{"id":999893,"date":"2024-12-27T09:45:14","date_gmt":"2024-12-27T01:45:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/999893.html"},"modified":"2024-12-27T09:45:17","modified_gmt":"2024-12-27T01:45:17","slug":"python%e6%95%a3%e7%82%b9%e5%9b%be%e5%a6%82%e4%bd%95%e5%8a%a0%e5%9b%be%e7%a4%ba","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/999893.html","title":{"rendered":"Python\u6563\u70b9\u56fe\u5982\u4f55\u52a0\u56fe\u793a"},"content":{"rendered":"

\"Python\u6563\u70b9\u56fe\u5982\u4f55\u52a0\u56fe\u793a\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684scatter\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u5e76\u901a\u8fc7legend\u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b<\/strong>\u3002\u5728\u6dfb\u52a0\u56fe\u4f8b\u65f6\uff0c\u901a\u5e38\u4f1a\u4e3a\u4e0d\u540c\u7684\u6570\u636e\u96c6\u6216\u7c7b\u522b\u5206\u914d\u4e0d\u540c\u7684\u989c\u8272\u6216\u6807\u8bb0\uff0c\u4ee5\u4fbf\u6e05\u6670\u5730\u5c55\u793a\u6570\u636e\u7684\u5206\u7c7b\u6216\u5206\u7ec4\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u56fe\u8868\u7684\u53ef\u8bfb\u6027\uff0c\u8fd8\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u80cc\u540e\u7684\u4fe1\u606f\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u7b80\u4ecb<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u7279\u522b\u9002\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u4ea4\u4e92\u5f0f\u548c\u52a8\u753b\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002\u5b83\u63d0\u4f9b\u4e86\u5404\u79cd\u56fe\u5f62\u7684\u7ed8\u5236\u529f\u80fd\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\uff0cMatplotlib\u662f\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n

Matplotlib\u7684\u6838\u5fc3\u662fpyplot\u5b50\u6a21\u5757\uff0c\u5b83\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eMATLAB\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u4f7f\u5f97\u7ed8\u56fe\u8fc7\u7a0b\u66f4\u52a0\u76f4\u89c2\u548c\u7b80\u5355\u3002\u901a\u8fc7pyplot\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u3001\u4fee\u6539\u548c\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u7ed8\u5236\u6563\u70b9\u56fe<\/h3>\n<\/p>\n

1. \u521b\u5efa\u6570\u636e<\/h4>\n<\/p>\n

\u5728\u7ed8\u5236\u6563\u70b9\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e9b\u6570\u636e\u3002\u901a\u5e38\u60c5\u51b5\u4e0b\uff0c\u6570\u636e\u53ef\u4ee5\u6765\u81ea\u4e8e\u6587\u4ef6\u3001\u6570\u636e\u5e93\u6216\u76f4\u63a5\u5728\u4ee3\u7801\u4e2d\u5b9a\u4e49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u521b\u5efa\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
x = np.random.rand(50)<\/p>\n
y = np.random.rand(50)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86NumPy\u5e93\u6765\u751f\u621050\u4e2a\u968f\u673a\u6570\uff0c\u5206\u522b\u4f5c\u4e3ax\u548cy\u5750\u6807\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528scatter\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y)<\/p>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
plt.title('Scatter Plot Example')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cscatter<\/code>\u51fd\u6570\u7528\u4e8e\u7ed8\u5236\u6563\u70b9\u56fe\uff0cxlabel<\/code>\u548cylabel<\/code>\u7528\u4e8e\u8bbe\u7f6e\u5750\u6807\u8f74\u6807\u7b7e\uff0ctitle<\/code>\u7528\u4e8e\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\uff0cshow<\/code>\u51fd\u6570\u7528\u4e8e\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u6dfb\u52a0\u56fe\u4f8b<\/h3>\n<\/p>\n
1. \u4e3a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5206\u914d\u4e0d\u540c\u7684\u989c\u8272\u6216\u6807\u8bb0<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u5206\u4e3a\u591a\u4e2a\u7c7b\u522b\uff0c\u5e76\u4e3a\u6bcf\u4e2a\u7c7b\u522b\u5206\u914d\u4e0d\u540c\u7684\u989c\u8272\u6216\u6807\u8bb0\uff0c\u4ee5\u4fbf\u5728\u56fe\u4e2d\u533a\u5206\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e09\u7ec4\u6570\u636e<\/p>\n
x1, y1 = np.random.rand(50), np.random.rand(50)<\/p>\n
x2, y2 = np.random.rand(50), np.random.rand(50)<\/p>\n
x3, y3 = np.random.rand(50), np.random.rand(50)<\/p>\n
\u7ed8\u5236\u4e0d\u540c\u989c\u8272\u7684\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x1, y1, c='r', label='Category 1')<\/p>\n
plt.scatter(x2, y2, c='g', label='Category 2')<\/p>\n
plt.scatter(x3, y3, c='b', label='Category 3')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e09\u7ec4\u6570\u636e\uff0c\u5e76\u901a\u8fc7c<\/code>\u53c2\u6570\u4e3a\u6bcf\u7ec4\u6570\u636e\u5206\u914d\u4e86\u4e0d\u540c\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528legend\u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b<\/h4>\n<\/p>\n
# \u6dfb\u52a0\u56fe\u4f8b<\/p>\n
plt.legend()<\/p>\n
<\/code><\/pre>\n<\/p>\n
legend<\/code>\u51fd\u6570\u7528\u4e8e\u6dfb\u52a0\u56fe\u4f8b\u3002\u901a\u8fc7\u4e3a\u6bcf\u7ec4\u6570\u636e\u6307\u5b9alabel<\/code>\u53c2\u6570\uff0clegend\u51fd\u6570\u5c06\u81ea\u52a8\u751f\u6210\u76f8\u5e94\u7684\u56fe\u4f8b\u9879\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5b9a\u5236\u56fe\u4f8b<\/h3>\n<\/p>\n
\u56fe\u4f8b\u7684\u6837\u5f0f\u548c\u4f4d\u7f6e\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\u5b9a\u5236\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
1. \u8bbe\u7f6e\u56fe\u4f8b\u7684\u4f4d\u7f6e<\/h4>\n<\/p>\n
# \u6dfb\u52a0\u56fe\u4f8b\u5e76\u8bbe\u7f6e\u4f4d\u7f6e<\/p>\n
plt.legend(loc='upper right')<\/p>\n
<\/code><\/pre>\n<\/p>\n
loc<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3002\u5e38\u7528\u7684\u4f4d\u7f6e\u9009\u9879\u5305\u62ecupper right<\/code>\u3001upper left<\/code>\u3001lower left<\/code>\u3001lower right<\/code>\u7b49\u3002<\/p>\n<\/p>\n
2. \u8bbe\u7f6e\u56fe\u4f8b\u7684\u5b57\u4f53\u5927\u5c0f<\/h4>\n<\/p>\n
# \u6dfb\u52a0\u56fe\u4f8b\u5e76\u8bbe\u7f6e\u5b57\u4f53\u5927\u5c0f<\/p>\n
plt.legend(fontsize='small')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7fontsize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u56fe\u4f8b\u6587\u5b57\u7684\u5927\u5c0f\u3002\u53ef\u9009\u503c\u5305\u62ecsmall<\/code>\u3001medium<\/code>\u3001large<\/code>\u7b49\u3002<\/p>\n<\/p>\n
3. \u81ea\u5b9a\u4e49\u56fe\u4f8b\u6807\u8bb0<\/h4>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u81ea\u5b9a\u4e49\u56fe\u4f8b\u6807\u8bb0\u7684\u6837\u5f0f\u3002\u53ef\u4ee5\u901a\u8fc7Handler<\/code>\u7c7b\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
from matplotlib.legend_handler import HandlerLine2D<\/p>\n
\u81ea\u5b9a\u4e49\u56fe\u4f8b\u6807\u8bb0\u6837\u5f0f<\/strong><\/h2>\n
plt.legend(handler_map={plt.Line2D: HandlerLine2D(numpoints=2)})<\/p>\n
<\/code><\/pre>\n<\/p>\n
HandlerLine2D<\/code>\u7c7b\u7684numpoints<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u56fe\u4f8b\u6807\u8bb0\u7684\u6837\u672c\u70b9\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u793a\u4f8b<\/h3>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u7ed8\u5236\u5e26\u6709\u56fe\u4f8b\u7684\u6563\u70b9\u56fe\uff0c\u5e76\u8fdb\u884c\u5404\u79cd\u5b9a\u5236\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x1, y1 = np.random.rand(50), np.random.rand(50)<\/p>\n
x2, y2 = np.random.rand(50), np.random.rand(50)<\/p>\n
x3, y3 = np.random.rand(50), np.random.rand(50)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x1, y1, c='r', label='Category 1')<\/p>\n
plt.scatter(x2, y2, c='g', label='Category 2')<\/p>\n
plt.scatter(x3, y3, c='b', label='Category 3')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend(loc='upper right', fontsize='medium')<\/p>\n
\u8bbe\u7f6e\u5750\u6807\u8f74\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
plt.title('Customized Scatter Plot with Legend')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5176\u4ed6\u53ef\u89c6\u5316\u6280\u5de7<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\uff0c\u9664\u4e86\u57fa\u672c\u7684\u6563\u70b9\u56fe\u548c\u56fe\u4f8b\u4e4b\u5916\uff0c\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u7684\u53ef\u89c6\u5316\u6280\u5de7\u548c\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528\u6807\u8bb0\u5927\u5c0f\u548c\u989c\u8272\u8868\u793a\u6570\u636e<\/h4>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u6211\u4eec\u4e0d\u4ec5\u9700\u8981\u901a\u8fc7\u4f4d\u7f6e\u6765\u8868\u793a\u6570\u636e\uff0c\u8fd8\u9700\u8981\u901a\u8fc7\u6807\u8bb0\u7684\u5927\u5c0f\u548c\u989c\u8272\u6765\u4f20\u8fbe\u66f4\u591a\u7684\u4fe1\u606f\u3002scatter<\/code>\u51fd\u6570\u7684s<\/code>\u548cc<\/code>\u53c2\u6570\u53ef\u4ee5\u7528\u4e8e\u8bbe\u7f6e\u6807\u8bb0\u7684\u5927\u5c0f\u548c\u989c\u8272\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u6807\u8bb0\u5927\u5c0f\u548c\u989c\u8272<\/p>\n
sizes = 100 * np.random.rand(50)<\/p>\n
colors = np.random.rand(50)<\/p>\n
plt.scatter(x, y, s=sizes, c=colors, alpha=0.5, cmap='viridis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0csizes<\/code>\u6570\u7ec4\u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u70b9\u7684\u5927\u5c0f\uff0ccolors<\/code>\u6570\u7ec4\u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u70b9\u7684\u989c\u8272\uff0calpha<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u900f\u660e\u5ea6\uff0ccmap<\/code>\u53c2\u6570\u7528\u4e8e\u9009\u62e9\u989c\u8272\u6620\u5c04\u3002<\/p>\n<\/p>\n
2. \u7ed8\u5236\u5e26\u56de\u5f52\u7ebf\u7684\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6563\u70b9\u56fe\u5e38\u5e38\u4e0e\u56de\u5f52\u5206\u6790\u7ed3\u5408\u4f7f\u7528\uff0c\u4ee5\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u8d8b\u52bf\u3002\u53ef\u4ee5\u4f7f\u7528numpy.polyfit<\/code>\u51fd\u6570\u62df\u5408\u56de\u5f52\u7ebf\uff0c\u5e76\u4f7f\u7528plot<\/code>\u51fd\u6570\u7ed8\u5236\u56de\u5f52\u7ebf\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u56de\u5f52\u7cfb\u6570<\/p>\n
slope, intercept = np.polyfit(x, y, 1)<\/p>\n
\u7ed8\u5236\u56de\u5f52\u7ebf<\/strong><\/h2>\n
plt.plot(x, slope * x + intercept, 'r')<\/p>\n
<\/code><\/pre>\n<\/p>\n
polyfit<\/code>\u51fd\u6570\u8fd4\u56de\u56de\u5f52\u7cfb\u6570(slope, intercept)<\/code>\uff0c\u7528\u4e8e\u5b9a\u4e49\u56de\u5f52\u7ebf\u7684\u65b9\u7a0b\u3002<\/p>\n<\/p>\n
3. \u6dfb\u52a0\u6ce8\u91ca<\/h4>\n<\/p>\n
\u6ce8\u91ca\u53ef\u4ee5\u7528\u4e8e\u6807\u6ce8\u7279\u6b8a\u7684\u6570\u636e\u70b9\u6216\u8bf4\u660e\u56fe\u8868\u7684\u67d0\u4e9b\u90e8\u5206\u3002annotate<\/code>\u51fd\u6570\u53ef\u7528\u4e8e\u6dfb\u52a0\u6ce8\u91ca\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u6ce8\u91ca<\/p>\n
plt.annotate('Special Point', xy=(x[0], y[0]), xytext=(x[0]+0.1, y[0]+0.1),<\/p>\n
             arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
<\/code><\/pre>\n<\/p>\n
xy<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u88ab\u6ce8\u91ca\u70b9\u7684\u5750\u6807\uff0cxytext<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6ce8\u91ca\u6587\u672c\u7684\u4f4d\u7f6e\uff0carrowprops<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u7bad\u5934\u7684\u6837\u5f0f\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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