{"id":969134,"date":"2024-12-27T05:13:47","date_gmt":"2024-12-26T21:13:47","guid":{"rendered":""},"modified":"2024-12-27T05:13:49","modified_gmt":"2024-12-26T21:13:49","slug":"python%e7%94%bb%e6%95%a3%e7%82%b9%e5%9b%be%e5%a6%82%e4%bd%95%e5%88%86%e7%bb%84","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/969134.html","title":{"rendered":"python\u753b\u6563\u70b9\u56fe\u5982\u4f55\u5206\u7ec4"},"content":{"rendered":"

\"python\u753b\u6563\u70b9\u56fe\u5982\u4f55\u5206\u7ec4\"<\/p>\n

\u5728Python\u4e2d\uff0c\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u8fdb\u884c\u5206\u7ec4\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u5b9e\u73b0\u3002\u5229\u7528\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u4e3a\u6bcf\u4e2a\u7ec4\u5206\u914d\u4e0d\u540c\u7684\u989c\u8272\u3001\u5f62\u72b6\u6216\u5927\u5c0f\u6765\u533a\u5206\u6570\u636e\u7ec4\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u6807\u8bb0\u3001\u4f7f\u7528\u4e0d\u540c\u7684\u5f62\u72b6\u8fdb\u884c\u6807\u8bc6\u3001\u4ee5\u53ca\u7ed3\u5408\u989c\u8272\u548c\u5f62\u72b6\u6765\u589e\u5f3a\u53ef\u89c6\u5316\u6548\u679c\u3002\u5177\u4f53\u5b9e\u73b0\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u7684\u6563\u70b9\u51fd\u6570\uff08scatter\uff09\uff0c\u4ee5\u53caSeaborn\u7684\u6563\u70b9\u56fe\u51fd\u6570\uff08scatterplot\uff09\u3002<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u5206\u7ec4\u662f\u6bd4\u8f83\u5e38\u89c1\u7684\u505a\u6cd5\uff0c\u901a\u5e38\u9700\u8981\u5148\u5c06\u6570\u636e\u6309\u7167\u7ec4\u522b\u8fdb\u884c\u5212\u5206\uff0c\u7136\u540e\u4e3a\u6bcf\u4e2a\u7ec4\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\u548c\u6807\u8bb0\u3002\u4e3a\u4e86\u66f4\u597d\u5730\u53ef\u89c6\u5316\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u56fe\u4f8b\uff08legend\uff09\u6765\u6807\u8bc6\u6bcf\u4e2a\u7ec4\u7684\u542b\u4e49\u3002Seaborn\u5e93\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u4fbf\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u76f4\u63a5\u4f20\u5165\u6570\u636e\u548c\u7ec4\u522b\u4fe1\u606f\u6765\u81ea\u52a8\u8fdb\u884c\u989c\u8272\u548c\u5f62\u72b6\u7684\u5206\u7ec4\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7ed8\u5236\u5206\u7ec4\u6563\u70b9\u56fe<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\u3002<\/p>\n<\/p>\n

1.1 \u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n

\u8981\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u9996\u5148\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5305\u542b\u4e09\u7ec4\u6570\u636e\uff0c\u6bcf\u7ec4\u6570\u636e\u6709\u4e0d\u540c\u7684x\u548cy\u503c\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
np.random.seed(0)<\/p>\n
x1, y1 = np.random.rand(2, 100) * 100<\/p>\n
x2, y2 = np.random.rand(2, 100) * 100<\/p>\n
x3, y3 = np.random.rand(2, 100) * 100<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u7ed8\u5236\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
\u4f7f\u7528Matplotlib\u7684scatter\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u5e76\u4e3a\u4e0d\u540c\u7684\u7ec4\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\u548c\u6807\u8bb0\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x1, y1, c='r', label='Group 1')<\/p>\n
plt.scatter(x2, y2, c='g', label='Group 2')<\/p>\n
plt.scatter(x3, y3, c='b', label='Group 3')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend()<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Scatter Plot with Groups')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u4f7f\u7528\u6807\u8bb0\u533a\u5206\u7ec4<\/h3>\n<\/p>\n
\u9664\u4e86\u989c\u8272\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u6807\u8bb0\u6765\u533a\u5206\u6570\u636e\u7ec4\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u4e0d\u540c\u6807\u8bb0<\/p>\n
plt.scatter(x1, y1, c='r', marker='o', label='Group 1')<\/p>\n
plt.scatter(x2, y2, c='g', marker='s', label='Group 2')<\/p>\n
plt.scatter(x3, y3, c='b', marker='^', label='Group 3')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001SEABORN\u7ed8\u5236\u5206\u7ec4\u6563\u70b9\u56fe<\/p>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u4fbf\u7684\u65b9\u6cd5\u6765\u7ed8\u5236\u7edf\u8ba1\u56fe\u8868\u3002<\/p>\n<\/p>\n
2.1 \u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n
Seaborn\u901a\u5e38\u4f7f\u7528Pandas\u6570\u636e\u6846\u6765\u5904\u7406\u6570\u636e\uff0c\u56e0\u6b64\u9700\u8981\u5c06\u6570\u636e\u6574\u7406\u4e3a\u6570\u636e\u6846\u683c\u5f0f\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u6570\u636e\u6846<\/strong><\/h2>\n
df = pd.DataFrame({<\/p>\n
    'x': np.concatenate([x1, x2, x3]),<\/p>\n
    'y': np.concatenate([y1, y2, y3]),<\/p>\n
    'group': ['Group 1'] * 100 + ['Group 2'] * 100 + ['Group 3'] * 100<\/p>\n
})<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u4f7f\u7528Seaborn\u7ed8\u5236\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
Seaborn\u7684scatterplot\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u5206\u7ec4\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u4f7f\u7528Seaborn\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(data=df, x='x', y='y', hue='group', style='group')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Scatter Plot with Groups using Seaborn')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6807\u8bb0<\/h3>\n<\/p>\n
Seaborn\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6807\u8bb0\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u6ee1\u8db3\u7279\u5b9a\u9700\u6c42\u3002<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6807\u8bb0<\/p>\n
sns.scatterplot(data=df, x='x', y='y', hue='group', style='group',<\/p>\n
                palette={'Group 1': 'red', 'Group 2': 'green', 'Group 3': 'blue'},<\/p>\n
                markers={'Group 1': 'o', 'Group 2': 's', 'Group 3': '^'})<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408\u989c\u8272\u548c\u5f62\u72b6\u8fdb\u884c\u5206\u7ec4<\/p>\n<\/p>\n
\u7ed3\u5408\u989c\u8272\u548c\u5f62\u72b6\u662f\u63d0\u9ad8\u6570\u636e\u53ef\u89c6\u5316\u6548\u679c\u7684\u6709\u6548\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
3.1 \u989c\u8272\u548c\u5f62\u72b6\u7ed3\u5408<\/h3>\n<\/p>\n
\u901a\u8fc7\u7ed3\u5408\u989c\u8272\u548c\u5f62\u72b6\uff0c\u53ef\u4ee5\u5728\u89c6\u89c9\u4e0a\u66f4\u660e\u663e\u5730\u533a\u5206\u4e0d\u540c\u7684\u6570\u636e\u7ec4\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u989c\u8272\u548c\u5f62\u72b6\u7ed3\u5408<\/p>\n
plt.scatter(x1, y1, c='r', marker='o', label='Group 1')<\/p>\n
plt.scatter(x2, y2, c='g', marker='s', label='Group 2')<\/p>\n
plt.scatter(x3, y3, c='b', marker='^', label='Group 3')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u52a8\u6001\u8c03\u6574\u56fe\u4f8b\u548c\u6807\u8bb0<\/h3>\n<\/p>\n
\u52a8\u6001\u8c03\u6574\u56fe\u4f8b\u548c\u6807\u8bb0\u53ef\u4ee5\u8ba9\u56fe\u5f62\u66f4\u5177\u4ea4\u4e92\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
# \u52a8\u6001\u8c03\u6574\u56fe\u4f8b<\/p>\n
handles, labels = plt.gca().get_legend_handles_labels()<\/p>\n
order = [0, 2, 1]  # \u6839\u636e\u9700\u8981\u8c03\u6574\u56fe\u4f8b\u987a\u5e8f<\/p>\n
plt.legend([handles[idx] for idx in order], [labels[idx] for idx in order])<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u5728Python\u4e2d\u7ed8\u5236\u5206\u7ec4\u6563\u70b9\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u3001\u5f62\u72b6\u548c\u7ec4\u5408\u6765\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u89c6\u5316\u6548\u679c<\/strong>\u3002Matplotlib\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u800cSeaborn\u7b80\u5316\u4e86\u8bb8\u591a\u5e38\u89c1\u7684\u7ed8\u56fe\u4efb\u52a1\u3002\u6839\u636e\u6570\u636e\u7684\u590d\u6742\u6027\u548c\u7279\u5b9a\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u5c55\u793a\u6570\u636e\u662f\u5173\u952e\u3002\u901a\u8fc7\u5408\u7406\u5730\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u51fa\u6e05\u6670\u4e14\u4fe1\u606f\u4e30\u5bcc\u7684\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u4e0d\u540c\u989c\u8272\u6216\u5f62\u72b6\u533a\u5206\u6563\u70b9\u56fe\u4e2d\u7684\u4e0d\u540c\u7ec4\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u7b49\u5e93\u6765\u521b\u5efa\u6563\u70b9\u56fe\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u3002\u901a\u8fc7\u4e3a\u4e0d\u540c\u7ec4\u5b9a\u4e49\u7279\u5b9a\u7684\u989c\u8272\u6216\u5f62\u72b6\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u4f8b\u5982\uff0c\u5728Seaborn\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528hue<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u5206\u7ec4\u53d8\u91cf\uff0c\u8fd9\u6837\u6563\u70b9\u56fe\u4e2d\u7684\u6bcf\u4e2a\u7ec4\u90fd\u4f1a\u4ee5\u4e0d\u540c\u989c\u8272\u663e\u793a\u3002Matplotlib\u540c\u6837\u53ef\u4ee5\u901a\u8fc7scatter()<\/code>\u51fd\u6570\u4e2d\u7684c<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u6bcf\u4e2a\u70b9\u7684\u989c\u8272\u3002<\/p>\n
\u5728\u7ed8\u5236\u6563\u70b9\u56fe\u65f6\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\uff1f<\/strong>
\u5904\u7406\u7f3a\u5931\u503c\u662f\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u5728\u7ed8\u5236\u6563\u70b9\u56fe\u4e4b\u524d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684dropna()<\/code>\u51fd\u6570\u6765\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u51fd\u6570\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u7b49\u66ff\u6362\u7f3a\u5931\u503c\u3002\u786e\u4fdd\u5904\u7406\u540e\u7684\u6570\u636e\u96c6\u80fd\u591f\u51c6\u786e\u53cd\u6620\u4e0d\u540c\u7ec4\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u7531\u4e8e\u7f3a\u5931\u503c\u5bfc\u81f4\u7684\u8bef\u5bfc\u6027\u7ed3\u679c\u3002<\/p>\n
\u5982\u4f55\u5728\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u6807\u7b7e\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8bc6\u522b\u5404\u4e2a\u7ec4\u7684\u7279\u5f81\uff1f<\/strong>
\u4e3a\u4e86\u589e\u5f3a\u6563\u70b9\u56fe\u7684\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684annotate()<\/code>\u51fd\u6570\u4e3a\u7279\u5b9a\u70b9\u6dfb\u52a0\u6807\u7b7e\u3002\u8fd9\u5c06\u6709\u52a9\u4e8e\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6bcf\u4e2a\u70b9\u7684\u542b\u4e49\u548c\u5b83\u6240\u5c5e\u7684\u7ec4\u3002\u6b64\u5916\uff0cSeaborn\u63d0\u4f9b\u7684scatterplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u5728\u56fe\u4e2d\u663e\u793a\u6570\u636e\u70b9\u7684\u6807\u7b7e\uff0c\u63d0\u5347\u6574\u4f53\u7684\u53ef\u89c6\u5316\u6548\u679c\u3002\u4f7f\u7528\u6807\u7b7e\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u6bcf\u4e2a\u7ec4\u7684\u7279\u5f81\uff0c\u4fbf\u4e8e\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u8fdb\u884c\u5206\u7ec4\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u5b9e\u73b0\u3002\u5229\u7528\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef […]","protected":false},"author":3,"featured_media":969142,"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\/969134"}],"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=969134"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969134\/revisions"}],"predecessor-version":[{"id":969145,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969134\/revisions\/969145"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/969142"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=969134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=969134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=969134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}