{"id":1093383,"date":"2025-01-08T14:30:36","date_gmt":"2025-01-08T06:30:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1093383.html"},"modified":"2025-01-08T14:30:39","modified_gmt":"2025-01-08T06:30:39","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e5%87%a0%e4%b8%87%e6%9d%a1%e6%9b%b2%e7%ba%bf-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1093383.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24210015\/fdbbc4a1-9f31-4b52-824d-db394b9f6bd0.webp\" alt=\"python\u5982\u4f55\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\" \/><\/p>\n<p><p> <strong>Python\u5982\u4f55\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf<\/strong><\/p>\n<\/p>\n<p><p><strong>\u4f7f\u7528Python\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5e38\u7528\u7684\u6709\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Plotly\u5e93\u3001\u4f7f\u7528Bokeh\u5e93\u3001\u4f7f\u7528Holoviews\u5e93\u3002<\/strong>\u8fd9\u4e9b\u5e93\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u5176\u4e2dMatplotlib\u5e93\u4ee5\u5176\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u5e7f\u6cdb\u7684\u5e94\u7528\u800c\u6700\u4e3a\u5e38\u7528\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u63d0\u9ad8\u7ed8\u56fe\u6027\u80fd\uff0c\u5904\u7406\u5927\u6570\u636e\u91cf\u65f6\uff0c\u6211\u4eec\u9700\u8981\u6ce8\u610f\u51e0\u4e2a\u65b9\u9762\uff1a\u4f18\u5316\u6570\u636e\u5904\u7406\u6d41\u7a0b\u3001\u51cf\u5c11\u4e0d\u5fc5\u8981\u7684\u7ed8\u56fe\u5143\u7d20\u3001\u4f7f\u7528\u5408\u9002\u7684\u7ed8\u56fe\u5de5\u5177\u548c\u65b9\u6cd5\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u53ef\u4ee5\u91c7\u7528\u4ee5\u4e0b\u7b56\u7565\uff1a<\/p>\n<\/p>\n<p><p><strong>\u4e00\u3001\u4f7f\u7528Matplotlib\u5e93<\/strong><\/p>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u529f\u80fd\u975e\u5e38\u5f3a\u5927\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u5b83\u7684\u57fa\u672c\u7528\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>y = np.sin(x)<\/p>\n<h2><strong>\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n<p>plt.plot(x, y)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u751f\u6210\u548c\u7ed8\u5236\u4e00\u6761\u66f2\u7ebf\u3002\u5982\u679c\u6211\u4eec\u8981\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528\u5faa\u73af\u751f\u6210\u591a\u4e2a\u6570\u636e\u96c6\uff0c\u5e76\u9010\u4e00\u7ed8\u5236\u5b83\u4eec\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>n_curves = 10000<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n<p>fig, ax = plt.subplots()<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for i in range(n_curves):<\/p>\n<p>    y = np.sin(x + i * 0.01)  # \u6bcf\u6761\u66f2\u7ebf\u6709\u4e0d\u540c\u7684\u504f\u79fb\u91cf<\/p>\n<p>    ax.plot(x, y)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u5faa\u73af\u751f\u6210\u4e8610000\u6761\u66f2\u7ebf\uff0c\u5e76\u5c06\u5b83\u4eec\u7ed8\u5236\u5728\u540c\u4e00\u5f20\u56fe\u4e0a\u3002<\/p>\n<\/p>\n<p><p><strong>\u4f18\u5316\u6570\u636e\u5904\u7406\u6d41\u7a0b<\/strong><\/p>\n<\/p>\n<p><p>\u7ed8\u5236\u5927\u91cf\u6570\u636e\u65f6\uff0c\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u81f3\u5173\u91cd\u8981\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u7b49\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u5e93\u6765\u751f\u6210\u548c\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>n_curves = 10000<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n<p>fig, ax = plt.subplots()<\/p>\n<h2><strong>\u4f7f\u7528NumPy\u6570\u7ec4\u6279\u91cf\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>y = np.sin(x[:, None] + np.arange(n_curves) * 0.01)<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>ax.plot(x, y)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u6570\u7ec4\u6279\u91cf\u751f\u6210\u6570\u636e\uff0c\u5e76\u4e00\u6b21\u6027\u7ed8\u5236\u6240\u6709\u66f2\u7ebf\u3002\u8fd9\u79cd\u65b9\u6cd5\u6bd4\u5faa\u73af\u7ed8\u5236\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/p>\n<p><p><strong>\u4e8c\u3001\u4f7f\u7528Plotly\u5e93<\/strong><\/p>\n<\/p>\n<p><p>Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u9002\u7528\u4e8eWeb\u5e94\u7528\u7a0b\u5e8f\u3002\u5b83\u7684\u57fa\u672c\u7528\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import plotly.graph_objects as go<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>n_curves = 10000<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n<p>fig = go.Figure()<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for i in range(n_curves):<\/p>\n<p>    y = np.sin(x + i * 0.01)<\/p>\n<p>    fig.add_trace(go.Scatter(x=x, y=y, mode=&#39;lines&#39;))<\/p>\n<p>fig.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Plotly\u7684\u7ed8\u56fe\u6027\u80fd\u8f83\u597d\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u91cf\u7684\u7ed8\u56fe\u4efb\u52a1\u3002\u540c\u65f6\uff0cPlotly\u751f\u6210\u7684\u56fe\u8868\u662f\u4ea4\u4e92\u5f0f\u7684\uff0c\u53ef\u4ee5\u8fdb\u884c\u7f29\u653e\u3001\u5e73\u79fb\u7b49\u64cd\u4f5c\uff0c\u975e\u5e38\u9002\u5408Web\u5e94\u7528\u3002<\/p>\n<\/p>\n<p><p><strong>\u4e09\u3001\u4f7f\u7528Bokeh\u5e93<\/strong><\/p>\n<\/p>\n<p><p>Bokeh\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5927\u6570\u636e\u91cf\u7684\u53ef\u89c6\u5316\u3002\u5b83\u7684\u57fa\u672c\u7528\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from bokeh.plotting import figure, show<\/p>\n<p>from bokeh.io import output_notebook<\/p>\n<p>import numpy as np<\/p>\n<p>output_notebook()<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>n_curves = 10000<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n<p>p = figure()<\/p>\n<h2><strong>\u7ed8\u5236\u591a\u6761\u66f2\u7ebf<\/strong><\/h2>\n<p>for i in range(n_curves):<\/p>\n<p>    y = np.sin(x + i * 0.01)<\/p>\n<p>    p.line(x, y)<\/p>\n<p>show(p)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Bokeh\u751f\u6210\u7684\u56fe\u8868\u4e5f\u662f\u4ea4\u4e92\u5f0f\u7684\uff0c\u9002\u7528\u4e8eWeb\u5e94\u7528\u3002\u4e0ePlotly\u7c7b\u4f3c\uff0cBokeh\u4e5f\u5177\u6709\u8f83\u597d\u7684\u7ed8\u56fe\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><p><strong>\u56db\u3001\u4f7f\u7528Holoviews\u5e93<\/strong><\/p>\n<\/p>\n<p><p>Holoviews\u662f\u4e00\u4e2a\u7528\u4e8e\u7b80\u5316\u6570\u636e\u53ef\u89c6\u5316\u7684\u5e93\uff0c\u652f\u6301\u591a\u79cd\u7ed8\u56fe\u5e93\uff08\u5982Matplotlib\u3001Bokeh\u7b49\uff09\u3002\u5b83\u7684\u57fa\u672c\u7528\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import holoviews as hv<\/p>\n<p>import numpy as np<\/p>\n<p>hv.extension(&#39;bokeh&#39;)<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 1000)<\/p>\n<p>n_curves = 10000<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n<p>curves = [hv.Curve((x, np.sin(x + i * 0.01))) for i in range(n_curves)]<\/p>\n<p>overlay = hv.Overlay(curves)<\/p>\n<p>hv.show(overlay)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Holoviews\u7b80\u5316\u4e86\u7ed8\u56fe\u6d41\u7a0b\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u91cf\u7684\u53ef\u89c6\u5316\u4efb\u52a1\u3002\u5b83\u652f\u6301\u591a\u79cd\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5de5\u5177\u3002<\/p>\n<\/p>\n<p><p><strong>\u603b\u7ed3<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528Python\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Plotly\u3001Bokeh\u548cHoloviews\u3002\u5728\u5904\u7406\u5927\u6570\u636e\u91cf\u65f6\uff0c\u6211\u4eec\u9700\u8981\u4f18\u5316\u6570\u636e\u5904\u7406\u6d41\u7a0b\uff0c\u51cf\u5c11\u4e0d\u5fc5\u8981\u7684\u7ed8\u56fe\u5143\u7d20\uff0c\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5de5\u5177\u548c\u65b9\u6cd5\u3002\u901a\u8fc7\u5408\u7406\u4f7f\u7528\u8fd9\u4e9b\u5e93\u548c\u4f18\u5316\u7b56\u7565\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\uff0c\u5b9e\u73b0\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u9ad8\u6548\u7ed8\u5236\u5927\u91cf\u66f2\u7ebf\uff1f<\/strong><br \/>\u7ed8\u5236\u51e0\u4e07\u6761\u66f2\u7ebf\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6027\u80fd\u95ee\u9898\uff0c\u56e0\u6b64\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u4e00\u4e9b\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93\uff0c\u5982Matplotlib\u6216Plotly\u3002\u786e\u4fdd\u4f60\u4f7f\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u6765\u5b58\u50a8\u548c\u5904\u7406\u6570\u636e\uff0c\u6bd4\u5982NumPy\u6570\u7ec4\uff0c\u8fd9\u6837\u80fd\u663e\u8457\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002\u6b64\u5916\uff0c\u9488\u5bf9\u7ed8\u5236\u7684\u590d\u6742\u5ea6\uff0c\u53ef\u4ee5\u9009\u62e9\u53ea\u7ed8\u5236\u90e8\u5206\u66f2\u7ebf\uff0c\u6216\u4f7f\u7528\u805a\u5408\u6280\u672f\u6765\u7b80\u5316\u663e\u793a\u3002<\/p>\n<p><strong>\u5728\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u65f6\uff0c\u5982\u4f55\u63a7\u5236\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\uff1f<\/strong><br 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