{"id":1065851,"date":"2024-12-31T16:21:08","date_gmt":"2024-12-31T08:21:08","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1065851.html"},"modified":"2024-12-31T16:21:11","modified_gmt":"2024-12-31T08:21:11","slug":"python%e5%a6%82%e4%bd%95%e4%b8%ba%e7%82%b9%e6%b7%bb%e5%8a%a0%e4%b8%8d%e5%90%8c%e7%9a%84%e5%bd%a2%e7%8a%b6","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1065851.html","title":{"rendered":"python\u5982\u4f55\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6"},"content":{"rendered":"

\"python\u5982\u4f55\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\"<\/p>\n

\u5728Python\u4e2d\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u56fe\u5f62\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u3002<\/strong> \u4f7f\u7528\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3a\u70b9\u8bbe\u7f6e\u4e0d\u540c\u7684\u5f62\u72b6\u3001\u989c\u8272\u548c\u5927\u5c0f\uff0c\u4ee5\u4f7f\u6570\u636e\u53ef\u89c6\u5316\u66f4\u5177\u8868\u73b0\u529b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f7f\u7528Matplotlib\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u5f62\u72b6\u7684\u8be6\u7ec6\u4ecb\u7ecd\u3002<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u521b\u5efa\u5404\u79cd\u56fe\u8868\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001Matplotlib\u7b80\u4ecb<\/h3>\n<\/p>\n

Matplotlib \u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u753b\u548c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u7684\u7efc\u5408\u5e93\u3002\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0cMatplotlib \u662f\u4e00\u6b3e\u975e\u5e38\u6d41\u884c\u7684\u5de5\u5177\uff0c\u5e7f\u6cdb\u7528\u4e8e\u521b\u5efa\u5404\u79cd\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u3002\u901a\u8fc7Matplotlib\uff0c\u53ef\u4ee5\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u5916\u89c2\uff0c\u5305\u62ec\u70b9\u7684\u5f62\u72b6\u3001\u989c\u8272\u3001\u5927\u5c0f\u7b49\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u5b89\u88c5Matplotlib<\/h3>\n<\/p>\n

\u5728\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8fd9\u4e2a\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
\u5728\u4e86\u89e3\u5982\u4f55\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u638c\u63e1\u57fa\u672c\u7684\u7ed8\u56fe\u6280\u5de7\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6563\u70b9\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y)<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6<\/h3>\n<\/p>\n
\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7marker<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u70b9\u7684\u5f62\u72b6\u3002\u5e38\u89c1\u7684\u5f62\u72b6\u5305\u62ec\u5706\u5708\u3001\u65b9\u5f62\u3001\u4e09\u89d2\u5f62\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684marker<\/code>\u53c2\u6570\u503c\u53ca\u5176\u5bf9\u5e94\u7684\u5f62\u72b6\uff1a<\/p>\n<\/p>\n
    \n
  • o<\/code>\uff1a\u5706\u5708<\/li>\n
  • s<\/code>\uff1a\u65b9\u5f62<\/li>\n
  • ^<\/code>\uff1a\u4e0a\u4e09\u89d2<\/li>\n
  • v<\/code>\uff1a\u4e0b\u4e09\u89d2<\/li>\n
  • ><\/code>\uff1a\u53f3\u4e09\u89d2<\/li>\n
  • <<\/code>\uff1a\u5de6\u4e09\u89d2<\/li>\n
  • *<\/code>\uff1a\u661f\u5f62<\/li>\n
  • x<\/code>\uff1aX\u5f62<\/li>\n
  • D<\/code>\uff1a\u83f1\u5f62<\/li>\n<\/ul>\n
    \u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    x = [1, 2, 3, 4, 5]<\/p>\n
    y = [2, 3, 5, 7, 11]<\/p>\n
    \u4e0d\u540c\u5f62\u72b6\u7684\u70b9<\/strong><\/h2>\n
    shapes = ['o', 's', '^', 'v', '>']<\/p>\n
    \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
    for i in range(len(x)):<\/p>\n
        plt.scatter(x[i], y[i], marker=shapes[i], label=f'Shape {shapes[i]}')<\/p>\n
    \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
    plt.legend()<\/p>\n
    \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u989c\u8272\u548c\u5927\u5c0f<\/h3>\n<\/p>\n
    \u9664\u4e86\u5f62\u72b6\u4e4b\u5916\uff0c\u8fd8\u53ef\u4ee5\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u989c\u8272\u548c\u5927\u5c0f\u3002\u53ef\u4ee5\u4f7f\u7528c<\/code>\u53c2\u6570\u8bbe\u7f6e\u989c\u8272\uff0cs<\/code>\u53c2\u6570\u8bbe\u7f6e\u5927\u5c0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    x = [1, 2, 3, 4, 5]<\/p>\n
    y = [2, 3, 5, 7, 11]<\/p>\n
    \u4e0d\u540c\u989c\u8272\u548c\u5927\u5c0f\u7684\u70b9<\/strong><\/h2>\n
    colors = ['r', 'g', 'b', 'c', 'm']<\/p>\n
    sizes = [50, 100, 150, 200, 250]<\/p>\n
    \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
    for i in range(len(x)):<\/p>\n
        plt.scatter(x[i], y[i], marker=shapes[i], color=colors[i], s=sizes[i], label=f'Shape {shapes[i]}')<\/p>\n
    \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
    plt.legend()<\/p>\n
    \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u7ed3\u5408\u4f7f\u7528\u4e0d\u540c\u7684\u5f62\u72b6\u3001\u989c\u8272\u548c\u5927\u5c0f<\/h3>\n<\/p>\n
    \u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u4e0d\u540c\u7684\u5f62\u72b6\u3001\u989c\u8272\u548c\u5927\u5c0f\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u52a0\u4e30\u5bcc\u548c\u591a\u6837\u5316\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7ed3\u5408\u4f7f\u7528\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    x = [1, 2, 3, 4, 5]<\/p>\n
    y = [2, 3, 5, 7, 11]<\/p>\n
    \u4e0d\u540c\u5f62\u72b6\u7684\u70b9<\/strong><\/h2>\n
    shapes = ['o', 's', '^', 'v', '>']<\/p>\n
    colors = ['r', 'g', 'b', 'c', 'm']<\/p>\n
    sizes = [50, 100, 150, 200, 250]<\/p>\n
    \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
    for i in range(len(x)):<\/p>\n
        plt.scatter(x[i], y[i], marker=shapes[i], color=colors[i], s=sizes[i], label=f'Shape {shapes[i]}')<\/p>\n
    \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
    plt.legend()<\/p>\n
    \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e03\u3001Seaborn\u5e93\u7ed8\u56fe<\/h3>\n<\/p>\n
    Seaborn \u662f\u57fa\u4e8e Matplotlib \u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u7ed8\u56fe\u63a5\u53e3\u3002\u867d\u7136 Seaborn \u9ed8\u8ba4\u4e0d\u652f\u6301\u8bbe\u7f6e\u70b9\u7684\u5f62\u72b6\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7 sns.scatterplot<\/code> \u51fd\u6570\u7684 style<\/code> \u53c2\u6570\u5b9e\u73b0\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    import seaborn as sns<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    data = {'x': [1, 2, 3, 4, 5], 'y': [2, 3, 5, 7, 11], 'shape': ['o', 's', '^', 'v', '>']}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
    sns.scatterplot(data=df, x='x', y='y', style='shape')<\/p>\n
    \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516b\u3001Plotly\u5e93\u7ed8\u56fe<\/h3>\n<\/p>\n
    Plotly \u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u66f4\u52a0\u590d\u6742\u7684\u56fe\u8868\u548c\u4ea4\u4e92\u6548\u679c\u3002\u53ef\u4ee5\u4f7f\u7528 plotly.express<\/code> \u6a21\u5757\u7684 scatter<\/code> \u51fd\u6570\u6765\u521b\u5efa\u6563\u70b9\u56fe\uff0c\u5e76\u901a\u8fc7 symbol<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u5f62\u72b6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    import plotly.express as px<\/p>\n
    \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    data = {'x': [1, 2, 3, 4, 5], 'y': [2, 3, 5, 7, 11], 'shape': ['circle', 'square', 'triangle-up', 'triangle-down', 'diamond']}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
    fig = px.scatter(df, x='x', y='y', symbol='shape')<\/p>\n
    \u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\uff0c\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u7684Matplotlib\u5e93\u4e3a\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\u3001\u989c\u8272\u548c\u5927\u5c0f\u3002\u6b64\u5916\uff0c\u8fd8\u7b80\u8981\u4ecb\u7ecd\u4e86Seaborn\u548cPlotly\u5e93\u7684\u76f8\u5173\u7528\u6cd5\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u6280\u5de7\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u52a0\u4e30\u5bcc\u591a\u6837\u7684\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5206\u6790\u548c\u5c55\u793a\u6570\u636e\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u8fdb\u4e00\u6b65\u63d0\u5347\u60a8\u7684\u6570\u636e\u53ef\u89c6\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4e3a\u6570\u636e\u70b9\u9009\u62e9\u4e0d\u540c\u7684\u5f62\u72b6\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u8f7b\u677e\u4e3a\u6570\u636e\u70b9\u6dfb\u52a0\u4e0d\u540c\u7684\u5f62\u72b6\u3002\u901a\u8fc7\u8bbe\u7f6escatter\u51fd\u6570\u4e2d\u7684marker\u53c2\u6570\uff0c\u53ef\u4ee5\u9009\u62e9\u591a\u79cd\u5f62\u72b6\uff0c\u5982\u5706\u5708\uff08'o'\uff09\u3001\u65b9\u5f62\uff08's'\uff09\u3001\u4e09\u89d2\u5f62\uff08'^'\uff09\u7b49\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u81ea\u5b9a\u4e49\u5f62\u72b6\u7684\u56fe\u6807\u6587\u4ef6\uff0c\u6765\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n
    \u5982\u4f55\u5728Python\u4e2d\u6839\u636e\u6761\u4ef6\u4e3a\u70b9\u5206\u914d\u4e0d\u540c\u7684\u5f62\u72b6\uff1f<\/strong>
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