{"id":969482,"date":"2024-12-27T05:17:25","date_gmt":"2024-12-26T21:17:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/969482.html"},"modified":"2024-12-27T05:17:27","modified_gmt":"2024-12-26T21:17:27","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%be%93%e5%87%ba%e5%9b%be%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/969482.html","title":{"rendered":"\u5982\u4f55\u7528python\u8f93\u51fa\u56fe\u8868"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24183733\/d7908abc-41f9-44bf-a3e7-f86d92f590fa.webp\" alt=\"\u5982\u4f55\u7528python\u8f93\u51fa\u56fe\u8868\" \/><\/p>\n<p><p> \u5f00\u5934\u6bb5\u843d\uff1a<br \/>\u8981\u7528Python\u8f93\u51fa\u56fe\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a<strong>Matplotlib\u3001Seaborn\u3001Pandas\u7684\u7ed8\u56fe\u529f\u80fd\u3001Plotly\u3001Bokeh<\/strong>\u3002\u5176\u4e2d\uff0c<strong>Matplotlib\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93<\/strong>\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\u3002Matplotlib\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u7b80\u5355\u7684\u547d\u4ee4\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u4e3a\u4e86\u8f93\u51fa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\u3002\u63a5\u7740\uff0c\u901a\u8fc7\u5bfc\u5165\u5e93\u5e76\u4f7f\u7528\u5176\u63d0\u4f9b\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u6839\u636e\u6570\u636e\u751f\u6210\u6240\u9700\u7684\u56fe\u8868\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u56fe\u8868\u7684\u6807\u9898\u3001\u6807\u7b7e\u3001\u989c\u8272\u3001\u7ebf\u578b\u7b49\u53c2\u6570\uff0c\u8fdb\u4e00\u6b65\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u5916\u89c2\u3002\u638c\u63e1Matplotlib\u4e0d\u4ec5\u80fd\u5e2e\u52a9\u4f60\u5728\u6570\u636e\u5206\u6790\u4e2d\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\uff0c\u8fd8\u80fd\u63d0\u9ad8\u4f60\u5728Python\u7f16\u7a0b\u4e2d\u7684\u6280\u80fd\u6c34\u5e73\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001MATPLOTLIB\u7684\u5b89\u88c5\u4e0e\u57fa\u672c\u7528\u6cd5<\/p>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u8981\u4f7f\u7528Matplotlib\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u5b89\u88c5Matplotlib\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u3002\u6253\u5f00\u547d\u4ee4\u884c\u7ec8\u7aef\uff08Windows\u7cfb\u7edf\u4e2d\u53ef\u4ee5\u4f7f\u7528cmd\u6216PowerShell\uff0cmacOS\u548cLinux\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u7ec8\u7aef\uff09\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5373\u53ef\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\u5e76\u5f00\u59cb\u4f7f\u7528\u3002\u901a\u5e38\uff0c\u6211\u4eec\u4f1a\u5bfc\u5165Matplotlib\u7684pyplot\u6a21\u5757\uff0c\u5e76\u5c06\u5176\u91cd\u547d\u540d\u4e3aplt\uff0c\u65b9\u4fbf\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u5bfc\u5165\u5e93\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684\u57fa\u672c\u529f\u80fd\u6765\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u6570\u636e<\/strong><\/h2>\n<p>x = [1, 2, 3, 4, 5]<\/p>\n<p>y = [2, 3, 5, 7, 11]<\/p>\n<h2><strong>\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n<p>plt.plot(x, y)<\/p>\n<h2><strong>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n<p>plt.title(&#39;Simple Line Plot&#39;)<\/p>\n<p>plt.xlabel(&#39;x-axis&#39;)<\/p>\n<p>plt.ylabel(&#39;y-axis&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u4e2a\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Matplotlib\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff0c\u5e76\u4e3a\u56fe\u8868\u6dfb\u52a0\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001SEABORN\u589e\u5f3a\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n<p><p>Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u66f4\u9ad8\u7ea7\u548c\u66f4\u590d\u6742\u7684\u56fe\u8868\u7c7b\u578b\uff0c\u9002\u5408\u7528\u4e8e\u7edf\u8ba1\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002Seaborn\u7684\u4e00\u4e2a\u663e\u8457\u7279\u70b9\u662f\u5b83\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u6f02\u4eae\u7684\u7edf\u8ba1\u56fe\u8868\u3002<\/p>\n<\/p>\n<p><p>\u8981\u4f7f\u7528Seaborn\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install seaborn<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165Seaborn\u6765\u4f7f\u7528\u5176\u529f\u80fd\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Seaborn\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u5206\u5e03\u56fe\u3001\u5173\u7cfb\u56fe\u3001\u5206\u7c7b\u56fe\u548c\u77e9\u9635\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Seaborn\u521b\u5efa\u5206\u5e03\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n<p>data = sns.load_dataset(&#39;iris&#39;)<\/p>\n<h2><strong>\u521b\u5efa\u5206\u5e03\u56fe<\/strong><\/h2>\n<p>sns.histplot(data[&#39;sepal_length&#39;], kde=True)<\/p>\n<h2><strong>\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n<p>plt.title(&#39;Distribution of Sepal Length&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Seaborn\u81ea\u5e26\u7684iris\u6570\u636e\u96c6\uff0c\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u5e26\u6709\u6838\u5bc6\u5ea6\u4f30\u8ba1\u7684\u76f4\u65b9\u56fe\u3002Seaborn\u7684\u8bed\u6cd5\u66f4\u4e3a\u7b80\u6d01\uff0c\u5e76\u4e14\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u4e0ePandas\u7ed3\u5408\u4f7f\u7528\uff0c\u9002\u5408\u7528\u6765\u5904\u7406\u548c\u53ef\u89c6\u5316\u590d\u6742\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001PANDAS\u7684\u7ed8\u56fe\u529f\u80fd<\/p>\n<\/p>\n<p><p>Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u81ea\u5e26\u4e86\u7b80\u5355\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u57fa\u4e8eMatplotlib\u5b9e\u73b0\u3002Pandas\u7684\u7ed8\u56fe\u529f\u80fd\u53ef\u4ee5\u76f4\u63a5\u4f5c\u7528\u4e8eDataFrame\u548cSeries\u5bf9\u8c61\uff0c\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n<\/p>\n<p><p>\u8981\u4f7f\u7528Pandas\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5Pandas\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pandas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u5bfc\u5165Pandas\u540e\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528DataFrame\u6216Series\u5bf9\u8c61\u7684plot\u65b9\u6cd5\u6765\u7ed8\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efaDataFrame<\/strong><\/h2>\n<p>data = {&#39;x&#39;: [1, 2, 3, 4, 5], &#39;y&#39;: [2, 3, 5, 7, 11]}<\/p>\n<p>df = pd.DataFrame(data)<\/p>\n<h2><strong>\u4f7f\u7528Pandas\u7ed8\u56fe<\/strong><\/h2>\n<p>df.plot(x=&#39;x&#39;, y=&#39;y&#39;, kind=&#39;line&#39;, title=&#39;Pandas Line Plot&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684DataFrame\uff0c\u5e76\u4f7f\u7528Pandas\u7684plot\u65b9\u6cd5\u521b\u5efa\u4e86\u4e00\u5f20\u6298\u7ebf\u56fe\u3002Pandas\u7684\u7ed8\u56fe\u529f\u80fd\u975e\u5e38\u9002\u5408\u7528\u4e8e\u5feb\u901f\u751f\u6210\u56fe\u8868\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001PLOTLY\u7528\u4e8e\u4ea4\u4e92\u5f0f\u56fe\u8868<\/p>\n<\/p>\n<p><p>Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684Python\u5e93\u3002\u4e0eMatplotlib\u548cSeaborn\u4e0d\u540c\uff0cPlotly\u751f\u6210\u7684\u56fe\u8868\u662f\u4ea4\u4e92\u5f0f\u7684\uff0c\u53ef\u4ee5\u5728\u7f51\u9875\u4e2d\u5c55\u793a\u3002<\/p>\n<\/p>\n<p><p>\u8981\u4f7f\u7528Plotly\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install plotly<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5bfc\u5165Plotly\u5e76\u5f00\u59cb\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Plotly\u6298\u7ebf\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import plotly.graph_objects as go<\/p>\n<h2><strong>\u6570\u636e<\/strong><\/h2>\n<p>x = [1, 2, 3, 4, 5]<\/p>\n<p>y = [2, 3, 5, 7, 11]<\/p>\n<h2><strong>\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n<p>fig = go.Figure(data=go.Scatter(x=x, y=y, mode=&#39;lines&#39;))<\/p>\n<h2><strong>\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n<p>fig.update_layout(title=&#39;Plotly Line Plot&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>fig.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Plotly\u4e0d\u4ec5\u53ef\u4ee5\u521b\u5efa\u6298\u7ebf\u56fe\uff0c\u8fd8\u53ef\u4ee5\u521b\u5efa\u5176\u4ed6\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6761\u5f62\u56fe\u3001\u997c\u56fe\u3001\u6c14\u6ce1\u56fe\u7b49\u3002\u800c\u4e14\u7531\u4e8e\u5176\u4ea4\u4e92\u5f0f\u7684\u7279\u70b9\uff0cPlotly\u975e\u5e38\u9002\u5408\u7528\u4e8e\u9700\u8981\u4e0e\u7528\u6237\u4ea4\u4e92\u7684Web\u5e94\u7528\u7a0b\u5e8f\u4e2d\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001BOKEH\u9002\u7528\u4e8e\u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316<\/p>\n<\/p>\n<p><p>Bokeh\u662f\u53e6\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684Python\u5e93\uff0c\u7279\u522b\u9002\u5408\u7528\u4e8e\u5927\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\u548c\u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<p><p>\u8981\u4f7f\u7528Bokeh\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install bokeh<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5bfc\u5165Bokeh\u5e76\u5f00\u59cb\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Bokeh\u6298\u7ebf\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from bokeh.plotting import figure, show<\/p>\n<h2><strong>\u6570\u636e<\/strong><\/h2>\n<p>x = [1, 2, 3, 4, 5]<\/p>\n<p>y = [2, 3, 5, 7, 11]<\/p>\n<h2><strong>\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n<p>p = figure(title=&quot;Bokeh Line Plot&quot;, x_axis_label=&#39;x&#39;, y_axis_label=&#39;y&#39;)<\/p>\n<h2><strong>\u6dfb\u52a0\u6298\u7ebf<\/strong><\/h2>\n<p>p.line(x, y, legend_label=&quot;Line&quot;, line_width=2)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n<p>show(p)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Bokeh\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u8868\u7c7b\u578b\u548c\u4ea4\u4e92\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u590d\u6742\u7684\u53ef\u89c6\u5316\u5e94\u7528\u3002\u4e0ePlotly\u7c7b\u4f3c\uff0cBokeh\u751f\u6210\u7684\u56fe\u8868\u4e5f\u53ef\u4ee5\u5728\u7f51\u9875\u4e2d\u5c55\u793a\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u521b\u5efa\u6570\u636e\u4eea\u8868\u677f\u548c\u6570\u636e\u5206\u6790\u62a5\u544a\u3002<\/p>\n<\/p>\n<p><p>\u516d\u3001\u603b\u7ed3\u4e0e\u6bd4\u8f83<\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u6709\u591a\u79cd\u5e93\u53ef\u7528\u4e8e\u8f93\u51fa\u56fe\u8868\uff0c\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\u3002<strong>Matplotlib<\/strong>\u662f\u6700\u57fa\u672c\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7528\u4e8e\u57fa\u7840\u7684\u56fe\u8868\u7ed8\u5236\uff1b<strong>Seaborn<\/strong>\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u9002\u5408\u7528\u4e8e\u6570\u636e\u5206\u6790\uff1b<strong>Pandas<\/strong>\u7684\u7ed8\u56fe\u529f\u80fd\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u56fe\u8868\uff0c\u9002\u5408\u4e0e\u6570\u636e\u5904\u7406\u7ed3\u5408\u4f7f\u7528\uff1b<strong>Plotly<\/strong>\u548c<strong>Bokeh<\/strong>\u5219\u9002\u5408\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u5c24\u5176\u9002\u5408\u7528\u4e8eWeb\u5e94\u7528\u548c\u590d\u6742\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<p><p>\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u548c\u5e94\u7528\u573a\u666f\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u5e93\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u6570\u636e\u53ef\u89c6\u5316\u7684\u6548\u7387\u548c\u6548\u679c\u3002\u65e0\u8bba\u662f\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u5206\u6790\u8fd8\u662f\u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u5c55\u793a\uff0cPython\u7684\u8fd9\u4e9b\u7ed8\u56fe\u5e93\u90fd\u80fd\u6ee1\u8db3\u4f60\u7684\u9700\u6c42\u3002\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u4e0d\u4ec5\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5c55\u793a\u6570\u636e\uff0c\u8fd8\u80fd\u63d0\u5347\u4f60\u7684\u6570\u636e\u5206\u6790\u548c\u7f16\u7a0b\u80fd\u529b\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u8868\uff1f<\/strong><br \/>Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u7ed8\u5236\u5404\u7c7b\u56fe\u8868\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002Matplotlib\u662f\u57fa\u7840\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u7b80\u5355\u7684\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u7b49\uff1bSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\uff1b\u800cPlotly\u5219\u9002\u5408\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u6839\u636e\u9879\u76ee\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u6709\u6548\u63d0\u5347\u56fe\u8868\u7684\u8868\u73b0\u529b\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u7ed8\u5236\u56fe\u8868\u9700\u8981\u5b89\u88c5\u54ea\u4e9b\u5e93\uff1f<\/strong><br \/>\u4e3a\u4e86\u7ed8\u5236\u56fe\u8868\uff0c\u901a\u5e38\u9700\u8981\u5b89\u88c5\u4e00\u4e9b\u7b2c\u4e09\u65b9\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u662fMatplotlib\u548cNumPy\uff0cSeaborn\u901a\u5e38\u4f9d\u8d56\u4e8eMatplotlib\u3002\u6b64\u5916\uff0c\u5982\u679c\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u53ef\u4ee5\u8003\u8651\u5b89\u88c5Plotly\u3002\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4\u884c\u4f7f\u7528<code>pip install matplotlib seaborn plotly<\/code>\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\u3002<\/p>\n<p><strong>\u5982\u4f55\u81ea\u5b9a\u4e49Python\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong><br \/>Python\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\u53ef\u4ee5\u901a\u8fc7\u5e93\u63d0\u4f9b\u7684\u53c2\u6570\u8fdb\u884c\u81ea\u5b9a\u4e49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Matplotlib\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7<code>plt.plot()<\/code>\u51fd\u6570\u4e2d\u7684<code>color<\/code>\u548c<code>linestyle<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u7ebf\u6761\u989c\u8272\u548c\u6837\u5f0f\u3002Seaborn\u53ef\u4ee5\u901a\u8fc7\u4e3b\u9898\u8bbe\u7f6e\u548c\u8c03\u8272\u677f\u6765\u7f8e\u5316\u56fe\u8868\u3002\u5177\u4f53\u7684\u81ea\u5b9a\u4e49\u65b9\u6cd5\u53ef\u4ee5\u53c2\u8003\u76f8\u5173\u5e93\u7684\u6587\u6863\uff0c\u4ee5\u5b9e\u73b0\u66f4\u7b26\u5408\u9700\u6c42\u7684\u89c6\u89c9\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a\u8981\u7528Python\u8f93\u51fa\u56fe\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1aMatplotlib\u3001Seaborn\u3001Pandas\u7684 [&hellip;]","protected":false},"author":3,"featured_media":969487,"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\/969482"}],"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=969482"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969482\/revisions"}],"predecessor-version":[{"id":969492,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969482\/revisions\/969492"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/969487"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=969482"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=969482"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=969482"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}