{"id":1113700,"date":"2025-01-08T17:50:22","date_gmt":"2025-01-08T09:50:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1113700.html"},"modified":"2025-01-08T17:50:24","modified_gmt":"2025-01-08T09:50:24","slug":"%e5%9c%a8python%e4%b8%ad%e7%9a%84%e5%a6%82%e4%bd%95%e9%9a%8f%e6%9c%ba%e7%94%9f%e6%88%90%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1113700.html","title":{"rendered":"\u5728python\u4e2d\u7684\u5982\u4f55\u968f\u673a\u751f\u6210\u6570\u636e"},"content":{"rendered":"

\"\u5728python\u4e2d\u7684\u5982\u4f55\u968f\u673a\u751f\u6210\u6570\u636e\"<\/p>\n

\u5728Python\u4e2d\u968f\u673a\u751f\u6210\u6570\u636e\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528\u6807\u51c6\u5e93\u4e2d\u7684random\u6a21\u5757\u3001Numpy\u5e93\u3001Pandas\u5e93\u7b49\u3002\u4f7f\u7528random\u6a21\u5757\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528Pandas\u5e93<\/strong>\u3002\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u751f\u6210\u968f\u673a\u6570\u636e\uff0c\u5e76\u8ba8\u8bba\u5176\u4f18\u7f3a\u70b9\u53ca\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528random\u6a21\u5757<\/h3>\n<\/p>\n

Python\u7684\u6807\u51c6\u5e93random\u6a21\u5757\u63d0\u4f9b\u4e86\u591a\u79cd\u751f\u6210\u968f\u673a\u6570\u7684\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u51fd\u6570\u53ca\u5176\u7528\u6cd5\uff1a<\/p>\n<\/p>\n

1.1\u3001\u751f\u6210\u968f\u673a\u6574\u6570<\/h4>\n<\/p>\n

import random<\/p>\n
\u751f\u6210\u4e00\u4e2a\u57281\u5230100\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570<\/strong><\/h2>\n
random_integer = random.randint(1, 100)<\/p>\n
print(random_integer)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> random.randint(a, b)<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5728a\u548cb\uff08\u5305\u62eca\u548cb\uff09\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u975e\u5e38\u9002\u5408\u7528\u4e8e\u751f\u6210\u9700\u8981\u5728\u67d0\u4e2a\u8303\u56f4\u5185\u7684\u968f\u673a\u6574\u6570\u7684\u573a\u666f\uff0c\u5982\u751f\u6210\u968f\u673aID\u3001\u968f\u673a\u5e8f\u53f7\u7b49\u3002<\/p>\n<\/p>\n
1.2\u3001\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570<\/h4>\n<\/p>\n
import random<\/p>\n
\u751f\u6210\u4e00\u4e2a\u57280\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570<\/strong><\/h2>\n
random_float = random.random()<\/p>\n
print(random_float)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5728\u6307\u5b9a\u8303\u56f4\u5185\u7684\u968f\u673a\u6d6e\u70b9\u6570<\/strong><\/h2>\n
random_float_range = random.uniform(1.5, 5.5)<\/p>\n
print(random_float_range)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> random.random()<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u57280\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570\uff0c\u800crandom.uniform(a, b)<\/code>\u51fd\u6570\u5219\u8fd4\u56de\u4e00\u4e2a\u5728a\u548cb\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570\u3002\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570\u5e38\u7528\u4e8e\u6a21\u62df\u6982\u7387\u4e8b\u4ef6\u3001\u751f\u6210\u8fde\u7eed\u578b\u6570\u636e\u7b49\u3002<\/p>\n<\/p>\n
1.3\u3001\u751f\u6210\u968f\u673a\u5e8f\u5217<\/h4>\n<\/p>\n
import random<\/p>\n
\u4ece\u5217\u8868\u4e2d\u968f\u673a\u9009\u62e9\u4e00\u4e2a\u5143\u7d20<\/strong><\/h2>\n
elements = ['apple', 'banana', 'cherry']<\/p>\n
random_element = random.choice(elements)<\/p>\n
print(random_element)<\/p>\n
\u6253\u4e71\u5217\u8868\u987a\u5e8f<\/strong><\/h2>\n
random.shuffle(elements)<\/p>\n
print(elements)<\/p>\n
\u4ece\u5217\u8868\u4e2d\u968f\u673a\u9009\u62e9\u591a\u4e2a\u5143\u7d20<\/strong><\/h2>\n
random_elements = random.sample(elements, 2)<\/p>\n
print(random_elements)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> random.choice(seq)<\/code>\u51fd\u6570\u4ece\u5e8f\u5217seq\u4e2d\u968f\u673a\u9009\u62e9\u4e00\u4e2a\u5143\u7d20\uff0crandom.shuffle(seq)<\/code>\u51fd\u6570\u5c06\u5e8f\u5217seq\u7684\u5143\u7d20\u987a\u5e8f\u6253\u4e71\uff0c\u800crandom.sample(seq, k)<\/code>\u51fd\u6570\u5219\u4ece\u5e8f\u5217seq\u4e2d\u968f\u673a\u9009\u62e9k\u4e2a\u5143\u7d20\u3002\u751f\u6210\u968f\u673a\u5e8f\u5217\u9002\u7528\u4e8e\u9700\u8981\u968f\u673a\u62bd\u6837\u3001\u968f\u673a\u6392\u5217\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Numpy\u5e93<\/h3>\n<\/p>\n
Numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u968f\u673a\u6570\u751f\u6210\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u51fd\u6570\u53ca\u5176\u7528\u6cd5\uff1a<\/p>\n<\/p>\n
2.1\u3001\u751f\u6210\u968f\u673a\u6574\u6570<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u57280\u523010\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570<\/strong><\/h2>\n
random_integer = np.random.randint(0, 10)<\/p>\n
print(random_integer)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(3, 3)\u7684\u4e8c\u7ef4\u6570\u7ec4\uff0c\u5143\u7d20\u4e3a0\u523010\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570<\/strong><\/h2>\n
random_integers = np.random.randint(0, 10, (3, 3))<\/p>\n
print(random_integers)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> np.random.randint(low, high, size)<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5728low\u548chigh\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570\uff0csize\u53c2\u6570\u6307\u5b9a\u8f93\u51fa\u6570\u7ec4\u7684\u5f62\u72b6\u3002\u4f7f\u7528Numpy\u751f\u6210\u968f\u673a\u6574\u6570\u9002\u7528\u4e8e\u9700\u8981\u751f\u6210\u5927\u89c4\u6a21\u968f\u673a\u6570\u636e\u3001\u9ad8\u6548\u8ba1\u7b97\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
2.2\u3001\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u57280\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570<\/strong><\/h2>\n
random_float = np.random.random()<\/p>\n
print(random_float)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(2, 2)\u7684\u4e8c\u7ef4\u6570\u7ec4\uff0c\u5143\u7d20\u4e3a0\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570<\/strong><\/h2>\n
random_floats = np.random.random((2, 2))<\/p>\n
print(random_floats)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(2, 2)\u7684\u4e8c\u7ef4\u6570\u7ec4\uff0c\u5143\u7d20\u4e3a\u5747\u503c\u4e3a0\u3001\u6807\u51c6\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\u968f\u673a\u6570<\/strong><\/h2>\n
random_normals = np.random.randn(2, 2)<\/p>\n
print(random_normals)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> np.random.random(size)<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5f62\u72b6\u4e3asize\u7684\u6570\u7ec4\uff0c\u6570\u7ec4\u5143\u7d20\u57280\u52301\u4e4b\u95f4\u3002np.random.randn(d0, d1, ..., dn)<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5f62\u72b6\u4e3a(d0, d1, …, dn)\u7684\u6570\u7ec4\uff0c\u6570\u7ec4\u5143\u7d20\u7b26\u5408\u6807\u51c6\u6b63\u6001\u5206\u5e03\uff08\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\uff09\u3002\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570\u5e38\u7528\u4e8e\u6a21\u62df\u6982\u7387\u5206\u5e03\u3001\u751f\u6210\u8fde\u7eed\u578b\u6570\u636e\u7b49\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u5e93\u63d0\u4f9b\u4e86\u751f\u6210\u968f\u673a\u6570\u636e\u7684\u529f\u80fd\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u751f\u6210DataFrame\u683c\u5f0f\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u51fd\u6570\u53ca\u5176\u7528\u6cd5\uff1a<\/p>\n<\/p>\n
3.1\u3001\u751f\u6210\u968f\u673a\u6574\u6570DataFrame<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(5, 3)\u7684DataFrame\uff0c\u5143\u7d20\u4e3a0\u5230100\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570<\/strong><\/h2>\n
df_random_integers = pd.DataFrame(np.random.randint(0, 100, size=(5, 3)), columns=['A', 'B', 'C'])<\/p>\n
print(df_random_integers)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> \u4f7f\u7528Numpy\u751f\u6210\u968f\u673a\u6574\u6570\u6570\u7ec4\uff0c\u518d\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u683c\u5f0f\u3002\u751f\u6210\u968f\u673a\u6574\u6570DataFrame\u9002\u7528\u4e8e\u9700\u8981\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
3.2\u3001\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570DataFrame<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(5, 3)\u7684DataFrame\uff0c\u5143\u7d20\u4e3a0\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570<\/strong><\/h2>\n
df_random_floats = pd.DataFrame(np.random.random(size=(5, 3)), columns=['A', 'B', 'C'])<\/p>\n
print(df_random_floats)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5f62\u72b6\u4e3a(5, 3)\u7684DataFrame\uff0c\u5143\u7d20\u4e3a\u5747\u503c\u4e3a0\u3001\u6807\u51c6\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\u968f\u673a\u6570<\/strong><\/h2>\n
df_random_normals = pd.DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])<\/p>\n
print(df_random_normals)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u8ff0\uff1a<\/strong> \u4f7f\u7528Numpy\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570\u6570\u7ec4\uff0c\u518d\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u683c\u5f0f\u3002\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570DataFrame\u5e38\u7528\u4e8e\u6570\u636e\u6a21\u62df\u3001\u751f\u6210\u6d4b\u8bd5\u6570\u636e\u7b49\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
4.1\u3001\u5e94\u7528\u573a\u666f<\/h4>\n<\/p>\n
    \n
  1. \u6570\u636e\u6a21\u62df\uff1a<\/strong> \u5728\u673a\u5668\u5b66\u4e60<\/a>\u548c\u6570\u636e\u79d1\u5b66\u4e2d\uff0c\u5e38\u5e38\u9700\u8981\u751f\u6210\u968f\u673a\u6570\u636e\u6765\u6a21\u62df\u771f\u5b9e\u6570\u636e\uff0c\u4ee5\u8fdb\u884c\u7b97\u6cd5\u6d4b\u8bd5\u548c\u9a8c\u8bc1\u3002<\/li>\n
  2. \u6e38\u620f\u5f00\u53d1\uff1a<\/strong> \u5728\u6e38\u620f\u5f00\u53d1\u4e2d\uff0c\u968f\u673a\u6570\u7528\u4e8e\u751f\u6210\u968f\u673a\u4e8b\u4ef6\u3001\u968f\u673a\u5730\u56fe\u7b49\uff0c\u589e\u52a0\u6e38\u620f\u7684\u53ef\u73a9\u6027\u548c\u6311\u6218\u6027\u3002<\/li>\n
  3. \u7edf\u8ba1\u5206\u6790\uff1a<\/strong> \u968f\u673a\u6570\u751f\u6210\u5728\u7edf\u8ba1\u5206\u6790\u4e2d\u7528\u4e8e\u8499\u7279\u5361\u7f57\u6a21\u62df\u3001\u62bd\u6837\u7b49\u3002<\/li>\n
  4. \u5b89\u5168\u52a0\u5bc6\uff1a<\/strong> \u968f\u673a\u6570\u5728\u5b89\u5168\u52a0\u5bc6\u4e2d\u7528\u4e8e\u751f\u6210\u5bc6\u94a5\u3001\u521d\u59cb\u5316\u5411\u91cf\u7b49\uff0c\u4fdd\u8bc1\u6570\u636e\u7684\u5b89\u5168\u6027\u3002<\/li>\n<\/ol>\n
    4.2\u3001\u6ce8\u610f\u4e8b\u9879<\/h4>\n<\/p>\n
      \n
    1. \u79cd\u5b50\u8bbe\u7f6e\uff1a<\/strong> \u4e3a\u4e86\u4fdd\u8bc1\u5b9e\u9a8c\u7684\u53ef\u91cd\u590d\u6027\uff0c\u5e38\u5e38\u9700\u8981\u8bbe\u7f6e\u968f\u673a\u6570\u751f\u6210\u5668\u7684\u79cd\u5b50\u3002\u53ef\u4ee5\u4f7f\u7528random.seed()<\/code>\u548cnp.random.seed()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u79cd\u5b50\u3002<\/li>\n
    2. \u6027\u80fd\u8003\u8651\uff1a<\/strong> \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u751f\u6210\uff0c\u5efa\u8bae\u4f7f\u7528Numpy\u5e93\uff0c\u56e0\u4e3a\u5176\u6027\u80fd\u4f18\u4e8e\u6807\u51c6\u5e93random\u6a21\u5757\u3002<\/li>\n
    3. \u5206\u5e03\u9009\u62e9\uff1a<\/strong> \u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u5408\u9002\u7684\u6982\u7387\u5206\u5e03\uff0c\u5982\u5747\u5300\u5206\u5e03\u3001\u6b63\u6001\u5206\u5e03\u3001\u6cca\u677e\u5206\u5e03\u7b49\u3002<\/li>\n<\/ol>\n
      \u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
      Python\u63d0\u4f9b\u4e86\u591a\u79cd\u751f\u6210\u968f\u673a\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u4e3b\u8981\u5305\u62ec\u6807\u51c6\u5e93\u4e2d\u7684random\u6a21\u5757\u3001Numpy\u5e93\u3001Pandas\u5e93\u7b49\u3002\u4f7f\u7528random\u6a21\u5757\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528Pandas\u5e93<\/strong>\u3002\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u968f\u673a\u6570\u636e\u3002\u901a\u8fc7\u5408\u7406\u8bbe\u7f6e\u968f\u673a\u6570\u751f\u6210\u5668\u7684\u79cd\u5b50\u3001\u9009\u62e9\u5408\u9002\u7684\u6982\u7387\u5206\u5e03\uff0c\u53ef\u4ee5\u4fdd\u8bc1\u5b9e\u9a8c\u7684\u53ef\u91cd\u590d\u6027\u548c\u6570\u636e\u7684\u771f\u5b9e\u6027\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u5728Python\u4e2d\u751f\u6210\u968f\u673a\u6570\u636e\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u968f\u673a\u6570\uff1f<\/strong>
      \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684random<\/code>\u6a21\u5757\u6765\u751f\u6210\u968f\u673a\u6570\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528random.randint(a, b)<\/code>\u751f\u6210\u4e00\u4e2a\u8303\u56f4\u5728a\u5230b\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570\uff0c\u6216\u4f7f\u7528random.uniform(a, b)<\/code>\u751f\u6210\u4e00\u4e2a\u8303\u56f4\u5728a\u5230b\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u6240\u9700\u7684\u968f\u673a\u6570\u3002<\/p>\n
      Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u968f\u673a\u6570\u636e\uff1f<\/strong>
      \u9664\u4e86random<\/code>\u6a21\u5757\uff0cPython\u8fd8\u6709\u5176\u4ed6\u5e93\u53ef\u4ee5\u751f\u6210\u968f\u673a\u6570\u636e\u3002\u4f8b\u5982\uff0cnumpy<\/code>\u5e93\u4e2d\u7684numpy.random<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u66f4\u4e3a\u4e30\u5bcc\u7684\u968f\u673a\u6570\u751f\u6210\u529f\u80fd\uff0c\u5305\u62ec\u751f\u6210\u968f\u673a\u6570\u7ec4\u3001\u4ece\u7279\u5b9a\u5206\u5e03\u4e2d\u62bd\u6837\u7b49\u3002\u6b64\u5916\uff0cpandas<\/code>\u5e93\u4e5f\u6709\u76f8\u5173\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u5e26\u6709\u968f\u673a\u6570\u636e\u7684DataFrame\uff0c\u4ee5\u4fbf\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n
      \u5982\u4f55\u751f\u6210\u968f\u673a\u5b57\u7b26\u4e32\u6216\u968f\u673a\u6837\u672c\u6570\u636e\uff1f<\/strong>
      \u5982\u679c\u4f60\u9700\u8981\u751f\u6210\u968f\u673a\u5b57\u7b26\u4e32\uff0c\u53ef\u4ee5\u4f7f\u7528random<\/code>\u6a21\u5757\u4e2d\u7684random.choices()<\/code>\u51fd\u6570\uff0c\u7ed3\u5408string<\/code>\u6a21\u5757\u4e2d\u7684\u5b57\u7b26\u96c6\uff0c\u5982\u5b57\u6bcd\u548c\u6570\u5b57\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
      import random\nimport string\n\nlength = 10  # \u5b57\u7b26\u4e32\u957f\u5ea6\nrandom_string = ''.join(random.choices(string.ascii_letters + string.digits, k=length))\nprint(random_string)\n<\/code><\/pre>\n
      \u6b64\u5916\uff0c\u4f7f\u7528Faker<\/code>\u5e93\u53ef\u4ee5\u751f\u6210\u968f\u673a\u7684\u6837\u672c\u6570\u636e\uff0c\u5982\u59d3\u540d\u3001\u5730\u5740\u3001\u7535\u8bdd\u53f7\u7801\u7b49\uff0c\u8fd9\u5bf9\u4e8e\u6d4b\u8bd5\u548c\u5f00\u53d1\u975e\u5e38\u6709\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u968f\u673a\u751f\u6210\u6570\u636e\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528\u6807\u51c6\u5e93\u4e2d\u7684random\u6a21\u5757\u3001Numpy\u5e93\u3001Pandas\u5e93 […]","protected":false},"author":3,"featured_media":1113704,"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\/1113700"}],"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=1113700"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1113700\/revisions"}],"predecessor-version":[{"id":1113705,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1113700\/revisions\/1113705"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1113704"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1113700"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1113700"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1113700"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}