{"id":1107929,"date":"2025-01-08T16:54:47","date_gmt":"2025-01-08T08:54:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1107929.html"},"modified":"2025-01-08T16:54:50","modified_gmt":"2025-01-08T08:54:50","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e6%ad%a3%e6%80%81%e5%88%86%e5%b8%83%e6%9b%b2%e7%ba%bf%e5%9b%be-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1107929.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\"<\/p>\n

\u4f7f\u7528Python\u753b\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u6709\uff1a\u4f7f\u7528matplotlib\u5e93\u3001\u4f7f\u7528seaborn\u5e93\u3001\u4f7f\u7528scipy\u5e93\u3001\u4f7f\u7528numpy\u5e93\u3002<\/strong> \u5176\u4e2d\uff0cmatplotlib\u5e93<\/strong>\u662f\u6700\u57fa\u7840\u4e5f\u662f\u6700\u5e38\u7528\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7ed8\u5236\u51fa\u5404\u79cd\u5404\u6837\u7684\u56fe\u5f62\uff0c\u5305\u62ec\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528matplotlib\u5e93\u6765\u753b\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u5fc5\u8981\u7684Python\u5e93<\/strong><\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u7ecf\u5b89\u88c5\u4e86\u6240\u9700\u7684\u5e93\u3002\u6211\u4eec\u4e3b\u8981\u4f7f\u7528matplotlib\u548cnumpy\u4e24\u4e2a\u5e93\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n

pip install matplotlib numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5bfc\u5165\u5e93\u5e76\u751f\u6210\u6570\u636e<\/strong><\/p>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff0c\u5e76\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy\u5e93\u6765\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u6b63\u6001\u5206\u5e03\u6570\u636e<\/strong><\/h2>\n
mu, sigma = 0, 0.1  # \u5747\u503c\u548c\u6807\u51c6\u5dee<\/p>\n
data = np.random.normal(mu, sigma, 1000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.random.normal<\/code>\u51fd\u6570\u751f\u6210\u4e861000\u4e2a\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u70b9\uff0c\u5176\u4e2d\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a0.1\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/p>\n<\/p>\n
\u751f\u6210\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u6765\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
count, bins, ignored = plt.hist(data, 30, density=True, alpha=0.6, color='g')<\/p>\n
\u4f7f\u7528\u516c\u5f0f\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/h2>\n
plt.plot(bins, 1\/(sigma * np.sqrt(2 * np.pi)) * np.exp(- (bins - mu)<strong>2 \/ (2 * sigma<\/strong>2)), linewidth=2, color='r')<\/p>\n
plt.title('Normal Distribution')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Probability')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528plt.hist<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u6570\u636e\u7684\u76f4\u65b9\u56fe\uff0c\u5e76\u5c06\u76f4\u65b9\u56fe\u7684\u989c\u8272\u8bbe\u7f6e\u4e3a\u7eff\u8272\uff08color='g'<\/code>\uff09\uff0c\u900f\u660e\u5ea6\u8bbe\u7f6e\u4e3a0.6\uff08alpha=0.6<\/code>\uff09\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4f7f\u7528\u6b63\u6001\u5206\u5e03\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u516c\u5f0f\u7ed8\u5236\u4e86\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\uff0c\u5e76\u5c06\u66f2\u7ebf\u7684\u989c\u8272\u8bbe\u7f6e\u4e3a\u7ea2\u8272\uff08color='r'<\/code>\uff09\uff0c\u7ebf\u6761\u5bbd\u5ea6\u8bbe\u7f6e\u4e3a2\uff08linewidth=2<\/code>\uff09\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f18\u5316\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe<\/strong><\/p>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u5f62\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u4e8e\u7406\u89e3\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u56fe\u5f62\u8fdb\u884c\u4e00\u4e9b\u4f18\u5316\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\u7ebf\u3001\u8bbe\u7f6e\u56fe\u4f8b\u3001\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f18\u5316\u540e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u4f18\u5316\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe<\/p>\n
plt.figure(figsize=(12, 8))<\/p>\n
count, bins, ignored = plt.hist(data, 30, density=True, alpha=0.6, color='g')<\/p>\n
plt.plot(bins, 1\/(sigma * np.sqrt(2 * np.pi)) * np.exp(- (bins - mu)<strong>2 \/ (2 * sigma<\/strong>2)), linewidth=2, color='r')<\/p>\n
\u6dfb\u52a0\u7f51\u683c\u7ebf<\/strong><\/h2>\n
plt.grid(True)<\/p>\n
\u8bbe\u7f6e\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend(['Normal Distribution Curve', 'Histogram'])<\/p>\n
\u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4<\/strong><\/h2>\n
plt.xlim(-0.5, 0.5)<\/p>\n
plt.ylim(0, 5)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Normal Distribution')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Probability')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.grid(True)<\/code>\u51fd\u6570\u6dfb\u52a0\u4e86\u7f51\u683c\u7ebf\uff0c\u4f7f\u7528plt.legend<\/code>\u51fd\u6570\u8bbe\u7f6e\u4e86\u56fe\u4f8b\uff0c\u5e76\u4f7f\u7528plt.xlim<\/code>\u548cplt.ylim<\/code>\u51fd\u6570\u8c03\u6574\u4e86\u5750\u6807\u8f74\u7684\u8303\u56f4\uff0c\u4f7f\u5f97\u56fe\u5f62\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u4e8e\u7406\u89e3\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528seaborn\u5e93\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/p>\n<\/p>\n
\u9664\u4e86matplotlib\u5e93\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528seaborn\u5e93\u6765\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u3002seaborn\u5e93\u662f\u57fa\u4e8ematplotlib\u5e93\u7684\u4e00\u4e2a\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u7ed8\u56fe\u63a5\u53e3\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528seaborn\u5e93\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u4f7f\u7528seaborn\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/h2>\n
sns.set(style="whitegrid")<\/p>\n
plt.figure(figsize=(12, 8))<\/p>\n
sns.histplot(data, kde=True, bins=30, color='g', alpha=0.6)<\/p>\n
\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Normal Distribution')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Probability')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528seaborn.histplot<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u6570\u636e\u7684\u76f4\u65b9\u56fe\uff0c\u5e76\u6dfb\u52a0\u4e86\u6838\u5bc6\u5ea6\u4f30\u8ba1\u66f2\u7ebf\uff08kde=True<\/code>\uff09\u3002seaborn\u5e93\u7684\u7ed8\u56fe\u63a5\u53e3\u975e\u5e38\u7b80\u6d01\uff0c\u53ea\u9700\u8981\u4e00\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u7ed8\u5236\u51fa\u7f8e\u89c2\u7684\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528scipy\u5e93\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/p>\n<\/p>\n
\u9664\u4e86matplotlib\u548cseaborn\u5e93\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528scipy\u5e93\u6765\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u3002scipy\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u7edf\u8ba1\u51fd\u6570\u548c\u5de5\u5177\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528scipy\u5e93\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from scipy.stats import norm<\/p>\n
\u4f7f\u7528scipy\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u66f2\u7ebf<\/strong><\/h2>\n
plt.figure(figsize=(12, 8))<\/p>\n
count, bins, ignored = plt.hist(data, 30, density=True, alpha=0.6, color='g')<\/p>\n
plt.plot(bins, norm.pdf(bins, mu, sigma), linewidth=2, color='r')<\/p>\n
\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Normal Distribution')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Probability')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528scipy.stats.norm.pdf<\/code>\u51fd\u6570\u8ba1\u7b97\u4e86\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\uff0c\u5e76\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u4e86\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/strong><\/p>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\uff0c\u5305\u62ec\u4f7f\u7528matplotlib\u5e93\u3001seaborn\u5e93\u548cscipy\u5e93\u7b49\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u51fa\u7f8e\u89c2\u7684\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\u3002\u5982\u679c\u4f60\u6709\u4efb\u4f55\u95ee\u9898\u6216\u5efa\u8bae\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u8ba8\u8bba\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 1. \u5728Python\u4e2d\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
\u8981\u5728Python\u4e2d\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\uff0c\u901a\u5e38\u9700\u8981\u4f7f\u7528matplotlib<\/code>\u5e93\u6765\u8fdb\u884c\u56fe\u5f62\u7ed8\u5236\uff0c\u4ee5\u53canumpy<\/code>\u5e93\u6765\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u3002\u6b64\u5916\uff0cscipy.stats<\/code>\u5e93\u4e2d\u7684norm<\/code>\u6a21\u5757\u4e5f\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\uff08PDF\uff09\u3002\u8fd9\u4e09\u4e2a\u5e93\u7684\u7ec4\u5408\u53ef\u4ee5\u5e2e\u52a9\u60a8\u9ad8\u6548\u5730\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u3002<\/p>\n
2. \u5982\u4f55\u8bbe\u7f6e\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7684\u53c2\u6570\uff1f<\/strong>
\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u7531\u4e24\u4e2a\u4e3b\u8981\u53c2\u6570\u51b3\u5b9a\uff1a\u5747\u503c\uff08mean\uff09\u548c\u6807\u51c6\u5dee\uff08standard deviation\uff09\u3002\u60a8\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8bbe\u7f6e\u8fd9\u4e9b\u53c2\u6570\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528numpy<\/code>\u5e93\u7684np.random.normal()<\/code>\u51fd\u6570\u751f\u6210\u6837\u672c\u6570\u636e\uff0c\u5e76\u6839\u636e\u8fd9\u4e9b\u6570\u636e\u8ba1\u7b97\u51fa\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u968f\u540e\u5c06\u5176\u5e94\u7528\u4e8e\u7ed8\u5236\u66f2\u7ebf\u3002<\/p>\n
3. \u5982\u4f55\u5728\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u4e2d\u6dfb\u52a0\u6570\u636e\u70b9\uff1f<\/strong>
\u5728\u7ed8\u5236\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u7684scatter()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u6570\u636e\u70b9\u3002\u8fd9\u4e9b\u70b9\u53ef\u4ee5\u662f\u60a8\u5b9e\u9645\u89c2\u6d4b\u5230\u7684\u6570\u636e\u6216\u968f\u673a\u751f\u6210\u7684\u6837\u672c\u3002\u901a\u8fc7\u8c03\u6574\u70b9\u7684\u989c\u8272\u548c\u5927\u5c0f\uff0c\u60a8\u53ef\u4ee5\u4f7f\u6570\u636e\u70b9\u5728\u66f2\u7ebf\u56fe\u4e2d\u66f4\u52a0\u7a81\u51fa\uff0c\u4ece\u800c\u66f4\u6e05\u6670\u5730\u5c55\u793a\u6570\u636e\u4e0e\u6b63\u6001\u5206\u5e03\u7684\u5173\u7cfb\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u753b\u6b63\u6001\u5206\u5e03\u66f2\u7ebf\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u6709\uff1a\u4f7f\u7528matplotlib\u5e93\u3001\u4f7f\u7528seaborn\u5e93\u3001\u4f7f […]","protected":false},"author":3,"featured_media":1107938,"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\/1107929"}],"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=1107929"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1107929\/revisions"}],"predecessor-version":[{"id":1107941,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1107929\/revisions\/1107941"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1107938"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1107929"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1107929"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1107929"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}