{"id":1110971,"date":"2025-01-08T17:24:46","date_gmt":"2025-01-08T09:24:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1110971.html"},"modified":"2025-01-08T17:24:49","modified_gmt":"2025-01-08T09:24:49","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e5%87%ba%e6%88%90%e7%bb%a9%e7%9a%84%e6%9f%b1%e7%8a%b6%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1110971.html","title":{"rendered":"\u5982\u4f55\u7528Python\u753b\u51fa\u6210\u7ee9\u7684\u67f1\u72b6\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u753b\u51fa\u6210\u7ee9\u7684\u67f1\u72b6\u56fe\"<\/p>\n

\u4f7f\u7528Python\u753b\u51fa\u6210\u7ee9\u7684\u67f1\u72b6\u56fe\u975e\u5e38\u7b80\u5355\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Pandas\u5e93\u7b49\u51e0\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002<\/strong> \u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u7ed8\u5236\u6210\u7ee9\u7684\u67f1\u72b6\u56fe\uff0c\u5e76\u4e3a\u6bcf\u79cd\u65b9\u6cd5\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u548c\u89e3\u91ca\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u67f1\u72b6\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u5e76\u5bfc\u5165\u4e86\u5fc5\u8981\u7684\u5e93\u3002\u4e3b\u8981\u4f7f\u7528\u7684\u5e93\u6709Matplotlib\u3001Seaborn\u548cPandas\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u67f1\u72b6\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u5b66\u751f\u7684\u6210\u7ee9\u6570\u636e\uff0c\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    '\u5b66\u751f': ['\u5f20\u4e09', '\u674e\u56db', '\u738b\u4e94', '\u8d75\u516d', '\u5b59\u4e03'],<\/p>\n
    '\u6210\u7ee9': [85, 90, 78, 92, 88]<\/p>\n
}<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5c06\u6570\u636e\u8f6c\u6362\u4e3aPandas DataFrame\u4ee5\u4fbf\u5904\u7406\uff1a<\/p>\n<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u6210\u7ee9\u67f1\u72b6\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
    \n
  1. \u521b\u5efa\u56fe\u5f62\u5bf9\u8c61\u548c\u5b50\u56fe\u5bf9\u8c61\u3002<\/li>\n
  2. \u4f7f\u7528bar<\/code>\u65b9\u6cd5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/li>\n
  3. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u3002<\/li>\n
  4. \u663e\u793a\u56fe\u5f62\u3002<\/li>\n<\/ol>\n
    # \u521b\u5efa\u56fe\u5f62\u5bf9\u8c61\u548c\u5b50\u56fe\u5bf9\u8c61<\/p>\n
    fig, ax = plt.subplots()<\/p>\n
    \u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
    ax.bar(df['\u5b66\u751f'], df['\u6210\u7ee9'], color='skyblue')<\/p>\n
    \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
    ax.set_title('\u5b66\u751f\u6210\u7ee9\u67f1\u72b6\u56fe')<\/p>\n
    ax.set_xlabel('\u5b66\u751f')<\/p>\n
    ax.set_ylabel('\u6210\u7ee9')<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Matplotlib\u7684bar<\/code>\u65b9\u6cd5\u6765\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u5e76\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u3001\u6807\u9898\u548c\u6807\u7b7e\u6765\u7f8e\u5316\u56fe\u5f62\u3002<\/p>\n<\/p>\n
    \u56db\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
    Seaborn\u662f\u57fa\u4e8eMatplotlib\u4e4b\u4e0a\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u6210\u7ee9\u67f1\u72b6\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528barplot<\/code>\u65b9\u6cd5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/li>\n
    2. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u3002<\/li>\n
    3. \u663e\u793a\u56fe\u5f62\u3002<\/li>\n<\/ol>\n
      # \u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n
      sns.barplot(x='\u5b66\u751f', y='\u6210\u7ee9', data=df, palette='viridis')<\/p>\n
      \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
      plt.title('\u5b66\u751f\u6210\u7ee9\u67f1\u72b6\u56fe')<\/p>\n
      plt.xlabel('\u5b66\u751f')<\/p>\n
      plt.ylabel('\u6210\u7ee9')<\/p>\n
      \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Seaborn\u7684barplot<\/code>\u65b9\u6cd5\u6765\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u5e76\u901a\u8fc7\u8bbe\u7f6e\u8c03\u8272\u677f\u3001\u6807\u9898\u548c\u6807\u7b7e\u6765\u7f8e\u5316\u56fe\u5f62\u3002<\/p>\n<\/p>\n
      \u4e94\u3001\u4f7f\u7528Pandas\u7ed8\u5236\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
      Pandas\u672c\u8eab\u4e5f\u63d0\u4f9b\u4e86\u7ed8\u56fe\u529f\u80fd\uff0c\u4f7f\u5f97\u76f4\u63a5\u4eceDataFrame\u7ed8\u5236\u56fe\u5f62\u53d8\u5f97\u975e\u5e38\u65b9\u4fbf\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u7ed8\u5236\u6210\u7ee9\u67f1\u72b6\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528plot<\/code>\u65b9\u6cd5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/li>\n
      2. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u3002<\/li>\n
      3. \u663e\u793a\u56fe\u5f62\u3002<\/li>\n<\/ol>\n
        # \u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n
        df.plot(kind='bar', x='\u5b66\u751f', y='\u6210\u7ee9', color='skyblue')<\/p>\n
        \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
        plt.title('\u5b66\u751f\u6210\u7ee9\u67f1\u72b6\u56fe')<\/p>\n
        plt.xlabel('\u5b66\u751f')<\/p>\n
        plt.ylabel('\u6210\u7ee9')<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Pandas\u7684plot<\/code>\u65b9\u6cd5\u6765\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u5e76\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u3001\u6807\u9898\u548c\u6807\u7b7e\u6765\u7f8e\u5316\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \u516d\u3001\u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/h3>\n<\/p>\n
        \u5728\u7ed8\u5236\u67f1\u72b6\u56fe\u65f6\uff0c\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u53ef\u4ee5\u4f7f\u56fe\u5f62\u66f4\u52a0\u76f4\u89c2\u3002\u4ee5\u4e0b\u662f\u4e3a\u67f1\u72b6\u56fe\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
        \u4f7f\u7528Matplotlib\u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/h4>\n<\/p>\n
        # \u521b\u5efa\u56fe\u5f62\u5bf9\u8c61\u548c\u5b50\u56fe\u5bf9\u8c61<\/p>\n
        fig, ax = plt.subplots()<\/p>\n
        \u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
        bars = ax.bar(df['\u5b66\u751f'], df['\u6210\u7ee9'], color='skyblue')<\/p>\n
        \u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/strong><\/h2>\n
        for bar in bars:<\/p>\n
            yval = bar.get_height()<\/p>\n
            plt.text(bar.get_x() + bar.get_width()\/2, yval, round(yval, 2), ha='center', va='bottom')<\/p>\n
        \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
        ax.set_title('\u5b66\u751f\u6210\u7ee9\u67f1\u72b6\u56fe')<\/p>\n
        ax.set_xlabel('\u5b66\u751f')<\/p>\n
        ax.set_ylabel('\u6210\u7ee9')<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u904d\u5386\u6bcf\u4e2a\u67f1\u5f62\u5bf9\u8c61\uff0c\u5e76\u4f7f\u7528plt.text<\/code>\u65b9\u6cd5\u5728\u6bcf\u4e2a\u67f1\u5b50\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u3002<\/p>\n<\/p>\n
        \u4f7f\u7528Seaborn\u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/h4>\n<\/p>\n
        # \u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n
        ax = sns.barplot(x='\u5b66\u751f', y='\u6210\u7ee9', data=df, palette='viridis')<\/p>\n
        \u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/strong><\/h2>\n
        for p in ax.patches:<\/p>\n
            ax.annotate(format(p.get_height(), '.2f'), <\/p>\n
                        (p.get_x() + p.get_width() \/ 2., p.get_height()), <\/p>\n
                        ha = 'center', va = 'center', <\/p>\n
                        xytext = (0, 9), <\/p>\n
                        textcoords = 'offset points')<\/p>\n
        \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
        plt.title('\u5b66\u751f\u6210\u7ee9\u67f1\u72b6\u56fe')<\/p>\n
        plt.xlabel('\u5b66\u751f')<\/p>\n
        plt.ylabel('\u6210\u7ee9')<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u904d\u5386\u6bcf\u4e2a\u8865\u4e01\u5bf9\u8c61\uff0c\u5e76\u4f7f\u7528ax.annotate<\/code>\u65b9\u6cd5\u5728\u6bcf\u4e2a\u67f1\u5b50\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u3002<\/p>\n<\/p>\n
        \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
        \u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Python\u7684Matplotlib\u3001Seaborn\u548cPandas\u5e93\u6765\u7ed8\u5236\u6210\u7ee9\u7684\u67f1\u72b6\u56fe\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u65e0\u8bba\u662f\u6570\u636e\u9884\u5904\u7406\u3001\u56fe\u5f62\u7ed8\u5236\u8fd8\u662f\u7f8e\u5316\u56fe\u5f62\uff0c\u8fd9\u4e9b\u5e93\u90fd\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u3001\u51c6\u786e\u5730\u5b8c\u6210\u53ef\u89c6\u5316\u4efb\u52a1\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u6570\u636e\u53ef\u89c6\u5316\u7684\u9053\u8def\u4e0a\u53d6\u5f97\u66f4\u591a\u6210\u5c31\uff01<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u5e93\u6765\u7ed8\u5236\u67f1\u72b6\u56fe\uff1f<\/strong>
        \u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u7528\u4e8e\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cSeaborn\u3002Matplotlib\u662f\u4e00\u4e2a\u57fa\u7840\u5e93\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u7c7b\u578b\u7684\u7ed8\u56fe\uff0c\u800cSeaborn\u5219\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u6269\u5c55\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u4fbf\u6377\u7684\u63a5\u53e3\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u8ba9\u7ed8\u56fe\u8fc7\u7a0b\u66f4\u52a0\u9ad8\u6548\u548c\u7b80\u5355\u3002<\/p>\n
        \u5982\u4f55\u51c6\u5907\u6570\u636e\u4ee5\u4fbf\u7ed8\u5236\u67f1\u72b6\u56fe\uff1f<\/strong>
        \u5728\u7ed8\u5236\u67f1\u72b6\u56fe\u4e4b\u524d\uff0c\u786e\u4fdd\u5c06\u6570\u636e\u6574\u7406\u6210\u9002\u5408\u683c\u5f0f\u3002\u901a\u5e38\u60c5\u51b5\u4e0b\uff0c\u6570\u636e\u5e94\u8be5\u5305\u542b\u4e24\u4e2a\u4e3b\u8981\u90e8\u5206\uff1a\u7c7b\u522b\uff08\u4f8b\u5982\uff0c\u5b66\u751f\u59d3\u540d\u6216\u79d1\u76ee\uff09\u548c\u6570\u503c\uff08\u4f8b\u5982\uff0c\u6210\u7ee9\uff09\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684Pandas\u5e93\u8f7b\u677e\u5904\u7406\u548c\u6e05\u6d17\u6570\u636e\uff0c\u4ee5\u4fbf\u5c06\u5176\u8f6c\u6362\u4e3a\u9002\u5408\u7ed8\u56fe\u7684\u683c\u5f0f\u3002<\/p>\n
        \u5982\u4f55\u81ea\u5b9a\u4e49\u67f1\u72b6\u56fe\u7684\u5916\u89c2\uff1f<\/strong>
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