{"id":1185292,"date":"2025-01-15T19:38:16","date_gmt":"2025-01-15T11:38:16","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1185292.html"},"modified":"2025-01-15T19:38:19","modified_gmt":"2025-01-15T11:38:19","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e6%b5%81%e5%9c%ba%e4%ba%91%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1185292.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u6d41\u573a\u4e91\u56fe"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25134457\/49346fad-cff5-4263-8c08-987e7dcc3df4.webp\" alt=\"python\u5982\u4f55\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\" \/><\/p>\n<p><p> <strong>Python\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u51c6\u5907\u6570\u636e\u3001\u9009\u62e9\u5e93\u3001\u8bbe\u7f6e\u753b\u5e03\u3001\u7ed8\u5236\u6d41\u7ebf\u3001\u6dfb\u52a0\u989c\u8272\u3002<\/strong> \u5176\u4e2d\uff0c<strong>\u9009\u62e9\u5e93<\/strong>\u662f\u5173\u952e\u6b65\u9aa4\u4e4b\u4e00\uff0c\u56e0\u4e3a\u4e0d\u540c\u7684\u7ed8\u56fe\u5e93\u6709\u4e0d\u540c\u7684\u529f\u80fd\u548c\u7279\u70b9\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u63cf\u8ff0\u4f7f\u7528Python\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u7684\u5177\u4f53\u6b65\u9aa4\u548c\u5b9e\u73b0\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u9009\u62e9\u5e93<\/h3>\n<\/p>\n<p><p>Python\u4e2d\u6709\u591a\u79cd\u5e93\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\uff0c\u5e38\u7528\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002\u8fd9\u4e9b\u5e93\u5404\u6709\u4f18\u7f3a\u70b9\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>Matplotlib<\/strong>\uff1a\u4f5c\u4e3aPython\u7684\u57fa\u7840\u7ed8\u56fe\u5e93\uff0c\u529f\u80fd\u5f3a\u5927\uff0c\u9002\u7528\u4e8e\u9759\u6001\u56fe\u8868\u7684\u7ed8\u5236\u3002<\/li>\n<li><strong>Seaborn<\/strong>\uff1a\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u9ed8\u8ba4\u7f8e\u89c2\u7684\u6837\u5f0f\u3002<\/li>\n<li><strong>Plotly<\/strong>\uff1a\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u7528\u4e8e\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u573a\u666f\u3002<\/li>\n<\/ol>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c<strong>Matplotlib<\/strong>\u662f\u6700\u5e38\u7528\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u7075\u6d3b\u6027\u9ad8\u3001\u4f7f\u7528\u5e7f\u6cdb\uff0c\u5e76\u4e14\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u5982NumPy\u548cPandas\u517c\u5bb9\u6027\u597d\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u5728\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u6d41\u573a\u6570\u636e\u3002\u6d41\u573a\u6570\u636e\u901a\u5e38\u5305\u542b\u901f\u5ea6\u573a\u7684\u77e2\u91cf\u4fe1\u606f\uff0c\u5373\u6bcf\u4e2a\u70b9\u4e0a\u7684\u901f\u5ea6\u5206\u91cf\uff08u, v\uff09\u3002\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u4ece\u5b9e\u9a8c\u6d4b\u91cf\u3001\u6570\u503c\u6a21\u62df\u6216\u5176\u4ed6\u6765\u6e90\u83b7\u5f97\u3002<\/p>\n<\/p>\n<p><p>\u793a\u4f8b\u6570\u636e\u51c6\u5907\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u7f51\u683c\u70b9<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 100)<\/p>\n<p>y = np.linspace(0, 10, 100)<\/p>\n<p>X, Y = np.meshgrid(x, y)<\/p>\n<h2><strong>\u5b9a\u4e49\u901f\u5ea6\u573a<\/strong><\/h2>\n<p>U = np.sin(X) * np.cos(Y)<\/p>\n<p>V = -np.cos(X) * np.sin(Y)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u8bbe\u7f6e\u753b\u5e03<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528Matplotlib\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u65f6\uff0c\u9996\u5148\u9700\u8981\u8bbe\u7f6e\u753b\u5e03\u3002\u753b\u5e03\u662f\u56fe\u50cf\u7684\u57fa\u7840\uff0c\u6240\u6709\u7684\u56fe\u5f62\u5143\u7d20\u90fd\u5c06\u5728\u753b\u5e03\u4e0a\u7ed8\u5236\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>fig, ax = plt.subplots(figsize=(10, 6))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u7ed8\u5236\u6d41\u7ebf<\/h3>\n<\/p>\n<p><p>\u6d41\u7ebf\u56fe\u662f\u6d41\u573a\u53ef\u89c6\u5316\u7684\u5e38\u89c1\u65b9\u6cd5\u4e4b\u4e00\uff0c\u5b83\u901a\u8fc7\u6d41\u7ebf\u5c55\u793a\u901f\u5ea6\u573a\u7684\u65b9\u5411\u548c\u5f3a\u5ea6\u3002Matplotlib\u63d0\u4f9b\u4e86<code>streamplot<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6d41\u7ebf\u56fe\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">strm = ax.streamplot(X, Y, U, V, color=&#39;b&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e94\u3001\u6dfb\u52a0\u989c\u8272<\/h3>\n<\/p>\n<p><p>\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u901f\u5ea6\u573a\u7684\u5f3a\u5ea6\uff0c\u53ef\u4ee5\u4e3a\u6d41\u7ebf\u6dfb\u52a0\u989c\u8272\u3002\u989c\u8272\u53ef\u4ee5\u6839\u636e\u901f\u5ea6\u7684\u5927\u5c0f\u6765\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">speed = np.sqrt(U&lt;strong&gt;2 + V&lt;\/strong&gt;2)<\/p>\n<p>strm = ax.streamplot(X, Y, U, V, color=speed, linewidth=2, cmap=&#39;viridis&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516d\u3001\u6dfb\u52a0\u5176\u4ed6\u56fe\u5f62\u5143\u7d20<\/h3>\n<\/p>\n<p><p>\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u8bfb\uff0c\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u3001\u8f74\u6807\u7b7e\u3001\u989c\u8272\u6761\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">ax.set_title(&#39;\u6d41\u573a\u4e91\u56fe&#39;)<\/p>\n<p>ax.set_xlabel(&#39;X \u65b9\u5411&#39;)<\/p>\n<p>ax.set_ylabel(&#39;Y \u65b9\u5411&#39;)<\/p>\n<p>fig.colorbar(strm.lines)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e03\u3001\u4f18\u5316\u548c\u4fdd\u5b58\u56fe\u8868<\/h3>\n<\/p>\n<p><p>\u5728\u751f\u6210\u56fe\u8868\u540e\uff0c\u53ef\u4ee5\u5bf9\u56fe\u8868\u8fdb\u884c\u4f18\u5316\uff0c\u4f8b\u5982\u8c03\u6574\u989c\u8272\u6761\u7684\u4f4d\u7f6e\u3001\u8bbe\u7f6e\u56fe\u8868\u7684\u5206\u8fa8\u7387\u7b49\u3002\u6700\u540e\uff0c\u53ef\u4ee5\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u7247\u6587\u4ef6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">fig.tight_layout()<\/p>\n<p>fig.savefig(&#39;\u6d41\u573a\u4e91\u56fe.png&#39;, dpi=300)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516b\u3001\u4ee3\u7801\u793a\u4f8b<\/h3>\n<\/p>\n<p><p>\u5b8c\u6574\u7684\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u7f51\u683c\u70b9<\/strong><\/h2>\n<p>x = np.linspace(0, 10, 100)<\/p>\n<p>y = np.linspace(0, 10, 100)<\/p>\n<p>X, Y = np.meshgrid(x, y)<\/p>\n<h2><strong>\u5b9a\u4e49\u901f\u5ea6\u573a<\/strong><\/h2>\n<p>U = np.sin(X) * np.cos(Y)<\/p>\n<p>V = -np.cos(X) * np.sin(Y)<\/p>\n<h2><strong>\u8bbe\u7f6e\u753b\u5e03<\/strong><\/h2>\n<p>fig, ax = plt.subplots(figsize=(10, 6))<\/p>\n<h2><strong>\u8ba1\u7b97\u901f\u5ea6\u5927\u5c0f<\/strong><\/h2>\n<p>speed = np.sqrt(U&lt;strong&gt;2 + V&lt;\/strong&gt;2)<\/p>\n<h2><strong>\u7ed8\u5236\u6d41\u7ebf\u56fe<\/strong><\/h2>\n<p>strm = ax.streamplot(X, Y, U, V, color=speed, linewidth=2, cmap=&#39;viridis&#39;)<\/p>\n<h2><strong>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n<p>ax.set_title(&#39;\u6d41\u573a\u4e91\u56fe&#39;)<\/p>\n<p>ax.set_xlabel(&#39;X \u65b9\u5411&#39;)<\/p>\n<p>ax.set_ylabel(&#39;Y \u65b9\u5411&#39;)<\/p>\n<h2><strong>\u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n<p>fig.colorbar(strm.lines)<\/p>\n<h2><strong>\u4f18\u5316\u5e03\u5c40\u5e76\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n<p>fig.tight_layout()<\/p>\n<p>fig.savefig(&#39;\u6d41\u573a\u4e91\u56fe.png&#39;, dpi=300)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u662f\u6570\u636e\u53ef\u89c6\u5316\u7684\u91cd\u8981\u624b\u6bb5\uff0c\u901a\u8fc7\u6d41\u7ebf\u548c\u989c\u8272\u5c55\u793a\u901f\u5ea6\u573a\u7684\u65b9\u5411\u548c\u5f3a\u5ea6\u3002\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u4f7f\u7528Python\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u7684\u6b65\u9aa4\uff0c\u5305\u62ec\u51c6\u5907\u6570\u636e\u3001\u9009\u62e9\u5e93\u3001\u8bbe\u7f6e\u753b\u5e03\u3001\u7ed8\u5236\u6d41\u7ebf\u3001\u6dfb\u52a0\u989c\u8272\u7b49\u3002<strong>Matplotlib<\/strong>\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7075\u6d3b\u7684\u63a5\u53e3\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u53ef\u89c6\u5316\u4efb\u52a1\u3002\u901a\u8fc7\u4f18\u5316\u548c\u4fdd\u5b58\u56fe\u8868\uff0c\u53ef\u4ee5\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u56fe\u7247\uff0c\u4fbf\u4e8e\u5c55\u793a\u548c\u5206\u4eab\u3002\u5e0c\u671b\u672c\u6587\u80fd\u4e3a\u4f60\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u8fdb\u884c\u6d41\u573a\u4e91\u56fe\u7684\u7ed8\u5236\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u6d41\u573a\u4e91\u56fe\u662f\u4ec0\u4e48\uff0c\u5b83\u6709\u4ec0\u4e48\u5e94\u7528\uff1f<\/strong><br \/>\u6d41\u573a\u4e91\u56fe\u662f\u7528\u4e8e\u53ef\u89c6\u5316\u6d41\u4f53\u8fd0\u52a8\u7684\u4e00\u79cd\u56fe\u5f62\u8868\u793a\u65b9\u5f0f\uff0c\u901a\u5e38\u7528\u4e8e\u5de5\u7a0b\u3001\u6c14\u8c61\u3001\u822a\u7a7a\u822a\u5929\u7b49\u9886\u57df\u3002\u901a\u8fc7\u6d41\u573a\u4e91\u56fe\uff0c\u80fd\u591f\u76f4\u89c2\u5730\u89c2\u5bdf\u6d41\u4f53\u7684\u901f\u5ea6\u3001\u65b9\u5411\u548c\u5206\u5e03\u60c5\u51b5\uff0c\u5e2e\u52a9\u5de5\u7a0b\u5e08\u548c\u79d1\u5b66\u5bb6\u5206\u6790\u6d41\u4f53\u884c\u4e3a\uff0c\u4f18\u5316\u8bbe\u8ba1\u548c\u9884\u6d4b\u6c14\u8c61\u53d8\u5316\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong><br \/>\u7ed8\u5236\u6d41\u573a\u4e91\u56fe\u901a\u5e38\u9700\u8981\u4f7f\u7528\u4e00\u4e9b\u6570\u636e\u53ef\u89c6\u5316\u548c\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u5982Matplotlib\u3001NumPy\u548cSciPy\u3002Matplotlib\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u800cNumPy\u548cSciPy\u5219\u7528\u4e8e\u5904\u7406\u6d41\u4f53\u6570\u636e\u548c\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3002\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u6d41\u573a\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528Mayavi\u6216Plotly\u7b49\u5e93\u3002<\/p>\n<p><strong>\u5982\u4f55\u5904\u7406\u6d41\u573a\u6570\u636e\u4ee5\u4fbf\u7ed8\u5236\u4e91\u56fe\uff1f<\/strong><br 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