{"id":980058,"date":"2024-12-27T06:51:28","date_gmt":"2024-12-26T22:51:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/980058.html"},"modified":"2024-12-27T06:51:30","modified_gmt":"2024-12-26T22:51:30","slug":"python%e5%a6%82%e4%bd%95%e5%88%b6%e4%bd%9c%e7%8e%af%e5%bd%a2%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/980058.html","title":{"rendered":"Python\u5982\u4f55\u5236\u4f5c\u73af\u5f62\u56fe"},"content":{"rendered":"

\"Python\u5982\u4f55\u5236\u4f5c\u73af\u5f62\u56fe\"<\/p>\n

\u5236\u4f5cPython\u73af\u5f62\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u73af\u5f62\u56fe\u3001\u5b9a\u5236\u56fe\u5f62\u5916\u89c2<\/strong>\u3002\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u521b\u5efa\u4e00\u4e2a\u57fa\u672c\u7684\u73af\u5f62\u56fe\uff0c\u5e76\u8fdb\u4e00\u6b65\u63a2\u8ba8\u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u5f62\u4ee5\u63d0\u9ad8\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u73af\u5f62\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165Python\u4e2d\u7684\u5fc5\u8981\u5e93\u3002\u901a\u5e38\u60c5\u51b5\u4e0b\uff0cMatplotlib\u662f\u521b\u5efa\u56fe\u5f62\u7684\u9996\u9009\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\u6765\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u6b64\u5916\uff0cNumPy\u4e5f\u53ef\u80fd\u4f1a\u7528\u6765\u521b\u5efa\u6216\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u753b\u548c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u7684\u5168\u9762\u5e93\uff0c\u800cNumPy\u662f\u4e00\u4e2a\u652f\u6301\u5927\u578b\u591a\u7ef4\u6570\u7ec4\u4e0e\u77e9\u9635\u8fd0\u7b97\u7684\u5e93\u3002\u901a\u5e38\uff0c\u6211\u4eec\u4f1a\u7528NumPy\u6765\u5904\u7406\u548c\u751f\u6210\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u5c06\u7528\u4e8e\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/p>\n<\/p>\n
\u5728\u7ed8\u5236\u73af\u5f62\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u3002\u73af\u5f62\u56fe\u662f\u4e00\u79cd\u53d8\u4f53\u7684\u997c\u56fe\uff0c\u56e0\u6b64\u6570\u636e\u901a\u5e38\u662f\u5404\u90e8\u5206\u6240\u5360\u6574\u4f53\u7684\u6bd4\u4f8b\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u6216\u6570\u7ec4\u6765\u5b58\u50a8\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u793a\u4f8b\u6570\u636e<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['Category A', 'Category B', 'Category C', 'Category D']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u56db\u4e2a\u7c7b\u522b\u7684\u5927\u5c0f\uff0c\u5b83\u4eec\u5206\u522b\u5360\u603b\u6570\u7684\u4e0d\u540c\u6bd4\u4f8b\u3002\u6bcf\u4e2a\u7c7b\u522b\u90fd\u6709\u4e00\u4e2a\u76f8\u5e94\u7684\u6807\u7b7e\uff0c\u4ee5\u4fbf\u5728\u56fe\u8868\u4e2d\u6807\u8bc6\u5b83\u4eec\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u73af\u5f62\u56fe<\/p>\n<\/p>\n
\u6709\u4e86\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u73af\u5f62\u56fe\u3002\u73af\u5f62\u56fe\u662f\u4e00\u79cd\u901a\u8fc7\u5728\u997c\u56fe\u4e2d\u5fc3\u6316\u7a7a\u6765\u663e\u793a\u6570\u636e\u6bd4\u4f8b\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u73af\u5f62\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.pie(sizes, labels=labels, startangle=90, wedgeprops={'width': 0.3})<\/p>\n
\u4fdd\u6301\u73af\u5f62\u56fe\u7684\u6bd4\u4f8b<\/strong><\/h2>\n
ax.axis('equal')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528ax.pie()<\/code>\u65b9\u6cd5\u6765\u7ed8\u5236\u73af\u5f62\u56fe\u3002wedgeprops<\/code>\u53c2\u6570\u4e2d\u7684width<\/code>\u5c5e\u6027\u7528\u4e8e\u63a7\u5236\u73af\u5f62\u56fe\u4e2d\u5fc3\u7684\u7a7a\u767d\u7a0b\u5ea6\uff0c\u4ece\u800c\u5f62\u6210\u4e00\u4e2a\u73af\u5f62\u3002startangle<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u56fe\u5f62\u5f00\u59cb\u7ed8\u5236\u7684\u89d2\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
\u4fdd\u6301\u73af\u5f62\u56fe\u6bd4\u4f8b<\/strong>\uff1a\u901a\u8fc7ax.axis('equal')<\/code>\u8bbe\u7f6e\u5750\u6807\u8f74\u6bd4\u4f8b\uff0c\u4f7f\u5f97\u7ed8\u5236\u7684\u73af\u5f62\u56fe\u662f\u4e00\u4e2a\u5b8c\u7f8e\u7684\u5706\u5f62\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5b9a\u5236\u56fe\u5f62\u5916\u89c2<\/p>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u73af\u5f62\u56fe\u66f4\u52a0\u7f8e\u89c2\u548c\u4fe1\u606f\u4e30\u5bcc\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u5176\u8fdb\u884c\u5404\u79cd\u5b9a\u5236\u3002\u4f8b\u5982\uff0c\u6dfb\u52a0\u989c\u8272\u3001\u9634\u5f71\u6548\u679c\u3001\u6570\u636e\u6807\u7b7e\u7b49\u3002<\/p>\n<\/p>\n
    \n
  1. \u6dfb\u52a0\u989c\u8272<\/strong><\/li>\n<\/ol>\n
    \u53ef\u4ee5\u901a\u8fc7colors<\/code>\u53c2\u6570\u4e3a\u6bcf\u4e2a\u90e8\u5206\u6307\u5b9a\u989c\u8272\uff1a<\/p>\n<\/p>\n
    colors = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen']<\/p>\n
    ax.pie(sizes, labels=labels, startangle=90, wedgeprops={'width': 0.3}, colors=colors)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6dfb\u52a0\u9634\u5f71\u6548\u679c<\/strong><\/li>\n<\/ol>\n
      \u901a\u8fc7\u8bbe\u7f6eshadow=True<\/code>\uff0c\u53ef\u4ee5\u4e3a\u73af\u5f62\u56fe\u6dfb\u52a0\u9634\u5f71\u6548\u679c\uff0c\u4f7f\u5176\u770b\u8d77\u6765\u66f4\u5177\u7acb\u4f53\u611f\uff1a<\/p>\n<\/p>\n
      ax.pie(sizes, labels=labels, startangle=90, wedgeprops={'width': 0.3}, colors=colors, shadow=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u663e\u793a\u6570\u636e\u6807\u7b7e<\/strong><\/li>\n<\/ol>\n
        \u53ef\u4ee5\u4f7f\u7528autopct<\/code>\u53c2\u6570\u5728\u56fe\u4e2d\u76f4\u63a5\u663e\u793a\u6bcf\u4e2a\u90e8\u5206\u6240\u5360\u6bd4\u4f8b\uff1a<\/p>\n<\/p>\n
        ax.pie(sizes, labels=labels, startangle=90, wedgeprops={'width': 0.3}, colors=colors, autopct='%1.1f%%')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u81ea\u5b9a\u4e49\u73af\u5f62\u56fe\u7684\u6807\u9898<\/strong><\/li>\n<\/ol>\n
          \u901a\u8fc7plt.title()<\/code>\u51fd\u6570\u53ef\u4ee5\u4e3a\u73af\u5f62\u56fe\u6dfb\u52a0\u6807\u9898\uff1a<\/p>\n<\/p>\n
          plt.title('Distribution of Categories')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e94\u3001\u7efc\u5408\u793a\u4f8b<\/p>\n<\/p>\n
          \u5c06\u4e0a\u8ff0\u6240\u6709\u5b9a\u5236\u9009\u9879\u7ed3\u5408\u5728\u4e00\u8d77\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u51fa\u4e00\u4e2a\u7efc\u5408\u7684\u73af\u5f62\u56fe\uff1a<\/p>\n<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
          sizes = [15, 30, 45, 10]<\/p>\n
          labels = ['Category A', 'Category B', 'Category C', 'Category D']<\/p>\n
          colors = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen']<\/p>\n
          \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
          fig, ax = plt.subplots()<\/p>\n
          \u7ed8\u5236\u73af\u5f62\u56fe<\/strong><\/h2>\n
          ax.pie(sizes, labels=labels, startangle=90, wedgeprops={'width': 0.3}, colors=colors, shadow=True, autopct='%1.1f%%')<\/p>\n
          \u4fdd\u6301\u6bd4\u4f8b<\/strong><\/h2>\n
          ax.axis('equal')<\/p>\n
          \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
          plt.title('Distribution of Categories')<\/p>\n
          \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u7f8e\u89c2\u4e14\u4fe1\u606f\u4e30\u5bcc\u7684\u73af\u5f62\u56fe\uff0c\u5e76\u901a\u8fc7\u5b9a\u5236\u989c\u8272\u3001\u9634\u5f71\u548c\u6807\u7b7e\u6765\u63d0\u9ad8\u5176\u53ef\u8bfb\u6027\u3002\u73af\u5f62\u56fe\u662f\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u4e00\u79cd\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u5c55\u793a\u5404\u90e8\u5206\u5360\u603b\u6570\u7684\u6bd4\u4f8b\u3002\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u4efb\u4f55\u4eba\u90fd\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u5730\u521b\u5efa\u4e13\u4e1a\u7684\u73af\u5f62\u56fe\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u73af\u5f62\u56fe\uff1f<\/strong>
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u73af\u5f62\u56fe\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\u3002\u63a5\u7740\uff0c\u51c6\u5907\u6570\u636e\u5e76\u4f7f\u7528plt.pie()<\/code>\u51fd\u6570\u7ed8\u5236\u997c\u56fe\uff0c\u518d\u901a\u8fc7\u8c03\u6574wedgeprops<\/code>\u53c2\u6570\u6765\u521b\u5efa\u73af\u5f62\u56fe\u3002\u9700\u8981\u8bbe\u7f6e\u4e00\u4e2aradius<\/code>\u53c2\u6570\u6765\u63a7\u5236\u73af\u7684\u539a\u5ea6\uff0c\u4ece\u800c\u5b9e\u73b0\u73af\u5f62\u6548\u679c\u3002<\/p>\n
          \u6211\u9700\u8981\u51c6\u5907\u54ea\u4e9b\u6570\u636e\u6765\u5236\u4f5c\u73af\u5f62\u56fe\uff1f<\/strong>
          \u5236\u4f5c\u73af\u5f62\u56fe\u65f6\uff0c\u901a\u5e38\u9700\u8981\u4e00\u4e2a\u5305\u542b\u5404\u90e8\u5206\u503c\u7684\u5217\u8868\u6216\u6570\u7ec4\uff0c\u4ee5\u53ca\u5bf9\u5e94\u7684\u6807\u7b7e\u3002\u6570\u636e\u53ef\u4ee5\u662f\u4efb\u4f55\u7c7b\u578b\u7684\u6570\u503c\uff0c\u4f8b\u5982\u9500\u552e\u989d\u3001\u4eba\u53e3\u5206\u5e03\u7b49\u3002\u786e\u4fdd\u6570\u636e\u7684\u603b\u548c\u9002\u5408\u5f62\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u73af\u5f62\u56fe\uff0c\u901a\u5e38\u4e3a100%\u6216\u603b\u6570\u503c\u7684\u6bd4\u4f8b\u3002<\/p>\n
          \u73af\u5f62\u56fe\u53ef\u4ee5\u7528\u6765\u8868\u793a\u54ea\u4e9b\u7c7b\u578b\u7684\u6570\u636e\uff1f<\/strong>
          \u73af\u5f62\u56fe\u7279\u522b\u9002\u5408\u7528\u4e8e\u5c55\u793a\u5404\u90e8\u5206\u5728\u6574\u4f53\u4e2d\u7684\u6bd4\u4f8b\u5173\u7cfb\uff0c\u5e38\u89c1\u4e8e\u5e02\u573a\u4efd\u989d\u3001\u7528\u6237\u7c7b\u522b\u5206\u5e03\u3001\u9884\u7b97\u5206\u914d\u7b49\u60c5\u51b5\u3002\u5b83\u80fd\u591f\u6e05\u6670\u5730\u5c55\u793a\u4e0d\u540c\u90e8\u5206\u4e4b\u95f4\u7684\u76f8\u5bf9\u5927\u5c0f\uff0c\u5e2e\u52a9\u8bfb\u8005\u5feb\u901f\u7406\u89e3\u6570\u636e\u7684\u6784\u6210\u3002\u4f7f\u7528\u73af\u5f62\u56fe\u65f6\uff0c\u6700\u597d\u907f\u514d\u8fc7\u591a\u7684\u5206\u7ec4\uff0c\u4ee5\u4fdd\u6301\u56fe\u5f62\u7684\u6e05\u6670\u5ea6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5236\u4f5cPython\u73af\u5f62\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u73af\u5f62\u56fe\u3001\u5b9a\u5236\u56fe\u5f62\u5916 […]","protected":false},"author":3,"featured_media":980063,"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\/980058"}],"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=980058"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/980058\/revisions"}],"predecessor-version":[{"id":980065,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/980058\/revisions\/980065"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/980063"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=980058"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=980058"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=980058"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}