{"id":1059549,"date":"2024-12-31T15:26:08","date_gmt":"2024-12-31T07:26:08","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1059549.html"},"modified":"2024-12-31T15:26:10","modified_gmt":"2024-12-31T07:26:10","slug":"%e5%b9%b3%e5%9d%87%e5%80%bc%e5%9c%a8python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%a1%a8%e7%a4%ba","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1059549.html","title":{"rendered":"\u5e73\u5747\u503c\u5728python\u4e2d\u5982\u4f55\u8868\u793a"},"content":{"rendered":"

\"\u5e73\u5747\u503c\u5728python\u4e2d\u5982\u4f55\u8868\u793a\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5e73\u5747\u503c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u548c\u5e93\u51fd\u6570\u6765\u8ba1\u7b97\u3002\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()\u3001\u5229\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570\u3001\u4ee5\u53ca\u5229\u7528NumPy\u5e93\u4e2d\u7684mean()\u51fd\u6570<\/strong>\uff0c\u5176\u4e2d\uff0c\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()<\/strong> \u662f\u6700\u57fa\u672c\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()<\/p>\n<\/p>\n

\u901a\u8fc7\u5185\u7f6e\u51fd\u6570sum()\u548clen()\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u4e00\u4e2a\u5217\u8868\u7684\u5e73\u5747\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\u3002<\/p>\n<\/p>\n

# \u793a\u4f8b<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
average = sum(data) \/ len(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u65b9\u6cd5\u4e2d\uff0csum()\u51fd\u6570\u8ba1\u7b97\u5217\u8868\u4e2d\u6240\u6709\u5143\u7d20\u7684\u603b\u548c\uff0c\u800clen()\u51fd\u6570\u8ba1\u7b97\u5217\u8868\u4e2d\u7684\u5143\u7d20\u4e2a\u6570\u3002\u7136\u540e\u5c06\u603b\u548c\u9664\u4ee5\u5143\u7d20\u4e2a\u6570\u5373\u53ef\u5f97\u5230\u5e73\u5747\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u89c2\uff0c\u9002\u7528\u4e8e\u57fa\u672c\u7684\u5e73\u5747\u503c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5229\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570<\/p>\n<\/p>\n
Python\u7684statistics\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u5176\u4e2dmean()\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u5e73\u5747\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u66f4\u591a\u7edf\u8ba1\u5206\u6790\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
# \u5bfc\u5165statistics\u5e93<\/p>\n
import statistics<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
average = statistics.mean(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
statistics.mean()\u51fd\u6570\u5185\u90e8\u5df2\u7ecf\u5b9e\u73b0\u4e86\u5e73\u5747\u503c\u7684\u8ba1\u7b97\u903b\u8f91\uff0c\u4f7f\u7528\u5b83\u53ef\u4ee5\u4f7f\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u660e\u4e86\uff0c\u9002\u5408\u5728\u9700\u8981\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u8ba1\u7b97\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5229\u7528NumPy\u5e93\u4e2d\u7684mean()\u51fd\u6570<\/p>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u5176\u4e2dmean()\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\u3002NumPy\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u591a\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
# \u5bfc\u5165NumPy\u5e93<\/p>\n
import numpy as np<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
data = np.array([1, 2, 3, 4, 5])<\/p>\n
average = np.mean(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
NumPy\u7684mean()\u51fd\u6570\u4e0d\u4ec5\u53ef\u4ee5\u8ba1\u7b97\u4e00\u7ef4\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff0c\u8fd8\u53ef\u4ee5\u8ba1\u7b97\u591a\u7ef4\u6570\u7ec4\u6cbf\u6307\u5b9a\u8f74\u7684\u5e73\u5747\u503c\uff0c\u975e\u5e38\u9002\u5408\u5927\u6570\u636e\u5904\u7406\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()<\/h3>\n<\/p>\n
\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()\u8ba1\u7b97\u5e73\u5747\u503c\u662f\u4e00\u79cd\u57fa\u672c\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\u3002\u6211\u4eec\u5148\u6765\u770b\u4e00\u4e2a\u5177\u4f53\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
average = sum(data) \/ len(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5305\u542b5\u4e2a\u6574\u6570\u7684\u5217\u8868data\u3002\u7136\u540e\uff0c\u4f7f\u7528sum(data)\u8ba1\u7b97\u5217\u8868\u4e2d\u6240\u6709\u5143\u7d20\u7684\u603b\u548c\uff0c\u7ed3\u679c\u662f15\u3002\u63a5\u7740\uff0c\u4f7f\u7528len(data)\u8ba1\u7b97\u5217\u8868\u4e2d\u7684\u5143\u7d20\u4e2a\u6570\uff0c\u7ed3\u679c\u662f5\u3002\u6700\u540e\uff0c\u5c06\u603b\u548c\u9664\u4ee5\u5143\u7d20\u4e2a\u6570\uff0c\u537315 \/ 5\uff0c\u5f97\u5230\u7684\u5e73\u5747\u503c\u662f3.0\u3002<\/p>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u7684\u4f18\u70b9\u662f\u7b80\u5355\u76f4\u89c2\uff0c\u9002\u7528\u4e8e\u57fa\u672c\u7684\u5e73\u5747\u503c\u8ba1\u7b97\u3002\u5b83\u4e0d\u4f9d\u8d56\u4e8e\u5916\u90e8\u5e93\uff0c\u56e0\u6b64\u5728\u4efb\u4f55\u73af\u5883\u4e0b\u90fd\u53ef\u4ee5\u4f7f\u7528\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u548c\u7edf\u8ba1\u5206\u6790\uff0c\u8fd9\u79cd\u65b9\u6cd5\u53ef\u80fd\u663e\u5f97\u6709\u4e9b\u4e0d\u8db3\u3002<\/p>\n<\/p>\n
\u4f7f\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570<\/h3>\n<\/p>\n
Python\u7684statistics\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u5176\u4e2dmean()\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u5e73\u5747\u503c\u3002\u4f7f\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570\u53ef\u4ee5\u4f7f\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u660e\u4e86\uff0c\u9002\u5408\u5728\u9700\u8981\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u8ba1\u7b97\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n
\u6211\u4eec\u6765\u770b\u4e00\u4e2a\u5177\u4f53\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u5bfc\u5165statistics\u5e93<\/p>\n
import statistics<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
average = statistics.mean(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86statistics\u5e93\u3002\u7136\u540e\uff0c\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5305\u542b5\u4e2a\u6574\u6570\u7684\u5217\u8868data\u3002\u4f7f\u7528statistics.mean(data)\u8ba1\u7b97\u5217\u8868\u7684\u5e73\u5747\u503c\uff0c\u7ed3\u679c\u662f3\u3002<\/p>\n<\/p>\n
statistics\u5e93\u7684mean()\u51fd\u6570\u5185\u90e8\u5df2\u7ecf\u5b9e\u73b0\u4e86\u5e73\u5747\u503c\u7684\u8ba1\u7b97\u903b\u8f91\uff0c\u56e0\u6b64\u4f7f\u7528\u5b83\u53ef\u4ee5\u907f\u514d\u624b\u52a8\u8ba1\u7b97\u603b\u548c\u548c\u5143\u7d20\u4e2a\u6570\uff0c\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u8ba1\u7b97\u7684\u573a\u666f\uff0c\u56e0\u4e3astatistics\u5e93\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u6709\u7528\u7684\u7edf\u8ba1\u51fd\u6570\u3002<\/p>\n<\/p>\n
\u4f7f\u7528NumPy\u5e93\u4e2d\u7684mean()\u51fd\u6570<\/h3>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u5176\u4e2dmean()\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\u3002NumPy\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u591a\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
\u6211\u4eec\u6765\u770b\u4e00\u4e2a\u5177\u4f53\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u5bfc\u5165NumPy\u5e93<\/p>\n
import numpy as np<\/p>\n
\u793a\u4f8b<\/strong><\/h2>\n
data = np.array([1, 2, 3, 4, 5])<\/p>\n
average = np.mean(data)<\/p>\n
print(f"\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86NumPy\u5e93\uff0c\u5e76\u4f7f\u7528np.array()\u51fd\u6570\u521b\u5efa\u4e86\u4e00\u4e2aNumPy\u6570\u7ec4data\u3002\u7136\u540e\uff0c\u4f7f\u7528np.mean(data)\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff0c\u7ed3\u679c\u662f3.0\u3002<\/p>\n<\/p>\n
NumPy\u7684mean()\u51fd\u6570\u4e0d\u4ec5\u53ef\u4ee5\u8ba1\u7b97\u4e00\u7ef4\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff0c\u8fd8\u53ef\u4ee5\u8ba1\u7b97\u591a\u7ef4\u6570\u7ec4\u6cbf\u6307\u5b9a\u8f74\u7684\u5e73\u5747\u503c\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u4e8c\u7ef4\u6570\u7ec4\u6bcf\u4e00\u884c\u6216\u6bcf\u4e00\u5217\u7684\u5e73\u5747\u503c\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b<\/p>\n
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
average_all = np.mean(data)         # \u6240\u6709\u5143\u7d20\u7684\u5e73\u5747\u503c<\/p>\n
average_axis0 = np.mean(data, axis=0) # \u6bcf\u5217\u7684\u5e73\u5747\u503c<\/p>\n
average_axis1 = np.mean(data, axis=1) # \u6bcf\u884c\u7684\u5e73\u5747\u503c<\/p>\n
print(f"\u6240\u6709\u5143\u7d20\u7684\u5e73\u5747\u503c\u662f: {average_all}")<\/p>\n
print(f"\u6bcf\u5217\u7684\u5e73\u5747\u503c\u662f: {average_axis0}")<\/p>\n
print(f"\u6bcf\u884c\u7684\u5e73\u5747\u503c\u662f: {average_axis1}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a3×3\u7684\u4e8c\u7ef4\u6570\u7ec4data\u3002np.mean(data)\u8ba1\u7b97\u6240\u6709\u5143\u7d20\u7684\u5e73\u5747\u503c\uff0c\u7ed3\u679c\u662f5.0\u3002np.mean(data, axis=0)\u8ba1\u7b97\u6bcf\u5217\u7684\u5e73\u5747\u503c\uff0c\u7ed3\u679c\u662f[4.0, 5.0, 6.0]\u3002np.mean(data, axis=1)\u8ba1\u7b97\u6bcf\u884c\u7684\u5e73\u5747\u503c\uff0c\u7ed3\u679c\u662f[2.0, 5.0, 8.0]\u3002<\/p>\n<\/p>\n
NumPy\u7684mean()\u51fd\u6570\u529f\u80fd\u5f3a\u5927\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u591a\u7ef4\u6570\u7ec4\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
\u7efc\u5408\u6bd4\u8f83<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u8ba1\u7b97\u5e73\u5747\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\u9009\u62e9\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u9002\u7528\u573a\u666f\u548c\u4f18\u7f3a\u70b9\u3002\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\uff0c\u4ee3\u7801\u7b80\u5355\u76f4\u89c2\uff1b\u5229\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u8ba1\u7b97\u7684\u573a\u666f\uff0c\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u660e\u4e86\uff1b\u5229\u7528NumPy\u5e93\u4e2d\u7684mean()\u51fd\u6570\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u591a\u7ef4\u6570\u7ec4\uff0c\u529f\u80fd\u5f3a\u5927\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u6548\u7387\u3002\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u65f6\uff0c\u5efa\u8bae\u4f18\u5148\u8003\u8651\u4f7f\u7528NumPy\u5e93\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\u548c\u9ad8\u6548\u7684\u8ba1\u7b97\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u4ee3\u7801\u793a\u4f8b\u53ca\u5b9e\u9645\u5e94\u7528<\/h3>\n<\/p>\n
\u4e0b\u9762\u6211\u4eec\u5c06\u7ed3\u5408\u5b9e\u9645\u5e94\u7528\u573a\u666f\uff0c\u5c55\u793a\u5982\u4f55\u5728\u4e0d\u540c\u573a\u666f\u4e0b\u9009\u62e9\u548c\u4f7f\u7528\u5408\u9002\u7684\u65b9\u6cd5\u8ba1\u7b97\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
\u793a\u4f8b\u4e00\uff1a\u8ba1\u7b97\u5b66\u751f\u6210\u7ee9\u7684\u5e73\u5747\u5206\u6570<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u5217\u8868\uff0c\u9700\u8981\u8ba1\u7b97\u8fd9\u4e9b\u6210\u7ee9\u7684\u5e73\u5747\u5206\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()<\/p>\n
scores = [85, 90, 78, 92, 88]<\/p>\n
average_score = sum(scores) \/ len(scores)<\/p>\n
print(f"\u5b66\u751f\u6210\u7ee9\u7684\u5e73\u5747\u5206\u6570\u662f: {average_score}")<\/p>\n
\u4f7f\u7528statistics\u5e93<\/strong><\/h2>\n
import statistics<\/p>\n
average_score = statistics.mean(scores)<\/p>\n
print(f"\u5b66\u751f\u6210\u7ee9\u7684\u5e73\u5747\u5206\u6570\u662f: {average_score}")<\/p>\n
\u4f7f\u7528NumPy\u5e93<\/strong><\/h2>\n
import numpy as np<\/p>\n
scores_array = np.array(scores)<\/p>\n
average_score = np.mean(scores_array)<\/p>\n
print(f"\u5b66\u751f\u6210\u7ee9\u7684\u5e73\u5747\u5206\u6570\u662f: {average_score}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u4e09\u79cd\u65b9\u6cd5\u8ba1\u7b97\u7684\u5e73\u5747\u5206\u6570\u90fd\u662f86.6\u3002\u5bf9\u4e8e\u8fd9\u79cd\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\uff0c\u4efb\u610f\u4e00\u79cd\u65b9\u6cd5\u90fd\u53ef\u4ee5\u6ee1\u8db3\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u793a\u4f8b\u4e8c\uff1a\u8ba1\u7b97\u80a1\u7968\u4ef7\u683c\u7684\u5e73\u5747\u503c<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u53ea\u80a1\u7968\u4ef7\u683c\u7684\u4e8c\u7ef4\u6570\u7ec4\uff0c\u9700\u8981\u8ba1\u7b97\u6bcf\u53ea\u80a1\u7968\u7684\u5e73\u5747\u4ef7\u683c\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528NumPy\u5e93<\/p>\n
import numpy as np<\/p>\n
\u793a\u4f8b\u6570\u636e\uff1a\u6bcf\u884c\u8868\u793a\u4e00\u53ea\u80a1\u7968\u7684\u4ef7\u683c\u53d8\u5316<\/strong><\/h2>\n
stock_prices = np.array([<\/p>\n
    [100, 102, 105, 107, 110],<\/p>\n
    [200, 198, 195, 193, 190],<\/p>\n
    [50, 52, 54, 56, 58]<\/p>\n
])<\/p>\n
\u8ba1\u7b97\u6bcf\u53ea\u80a1\u7968\u7684\u5e73\u5747\u4ef7\u683c<\/strong><\/h2>\n
average_prices = np.mean(stock_prices, axis=1)<\/p>\n
print(f"\u6bcf\u53ea\u80a1\u7968\u7684\u5e73\u5747\u4ef7\u683c\u662f: {average_prices}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u6bcf\u53ea\u80a1\u7968\u7684\u5e73\u5747\u4ef7\u683c\u3002\u7ed3\u679c\u662f[104.8, 195.2, 54.0]\u3002NumPy\u7684mean()\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u591a\u7ef4\u6570\u7ec4\uff0c\u9002\u7528\u4e8e\u8fd9\u79cd\u9700\u8981\u8ba1\u7b97\u6bcf\u53ea\u80a1\u7968\u5e73\u5747\u4ef7\u683c\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u793a\u4f8b\u4e09\uff1a\u5904\u7406\u7f3a\u5931\u6570\u636e<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u4f1a\u9047\u5230\u5305\u542b\u7f3a\u5931\u503c\u7684\u6570\u636e\u3002\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528NumPy\u5e93<\/p>\n
import numpy as np<\/p>\n
\u793a\u4f8b\u6570\u636e\uff1a\u5305\u542b\u7f3a\u5931\u503c\uff08\u7528np.nan\u8868\u793a\uff09<\/strong><\/h2>\n
data_with_nan = np.array([1, 2, np.nan, 4, 5])<\/p>\n
\u8ba1\u7b97\u5ffd\u7565\u7f3a\u5931\u503c\u7684\u5e73\u5747\u503c<\/strong><\/h2>\n
average = np.nanmean(data_with_nan)<\/p>\n
print(f"\u5ffd\u7565\u7f3a\u5931\u503c\u7684\u5e73\u5747\u503c\u662f: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528np.nanmean()\u51fd\u6570\u8ba1\u7b97\u5ffd\u7565\u7f3a\u5931\u503c\u7684\u5e73\u5747\u503c\u3002\u7ed3\u679c\u662f3.0\u3002np.nanmean()\u51fd\u6570\u4f1a\u81ea\u52a8\u5ffd\u7565\u6570\u7ec4\u4e2d\u7684np.nan\u503c\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u5305\u542b\u7f3a\u5931\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u8ba1\u7b97\u5e73\u5747\u503c\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u6548\u7387\u3002\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\uff1b\u5229\u7528statistics\u5e93\u4e2d\u7684mean()\u51fd\u6570\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u8ba1\u7b97\u7684\u573a\u666f\uff1b\u5229\u7528NumPy\u5e93\u4e2d\u7684mean()\u51fd\u6570\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u591a\u7ef4\u6570\u7ec4\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7ed3\u5408\u5177\u4f53\u573a\u666f\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u6ee1\u8db3\u9700\u6c42\u5e76\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u4e00\u7ec4\u6570\u5b57\u7684\u5e73\u5747\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684sum()<\/code>\u548clen()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u4e00\u7ec4\u6570\u5b57\u7684\u5e73\u5747\u503c\u3002\u5177\u4f53\u6b65\u9aa4\u662f\u5148\u8ba1\u7b97\u6240\u6709\u6570\u5b57\u7684\u603b\u548c\uff0c\u7136\u540e\u5c06\u603b\u548c\u9664\u4ee5\u6570\u5b57\u7684\u6570\u91cf\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6709\u4e00\u4e2a\u5217\u8868numbers = [10, 20, 30, 40]<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8ba1\u7b97\u5e73\u5747\u503c\uff1a  <\/p>\n
average = sum(numbers) \/ len(numbers)\nprint(average)  # \u8f93\u51fa 25.0\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u5f0f\u7b80\u5355\u6613\u61c2\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u57fa\u672c\u60c5\u51b5\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u4f7f\u7528Python\u7684\u7b2c\u4e09\u65b9\u5e93\u6765\u8ba1\u7b97\u5e73\u5747\u503c\uff1f<\/strong>
\u786e\u5b9e\u5982\u6b64\uff0cPython\u7684\u7b2c\u4e09\u65b9\u5e93\u5982NumPy\u63d0\u4f9b\u4e86\u66f4\u9ad8\u6548\u7684\u8ba1\u7b97\u65b9\u6cd5\u3002\u4f7f\u7528NumPy\u65f6\uff0c\u53ef\u4ee5\u8c03\u7528numpy.mean()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5NumPy\u5e93\uff0c\u7136\u540e\u53ef\u4ee5\u8fd9\u6837\u4f7f\u7528\uff1a  <\/p>\n
import numpy as np\n\nnumbers = [10, 20, 30, 40]\naverage = np.mean(numbers)\nprint(average)  # \u8f93\u51fa 25.0\n<\/code><\/pre>\n
\u4f7f\u7528NumPy\u4e0d\u4ec5\u80fd\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u8fd8\u80fd\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u5305\u542bNaN\u503c\u7684\u5e73\u5747\u503c\u8ba1\u7b97\uff1f<\/strong>
\u5728\u5904\u7406\u5305\u542bNaN\uff08\u7f3a\u5931\u503c\uff09\u7684\u6570\u636e\u65f6\uff0c\u4f7f\u7528NumPy\u7684np.nanmean()<\/code>\u51fd\u6570\u53ef\u4ee5\u6709\u6548\u5ffd\u7565\u8fd9\u4e9bNaN\u503c\u8fdb\u884c\u5e73\u5747\u503c\u7684\u8ba1\u7b97\u3002\u4f8b\u5982\uff1a  <\/p>\n
import numpy as np\n\nnumbers = [10, 20, np.nan, 40]\naverage = np.nanmean(numbers)\nprint(average)  # \u8f93\u51fa 23.3333\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u786e\u4fdd\u8ba1\u7b97\u7ed3\u679c\u4e0d\u4f1a\u53d7\u5230\u7f3a\u5931\u503c\u7684\u5f71\u54cd\uff0c\u9002\u5408\u4e8e\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\u7684\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5e73\u5747\u503c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u548c\u5e93\u51fd\u6570\u6765\u8ba1\u7b97\u3002\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u548clen()\u3001\u5229 […]","protected":false},"author":3,"featured_media":1059563,"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\/1059549"}],"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=1059549"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1059549\/revisions"}],"predecessor-version":[{"id":1059566,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1059549\/revisions\/1059566"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1059563"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1059549"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1059549"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1059549"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}