{"id":1019549,"date":"2024-12-27T13:01:39","date_gmt":"2024-12-27T05:01:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1019549.html"},"modified":"2024-12-27T13:01:43","modified_gmt":"2024-12-27T05:01:43","slug":"python%e7%94%bb%e5%9b%be%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1019549.html","title":{"rendered":"python\u753b\u56fe\u5982\u4f55\u6dfb\u52a0\u989c\u8272"},"content":{"rendered":"

\"python\u753b\u56fe\u5982\u4f55\u6dfb\u52a0\u989c\u8272\"<\/p>\n

\u5728Python\u4e2d\u4e3a\u7ed8\u56fe\u6dfb\u52a0\u989c\u8272\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001\u4ee5\u53caPandas\u5185\u7f6e\u7684\u7ed8\u56fe\u529f\u80fd<\/strong>\u3002\u8fd9\u4e9b\u5de5\u5177\u90fd\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u989c\u8272\u9009\u9879\u548c\u81ea\u5b9a\u4e49\u529f\u80fd\u3002Matplotlib\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u652f\u6301RGB\u3001RGBA\u3001\u5341\u516d\u8fdb\u5236\u548cCSS\u989c\u8272\u540d\u79f0\u7b49\u591a\u79cd\u989c\u8272\u8868\u793a\u65b9\u6cd5<\/strong>\u3002\u901a\u8fc7\u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04\uff08colormap\uff09\u548c\u4f7f\u7528\u989c\u8272\u5faa\u73af\uff0c\u7528\u6237\u53ef\u4ee5\u521b\u5efa\u591a\u6837\u5316\u548c\u7cbe\u7f8e\u7684\u56fe\u5f62\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Matplotlib\u4e2d\u6dfb\u52a0\u989c\u8272\u3002<\/p>\n<\/p>\n

\u5728Matplotlib\u4e2d\uff0c\u989c\u8272\u7684\u6dfb\u52a0\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u5982\u6307\u5b9a\u989c\u8272\u540d\u79f0\u3001\u4f7f\u7528RGB\u5143\u7ec4\u6216\u8005\u5341\u516d\u8fdb\u5236\u4ee3\u7801\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u53c2\u6570\uff0c\u4f8b\u5982color<\/code>\u3001facecolor<\/code>\u3001edgecolor<\/code>\u7b49\uff0c\u4e3a\u56fe\u5f62\u5143\u7d20\uff08\u5982\u7ebf\u6761\u3001\u586b\u5145\u533a\u57df\u3001\u8fb9\u6846\u7b49\uff09\u6dfb\u52a0\u989c\u8272\u3002Matplotlib\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5185\u7f6e\u7684\u989c\u8272\u6620\u5c04\u548c\u8c03\u8272\u677f\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7cmap<\/code>\u53c2\u6570\u8f7b\u677e\u5730\u4e3a\u6570\u636e\u96c6\u4e2d\u7684\u5404\u4e2a\u5143\u7d20\u6307\u5b9a\u989c\u8272\u3002\u4f7f\u7528\u8fd9\u4e9b\u529f\u80fd\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u6027\u8d28\u548c\u4e2a\u4eba\u559c\u597d\uff0c\u521b\u5efa\u66f4\u52a0\u76f4\u89c2\u548c\u7f8e\u89c2\u7684\u53ef\u89c6\u5316\u56fe\u5f62\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001MATPLOTLIB\u4e2d\u7684\u989c\u8272\u6307\u5b9a<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u989c\u8272\u6307\u5b9a\u65b9\u6cd5\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u989c\u8272\u540d\u79f0\u548c\u7b80\u5199<\/strong>
    Matplotlib\u652f\u6301\u591a\u79cd\u989c\u8272\u540d\u79f0\u548c\u7b80\u5199\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u989c\u8272\u540d\u79f0\u5b57\u7b26\u4e32\uff0c\u5982 'red'<\/code>, 'blue'<\/code>, 'green'<\/code>\u7b49\uff0c\u6216\u8005\u4f7f\u7528\u7b80\u5199\u5b57\u7b26\uff0c\u5982 'r'<\/code>, 'b'<\/code>, 'g'<\/code>\u7b49\u3002\u6b64\u5916\uff0cMatplotlib\u8fd8\u652f\u6301CSS4\u989c\u8272\u540d\u79f0\uff0c\u5982 'darkorange'<\/code>, 'cyan'<\/code>\u7b49\u3002\u8fd9\u79cd\u65b9\u5f0f\u7b80\u5355\u6613\u7528\uff0c\u9002\u5408\u5feb\u901f\u751f\u6210\u56fe\u5f62\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    plt.plot([1, 2, 3], [4, 5, 6], color='red')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    RGB\u548cRGBA\u5143\u7ec4<\/strong>
    \u901a\u8fc7\u6307\u5b9aRGB\u6216RGBA\u5143\u7ec4\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49\u989c\u8272\u7684\u7cbe\u786e\u5ea6\u3002RGB\u5143\u7ec4\u7531\u4e09\u4e2a\u503c\uff08\u4ecb\u4e8e0\u52301\u4e4b\u95f4\uff09\u7ec4\u6210\uff0c\u5206\u522b\u4ee3\u8868\u7ea2\u3001\u7eff\u3001\u84dd\u4e09\u79cd\u989c\u8272\u7684\u5f3a\u5ea6\uff1bRGBA\u5143\u7ec4\u5219\u591a\u4e86\u4e00\u4e2a\u8868\u793a\u900f\u660e\u5ea6\u7684\u503c\u3002\u8fd9\u79cd\u65b9\u5f0f\u9002\u5408\u9700\u8981\u7cbe\u7ec6\u63a7\u5236\u989c\u8272\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n
    plt.plot([1, 2, 3], [4, 5, 6], color=(0.1, 0.2, 0.5))  # RGB<\/p>\n
    plt.plot([1, 2, 3], [4, 5, 6], color=(0.1, 0.2, 0.5, 0.3))  # RGBA<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  3. \n
    \u5341\u516d\u8fdb\u5236\u989c\u8272\u7801<\/strong>
    \u4f7f\u7528\u5341\u516d\u8fdb\u5236\u4ee3\u7801\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u989c\u8272\u6307\u5b9a\u65b9\u5f0f\uff0c\u5c24\u5176\u662f\u5728\u7f51\u9875\u8bbe\u8ba1\u4e2d\u3002Matplotlib\u4e5f\u652f\u6301\u8fd9\u79cd\u8868\u793a\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c'#FF5733'<\/code>\u8868\u793a\u4e00\u79cd\u6a59\u8272\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u9700\u8981\u4e0e\u7f51\u9875\u8bbe\u8ba1\u98ce\u683c\u4e00\u81f4\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
    plt.plot([1, 2, 3], [4, 5, 6], color='#FF5733')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u4f7f\u7528COLORMAP\u4e3a\u6570\u636e\u6dfb\u52a0\u989c\u8272<\/p>\n<\/p>\n
    Colormap\uff08\u989c\u8272\u6620\u5c04\uff09\u662fMatplotlib\u4e2d\u7528\u4e8e\u5c06\u6570\u503c\u6620\u5c04\u4e3a\u989c\u8272\u7684\u4e00\u79cd\u5de5\u5177\u3002\u5b83\u5728\u53ef\u89c6\u5316\u6570\u636e\u65f6\u975e\u5e38\u6709\u7528\uff0c\u5c24\u5176\u662f\u5f53\u6211\u4eec\u9700\u8981\u8868\u793a\u6570\u636e\u7684\u4e0d\u540c\u5f3a\u5ea6\u6216\u5206\u7c7b\u65f6\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u9884\u5b9a\u4e49\u7684Colormaps<\/strong>
      Matplotlib\u63d0\u4f9b\u4e86\u591a\u79cd\u9884\u5b9a\u4e49\u7684Colormap\uff0c\u5305\u62ec\u8fde\u7eed\u578b\u548c\u79bb\u6563\u578b\u3002\u5e38\u89c1\u7684\u6709 viridis<\/code>, plasma<\/code>, inferno<\/code>, magma<\/code>, cividis<\/code>, Greys<\/code>, Purples<\/code>\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9bColormap\uff0c\u7528\u6237\u53ef\u4ee5\u4e3a\u6839\u636e\u6570\u636e\u503c\u81ea\u52a8\u5206\u914d\u989c\u8272\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      x = np.random.rand(100)<\/p>\n
      y = np.random.rand(100)<\/p>\n
      colors = np.random.rand(100)<\/p>\n
      plt.scatter(x, y, c=colors, cmap='viridis')<\/p>\n
      plt.colorbar()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u81ea\u5b9a\u4e49Colormap<\/strong>
      \u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7ec4\u5408\u5df2\u6709\u7684Colormap\u6216\u81ea\u5b9a\u4e49\u989c\u8272\u5217\u8868\u6765\u521b\u5efa\u65b0\u7684Colormap\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u9700\u8981\u7279\u6b8a\u989c\u8272\u7ec4\u5408\u6216\u8bbe\u8ba1\u7279\u5b9a\u98ce\u683c\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
      from matplotlib.colors import LinearSegmentedColormap<\/p>\n
      custom_cmap = LinearSegmentedColormap.from_list('mycmap', ['blue', 'white', 'red'])<\/p>\n
      plt.scatter(x, y, c=colors, cmap=custom_cmap)<\/p>\n
      plt.colorbar()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u989c\u8272\u5faa\u73af\u4e0e\u6837\u5f0f<\/p>\n<\/p>\n
      \u989c\u8272\u5faa\u73af\u5141\u8bb8\u7528\u6237\u5728\u591a\u6761\u7ebf\u6761\u6216\u591a\u4e2a\u6570\u636e\u96c6\u4e4b\u95f4\u81ea\u52a8\u5e94\u7528\u4e0d\u540c\u7684\u989c\u8272\u3002\u8fd9\u5bf9\u4e8e\u9700\u8981\u5728\u540c\u4e00\u56fe\u4e2d\u663e\u793a\u591a\u7ec4\u6570\u636e\u65f6\u7279\u522b\u6709\u7528\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u9ed8\u8ba4\u989c\u8272\u5faa\u73af<\/strong>
        Matplotlib\u6709\u4e00\u7ec4\u9ed8\u8ba4\u7684\u989c\u8272\u5faa\u73af\uff0c\u5f53\u7ed8\u5236\u591a\u6761\u7ebf\u65f6\uff0c\u5b83\u4eec\u4f1a\u88ab\u4f9d\u6b21\u4f7f\u7528\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7plt.rcParams['axes.prop_cycle']<\/code>\u67e5\u770b\u6216\u4fee\u6539\u8fd9\u4e9b\u9ed8\u8ba4\u989c\u8272\u3002<\/p>\n<\/p>\n
        plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
        plt.plot([1, 2, 3], [6, 5, 4])<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u81ea\u5b9a\u4e49\u989c\u8272\u5faa\u73af<\/strong>
        \u7528\u6237\u53ef\u4ee5\u901a\u8fc7cycler<\/code>\u6a21\u5757\u81ea\u5b9a\u4e49\u989c\u8272\u5faa\u73af\uff0c\u4ee5\u4fbf\u5728\u7ed8\u56fe\u65f6\u81ea\u52a8\u5e94\u7528\u6240\u9700\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n
        from cycler import cycler<\/p>\n
        plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y'])))<\/p>\n
        plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
        plt.plot([1, 2, 3], [6, 5, 4])<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001SEABORN\u4e0e\u989c\u8272\u7ba1\u7406<\/p>\n<\/p>\n
        Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          Seaborn\u7684\u8c03\u8272\u677f<\/strong>
          Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u8c03\u8272\u677f\uff0c\u5982deep<\/code>, muted<\/code>, bright<\/code>, pastel<\/code>, dark<\/code>, colorblind<\/code>\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e9b\u8c03\u8272\u677f\u8f7b\u677e\u521b\u5efa\u534f\u8c03\u4e00\u81f4\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
          import seaborn as sns<\/p>\n
          sns.set_palette("pastel")<\/p>\n
          sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
          sns.lineplot(x=[1, 2, 3], y=[6, 5, 4])<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u81ea\u5b9a\u4e49Seaborn\u8c03\u8272\u677f<\/strong>
          \u7528\u6237\u53ef\u4ee5\u901a\u8fc7color_palette<\/code>\u51fd\u6570\u81ea\u5b9a\u4e49\u8c03\u8272\u677f\uff0c\u4ee5\u5b9e\u73b0\u7279\u5b9a\u7684\u989c\u8272\u9700\u6c42\u3002<\/p>\n<\/p>\n
          custom_palette = sns.color_palette(["#FF5733", "#33FF57", "#3357FF"])<\/p>\n
          sns.set_palette(custom_palette)<\/p>\n
          sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
          sns.lineplot(x=[1, 2, 3], y=[6, 5, 4])<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001PANDAS\u4e2d\u7684\u989c\u8272\u5e94\u7528<\/p>\n<\/p>\n
          Pandas\u662f\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5b83\u4e5f\u5185\u7f6e\u4e86\u7b80\u5355\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u652f\u6301\u989c\u8272\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u57fa\u672c\u989c\u8272\u5e94\u7528<\/strong>
            \u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7plot<\/code>\u65b9\u6cd5\u7684color<\/code>\u53c2\u6570\u6307\u5b9a\u989c\u8272\u3002<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})<\/p>\n
            df.plot(kind='line', color=['red', 'blue'])<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u4f7f\u7528Colormap<\/strong>
            Pandas\u7684\u7ed8\u56fe\u529f\u80fd\u4e5f\u652f\u6301\u4f7f\u7528Colormap\uff0c\u4e3a\u6570\u636e\u81ea\u52a8\u5206\u914d\u989c\u8272\u3002<\/p>\n<\/p>\n
            df.plot(kind='scatter', x='A', y='B', c='A', cmap='viridis')<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u989c\u8272\u7684\u6700\u4f73\u5b9e\u8df5<\/p>\n<\/p>\n
            \u5728\u4f7f\u7528\u989c\u8272\u65f6\uff0c\u4e86\u89e3\u548c\u9075\u5faa\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u53ef\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u989c\u8272\u9009\u62e9\u7684\u53ef\u8bfb\u6027<\/strong>
              \u9009\u62e9\u989c\u8272\u65f6\uff0c\u5e94\u786e\u4fdd\u989c\u8272\u4e4b\u95f4\u7684\u5bf9\u6bd4\u8db3\u591f\uff0c\u4ee5\u4fbf\u4e8e\u533a\u5206\u4e0d\u540c\u7684\u6570\u636e\u96c6\u3002\u540c\u65f6\uff0c\u5bf9\u4e8e\u8272\u76f2\u7528\u6237\uff0c\u9009\u62e9\u8272\u76f2\u53cb\u597d\u7684\u8c03\u8272\u677f\u4e5f\u662f\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u989c\u8272\u7684\u4e00\u81f4\u6027<\/strong>
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