{"id":976036,"date":"2024-12-27T06:18:19","date_gmt":"2024-12-26T22:18:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/976036.html"},"modified":"2024-12-27T06:18:21","modified_gmt":"2024-12-26T22:18:21","slug":"python%e4%bd%9c%e5%9b%be%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e6%a0%87%e9%a2%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/976036.html","title":{"rendered":"python\u4f5c\u56fe\u5982\u4f55\u6dfb\u52a0\u6807\u9898"},"content":{"rendered":"

\"python\u4f5c\u56fe\u5982\u4f55\u6dfb\u52a0\u6807\u9898\"<\/p>\n

\u5728Python\u4e2d\u4f5c\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u6dfb\u52a0\u6807\u9898\u3001\u8bbe\u7f6e\u5b57\u4f53\u5927\u5c0f\u3001\u8c03\u6574\u4f4d\u7f6e\u7b49\u3002<\/strong>\u4f7f\u7528plt.title()<\/code>\u51fd\u6570\u53ef\u4ee5\u6dfb\u52a0\u548c\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6807\u9898\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7plt.title("Your Title", fontsize=16, loc='center')<\/code>\u6765\u8bbe\u7f6e\u6807\u9898\u3001\u5b57\u4f53\u5927\u5c0f\u548c\u4f4d\u7f6e\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u56fe\u8868\u66f4\u5177\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\u6765\u6dfb\u52a0\u6807\u9898\uff0c\u4ee5\u53ca\u4e00\u4e9b\u76f8\u5173\u7684\u8bbe\u7f6e\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib\u5e93\u7b80\u4ecb<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u7528\u4e8e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u5176\u7075\u6d3b\u6027\u548c\u529f\u80fd\u5f3a\u5927\uff0c\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u9886\u57df\u7684\u9996\u9009\u5de5\u5177\u4e4b\u4e00\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165Matplotlib<\/h4>\n<\/p>\n
\u5728Python\u811a\u672c\u4e2d\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6dfb\u52a0\u6807\u9898\u57fa\u672c\u65b9\u6cd5<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u56fe\u8868\u65f6\uff0c\u6807\u9898\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u7ec4\u6210\u90e8\u5206\u3002\u5b83\u5e2e\u52a9\u89c2\u4f17\u5feb\u901f\u7406\u89e3\u56fe\u8868\u7684\u4e3b\u9898\u548c\u5185\u5bb9\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528plt.title()<\/code>\u51fd\u6570<\/h4>\n<\/p>\n
plt.title()<\/code>\u51fd\u6570\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u7528\u4e8e\u4e3a\u5f53\u524d\u56fe\u8868\u6dfb\u52a0\u6807\u9898\u3002\u5176\u57fa\u672c\u8bed\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
plt.title("Your Title", fontsize=16, loc='center')<\/p>\n
<\/code><\/pre>\n<\/p>\n
    \n
  • "Your Title"<\/code>\uff1a\u5b57\u7b26\u4e32\u5f62\u5f0f\u7684\u56fe\u8868\u6807\u9898\u3002<\/li>\n
  • fontsize<\/code>\uff1a\u53ef\u9009\u53c2\u6570\uff0c\u7528\u4e8e\u8bbe\u7f6e\u6807\u9898\u7684\u5b57\u4f53\u5927\u5c0f\u3002<\/li>\n
  • loc<\/code>\uff1a\u53ef\u9009\u53c2\u6570\uff0c\u7528\u4e8e\u8bbe\u7f6e\u6807\u9898\u7684\u4f4d\u7f6e\uff0c\u9ed8\u8ba4\u503c\u4e3a'center'<\/code>\u3002\u53ef\u4ee5\u9009\u62e9'left'<\/code>\u3001'right'<\/code>\u6216'center'<\/code>\u3002<\/li>\n<\/ul>\n
    2\u3001\u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4e3a\u6298\u7ebf\u56fe\u6dfb\u52a0\u6807\u9898\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    x = [1, 2, 3, 4, 5]<\/p>\n
    y = [2, 3, 5, 7, 11]<\/p>\n
    plt.plot(x, y)<\/p>\n
    plt.title("Prime Numbers", fontsize=16, loc='center')<\/p>\n
    plt.xlabel("Index")<\/p>\n
    plt.ylabel("Value")<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff0c\u56fe\u8868\u7684\u6807\u9898\u4e3a\u201cPrime Numbers\u201d\u3002<\/p>\n<\/p>\n
    \u4e09\u3001\u6807\u9898\u7684\u9ad8\u7ea7\u8bbe\u7f6e<\/h3>\n<\/p>\n
    \u9664\u4e86\u57fa\u672c\u7684\u6807\u9898\u8bbe\u7f6e\u4e4b\u5916\uff0cMatplotlib\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u9009\u9879\uff0c\u4f7f\u5f97\u6807\u9898\u7684\u6837\u5f0f\u66f4\u52a0\u4e30\u5bcc\u3002<\/p>\n<\/p>\n
    1\u3001\u4f7f\u7528fontdict<\/code>\u53c2\u6570<\/h4>\n<\/p>\n
    fontdict<\/code>\u53c2\u6570\u53ef\u4ee5\u7528\u4e8e\u66f4\u8be6\u7ec6\u5730\u63a7\u5236\u6807\u9898\u7684\u5b57\u4f53\u5c5e\u6027\uff0c\u5305\u62ec\u5b57\u4f53\u7c7b\u578b\u3001\u989c\u8272\u3001\u52a0\u7c97\u7b49\uff1a<\/p>\n<\/p>\n
    title_font = {'fontsize': 16, 'fontweight': 'bold', 'color': 'blue'}<\/p>\n
    plt.title("Advanced Title", fontdict=title_font)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u4f7f\u7528position<\/code>\u53c2\u6570<\/h4>\n<\/p>\n
    position<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6807\u9898\u5728\u56fe\u8868\u4e0a\u7684\u786e\u5207\u4f4d\u7f6e\uff0c\u901a\u8fc7\u4e00\u4e2a\u4e8c\u5143\u7ec4\u6765\u8868\u793a\u4f4d\u7f6e\uff0c\u5982(x, y)<\/code>\uff0c\u5176\u4e2dx\u548cy\u7684\u53d6\u503c\u8303\u56f4\u57280\u52301\u4e4b\u95f4\uff1a<\/p>\n<\/p>\n
    plt.title("Custom Position", position=(0.5, 0.95))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u6807\u9898\u591a\u884c\u6587\u672c<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u901a\u8fc7\u6362\u884c\u7b26\\n<\/code>\u6765\u521b\u5efa\u591a\u884c\u6807\u9898\uff1a<\/p>\n<\/p>\n
    plt.title("Line 1\\nLine 2", fontsize=12)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u5728\u5b50\u56fe\u4e2d\u6dfb\u52a0\u6807\u9898<\/h3>\n<\/p>\n
    \u5728\u591a\u5b50\u56fe\u60c5\u5883\u4e2d\uff0c\u4e3a\u6bcf\u4e2a\u5b50\u56fe\u5206\u522b\u6dfb\u52a0\u6807\u9898\u662f\u5f88\u5e38\u89c1\u7684\u9700\u6c42\u3002Matplotlib\u4e2d\u7684subplot<\/code>\u529f\u80fd\u4f7f\u5f97\u8fd9\u4e00\u4efb\u52a1\u53d8\u5f97\u7b80\u5355\u3002<\/p>\n<\/p>\n
    1\u3001\u521b\u5efa\u5b50\u56fe<\/h4>\n<\/p>\n
    \u9996\u5148\uff0c\u901a\u8fc7plt.subplot()<\/code>\u51fd\u6570\u521b\u5efa\u591a\u4e2a\u5b50\u56fe\uff1a<\/p>\n<\/p>\n
    fig, axs = plt.subplots(2, 2)  # \u521b\u5efa2x2\u7684\u5b50\u56fe<\/p>\n
    \u7ed8\u5236\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
    axs[0, 0].plot(x, y)<\/p>\n
    axs[0, 0].set_title("Subplot 1")<\/p>\n
    \u7ed8\u5236\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
    axs[0, 1].plot(x, y)<\/p>\n
    axs[0, 1].set_title("Subplot 2")<\/p>\n
    \u7ed8\u5236\u7b2c\u4e09\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
    axs[1, 0].plot(x, y)<\/p>\n
    axs[1, 0].set_title("Subplot 3")<\/p>\n
    \u7ed8\u5236\u7b2c\u56db\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
    axs[1, 1].plot(x, y)<\/p>\n
    axs[1, 1].set_title("Subplot 4")<\/p>\n
    plt.tight_layout()  # \u81ea\u52a8\u8c03\u6574\u5b50\u56fe\u4e4b\u95f4\u7684\u95f4\u8ddd<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u6dfb\u52a0\u5168\u5c40\u6807\u9898<\/h4>\n<\/p>\n
    \u5982\u679c\u9700\u8981\u4e3a\u6574\u4e2a\u56fe\u5f62\u6dfb\u52a0\u4e00\u4e2a\u5168\u5c40\u6807\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528fig.suptitle()<\/code>\uff1a<\/p>\n<\/p>\n
    fig.suptitle("Global Title for All Subplots", fontsize=16)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u7ed3\u5408\u5176\u4ed6\u5e93\u589e\u5f3a\u56fe\u8868\u6807\u9898<\/h3>\n<\/p>\n
    \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u5176\u4ed6\u5e93\u6765\u589e\u5f3a\u56fe\u8868\u7684\u529f\u80fd\u548c\u7f8e\u89c2\u6027\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Seaborn\u6216Plotly\u7b49\u5e93\u53ef\u4ee5\u521b\u5efa\u66f4\u5177\u5438\u5f15\u529b\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
    1\u3001\u4f7f\u7528Seaborn<\/h4>\n<\/p>\n
    Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7f8e\u89c2\u548c\u9ad8\u7ea7\u7684\u56fe\u8868\u6837\u5f0f\u3002<\/p>\n<\/p>\n
    import seaborn as sns<\/p>\n
    sns.set_theme(style="darkgrid")<\/p>\n
    plt.figure(figsize=(8, 6))<\/p>\n
    sns.lineplot(x=x, y=y)<\/p>\n
    plt.title("Seaborn Styled Title", fontsize=16)<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u4f7f\u7528Plotly<\/h4>\n<\/p>\n
    Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u56fe\u8868\u5e93\uff0c\u9002\u5408\u9700\u8981\u52a8\u6001\u4ea4\u4e92\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
    import plotly.express as px<\/p>\n
    df = px.data.iris()<\/p>\n
    fig = px.scatter(df, x="sepal_width", y="sepal_length", title="Interactive Title")<\/p>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u5e38\u89c1\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6848<\/h3>\n<\/p>\n
    \u5728\u4f7f\u7528Matplotlib\u6dfb\u52a0\u6807\u9898\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\uff1a<\/p>\n<\/p>\n
    1\u3001\u6807\u9898\u88ab\u88c1\u526a<\/h4>\n<\/p>\n
    \u5f53\u6807\u9898\u8fc7\u957f\u6216\u4f4d\u7f6e\u8bbe\u7f6e\u4e0d\u5f53\u65f6\uff0c\u53ef\u80fd\u4f1a\u88ab\u88c1\u526a\u3002\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574plt.tight_layout()<\/code>\u6216\u589e\u52a0\u56fe\u8868\u7684figsize<\/code>\u6765\u89e3\u51b3\uff1a<\/p>\n<\/p>\n
    plt.figure(figsize=(10, 8))<\/p>\n
    plt.tight_layout(pad=2.0)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u5b57\u4f53\u4e0d\u652f\u6301\u7279\u5b9a\u5b57\u7b26<\/h4>\n<\/p>\n
    \u6709\u65f6\uff0c\u9ed8\u8ba4\u5b57\u4f53\u53ef\u80fd\u4e0d\u652f\u6301\u67d0\u4e9b\u7279\u6b8a\u5b57\u7b26\u6216\u8bed\u8a00\u3002\u53ef\u4ee5\u901a\u8fc7rcParams<\/code>\u6765\u8bbe\u7f6e\u5b57\u4f53\uff1a<\/p>\n<\/p>\n
    plt.rcParams['font.family'] = 'DejaVu Sans'<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u9898\u662f\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u4e00\u4e2a\u57fa\u672c\u800c\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u9898\uff0c\u4ee5\u53ca\u5982\u4f55\u5229\u7528\u9ad8\u7ea7\u8bbe\u7f6e\u548c\u7ed3\u5408\u5176\u4ed6\u5e93\u6765\u589e\u5f3a\u56fe\u8868\u7684\u7f8e\u89c2\u6027\u548c\u529f\u80fd\u6027\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u5355\u56fe\u8fd8\u662f\u590d\u6742\u7684\u591a\u5b50\u56fe\uff0cMatplotlib\u90fd\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u89e3\u51b3\u65b9\u6848\u6765\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u638c\u63e1Python\u4f5c\u56fe\u4e2d\u7684\u6807\u9898\u8bbe\u7f6e\u6280\u5de7\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u9898\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u8f7b\u677e\u5730\u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u9898\u3002\u4f7f\u7528plt.title("\u4f60\u7684\u6807\u9898")<\/code>\u51fd\u6570\u5373\u53ef\u8bbe\u7f6e\u6807\u9898\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u56fe\u5f62\u4e4b\u524d\u8c03\u7528\u8be5\u51fd\u6570\uff0c\u8fd9\u6837\u6807\u9898\u624d\u80fd\u6b63\u786e\u663e\u793a\u3002<\/p>\n
    \u662f\u5426\u53ef\u4ee5\u4e3a\u56fe\u8868\u6dfb\u52a0\u526f\u6807\u9898\uff1f<\/strong>
    \u662f\u7684\uff0cMatplotlib\u5141\u8bb8\u60a8\u4e3a\u56fe\u8868\u6dfb\u52a0\u526f\u6807\u9898\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528plt.suptitle("\u526f\u6807\u9898")<\/code>\u65b9\u6cd5\u5728\u4e3b\u6807\u9898\u4e0b\u65b9\u6dfb\u52a0\u526f\u6807\u9898\uff0c\u4ece\u800c\u66f4\u597d\u5730\u8bf4\u660e\u56fe\u8868\u5185\u5bb9\u3002<\/p>\n
    \u5982\u4f55\u81ea\u5b9a\u4e49\u6807\u9898\u7684\u5b57\u4f53\u548c\u989c\u8272\uff1f<\/strong>
    \u60a8\u53ef\u4ee5\u901a\u8fc7\u5728plt.title()<\/code>\u51fd\u6570\u4e2d\u6dfb\u52a0\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u6807\u9898\u7684\u5b57\u4f53\u3001\u989c\u8272\u548c\u5927\u5c0f\u3002\u4f8b\u5982\uff0cplt.title("\u4f60\u7684\u6807\u9898", fontsize=14, color='blue', fontweight='bold')<\/code>\u5c06\u8bbe\u7f6e\u6807\u9898\u4e3a\u84dd\u8272\u3001\u52a0\u7c97\uff0c\u5e76\u5c06\u5b57\u4f53\u5927\u5c0f\u8bbe\u7f6e\u4e3a14\u3002\u8fd9\u6837\u53ef\u4ee5\u4f7f\u6807\u9898\u66f4\u52a0\u7a81\u51fa\uff0c\u589e\u5f3a\u53ef\u8bfb\u6027\u3002<\/p>\n
    \u5728\u6dfb\u52a0\u6807\u9898\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u9519\u8bef\u9700\u8981\u907f\u514d\uff1f<\/strong>
    \u5e38\u89c1\u9519\u8bef\u5305\u62ec\u5728\u7ed8\u56fe\u4e4b\u524d\u6dfb\u52a0\u6807\u9898\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u6807\u9898\u65e0\u6cd5\u663e\u793a\uff1b\u672a\u6307\u5b9a\u6807\u9898\u6587\u672c\u5185\u5bb9\uff1b\u4ee5\u53ca\u4f7f\u7528\u4e0d\u652f\u6301\u7684\u53c2\u6570\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u56fe\u5f62\u540e\u518d\u6dfb\u52a0\u6807\u9898\uff0c\u5e76\u68c0\u67e5\u53c2\u6570\u662f\u5426\u6b63\u786e\uff0c\u4ee5\u907f\u514d\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u4f5c\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u6dfb\u52a0\u6807\u9898\u3001\u8bbe\u7f6e\u5b57\u4f53\u5927\u5c0f\u3001\u8c03\u6574\u4f4d\u7f6e\u7b49\u3002\u4f7f\u7528plt.tit […]","protected":false},"author":3,"featured_media":976044,"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\/976036"}],"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=976036"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/976036\/revisions"}],"predecessor-version":[{"id":976045,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/976036\/revisions\/976045"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/976044"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=976036"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=976036"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=976036"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}