{"id":922885,"date":"2024-12-26T14:40:06","date_gmt":"2024-12-26T06:40:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/922885.html"},"modified":"2024-12-26T14:40:08","modified_gmt":"2024-12-26T06:40:08","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%bb%98%e5%87%bd%e6%95%b0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/922885.html","title":{"rendered":"\u5982\u4f55\u7528python\u7ed8\u51fd\u6570\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7ed8\u51fd\u6570\u56fe\"<\/p>\n

\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3002\u8fd9\u4e9b\u5de5\u5177\u5404\u6709\u5176\u72ec\u7279\u7684\u529f\u80fd\u548c\u7279\u70b9\u3002Matplotlib\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u5404\u79cd\u9759\u6001\u56fe\u8868\u3002Seaborn\u5219\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u6269\u5c55\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u548c\u7b80\u4fbf\u7684\u7ed8\u56fe\u63a5\u53e3\u3002Plotly\u5219\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u9700\u8981\u52a8\u6001\u4ea4\u4e92\u7684\u573a\u666f\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u51fd\u6570\u56fe\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u6982\u8ff0<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u51fd\u6570\u56fe\u65f6\uff0c\u901a\u5e38\u9700\u8981\u7528\u5230\u4e24\u4e2a\u6a21\u5757\uff1apyplot<\/code>\u548cnumpy<\/code>\u3002pyplot<\/code>\u662fMatplotlib\u7684\u4e00\u4e2a\u6a21\u5757\uff0c\u7528\u4e8e\u7ed8\u52362D\u56fe\u5f62\uff0c\u800cnumpy<\/code>\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u652f\u6301\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\u3002<\/p>\n<\/p>\n

Matplotlib\u7684\u6838\u5fc3\u662fFigure\u5bf9\u8c61\uff0c\u6bcf\u4e00\u4e2aFigure\u5bf9\u8c61\u90fd\u53ef\u4ee5\u5305\u542b\u4e00\u4e2a\u6216\u591a\u4e2aAxes\u5bf9\u8c61\u3002Axes\u5bf9\u8c61\u662f\u56fe\u8868\u7684\u5b9e\u9645\u7ed8\u5236\u533a\u57df\uff0c\u6240\u6709\u7684\u7ed8\u56fe\u547d\u4ee4\u90fd\u662f\u9488\u5bf9Axes\u5bf9\u8c61\u8fdb\u884c\u7684\u3002\u901a\u8fc7\u7406\u89e3\u8fd9\u4e9b\u57fa\u672c\u6982\u5ff5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u638c\u63e1Matplotlib\u7684\u4f7f\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u57fa\u7840\u793a\u4f8b\uff1a\u7ed8\u5236\u7b80\u5355\u51fd\u6570\u56fe<\/p>\n<\/p>\n

\u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u51fd\u6570\u56fe\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\uff0c\u7136\u540e\u751f\u6210\u5176\u81ea\u53d8\u91cf\u7684\u53d6\u503c\u8303\u56f4\uff0c\u6700\u540e\u8ba1\u7b97\u5bf9\u5e94\u7684\u56e0\u53d8\u91cf\u503c\u5e76\u7ed8\u5236\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
def f(x):<\/p>\n
    return x  2<\/p>\n
\u751f\u6210x\u5750\u6807\u7684\u503c<\/strong><\/h2>\n
x = np.linspace(-10, 10, 100)<\/p>\n
\u8ba1\u7b97y\u5750\u6807\u7684\u503c<\/strong><\/h2>\n
y = f(x)<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
plt.plot(x, y, label='y = x^2')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Function Plot')<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('f(x)')<\/p>\n
\u663e\u793a\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0clinspace<\/code>\u51fd\u6570\u7528\u4e8e\u751f\u6210\u4ece-10\u523010\u4e4b\u95f4\u7684100\u4e2a\u5747\u5300\u5206\u5e03\u7684\u6570\u503c\uff0c\u8fd9\u4e9b\u6570\u503c\u4f5c\u4e3a\u51fd\u6570\u7684\u81ea\u53d8\u91cf\u3002\u901a\u8fc7\u8c03\u7528\u5b9a\u4e49\u7684\u51fd\u6570f(x)<\/code>\uff0c\u8ba1\u7b97\u5bf9\u5e94\u7684\u56e0\u53d8\u91cf\u503c\uff0c\u7136\u540e\u4f7f\u7528plot<\/code>\u51fd\u6570\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5b9a\u5236\u5316\u56fe\u5f62<\/p>\n<\/p>\n
Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5b9a\u5236\u5316\u9009\u9879\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u521b\u5efa\u66f4\u5177\u5438\u5f15\u529b\u548c\u4fe1\u606f\u91cf\u7684\u56fe\u5f62\u3002\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7ebf\u6761\u7684\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u7b7e\u7b49\uff0c\u4f7f\u56fe\u5f62\u66f4\u52a0\u6e05\u6670\u6613\u61c2\u3002<\/p>\n<\/p>\n
    \n
  1. \u7ebf\u6761\u6837\u5f0f\u548c\u989c\u8272<\/strong><\/li>\n<\/ol>\n
    \u901a\u8fc7\u8bbe\u7f6eplot<\/code>\u51fd\u6570\u7684\u53c2\u6570\uff0c\u53ef\u4ee5\u6539\u53d8\u7ebf\u6761\u7684\u6837\u5f0f\u548c\u989c\u8272\u3002\u4f8b\u5982\uff0c\u4f7f\u7528'-'<\/code>\u8868\u793a\u5b9e\u7ebf\uff0c'--'<\/code>\u8868\u793a\u865a\u7ebf\uff0c':'<\/code>\u8868\u793a\u70b9\u7ebf\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6ecolor<\/code>\u53c2\u6570\u6539\u53d8\u7ebf\u6761\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n
    plt.plot(x, y, linestyle='--', color='r', label='y = x^2')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6dfb\u52a0\u7f51\u683c\u7ebf<\/strong><\/li>\n<\/ol>\n
      \u7f51\u683c\u7ebf\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u56fe\u5f62\u4e2d\u7684\u6570\u636e\u5206\u5e03\u3002\u901a\u8fc7grid<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u6dfb\u52a0\u7f51\u683c\u7ebf\u3002<\/p>\n<\/p>\n
      plt.grid(True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4<\/strong><\/li>\n<\/ol>\n
        \u6709\u65f6\u4e3a\u4e86\u66f4\u597d\u5730\u663e\u793a\u51fd\u6570\u7684\u7279\u5f81\uff0c\u9700\u8981\u624b\u52a8\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u8303\u56f4\u3002\u53ef\u4ee5\u901a\u8fc7xlim<\/code>\u548cylim<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
        plt.xlim(-10, 10)<\/p>\n
        plt.ylim(0, 100)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u56db\u3001\u7ed8\u5236\u591a\u6761\u51fd\u6570\u66f2\u7ebf<\/p>\n<\/p>\n
        \u5728\u540c\u4e00\u5f20\u56fe\u4e2d\u7ed8\u5236\u591a\u6761\u51fd\u6570\u66f2\u7ebf\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6bd4\u8f83\u4e0d\u540c\u51fd\u6570\u7684\u7279\u6027\u3002\u5728Matplotlib\u4e2d\uff0c\u53ea\u9700\u591a\u6b21\u8c03\u7528plot<\/code>\u51fd\u6570\u5373\u53ef\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        import numpy as np<\/p>\n
        def f1(x):<\/p>\n
            return x  2<\/p>\n
        def f2(x):<\/p>\n
            return x  3<\/p>\n
        x = np.linspace(-10, 10, 100)<\/p>\n
        y1 = f1(x)<\/p>\n
        y2 = f2(x)<\/p>\n
        plt.plot(x, y1, label='y = x^2')<\/p>\n
        plt.plot(x, y2, linestyle='--', label='y = x^3')<\/p>\n
        plt.title('Multiple Functions Plot')<\/p>\n
        plt.xlabel('x')<\/p>\n
        plt.ylabel('f(x)')<\/p>\n
        plt.legend()<\/p>\n
        plt.grid(True)<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u901a\u8fc7\u5b9a\u4e49\u4e24\u4e2a\u4e0d\u540c\u7684\u51fd\u6570f1<\/code>\u548cf2<\/code>\uff0c\u5e76\u5206\u522b\u8ba1\u7b97\u5b83\u4eec\u7684\u56e0\u53d8\u91cf\u503c\uff0c\u7136\u540e\u901a\u8fc7\u4e24\u6b21\u8c03\u7528plot<\/code>\u51fd\u6570\uff0c\u5c06\u4e24\u6761\u66f2\u7ebf\u7ed8\u5236\u5728\u540c\u4e00\u5f20\u56fe\u4e0a\u3002<\/p>\n<\/p>\n
        \u4e94\u3001\u7ed8\u5236\u4e09\u7ef4\u51fd\u6570\u56fe<\/p>\n<\/p>\n
        \u5bf9\u4e8e\u4e00\u4e9b\u9700\u8981\u5c55\u793a\u4e09\u7ef4\u7279\u5f81\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4e09\u7ef4\u51fd\u6570\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        import numpy as np<\/p>\n
        from mpl_toolkits.mplot3d import Axes3D<\/p>\n
        \u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
        def f(x, y):<\/p>\n
            return np.sin(np.sqrt(x <strong> 2 + y <\/strong> 2))<\/p>\n
        \u751f\u6210x\u548cy\u5750\u6807\u7684\u503c<\/strong><\/h2>\n
        x = np.linspace(-6, 6, 30)<\/p>\n
        y = np.linspace(-6, 6, 30)<\/p>\n
        \u751f\u6210\u7f51\u683c<\/strong><\/h2>\n
        X, Y = np.meshgrid(x, y)<\/p>\n
        Z = f(X, Y)<\/p>\n
        \u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62<\/strong><\/h2>\n
        fig = plt.figure()<\/p>\n
        ax = plt.axes(projection='3d')<\/p>\n
        ax.plot_surface(X, Y, Z, cmap='viridis')<\/p>\n
        \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
        ax.set_title('3D Function Plot')<\/p>\n
        ax.set_xlabel('x')<\/p>\n
        ax.set_ylabel('y')<\/p>\n
        ax.set_zlabel('f(x, y)')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u9996\u5148\u5b9a\u4e49\u4e86\u4e00\u4e2a\u4e8c\u7ef4\u51fd\u6570f(x, y)<\/code>\u3002\u7136\u540e\u4f7f\u7528meshgrid<\/code>\u51fd\u6570\u751f\u6210\u4e8c\u7ef4\u7f51\u683c\uff0c\u5bf9\u5e94\u7684\u56e0\u53d8\u91cf\u503c\u5b58\u50a8\u5728Z<\/code>\u4e2d\u3002\u6700\u540e\uff0c\u901a\u8fc7plot_surface<\/code>\u51fd\u6570\u7ed8\u5236\u4e09\u7ef4\u66f2\u9762\u56fe\u3002<\/p>\n<\/p>\n
        \u516d\u3001\u4f7f\u7528SEABORN\u7ed8\u5236\u51fd\u6570\u56fe<\/p>\n<\/p>\n
        Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u4fbf\u7684\u7ed8\u56fe\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u914d\u8272\u65b9\u6848\u3002\u5728\u4e00\u4e9b\u573a\u666f\u4e0b\uff0c\u4f7f\u7528Seaborn\u53ef\u4ee5\u66f4\u52a0\u5feb\u901f\u5730\u521b\u5efa\u9ad8\u8d28\u91cf\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        import numpy as np<\/p>\n
        import seaborn as sns<\/p>\n
        \u542f\u7528Seaborn\u7684\u7f8e\u89c2\u4e3b\u9898<\/strong><\/h2>\n
        sns.set_theme()<\/p>\n
        \u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
        def f(x):<\/p>\n
            return np.sin(x)<\/p>\n
        x = np.linspace(0, 10, 100)<\/p>\n
        y = f(x)<\/p>\n
        \u4f7f\u7528Seaborn\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
        sns.lineplot(x=x, y=y, label='y = sin(x)')<\/p>\n
        plt.title('Seaborn Function Plot')<\/p>\n
        plt.xlabel('x')<\/p>\n
        plt.ylabel('f(x)')<\/p>\n
        plt.legend()<\/p>\n
        plt.grid(True)<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u8fd9\u4e2a\u793a\u4f8b\u53ef\u4ee5\u770b\u51fa\uff0c\u4f7f\u7528Seaborn\u7ed8\u56fe\u7684\u4ee3\u7801\u7ed3\u6784\u4e0eMatplotlib\u57fa\u672c\u76f8\u540c\uff0c\u4f46\u9ed8\u8ba4\u7684\u6837\u5f0f\u66f4\u52a0\u7f8e\u89c2\u3002<\/p>\n<\/p>\n
        \u4e03\u3001\u4f7f\u7528PLOTLY\u7ed8\u5236\u4ea4\u4e92\u5f0f\u51fd\u6570\u56fe<\/p>\n<\/p>\n
        Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u9700\u8981\u52a8\u6001\u4ea4\u4e92\u7684\u573a\u666f\u3002\u4f7f\u7528Plotly\u53ef\u4ee5\u521b\u5efa\u53ef\u7f29\u653e\u3001\u53ef\u65cb\u8f6c\u3001\u53ef\u5e73\u79fb\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        import plotly.graph_objs as go<\/p>\n
        import numpy as np<\/p>\n
        \u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
        def f(x):<\/p>\n
            return np.sin(x)<\/p>\n
        x = np.linspace(0, 10, 100)<\/p>\n
        y = f(x)<\/p>\n
        \u521b\u5efaPlotly\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
        trace = go.Scatter(x=x, y=y, mode='lines', name='y = sin(x)')<\/p>\n
        layout = go.Layout(title='Interactive Function Plot', xaxis=dict(title='x'), yaxis=dict(title='f(x)'))<\/p>\n
        fig = go.Figure(data=[trace], layout=layout)<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        fig.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u901a\u8fc7\u521b\u5efaScatter<\/code>\u5bf9\u8c61\u5b9a\u4e49\u56fe\u5f62\u7684\u7c7b\u578b\u548c\u6570\u636e\uff0c\u7136\u540e\u901a\u8fc7Layout<\/code>\u5bf9\u8c61\u8bbe\u7f6e\u56fe\u5f62\u7684\u5e03\u5c40\u548c\u6807\u9898\u3002\u6700\u540e\uff0c\u4f7f\u7528Figure<\/code>\u5bf9\u8c61\u5c06\u6570\u636e\u548c\u5e03\u5c40\u7ec4\u5408\u5728\u4e00\u8d77\uff0c\u5e76\u8c03\u7528show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \u603b\u7ed3\uff1a<\/p>\n<\/p>\n
        \u7ed8\u5236\u51fd\u6570\u56fe\u662f\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u5e38\u89c1\u7684\u9700\u6c42\u4e4b\u4e00\u3002\u901a\u8fc7\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u9759\u6001\u548c\u52a8\u6001\u7684\u51fd\u6570\u56fe\u8868\u3002\u5728\u9009\u62e9\u4f7f\u7528\u54ea\u4e2a\u5e93\u65f6\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u573a\u666f\u8fdb\u884c\u5224\u65ad\u3002Matplotlib\u9002\u5408\u9759\u6001\u548c\u590d\u6742\u56fe\u8868\u7684\u7ed8\u5236\uff0cSeaborn\u5219\u66f4\u6ce8\u91cd\u7f8e\u89c2\u548c\u7b80\u4fbf\uff0cPlotly\u5219\u662f\u52a8\u6001\u4ea4\u4e92\u56fe\u8868\u7684\u6700\u4f73\u9009\u62e9\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u5e0c\u671b\u80fd\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u7ed8\u5236\u51fd\u6570\u56fe\u3002<\/p>\n<\/p>\n
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         \u4f7f\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
        \u5728Python\u4e2d\uff0c\u7ed8\u5236\u51fd\u6570\u56fe\u901a\u5e38\u4f7f\u7528Matplotlib\u5e93\uff0c\u8fd9\u662f\u4e00\u4e2a\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u7ed8\u56fe\u5e93\u3002\u9664\u4e86Matplotlib\uff0cNumPy\u5e93\u4e5f\u5f88\u5e38\u7528\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u6570\u503c\u8ba1\u7b97\uff0c\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u9ad8\u6548\u3002\u6b64\u5916\uff0cSciPy\u5e93\u5728\u5904\u7406\u6570\u5b66\u51fd\u6570\u548c\u6570\u636e\u65f6\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/p>\n
        \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u6765\u5c55\u793a\u51fd\u6570\uff1f<\/strong>
        \u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u53d6\u51b3\u4e8e\u4f60\u60f3\u8981\u8868\u8fbe\u7684\u5185\u5bb9\u3002\u4f8b\u5982\uff0c\u5982\u679c\u662f\u5c55\u793a\u51fd\u6570\u7684\u8fde\u7eed\u53d8\u5316\uff0c\u6298\u7ebf\u56fe\u6216\u66f2\u7ebf\u56fe\u662f\u7406\u60f3\u9009\u62e9\uff1b\u5982\u679c\u9700\u8981\u6bd4\u8f83\u4e0d\u540c\u51fd\u6570\u7684\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u6761\u66f2\u7ebf\u5728\u540c\u4e00\u5750\u6807\u7cfb\u4e2d\u5c55\u793a\u3002\u5bf9\u4e8e\u79bb\u6563\u6570\u636e\uff0c\u6563\u70b9\u56fe\u4f1a\u66f4\u5408\u9002\u3002\u4e86\u89e3\u4e0d\u540c\u56fe\u5f62\u7684\u7279\u70b9\u53ef\u4ee5\u5e2e\u52a9\u66f4\u6e05\u6670\u5730\u4f20\u8fbe\u4fe1\u606f\u3002<\/p>\n
        \u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u6837\u5f0f\u548c\u6807\u7b7e\uff1f<\/strong>
        \u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u6539\u53d8\u7ebf\u6761\u7684\u989c\u8272\u3001\u7c7b\u578b\u548c\u5bbd\u5ea6\uff0c\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u3001\u5750\u6807\u8f74\u6807\u7b7e\u4ee5\u53ca\u56fe\u4f8b\u3002\u4f7f\u7528plt.title()<\/code>, plt.xlabel()<\/code>, plt.ylabel()<\/code>, plt.legend()<\/code>\u7b49\u51fd\u6570\u53ef\u4ee5\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\uff0c\u8ba9\u89c2\u4f17\u66f4\u5bb9\u6613\u7406\u89e3\u56fe\u8868\u6240\u4f20\u8fbe\u7684\u4fe1\u606f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3002\u8fd9\u4e9b\u5de5\u5177 […]","protected":false},"author":3,"featured_media":922888,"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\/922885"}],"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=922885"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/922885\/revisions"}],"predecessor-version":[{"id":922890,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/922885\/revisions\/922890"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/922888"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=922885"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=922885"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=922885"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}