{"id":1076073,"date":"2025-01-08T11:50:06","date_gmt":"2025-01-08T03:50:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1076073.html"},"modified":"2025-01-08T11:50:08","modified_gmt":"2025-01-08T03:50:08","slug":"python%e7%ae%b1%e5%9e%8b%e5%9b%be%e5%a6%82%e4%bd%95%e6%98%be%e7%a4%ba%e7%82%b9%e6%95%b0%e6%8d%ae-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1076073.html","title":{"rendered":"Python\u7bb1\u578b\u56fe\u5982\u4f55\u663e\u793a\u70b9\u6570\u636e"},"content":{"rendered":"

\"Python\u7bb1\u578b\u56fe\u5982\u4f55\u663e\u793a\u70b9\u6570\u636e\"<\/p>\n

Python\u7bb1\u578b\u56fe\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u663e\u793a\u70b9\u6570\u636e\u3001\u4f7f\u7528 seaborn \u5e93\u3001\u5229\u7528 matplotlib \u5e93<\/strong><\/p>\n<\/p>\n

\u7bb1\u578b\u56fe\uff08Boxplot\uff09\u662f\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u4e00\u79cd\u5e38\u89c1\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002Python \u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u5f0f\u6765\u7ed8\u5236\u7bb1\u578b\u56fe\uff0c\u5e76\u4e14\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u663e\u793a\u70b9\u6570\u636e\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u7ed8\u5236\u7bb1\u578b\u56fe\u5e76\u663e\u793a\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 Matplotlib \u548c Seaborn \u7ed8\u5236\u7bb1\u578b\u56fe\u5e76\u663e\u793a\u70b9\u6570\u636e<\/h3>\n<\/p>\n

1\u3001Matplotlib \u7ed8\u5236\u7bb1\u578b\u56fe<\/h4>\n<\/p>\n

Matplotlib \u662f Python \u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Matplotlib \u7ed8\u5236\u7bb1\u578b\u56fe\uff0c\u5e76\u4e14\u901a\u8fc7\u8bbe\u7f6e flierprops<\/code> \u53c2\u6570\u6765\u663e\u793a\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e9b\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(10)<\/p>\n
data = np.random.normal(100, 20, 200)<\/p>\n
\u7ed8\u5236\u7bb1\u578b\u56fe<\/strong><\/h2>\n
plt.boxplot(data, flierprops=dict(marker='o', color='r', alpha=0.5))<\/p>\n
plt.title('Boxplot with Matplotlib')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0cflierprops<\/code> \u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u5f02\u5e38\u503c\u7684\u6837\u5f0f\u3002marker<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u5f62\u72b6\uff0ccolor<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u989c\u8272\uff0calpha<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n
2\u3001Seaborn \u7ed8\u5236\u7bb1\u578b\u56fe<\/h4>\n<\/p>\n
Seaborn \u662f\u57fa\u4e8e Matplotlib \u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u7ed8\u56fe\u63a5\u53e3\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Seaborn \u6765\u7ed8\u5236\u7bb1\u578b\u56fe\uff0c\u5e76\u663e\u793a\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e9b\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(10)<\/p>\n
data = np.random.normal(100, 20, 200)<\/p>\n
\u7ed8\u5236\u7bb1\u578b\u56fe<\/strong><\/h2>\n
sns.boxplot(data, whis=1.5)<\/p>\n
sns.stripplot(data, jitter=True, color='r', alpha=0.5)<\/p>\n
plt.title('Boxplot with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 sns.boxplot<\/code> \u7ed8\u5236\u7bb1\u578b\u56fe\uff0c\u7136\u540e\u4f7f\u7528 sns.stripplot<\/code> \u7ed8\u5236\u6570\u636e\u70b9\u3002jitter<\/code> \u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u70b9\u7684\u6296\u52a8\uff0ccolor<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u989c\u8272\uff0calpha<\/code> \u53c2\u6570\u8bbe\u7f6e\u70b9\u7684\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u7bb1\u578b\u56fe<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6bd4\u591a\u7ec4\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Matplotlib \u6216 Seaborn \u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u7bb1\u578b\u56fe\uff0c\u5e76\u663e\u793a\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
1\u3001Matplotlib \u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u7bb1\u578b\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e9b\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(10)<\/p>\n
data1 = np.random.normal(100, 20, 200)<\/p>\n
data2 = np.random.normal(90, 15, 200)<\/p>\n
data3 = np.random.normal(80, 10, 200)<\/p>\n
data = [data1, data2, data3]<\/p>\n
\u7ed8\u5236\u7bb1\u578b\u56fe<\/strong><\/h2>\n
plt.boxplot(data, flierprops=dict(marker='o', color='r', alpha=0.5))<\/p>\n
plt.title('Multiple Boxplots with Matplotlib')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c06\u591a\u7ec4\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u5e76\u4f20\u9012\u7ed9 plt.boxplot<\/code> \u51fd\u6570\u8fdb\u884c\u7ed8\u56fe\u3002<\/p>\n<\/p>\n
2\u3001Seaborn \u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u7bb1\u578b\u56fe<\/h4>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
import pandas as pd<\/p>\n
\u751f\u6210\u4e00\u4e9b\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(10)<\/p>\n
data1 = np.random.normal(100, 20, 200)<\/p>\n
data2 = np.random.normal(90, 15, 200)<\/p>\n
data3 = np.random.normal(80, 10, 200)<\/p>\n
\u6784\u5efa DataFrame<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'Group': ['A']*200 + ['B']*200 + ['C']*200,<\/p>\n
    'Value': np.concatenate([data1, data2, data3])<\/p>\n
})<\/p>\n
\u7ed8\u5236\u7bb1\u578b\u56fe<\/strong><\/h2>\n
sns.boxplot(x='Group', y='Value', data=data, whis=1.5)<\/p>\n
sns.stripplot(x='Group', y='Value', data=data, jitter=True, color='r', alpha=0.5)<\/p>\n
plt.title('Multiple Boxplots with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c06\u591a\u7ec4\u6570\u636e\u6784\u5efa\u6210\u4e00\u4e2a DataFrame\uff0c\u5e76\u4f7f\u7528 sns.boxplot<\/code> \u548c sns.stripplot<\/code> \u5206\u522b\u7ed8\u5236\u7bb1\u578b\u56fe\u548c\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8be6\u7ec6\u89e3\u91ca\u663e\u793a\u6570\u636e\u70b9\u7684\u91cd\u8981\u6027<\/h3>\n<\/p>\n
\u5728\u7bb1\u578b\u56fe\u4e2d\u663e\u793a\u6570\u636e\u70b9\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u867d\u7136\u7bb1\u578b\u56fe\u53ef\u4ee5\u5c55\u793a\u6570\u636e\u7684\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u548c\u5f02\u5e38\u503c\uff0c\u4f46\u65e0\u6cd5\u5c55\u793a\u6bcf\u4e2a\u6570\u636e\u70b9\u7684\u5177\u4f53\u4f4d\u7f6e\u3002\u901a\u8fc7\u663e\u793a\u6570\u636e\u70b9\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u770b\u5230\u6570\u636e\u7684\u5bc6\u96c6\u7a0b\u5ea6\u3001\u5206\u5e03\u5f62\u6001\u548c\u5f02\u5e38\u503c\u7684\u5177\u4f53\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u5728\u5206\u6790\u5b9e\u9a8c\u6570\u636e\u65f6\uff0c\u663e\u793a\u6570\u636e\u70b9\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u51fa\u54ea\u4e9b\u6570\u636e\u70b9\u53ef\u80fd\u662f\u5f02\u5e38\u503c\uff0c\u5e76\u8fdb\u4e00\u6b65\u5206\u6790\u5176\u539f\u56e0\u3002\u5728\u6bd4\u8f83\u591a\u7ec4\u6570\u636e\u65f6\uff0c\u663e\u793a\u6570\u636e\u70b9\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u770b\u5230\u4e0d\u540c\u7ec4\u6570\u636e\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528 Matplotlib \u548c Seaborn \u7ed8\u5236\u7bb1\u578b\u56fe\uff0c\u5e76\u663e\u793a\u6570\u636e\u70b9\u3002\u5177\u4f53\u65b9\u6cd5\u5305\u62ec\uff1a<\/p>\n<\/p>\n
    \n
  • \u4f7f\u7528 flierprops<\/code> \u53c2\u6570\u8bbe\u7f6e Matplotlib \u4e2d\u7bb1\u578b\u56fe\u7684\u5f02\u5e38\u503c\u6837\u5f0f\u3002<\/li>\n
  • \u4f7f\u7528 sns.stripplot<\/code> \u5728 Seaborn \u7684\u7bb1\u578b\u56fe\u4e2d\u663e\u793a\u6570\u636e\u70b9\u3002<\/li>\n
  • \u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u7bb1\u578b\u56fe\uff0c\u5e76\u901a\u8fc7\u663e\u793a\u6570\u636e\u70b9\u8fdb\u884c\u5bf9\u6bd4\u5206\u6790\u3002<\/li>\n<\/ul>\n
    \u603b\u4e4b\uff0c\u5728\u7bb1\u578b\u56fe\u4e2d\u663e\u793a\u6570\u636e\u70b9\u53ef\u4ee5\u63d0\u4f9b\u66f4\u591a\u7684\u6570\u636e\u4fe1\u606f\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u65b9\u5f0f\u548c\u53c2\u6570\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u7bb1\u578b\u56fe\u4e2d\u6dfb\u52a0\u6570\u636e\u70b9\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u4e2d\u7684boxplot\u51fd\u6570\u6765\u521b\u5efa\u7bb1\u578b\u56fe\uff0c\u5e76\u901a\u8fc7\u4f7f\u7528scatter\u51fd\u6570\u6216stripplot\u51fd\u6570\u6765\u53e0\u52a0\u6570\u636e\u70b9\u3002\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\uff1a\u4f7f\u7528plt.boxplot()\u751f\u6210\u7bb1\u578b\u56fe\uff0c\u518d\u901a\u8fc7plt.scatter()\u6216sns.stripplot()\u6765\u5728\u7bb1\u578b\u56fe\u4e0a\u53e0\u52a0\u6570\u636e\u70b9\u3002<\/p>\n
    \u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5728Python\u4e2d\u7ed8\u5236\u7bb1\u578b\u56fe\uff1f<\/strong>
    Python\u4e2d\u7ed8\u5236\u7bb1\u578b\u56fe\u7684\u5e38\u7528\u5e93\u5305\u62ecmatplotlib\u3001seaborn\u548cplotly\u3002Matplotlib\u63d0\u4f9b\u4e86\u57fa\u7840\u7684\u7ed8\u56fe\u529f\u80fd\uff0cSeaborn\u5728\u6b64\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u7f8e\u89c2\u7684\u4e3b\u9898\u548c\u66f4\u7b80\u4fbf\u7684\u63a5\u53e3\uff0c\u800cPlotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u7684\u53ef\u89c6\u5316\u4f53\u9a8c\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u9002\u5408\u7684\u5e93\u53ef\u4ee5\u5927\u5927\u63d0\u5347\u7ed8\u56fe\u6548\u679c\u548c\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n
    \u600e\u6837\u81ea\u5b9a\u4e49\u7bb1\u578b\u56fe\u7684\u6837\u5f0f\u4ee5\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7bb1\u578b\u56fe\u7684\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u5176\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528matplotlib\u7684boxplot\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u8c03\u6574\u7bb1\u4f53\u7684\u989c\u8272\u3001\u8fb9\u6846\u6837\u5f0f\u3001\u79bb\u7fa4\u70b9\u7684\u5f62\u72b6\u7b49\u3002\u6b64\u5916\uff0cseaborn\u5e93\u4e5f\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u5305\u62ec\u4e3b\u9898\u6837\u5f0f\u3001\u8c03\u8272\u677f\u7b49\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u6570\u636e\u7279\u5f81\u548c\u5c55\u793a\u9700\u6c42\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u7bb1\u578b\u56fe\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u663e\u793a\u70b9\u6570\u636e\u3001\u4f7f\u7528 seaborn \u5e93\u3001\u5229\u7528 matplotlib \u5e93 \u7bb1\u578b\u56fe […]","protected":false},"author":3,"featured_media":1076078,"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\/1076073"}],"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=1076073"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1076073\/revisions"}],"predecessor-version":[{"id":1076080,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1076073\/revisions\/1076080"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1076078"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1076073"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1076073"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1076073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}