered":"\u7b2c14\u56de\u300cMatplotlib\u306e\u4f7f\u3044\u65b9\u300d"},"content":{"rendered":"\n

\u3053\u3093\u306b\u3061\u306f\u3001\u5c0f\u6fa4\u3067\u3059\u3002<\/p>\n\n\n\n

\u4eca\u56de\u306f\u3001Matplotlib\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002Matplotlib\u306f\u3001Python\u306e\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u3057\u3066\u5e83\u304f\u4f7f\u7528\u3055\u308c\u3066\u304a\u308a\u3001\u30c7\u30fc\u30bf\u3092\u8996\u899a\u7684\u306b\u8868\u73fe\u30fb\u5206\u6790\u3059\u308b\u305f\u3081\u306e\u975e\u5e38\u306b\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u591a\u69d8\u306a\u30b0\u30e9\u30d5\u3092\u7c21\u5358\u306b\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001NumPy\u3001SciPy\u3001Pandas\u306a\u3069\u306e\u4ed6\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u3082\u30b7\u30fc\u30e0\u30ec\u30b9\u306b\u7d71\u5408\u3067\u304d\u308b\u305f\u3081\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306b\u304a\u3044\u3066\u6b20\u304b\u305b\u306a\u3044\u5b58\u5728\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002Matplotlib\u3092\u4f7f\u3044\u3053\u306a\u3059\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u306e\u50be\u5411\u3084\u7279\u6027\u3092\u52b9\u679c\u7684\u306b\u4f1d\u3048\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n\n\n\n

\u6559\u79d1\u66f8\u300ePython\u306b\u3088\u308b\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u5206\u6790\u306e\u6559\u79d1\u66f8\uff08\u7b2c2\u7248\uff09<\/a>\u300f\u306e4.3\u7ae0\u300cMatplotlib\u300d\uff08178\u30da\u30fc\u30b8\u301c213\u30da\u30fc\u30b8\uff09\u3067\u306f\u3001\u3053\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4f7f\u7528\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u89e3\u8aac\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

<\/a>Matplotlib\u306e\u6982\u8981<\/strong><\/strong><\/h2>\n\n\n\n

Matplotlib\u306f\u3001Python\u30672D\u30843D\u306e\u30b0\u30e9\u30d5\u3092\u63cf\u753b\u3059\u308b\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002NumPy\u3068\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u7528\u3055\u308c\u308b\u3053\u3068\u304c\u591a\u304f\u3001\u30b7\u30f3\u30d7\u30eb\u306a\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u304b\u3089\u8907\u96d1\u306a\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u30843D\u30b0\u30e9\u30d5\u307e\u3067\u3001\u591a\u5f69\u306a\u30b0\u30e9\u30d5\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

Matplotlib\u3092\u4f7f\u7528\u3059\u308b\u306b\u306f\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002pip\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n

pip install matplotlib<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u305d\u3057\u3066\u3001Python\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u306f\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u4f7f\u3044\u307e\u3059\u3002pyplot\u30e2\u30b8\u30e5\u30fc\u30eb\u3092plt\u3068\u3044\u3046\u5225\u540d\u3067\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u306e\u304c\u4e00\u822c\u7684\u3067\u3059\u3002<\/p>\n\n\n\n

import matplotlib.pyplot as plt<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/a>pyplot\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9<\/strong><\/strong><\/h2>\n\n\n\n

Matplotlib\u306b\u306f\u3001\u4f7f\u3044\u3084\u3059\u3044\u9ad8\u6c34\u6e96\u306e\u63cf\u753b\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3067\u3042\u308bpyplot\u30e2\u30b8\u30e5\u30fc\u30eb\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u6570\u5024\u89e3\u6790\u30bd\u30d5\u30c8\u306eMATLAB\u306e\u3088\u3046\u306a\u30b3\u30de\u30f3\u30c9\u30b9\u30bf\u30a4\u30eb\u306e\u95a2\u6570\u3092\u63d0\u4f9b\u3057\u3001\u7c21\u5358\u306b\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3067\u304d\u308b\u3082\u306e\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306f\u3001\u30b7\u30f3\u30d7\u30eb\u306a\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u8868\u793a\u3059\u308b\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n\n\n\n

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 15, 20, 25, 30]

plt.plot(x, y)
plt.xlabel(‘x label’)
plt.ylabel(‘y label’)
plt.title(‘pyplot interface’)
plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

<\/a>\u63cf\u753b\u30aa\u30d6\u30b8\u30a7\u30af\u30c8<\/strong><\/strong><\/h2>\n\n\n\n

Matplotlib\u306e\u63cf\u753b\u306f\u3001\u4e3b\u306b\u4ee5\u4e0b\u306e2\u3064\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4e2d\u5fc3\u306b\u884c\u308f\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

    \n
  1. figure\uff08\u56f3\u5168\u4f53\uff09
    figure\u306f\u3001\u30b0\u30e9\u30d5\u5168\u4f53\u3092\u8868\u3059\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u3001\u30ad\u30e3\u30f3\u30d0\u30b9\u3068\u3082\u547c\u3070\u308c\u307e\u3059\u3002\u8907\u6570\u306eaxes\u3092\u542b\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u4e00\u3064\u306efigure\u5185\u306b\u8907\u6570\u306e\u30d7\u30ed\u30c3\u30c8\u3092\u914d\u7f6e\u3057\u3066\u8907\u96d1\u306a\u30ec\u30a4\u30a2\u30a6\u30c8\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
    fig = plt.figure()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \n
    1. axes\uff08\u30d7\u30ed\u30c3\u30c8\u9818\u57df\uff09
      axes\u306f\u3001\u5b9f\u969b\u306b\u30c7\u30fc\u30bf\u3092\u63cf\u753b\u3059\u308b\u9818\u57df\u3067\u3001\u30bf\u30a4\u30c8\u30eb\u3001\u8ef8\u30e9\u30d9\u30eb\u3001\u76ee\u76db\u308a\u306a\u3069\u306e\u8981\u7d20\u3092\u6301\u3061\u307e\u3059\u3002figure\u5185\u306b\u8907\u6570\u306eaxes\u3092\u914d\u7f6e\u3057\u3066\u30b5\u30d6\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
       ax = fig.add_subplot(1, 1, 1)  # 1\u884c1\u5217\u306e\u30b5\u30d6\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      <\/a>\u51fa\u529b\u65b9\u6cd5<\/strong><\/strong><\/h2>\n\n\n\n

      Matplotlib\u3067\u4f5c\u6210\u3057\u305f\u30b0\u30e9\u30d5\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u65b9\u6cd5\u3067\u51fa\u529b\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

        \n
      1. \u753b\u9762\u306b\u8868\u793a
        plt.show()\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u30b0\u30e9\u30d5\u3092\u753b\u9762\u306b\u8868\u793a\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
        plt.plot(x, y)
        plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
          \n
        1. \u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58
          plt.savefig()\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u30b0\u30e9\u30d5\u3092\u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3057\u307e\u3059\u3002PNG\u3001JPEG\u3001PDF\u3001SVG\u306a\u3069\u306e\u5f62\u5f0f\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3059\u3002\u30d5\u30a1\u30a4\u30eb\u5f62\u5f0f\u306f\u30d5\u30a1\u30a4\u30eb\u540d\u306e\u62e1\u5f35\u5b50\u304b\u3089\u81ea\u52d5\u5224\u5225\u3055\u308c\u307e\u3059\u3002
          \u4ee5\u4e0b\u306e\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u306f\u3001\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u300cgraph.png\u300d\u3068\u3057\u3001dpi\u3067\u89e3\u50cf\u5ea6\u3092\u6307\u5b9a\u3001bbox_inches=’tight’\u3067\u4f59\u767d\u3092\u6700\u5c0f\u9650\u306b\u8a2d\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
          plt.plot(x, y)
          plt.savefig(‘graph.png’, dpi=300, bbox_inches=’tight’)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
            \n
          1. Jupyter Notebook\u3067\u306e\u8868\u793a
            Jupyter Notebook\u3067\u30b0\u30e9\u30d5\u3092\u8868\u793a\u3059\u308b\u5834\u5408\u3001\u300c%matplotlib inline\u300d\u30de\u30b8\u30c3\u30af\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
            %matplotlib inline
            import matplotlib.pyplot as plt

            plt.plot(x, y)
            plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

            <\/a>\u30b0\u30e9\u30d5\u306e\u7a2e\u985e<\/strong><\/strong><\/h2>\n\n\n\n

            Matplotlib\u306f\u3055\u307e\u3056\u307e\u306a\u30b0\u30e9\u30d5\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3059\u3002\u4e3b\u306a\u3082\u306e\u3092\u4ee5\u4e0b\u306b\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

              \n
            1. \u6298\u308c\u7dda\u30b0\u30e9\u30d5\uff08Line Plot\uff09
              \u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3084\u9023\u7d9a\u30c7\u30fc\u30bf\u306e\u50be\u5411\u3092\u793a\u3059\u306e\u306b\u4f7f\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
              import matplotlib.pyplot as plt

              x = [1, 2, 3, 4, 5]
              y = [2, 3, 5, 7, 11]

              plt.plot(x, y)
              plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
              \"\"<\/figure>\n\n\n\n
                \n
              1. \u6563\u5e03\u56f3\uff08Scatter Plot\uff09
                \u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306e\u5206\u5e03\u3084\u76f8\u95a2\u95a2\u4fc2\u3092\u8996\u899a\u5316\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                import matplotlib.pyplot as plt

                x = [5, 7, 8, 7, 2, 17, 2, 9]
                y = [99, 86, 87, 88, 111, 86, 103, 87]

                plt.scatter(x, y)
                plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                \"\"<\/figure>\n\n\n\n
                  \n
                1. \u68d2\u30b0\u30e9\u30d5\uff08Bar Chart\uff09
                  \u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306e\u6bd4\u8f03\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                  import matplotlib.pyplot as plt

                  categories = [‘A’, ‘B’, ‘C’, ‘D’]
                  values = [10, 15, 7, 12]

                  plt.bar(categories, values)
                  plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n
                    \n
                  1. \u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\uff08Histogram\uff09
                    \u30c7\u30fc\u30bf\u306e\u983b\u5ea6\u5206\u5e03\u3092\u793a\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                    import matplotlib.pyplot as plt
                    import numpy as np

                    data = np.random.randn(1000)

                    plt.hist(data, bins=30)
                    plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                    \"\"<\/figure>\n\n\n\n
                      \n
                    1. \u7bb1\u3072\u3052\u56f3\uff08Box Plot\uff09
                      \u30c7\u30fc\u30bf\u306e\u5206\u5e03\u7279\u6027\uff08\u4e2d\u592e\u5024\u3001\u56db\u5206\u4f4d\u7bc4\u56f2\u306a\u3069\uff09\u3092\u8996\u899a\u5316\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                      import matplotlib.pyplot as plt
                      import numpy as np

                      data = [np.random.randn(100) for _ in range(5)]

                      plt.boxplot(data)
                      plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                      \"\"<\/figure>\n\n\n\n
                        \n
                      1. \u5186\u30b0\u30e9\u30d5\uff08Pie Chart\uff09
                        \u5168\u4f53\u306b\u5bfe\u3059\u308b\u5404\u90e8\u5206\u306e\u5272\u5408\u3092\u793a\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                        import matplotlib.pyplot as plt

                        labels = [‘A’, ‘B’, ‘C’, ‘D’]
                        sizes = [15, 30, 45, 10]

                        plt.pie(sizes, labels=labels, autopct=’%1.1f%%’)
                        plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                        \"\"<\/figure>\n\n\n\n
                          \n
                        1. \u7b49\u9ad8\u7dda\u56f3\uff08Contour Plot\uff09
                          3\u6b21\u5143\u30c7\u30fc\u30bf\u30922\u6b21\u5143\u3067\u8868\u73fe\u3057\u3001\u7b49\u9ad8\u7dda\u3067\u5024\u306e\u5909\u5316\u3092\u793a\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                          import matplotlib.pyplot as plt
                          import numpy as np

                          x = np.linspace(-3, 3, 100)
                          y = np.linspace(-3, 3, 100)
                          X, Y = np.meshgrid(x, y)
                          Z = np.sin(X**2 + Y**2)

                          plt.contour(X, Y, Z)
                          plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                          \"\"<\/figure>\n\n\n\n
                            \n
                          1. \u30d2\u30fc\u30c8\u30de\u30c3\u30d7\uff08Heatmap\uff09
                            \u884c\u5217\u5f62\u5f0f\u306e\u30c7\u30fc\u30bf\u3092\u8272\u306e\u6fc3\u6de1\u3067\u8868\u73fe\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
                            import matplotlib.pyplot as plt
                            import numpy as np

                            data = np.random.rand(10, 10)

                            plt.imshow(data, cmap=’hot’, interpolation=’nearest’)
                            plt.colorbar()
                            plt.show()<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                            \"\"<\/figure>\n\n\n\n

                            <\/a>\u307e\u3068\u3081<\/strong><\/strong><\/h2>\n\n\n\n

                            Matplotlib\u306f\u3001Python\u3067\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u57fa\u672c\u7684\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u7279\u5fb4\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

                              \n
                            • \u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3001\u6563\u5e03\u56f3\u3001\u68d2\u30b0\u30e9\u30d5\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u7a2e\u985e\u306e\u30b0\u30e9\u30d5\u3092\u7c21\u5358\u306b\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/li>\n\n\n\n
                            • pyplot\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30b7\u30f3\u30d7\u30eb\u306a\u30b3\u30fc\u30c9\u3067\u30b0\u30e9\u30d5\u3092\u63cf\u753b\u3067\u304d\u307e\u3059\u3002<\/li>\n\n\n\n
                            • \u8ef8\u3001\u30e9\u30d9\u30eb\u3001\u30bf\u30a4\u30c8\u30eb\u3001\u8272\u3001\u30b9\u30bf\u30a4\u30eb\u306a\u3069\u3001\u81ea\u7531\u306b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u53ef\u80fd\u3067\u3059\u3002<\/li>\n\n\n\n
                            • \u753b\u9762\u8868\u793a\u3001\u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u4fdd\u5b58\u3001Jupyter Notebook\u3067\u306e\u30a4\u30f3\u30e9\u30a4\u30f3\u8868\u793a\u306a\u3069\u3001\u7528\u9014\u306b\u5fdc\u3058\u3066\u3001\u3055\u307e\u3056\u307e\u306a\u65b9\u6cd5\u3067\u30b0\u30e9\u30d5\u3092\u51fa\u529b\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n

                              Matplotlib\u3092\u6d3b\u7528\u3059\u308c\u3070\u3001\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3084\u30ec\u30dd\u30fc\u30c8\u306e\u4f5c\u6210\u306b\u304a\u3044\u3066\u8996\u899a\u7684\u306b\u8a34\u6c42\u3067\u304d\u308b\u8cc7\u6599\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n

                              \u6b21\u56de\u304b\u3089\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u3067\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u308bscikit-learn\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\u6b21\u56de\u3082\u304a\u697d\u3057\u307f\u306b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u3053\u3093\u306b\u3061\u306f\u3001\u5c0f\u6fa4\u3067\u3059\u3002 \u4eca\u56de\u306f\u3001Matplotlib\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002Matplotlib\u306f\u3001Python\u306e\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u3057\u3066\u5e83\u304f\u4f7f\u7528\u3055\u308c\u3066\u304a\u308a\u3001\u30c7\u30fc\u30bf\u3092\u8996\u899a\u7684\u306b\u8868\u73fe\u30fb\u5206\u6790\u3059\u308b\u305f\u3081\u306e\u975e\u5e38\u306b\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053 […]","protected":false},"author":3,"featured_media":8143,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[17,32],"tags":[],"class_list":["post-8956","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-column","category-data"],"_links":{"self":[{"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/posts\/8956","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/comments?post=8956"}],"version-history":[{"count":0,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/posts\/8956\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/media\/8143"}],"wp:attachment":[{"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/media?parent=8956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/categories?post=8956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pythonic-exam.com\/wp-json\/wp\/v2\/tags?post=8956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}