{"id":971211,"date":"2024-12-27T05:33:01","date_gmt":"2024-12-26T21:33:01","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/971211.html"},"modified":"2024-12-27T05:33:03","modified_gmt":"2024-12-26T21:33:03","slug":"python%e5%9b%be%e8%a1%a8%e5%a6%82%e4%bd%95%e6%94%b9%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/971211.html","title":{"rendered":"python\u56fe\u8868\u5982\u4f55\u6539\u989c\u8272"},"content":{"rendered":"

\"python\u56fe\u8868\u5982\u4f55\u6539\u989c\u8272\"<\/p>\n

\u5728Python\u4e2d\uff0c\u6539\u53d8\u56fe\u8868\u7684\u989c\u8272\u4e3b\u8981\u901a\u8fc7\u8c03\u6574\u7ed8\u56fe\u5e93\uff08\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff09\u7684\u53c2\u6570\u6765\u5b9e\u73b0\u3002\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528\u989c\u8272\u540d\u79f0\u3001RGB\u6570\u503c\u3001\u5341\u516d\u8fdb\u5236\u4ee3\u7801\u7b49\u65b9\u5f0f\u8fdb\u884c\u989c\u8272\u8bbe\u7f6e\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u7684color<\/code>\u53c2\u6570\u3001Seaborn\u7684palette<\/code>\u53c2\u6570\u3001Plotly\u7684marker_color<\/code>\u5c5e\u6027\u7b49\u3002<\/strong>\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728\u8fd9\u4e9b\u5e93\u4e2d\u5b9e\u73b0\u56fe\u8868\u989c\u8272\u7684\u6539\u53d8\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u9645\u5e94\u7528\u7684\u793a\u4f8b\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u4e2d\u7684\u989c\u8272\u8bbe\u7f6e<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u5f62\u7ed8\u5236\u529f\u80fd\u548c\u9ad8\u5ea6\u7684\u81ea\u5b9a\u4e49\u6027\u3002<\/p>\n<\/p>\n

1.1\u3001\u57fa\u672c\u989c\u8272\u8bbe\u7f6e<\/p>\n<\/p>\n

\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7plot<\/code>\u51fd\u6570\u7684color<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u7ebf\u6761\u7684\u989c\u8272\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
x = [1, 2, 3, 4]<\/p>\n
y = [10, 20, 25, 30]<\/p>\n
plt.plot(x, y, color='red')  # \u4f7f\u7528\u989c\u8272\u540d\u79f0<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53e6\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528RGB\u6216\u5341\u516d\u8fdb\u5236\u4ee3\u7801\u6765\u8bbe\u7f6e\u989c\u8272\uff1a<\/p>\n<\/p>\n
plt.plot(x, y, color='#FF5733')  # \u4f7f\u7528\u5341\u516d\u8fdb\u5236\u4ee3\u7801<\/p>\n
plt.plot(x, y, color=(0.1, 0.2, 0.5))  # \u4f7f\u7528RGB\u503c<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u8bbe\u7f6e\u591a\u4e2a\u56fe\u5f62\u5143\u7d20\u7684\u989c\u8272<\/p>\n<\/p>\n
\u5728\u4e00\u4e2a\u56fe\u8868\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u8bbe\u7f6e\u4e0d\u540c\u5143\u7d20\u7684\u989c\u8272\uff0c\u5982\u7ebf\u6761\u3001\u586b\u5145\u533a\u57df\u3001\u7f51\u683c\u7ebf\u7b49\uff1a<\/p>\n<\/p>\n
    \n
  • \u7ebf\u6761\u989c\u8272<\/strong>\uff1a\u4f7f\u7528color<\/code>\u53c2\u6570\u3002<\/li>\n
  • \u586b\u5145\u989c\u8272<\/strong>\uff1a\u4f7f\u7528fill_between<\/code>\u51fd\u6570\u7684color<\/code>\u53c2\u6570\u3002<\/li>\n
  • \u7f51\u683c\u7ebf\u989c\u8272<\/strong>\uff1a\u4f7f\u7528grid<\/code>\u51fd\u6570\u7684color<\/code>\u53c2\u6570\u3002<\/li>\n<\/ul>\n
    \u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    plt.fill_between(x, y, color='lightblue')  # \u586b\u5145\u533a\u57df<\/p>\n
    plt.grid(color='gray', linestyle='--', linewidth=0.5)  # \u7f51\u683c\u7ebf<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3\u3001\u4f7f\u7528Colormap<\/p>\n<\/p>\n
    \u5bf9\u4e8e\u9700\u8981\u6e10\u53d8\u8272\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684Colormap\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    x = np.linspace(0, 10, 100)<\/p>\n
    y = np.sin(x)<\/p>\n
    plt.scatter(x, y, c=y, cmap='viridis')  # \u4f7f\u7528Colormap<\/p>\n
    plt.colorbar()  # \u6dfb\u52a0\u989c\u8272\u6761<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001SEABORN\u4e2d\u7684\u989c\u8272\u8bbe\u7f6e<\/p>\n<\/p>\n
    Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u7edf\u8ba1\u7c7b\u56fe\u8868\u7684\u7ed8\u5236\u3002\u5b83\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u7f8e\u89c2\u7684\u9ed8\u8ba4\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n
    2.1\u3001\u6539\u53d8\u8c03\u8272\u677f<\/p>\n<\/p>\n
    Seaborn\u901a\u8fc7palette<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u56fe\u8868\u7684\u8c03\u8272\u677f\uff1a<\/p>\n<\/p>\n
    import seaborn as sns<\/p>\n
    sns.set_palette('husl')  # \u8bbe\u7f6e\u5168\u5c40\u8c03\u8272\u677f<\/p>\n
    sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.2\u3001\u6307\u5b9a\u5355\u4e2a\u56fe\u8868\u7684\u989c\u8272<\/p>\n<\/p>\n
    \u53ef\u4ee5\u4e3a\u5355\u4e2a\u56fe\u8868\u6307\u5b9a\u989c\u8272\uff1a<\/p>\n<\/p>\n
    sns.lineplot(x=[1, 2, 3], y=[4, 5, 6], color='purple')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.3\u3001\u81ea\u5b9a\u4e49\u8c03\u8272\u677f<\/p>\n<\/p>\n
    Seaborn\u5141\u8bb8\u521b\u5efa\u81ea\u5b9a\u4e49\u8c03\u8272\u677f\uff1a<\/p>\n<\/p>\n
    custom_palette = sns.color_palette("hsv", 10)<\/p>\n
    sns.set_palette(custom_palette)<\/p>\n
    sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001PLOTLY\u4e2d\u7684\u989c\u8272\u8bbe\u7f6e<\/p>\n<\/p>\n
    Plotly\u662f\u4e00\u6b3e\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u521b\u5efa\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
    3.1\u3001\u57fa\u672c\u989c\u8272\u8bbe\u7f6e<\/p>\n<\/p>\n
    \u5728Plotly\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7marker_color<\/code>\u5c5e\u6027\u6765\u8bbe\u7f6e\u989c\u8272\uff1a<\/p>\n<\/p>\n
    import plotly.express as px<\/p>\n
    fig = px.line(x=[1, 2, 3], y=[4, 5, 6], line_shape='linear')<\/p>\n
    fig.update_traces(marker_color='green')<\/p>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2\u3001\u4f7f\u7528\u989c\u8272\u5e8f\u5217<\/p>\n<\/p>\n
    Plotly\u652f\u6301\u4f7f\u7528\u989c\u8272\u5e8f\u5217\uff08color sequences\uff09\u6765\u8bbe\u7f6e\u591a\u6761\u7ebf\u7684\u989c\u8272\uff1a<\/p>\n<\/p>\n
    fig = px.line(x=[1, 2, 3], y=[4, 5, 6], line_shape='linear')<\/p>\n
    fig.update_traces(line=dict(color='firebrick'))<\/p>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.3\u3001\u4f7f\u7528Colormaps<\/p>\n<\/p>\n
    \u7c7b\u4f3c\u4e8eMatplotlib\uff0cPlotly\u4e5f\u652f\u6301Colormaps\uff1a<\/p>\n<\/p>\n
    fig = px.scatter(x=[1, 2, 3], y=[4, 5, 6], color=[1, 2, 3], color_continuous_scale='Viridis')<\/p>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u5176\u4ed6\u6280\u5de7\u548c\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
    4.1\u3001\u9009\u62e9\u5408\u9002\u7684\u989c\u8272<\/p>\n<\/p>\n
    \u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u662f\u56fe\u8868\u8bbe\u8ba1\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\u3002\u5e94\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
      \n
    • \u53ef\u8bfb\u6027<\/strong>\uff1a\u786e\u4fdd\u6587\u5b57\u548c\u80cc\u666f\u7684\u989c\u8272\u5bf9\u6bd4\u8db3\u591f\u3002<\/li>\n
    • \u8272\u76f2\u53cb\u597d<\/strong>\uff1a\u8003\u8651\u4f7f\u7528\u8272\u76f2\u53cb\u597d\u7684\u8c03\u8272\u677f\u3002<\/li>\n
    • \u4e00\u81f4\u6027<\/strong>\uff1a\u5728\u76f8\u540c\u7c7b\u522b\u7684\u56fe\u8868\u4e2d\u4f7f\u7528\u4e00\u81f4\u7684\u989c\u8272\u7f16\u7801\u3002<\/li>\n<\/ul>\n
      4.2\u3001\u4f7f\u7528\u900f\u660e\u5ea6<\/p>\n<\/p>\n
      \u5728\u91cd\u53e0\u56fe\u5f62\u4e2d\uff0c\u4f7f\u7528\u900f\u660e\u5ea6\u53ef\u4ee5\u63d0\u9ad8\u53ef\u8bfb\u6027\uff1a<\/p>\n<\/p>\n
      plt.plot(x, y, alpha=0.5)  # \u8bbe\u7f6e\u900f\u660e\u5ea6<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      4.3\u3001\u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04<\/p>\n<\/p>\n
      \u5bf9\u4e8e\u590d\u6742\u7684\u56fe\u8868\uff0c\u53ef\u4ee5\u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04\u4ee5\u9002\u5e94\u7279\u5b9a\u9700\u6c42\uff1a<\/p>\n<\/p>\n
      from matplotlib.colors import ListedColormap<\/p>\n
      new_cmap = ListedColormap(['red', 'green', 'blue'])<\/p>\n
      plt.scatter(x, y, c=y, cmap=new_cmap)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u7075\u6d3b\u5730\u6539\u53d8\u56fe\u8868\u7684\u989c\u8272\uff0c\u4ece\u800c\u521b\u5efa\u51fa\u65e2\u7f8e\u89c2\u53c8\u5b9e\u7528\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002\u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u4e0d\u4ec5\u80fd\u589e\u5f3a\u56fe\u8868\u7684\u89c6\u89c9\u6548\u679c\uff0c\u8fd8\u80fd\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002\u56e0\u6b64\uff0c\u5728\u8bbe\u8ba1\u56fe\u8868\u65f6\uff0c\u989c\u8272\u7684\u9009\u62e9\u548c\u5e94\u7528\u662f\u4e00\u4e2a\u4e0d\u5bb9\u5ffd\u89c6\u7684\u91cd\u8981\u73af\u8282\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5728Python\u4e2d\u66f4\u6539Matplotlib\u56fe\u8868\u7684\u989c\u8272\uff1f<\/strong>
      \u5728\u4f7f\u7528Matplotlib\u5e93\u521b\u5efa\u56fe\u8868\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u53c2\u6570\u6765\u6539\u53d8\u56fe\u8868\u7684\u989c\u8272\u3002\u4f8b\u5982\uff0c\u5728\u7ed8\u5236\u7ebf\u56fe\u65f6\uff0c\u53ef\u4ee5\u5728plot\u51fd\u6570\u4e2d\u4f7f\u7528color\u53c2\u6570\uff0c\u4f20\u5165\u989c\u8272\u540d\u79f0\u3001\u5341\u516d\u8fdb\u5236\u503c\u6216RGB\u5143\u7ec4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
      import matplotlib.pyplot as plt\n\nx = [1, 2, 3, 4]\ny = [10, 20, 25, 30]\n\nplt.plot(x, y, color='blue')  # \u4f7f\u7528\u989c\u8272\u540d\u79f0\nplt.show()\n<\/code><\/pre>\n
      \u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528plt.set_color()\u65b9\u6cd5\u6765\u4fee\u6539\u5df2\u6709\u56fe\u8868\u7684\u989c\u8272\u3002<\/p>\n
      \u4f7f\u7528Seaborn\u5e93\u5982\u4f55\u8c03\u6574\u56fe\u8868\u7684\u914d\u8272\u65b9\u6848\uff1f<\/strong>
      Seaborn\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u8c03\u8272\u677f\uff0c\u53ef\u4ee5\u8f7b\u677e\u4e3a\u56fe\u8868\u8bbe\u7f6e\u7f8e\u89c2\u7684\u989c\u8272\u3002\u901a\u8fc7\u4f7f\u7528set_palette()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u8c03\u8272\u677f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528set_palette("pastel")<\/code>\u53ef\u4ee5\u5c06\u56fe\u8868\u989c\u8272\u8c03\u6574\u4e3a\u67d4\u548c\u7684\u8272\u8c03\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
      import seaborn as sns\nimport matplotlib.pyplot as plt\n\nsns.set_palette("pastel")\nsns.barplot(x=["A", "B", "C"], y=[4, 7, 1])\nplt.show()\n<\/code><\/pre>\n
      \u8fd9\u79cd\u65b9\u5f0f\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u5347\u56fe\u8868\u7684\u89c6\u89c9\u6548\u679c\uff0c\u8fd8\u80fd\u4f7f\u6570\u636e\u7684\u6bd4\u8f83\u66f4\u4e3a\u6e05\u6670\u3002<\/p>\n
      \u5982\u4f55\u5728Python\u4e2d\u4e3a\u4e0d\u540c\u7684\u6570\u636e\u7cfb\u5217\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\uff1f<\/strong>
      \u5982\u679c\u4f60\u5e0c\u671b\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u4e3a\u4e0d\u540c\u7684\u6570\u636e\u7cfb\u5217\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\uff0c\u53ef\u4ee5\u5728\u7ed8\u5236\u591a\u4e2a\u6570\u636e\u7cfb\u5217\u65f6\u5206\u522b\u6307\u5b9a\u989c\u8272\u3002\u4f8b\u5982\uff0c\u5728\u7ed8\u5236\u591a\u4e2a\u7ebf\u6761\u65f6\uff0c\u53ef\u4ee5\u4e3a\u6bcf\u6761\u7ebf\u6307\u5b9a\u4e0d\u540c\u7684\u989c\u8272\u53c2\u6570\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
      import matplotlib.pyplot as plt\n\nx = [1, 2, 3, 4]\ny1 = [10, 20, 30, 40]\ny2 = [15, 25, 35, 45]\n\nplt.plot(x, y1, color='red', label='Series 1')\nplt.plot(x, y2, color='green', label='Series 2')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
      \u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u6bd4\u8f83\u4e0d\u540c\u6570\u636e\u7cfb\u5217\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6539\u53d8\u56fe\u8868\u7684\u989c\u8272\u4e3b\u8981\u901a\u8fc7\u8c03\u6574\u7ed8\u56fe\u5e93\uff08\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff09\u7684 […]","protected":false},"author":3,"featured_media":971221,"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\/971211"}],"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=971211"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/971211\/revisions"}],"predecessor-version":[{"id":971226,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/971211\/revisions\/971226"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/971221"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=971211"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=971211"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=971211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}