{"id":1053671,"date":"2024-12-31T14:35:51","date_gmt":"2024-12-31T06:35:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1053671.html"},"modified":"2024-12-31T14:35:53","modified_gmt":"2024-12-31T06:35:53","slug":"python%e5%a6%82%e4%bd%95%e5%86%99%e5%87%ba%e8%87%aa%e7%84%b6%e6%8c%87%e6%95%b0e","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1053671.html","title":{"rendered":"python\u5982\u4f55\u5199\u51fa\u81ea\u7136\u6307\u6570e"},"content":{"rendered":"

\"python\u5982\u4f55\u5199\u51fa\u81ea\u7136\u6307\u6570e\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8ba1\u7b97\u548c\u8868\u793a\u81ea\u7136\u6307\u6570e\uff0c\u5982\u4f7f\u7528math\u6a21\u5757\u3001numpy\u6a21\u5757\u3001\u4ee5\u53ca\u901a\u8fc7\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u7b49\u65b9\u6cd5\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684exp\u51fd\u6570\u6765\u8ba1\u7b97e\u7684\u5e42\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165math\u6a21\u5757<\/p>\n<\/p>\n

\u8981\u4f7f\u7528math\u6a21\u5757\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u5b83\u3002math\u6a21\u5757\u662fPython\u7684\u6807\u51c6\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u6570\u5b66\u51fd\u6570\u548c\u5e38\u91cf\uff0c\u5305\u62ec\u81ea\u7136\u6307\u6570e\u3002<\/p>\n<\/p>\n

import math<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528math.exp\u51fd\u6570<\/p>\n<\/p>\n
math.exp\u51fd\u6570\u7528\u4e8e\u8ba1\u7b97e\u7684\u5e42\uff0c\u5373e^x\u3002\u53ef\u4ee5\u901a\u8fc7\u4f20\u5165\u53c2\u6570x\u6765\u8ba1\u7b97e\u7684\u5e42\u3002<\/p>\n<\/p>\n
import math<\/p>\n
\u8ba1\u7b97e^1<\/strong><\/h2>\n
result = math.exp(1)<\/p>\n
print(result)  # \u8f93\u51fa2.718281828459045<\/p>\n
<\/code><\/pre>\n<\/p>\n
math.exp\u51fd\u6570\u662f\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u6700\u5e38\u7528\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u4f7f\u7528\u4e86\u9ad8\u6548\u7684\u7b97\u6cd5\uff0c\u53ef\u4ee5\u5feb\u901f\u51c6\u786e\u5730\u8ba1\u7b97e\u7684\u5e42\u3002<\/strong><\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528math.e\u5e38\u91cf<\/p>\n<\/p>\n
math\u6a21\u5757\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5e38\u91cfmath.e\uff0c\u8868\u793a\u81ea\u7136\u6307\u6570e\u7684\u503c\u3002<\/p>\n<\/p>\n
import math<\/p>\n
\u8f93\u51fa\u81ea\u7136\u6307\u6570e\u7684\u503c<\/strong><\/h2>\n
print(math.e)  # \u8f93\u51fa2.718281828459045<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528numpy\u6a21\u5757<\/p>\n<\/p>\n
\u9664\u4e86math\u6a21\u5757\uff0cnumpy\u6a21\u5757\u4e5f\u63d0\u4f9b\u4e86\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u529f\u80fd\u3002numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u6570\u5b66\u51fd\u6570\u548c\u5e38\u91cf\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8ba1\u7b97e^1<\/strong><\/h2>\n
result = np.exp(1)<\/p>\n
print(result)  # \u8f93\u51fa2.718281828459045<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u901a\u8fc7\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u8ba1\u7b97\u81ea\u7136\u6307\u6570e<\/p>\n<\/p>\n
\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u662f\u4e00\u79cd\u6570\u5b66\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u8fd1\u4f3c\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u3002\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
e^x = 1 + x + x^2\/2! + x^3\/3! + …<\/p>\n<\/p>\n
\u901a\u8fc7\u7f16\u5199\u4e00\u4e2a\u51fd\u6570\u6765\u5b9e\u73b0\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\uff0c\u53ef\u4ee5\u8fd1\u4f3c\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u3002<\/p>\n<\/p>\n
def taylor_exp(x, n):<\/p>\n
    """<\/p>\n
    \u901a\u8fc7\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u8ba1\u7b97e^x<\/p>\n
    :param x: \u6307\u6570<\/p>\n
    :param n: \u5c55\u5f00\u9879\u6570<\/p>\n
    :return: \u8fd1\u4f3c\u8ba1\u7b97\u7ed3\u679c<\/p>\n
    """<\/p>\n
    result = 1<\/p>\n
    term = 1<\/p>\n
    for i in range(1, n + 1):<\/p>\n
        term *= x \/ i<\/p>\n
        result += term<\/p>\n
    return result<\/p>\n
\u8ba1\u7b97e^1\uff0c\u5c55\u5f00\u9879\u6570\u4e3a10<\/strong><\/h2>\n
result = taylor_exp(1, 10)<\/p>\n
print(result)  # \u8f93\u51fa2.7182818011463845<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u867d\u7136\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u53ef\u4ee5\u8fd1\u4f3c\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\uff0c\u4f46\u5b83\u7684\u51c6\u786e\u6027\u53d6\u51b3\u4e8e\u5c55\u5f00\u9879\u6570\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u5c55\u5f00\u9879\u6570\u8d8a\u591a\uff0c\u7ed3\u679c\u8d8a\u51c6\u786e\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u51e0\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5728Python\u4e2d\u8ba1\u7b97\u548c\u8868\u793a\u81ea\u7136\u6307\u6570e\u3002\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684exp\u51fd\u6570\u548c\u5e38\u91cfmath.e\u662f\u6700\u5e38\u7528\u548c\u9ad8\u6548\u7684\u65b9\u5f0f<\/strong>\uff0c\u800cnumpy\u6a21\u5757\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u7684\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3002\u6cf0\u52d2\u7ea7\u6570\u5c55\u5f00\u662f\u4e00\u79cd\u6570\u5b66\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8fd1\u4f3c\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\uff0c\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5efa\u8bae\u4f7f\u7528\u5185\u7f6e\u7684\u6570\u5b66\u51fd\u6570\u6765\u786e\u4fdd\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n
\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u548c\u8868\u793a\u81ea\u7136\u6307\u6570e\uff0c\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u6548\u7387\u548c\u51c6\u786e\u6027<\/strong>\u3002\u5728\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4e2d\uff0cnumpy\u6a21\u5757\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u590d\u6742\u8ba1\u7b97\u3002\u901a\u8fc7\u7406\u89e3\u548c\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5e94\u7528\u81ea\u7136\u6307\u6570e\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8ba1\u7b97\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u5b66\u5e93math<\/code>\u4e2d\u7684exp()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u503c\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import math\n\ne_value = math.e\nprint(e_value)\n<\/code><\/pre>\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u8f93\u51fa\u81ea\u7136\u6307\u6570e\u7684\u503c\uff0c\u7ea6\u4e3a2.71828\u3002<\/p>\n
\u6709\u6ca1\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u5728Python\u4e2d\u8fd1\u4f3c\u8ba1\u7b97e\u7684\u503c\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u6cf0\u52d2\u7ea7\u6570\u6765\u8fd1\u4f3c\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u503c\u3002\u6cf0\u52d2\u7ea7\u6570\u516c\u5f0f\u4e3a\uff1a
[ e \\approx \\sum_{n=0}^{N} \\frac{1}{n!} ]
\u901a\u8fc7\u589e\u52a0N\u7684\u503c\uff0c\u53ef\u4ee5\u63d0\u9ad8\u8fd1\u4f3c\u503c\u7684\u7cbe\u786e\u5ea6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
def calculate_e(terms):\n    e_approx = sum(1 \/ math.factorial(n) for n in range(terms))\n    return e_approx\n\nprint(calculate_e(100))  # \u8ba1\u7b97100\u9879\u7684\u8fd1\u4f3c\u503c\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u63d0\u4f9b\u4e86\u4e00\u79cd\u7075\u6d3b\u7684\u65b9\u5f0f\u6765\u63a7\u5236\u8ba1\u7b97\u7684\u7cbe\u5ea6\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\uff1f<\/strong>
NumPy\u5e93\u540c\u6837\u63d0\u4f9b\u4e86\u8ba1\u7b97\u81ea\u7136\u6307\u6570e\u7684\u529f\u80fd\u3002\u4f7f\u7528numpy.exp()<\/code>\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97e\u7684\u4efb\u610f\u5e42\u3002\u8981\u83b7\u53d6e\u7684\u503c\uff0c\u53ef\u4ee5\u4f20\u51651\u4f5c\u4e3a\u53c2\u6570\u3002\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a  <\/p>\n
import numpy as np\n\ne_value = np.exp(1)\nprint(e_value)\n<\/code><\/pre>\n
NumPy\u7684\u4f18\u52bf\u5728\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u7684\u6027\u80fd\u4f18\u5316\uff0c\u4f7f\u5176\u5728\u79d1\u5b66\u8ba1\u7b97\u4e2d\u975e\u5e38\u53d7\u6b22\u8fce\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8ba1\u7b97\u548c\u8868\u793a\u81ea\u7136\u6307\u6570e\uff0c\u5982\u4f7f\u7528math\u6a21\u5757\u3001numpy\u6a21\u5757\u3001\u4ee5\u53ca\u901a\u8fc7\u6cf0\u52d2\u7ea7\u6570\u5c55 […]","protected":false},"author":3,"featured_media":1053676,"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\/1053671"}],"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=1053671"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1053671\/revisions"}],"predecessor-version":[{"id":1053679,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1053671\/revisions\/1053679"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1053676"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1053671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1053671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1053671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}