{"id":294827,"date":"2024-05-20T15:20:06","date_gmt":"2024-05-20T07:20:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/294827.html"},"modified":"2024-05-20T15:20:19","modified_gmt":"2024-05-20T07:20:19","slug":"numpy-%e7%9a%84%e6%95%b0%e7%bb%84%e5%88%87%e7%89%87%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/294827.html","title":{"rendered":"numpy \u7684\u6570\u7ec4\u5207\u7247\u5982\u4f55\u8fdb\u884c"},"content":{"rendered":"

\"numpy<\/p>\n

\u6570\u7ec4\u5207\u7247\u5728NumPy\u4e2d\u662f\u5904\u7406\u6570\u636e\u7684\u57fa\u7840\u64cd\u4f5c\u4e4b\u4e00\u3002\u901a\u8fc7\u5207\u7247\uff0c\u53ef\u4ee5\u4ece\u5927\u578b\u6570\u7ec4\u4e2d\u63d0\u53d6\u51fa\u6570\u636e\u5b50\u96c6\u3002NumPy\u6570\u7ec4\u7684\u5207\u7247\u8bed\u6cd5\u9075\u5faa\u6807\u51c6Python\u5217\u8868\u5207\u7247\u7684\u65b9\u5f0f\uff1a\u4f7f\u7528\u8d77\u59cb\u7d22\u5f15\u3001\u7ec8\u6b62\u7d22\u5f15\u548c\u6b65\u957f<\/strong>\u3002\u6700\u76f4\u63a5\u7684\u5207\u7247\u53ef\u4ee5\u4f7f\u7528\u65b9\u62ec\u53f7[]<\/code>\u6765\u5b9e\u73b0\uff0c\u5207\u7247\u7684\u57fa\u672c\u683c\u5f0f\u662f\u6570\u7ec4[start:stop:step]<\/code>\u3002\u7279\u522b\u5730\uff0c\u8fd9\u79cd\u6280\u672f\u4e0d\u53ea\u662f\u9650\u4e8e\u4e00\u7ef4\u6570\u7ec4\uff0c\u5b83\u540c\u6837\u9002\u7528\u4e8e\u591a\u7ef4\u6570\u7ec4\uff0c\u901a\u8fc7\u63d0\u4f9b\u6bcf\u4e2a\u7ef4\u5ea6\u7684\u5207\u7247\u6765\u83b7\u53d6\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u57fa\u672c\u5207\u7247\u8bed\u6cd5<\/h3>\n<\/p>\n

\u5728\u4e00\u7ef4\u6570\u7ec4\u4e2d\u8fdb\u884c\u5207\u7247\u76f8\u5bf9\u7b80\u5355\u76f4\u89c2\u3002\u9996\u5148\uff0c\u521b\u5efa\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4\u5e76\u8fdb\u884c\u57fa\u672c\u7684\u5207\u7247\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
arr = np.arange(10)<\/p>\n
\u83b7\u53d6\u524d5\u4e2a\u5143\u7d20<\/strong><\/h2>\n
slice1 = arr[:5]<\/p>\n
\u83b7\u53d6\u7d22\u5f153\u81f37\u4e4b\u95f4\u7684\u5143\u7d20<\/strong><\/h2>\n
slice2 = arr[3:8]<\/p>\n
\u83b7\u53d6\u4ece\u7d22\u5f155\u5230\u6570\u7ec4\u5c3e\u7aef\u7684\u6240\u6709\u5143\u7d20<\/strong><\/h2>\n
slice3 = arr[5:]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u591a\u7ef4\u6570\u7ec4\u4e2d\uff0c\u5207\u7247\u5c06\u53d8\u5f97\u66f4\u52a0\u4e30\u5bcc\u548c\u5f3a\u5927<\/strong>\u3002\u6bcf\u4e2a\u7ef4\u5ea6\u90fd\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u9017\u53f7\u5206\u9694\u7684\u5207\u7247\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u591a\u7ef4\u6570\u7ec4\u5207\u7247<\/h3>\n<\/p>\n
\u5728\u591a\u7ef4\u6570\u7ec4\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u6cbf\u7740\u6bcf\u4e00\u4e2a\u7ef4\u5ea6\u8fdb\u884c\u5207\u7247\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a2\u7ef4\u6570\u7ec4<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
\u83b7\u53d6\u7b2c\u4e00\u884c\u7684\u6240\u6709\u5143\u7d20<\/strong><\/h2>\n
row_slice = matrix[0, :]<\/p>\n
\u83b7\u53d6\u7b2c\u4e00\u5217\u7684\u6240\u6709\u5143\u7d20<\/strong><\/h2>\n
column_slice = matrix[:, 0]<\/p>\n
\u83b7\u53d6\u5b50\u77e9\u9635<\/strong><\/h2>\n
sub_matrix = matrix[1:3, 1:3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u63d0\u4f9b\u5207\u7247\u65f6\uff0c\u5982\u679c\u7701\u7565\u4e86\u67d0\u4e2a\u7ef4\u5ea6\u7684\u5207\u7247\uff0c\u5219\u9ed8\u8ba4\u9009\u62e9\u8be5\u7ef4\u5ea6\u7684\u6240\u6709\u6570\u636e<\/strong>\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u9ad8\u7ea7\u7d22\u5f15\u548c\u5207\u7247<\/h3>\n<\/p>\n
NumPy\u63d0\u4f9b\u4e86\u76f8\u6bd4\u4e8e\u7b80\u5355\u7d22\u5f15\u66f4\u52a0\u7075\u6d3b\u7684\u65b9\u5f0f\uff0c\u79f0\u4e3a\u9ad8\u7ea7\u7d22\u5f15\u3002\u9ad8\u7ea7\u7d22\u5f15\u5141\u8bb8\u4f60\u4f7f\u7528\u6574\u6570\u6570\u7ec4\u6765\u6307\u5b9a\u83b7\u53d6\u6570\u636e\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u6574\u6570\u6570\u7ec4\u7d22\u5f15<\/p>\n
row_indices = np.array([0, 2])<\/p>\n
rows = matrix[row_indices, :]<\/p>\n
\u4f7f\u7528\u5e03\u5c14\u6570\u7ec4\u8fdb\u884c\u7d22\u5f15<\/strong><\/h2>\n
column_mask = np.array([True, False, True])<\/p>\n
columns = matrix[:, column_mask]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9ad8\u7ea7\u7d22\u5f15\u662f\u66f4\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4e3a\u7528\u6237\u5728\u590d\u6742\u6570\u636e\u96c6\u4e0a\u63d0\u4f9b\u4e86\u6781\u5927\u7684\u7075\u6d3b\u6027<\/strong>\u3002\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\uff0c\u751a\u81f3\u53ef\u4ee5\u57fa\u4e8e\u6570\u7ec4\u4e2d\u7684\u771f\u503c\u6765\u9009\u62e9\u6216\u6392\u9664\u6570\u636e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5207\u7247\u4e0e\u89c6\u56fe<\/h3>\n<\/p>\n
\u5728NumPy\u4e2d\uff0c\u6570\u7ec4\u5207\u7247\u8fd4\u56de\u7684\u51e0\u4e4e\u90fd\u662f\u6570\u7ec4\u6570\u636e\u7684\u89c6\u56fe\uff0c\u800c\u4e0d\u662f\u526f\u672c\u3002\u8fd9\u610f\u5473\u7740\uff0c\u4fee\u6539\u5207\u7247\u5c06\u5f71\u54cd\u5230\u539f\u59cb\u6570\u7ec4<\/strong>\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4<\/p>\n
new_array = np.array([1, 2, 3, 4, 5])<\/p>\n
\u5207\u7247\u751f\u6210\u89c6\u56fe<\/strong><\/h2>\n
view = new_array[1:4]<\/p>\n
view[1] = -1<\/p>\n
\u73b0\u5728original_array\u4e2d\u7684\u76f8\u5e94\u5143\u7d20\u4e5f\u88ab\u4fee\u6539\u4e86<\/strong><\/h2>\n
print(new_array) # \u8f93\u51fa: [1, 2, -1, 4, 5]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u884c\u4e3a\u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u975e\u5e38\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u907f\u514d\u4e86\u4e0d\u5fc5\u8981\u7684\u6570\u636e\u590d\u5236\uff0c\u4ece\u800c\u63d0\u9ad8\u4e86\u7a0b\u5e8f\u7684\u6548\u7387<\/strong>\u3002\u7136\u800c\uff0c\u5728\u9700\u8981\u72ec\u7acb\u4fee\u6539\u7684\u60c5\u51b5\u4e0b\uff0c\u5c31\u5fc5\u987b\u663e\u5f0f\u5730\u521b\u5efa\u526f\u672c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5207\u7247\u4e0e\u5185\u5b58\u4f7f\u7528<\/h3>\n<\/p>\n
\u5207\u7247\u5728\u5185\u5b58\u5229\u7528\u65b9\u9762\u975e\u5e38\u9ad8\u6548\u3002\u56e0\u4e3a\u5207\u7247\u64cd\u4f5c\u521b\u5efa\u7684\u53ea\u662f\u539f\u6570\u7ec4\u7684\u89c6\u56fe\uff0c\u6240\u4ee5\u5b83\u4e0d\u5360\u7528\u989d\u5916\u5185\u5b58\u7a7a\u95f4\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u4f7f\u7528\u5207\u7247\u4e0d\u4f1a\u5f71\u54cd\u7a0b\u5e8f\u7684\u5185\u5b58\u5360\u7528\u60c5\u51b5<\/strong>\u3002<\/p>\n<\/p>\n
# \u5373\u4f7f\u5728\u5927\u6570\u636e\u96c6\u4e0a\uff0c\u5207\u7247\u4e5f\u51e0\u4e4e\u4e0d\u5360\u7528\u989d\u5916\u5185\u5b58<\/p>\n
large_array = np.arange(1000000)<\/p>\n
small_slice = large_array[:10]<\/p>\n
small_slice\u4e0d\u5360\u7528\u989d\u5916\u5185\u5b58<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u5185\u5b58\u6548\u7387\u610f\u5473\u7740\u5728\u5904\u7406\u6781\u5927\u7684\u6570\u636e\u96c6\u65f6\uff0cNumPy\u80fd\u591f\u63d0\u4f9b\u975e\u51e1\u7684\u6027\u80fd\uff0c\u800c\u65e0\u9700\u62c5\u5fe7\u5185\u5b58\u6ea2\u51fa\u95ee\u9898<\/strong>\u3002<\/p>\n<\/p>\n
\u516d\u3001\u6ce8\u610f\u4e8b\u9879\u548c\u5e38\u89c1\u9677\u9631<\/h3>\n<\/p>\n
\u5c3d\u7ba1\u6570\u7ec4\u5207\u7247\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4f46\u4f7f\u7528\u4e0d\u5f53\u4e5f\u53ef\u80fd\u4ea7\u751f\u4e00\u4e9b\u95ee\u9898\u3002<\/p>\n<\/p>\n
    \n
  • \u5207\u7247\u521b\u5efa\u7684\u662f\u89c6\u56fe\u800c\u975e\u526f\u672c<\/strong>\uff0c\u8fd9\u53ef\u80fd\u4f1a\u5728\u4e0d\u7ecf\u610f\u95f4\u4fee\u6539\u539f\u59cb\u6570\u636e\uff1b<\/li>\n
  • \u591a\u7ef4\u6570\u7ec4\u7684\u5207\u7247\u6bd4\u4e00\u7ef4\u6570\u7ec4\u7684\u5207\u7247\u66f4\u5bb9\u6613\u4ea7\u751f\u6df7\u6dc6\uff1b<\/li>\n
  • \u9ad8\u7ea7\u7d22\u5f15\u751f\u6210\u7684\u662f\u6570\u7ec4\u7684\u526f\u672c\u800c\u975e\u89c6\u56fe\uff1b<\/li>\n
  • \u5207\u7247\u7684\u8d77\u59cb\u7d22\u5f15\u3001\u7ec8\u6b62\u7d22\u5f15\u548c\u6b65\u957f\u90fd\u53ef\u4ee5\u662f\u8d1f\u6570<\/strong>\uff0c\u8868\u793a\u4ece\u6570\u7ec4\u7684\u5c3e\u90e8\u5f00\u59cb\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n
    # \u4f7f\u7528\u8d1f\u7d22\u5f15\u8fdb\u884c\u5207\u7247<\/p>\n
    negative_slice = new_array[-4:-1]<\/p>\n
    \u4f7f\u7528\u6b65\u957f\u8fdb\u884c\u5207\u7247<\/strong><\/h2>\n
    step_slice = new_array[::2]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4f7f\u7528NumPy\u65f6\uff0c\u8981\u7279\u522b\u6ce8\u610f\u7ef4\u62a4\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u907f\u514d\u610f\u5916\u7684\u6570\u636e\u4fee\u6539<\/strong>\u3002\u901a\u5e38\uff0c\u5728\u5b9e\u8df5\u4e2d\u7ecf\u5e38\u4f1a\u5148\u5bf9\u590d\u6742\u7684\u5207\u7247\u64cd\u4f5c\u8fdb\u884c\u5c0f\u8303\u56f4\u7684\u6d4b\u8bd5\uff0c\u4ee5\u786e\u4fdd\u5b83\u4eec\u6309\u7167\u9884\u671f\u5de5\u4f5c\u3002<\/p>\n<\/p>\n
    \u4ee5\u4e0a\u5c31\u662fNumPy\u6570\u7ec4\u5207\u7247\u7684\u64cd\u4f5c\u65b9\u6cd5\u548c\u4e3b\u8981\u7279\u70b9\u3002\u638c\u63e1\u4e86\u8fd9\u4e9b\u5207\u7247\u6280\u672f\uff0c\u5c31\u53ef\u4ee5\u6709\u6548\u5730\u4ece\u5927\u578b\u6570\u636e\u96c6\u4e2d\u63d0\u53d6\u4fe1\u606f\uff0c\u8fdb\u800c\u7528\u4e8e\u5404\u79cd\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4efb\u52a1\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
    1. \u6570\u7ec4\u5207\u7247\u5728 numpy \u4e2d\u5982\u4f55\u5b9e\u73b0\uff1f<\/strong><\/p>\n
    \u6570\u7ec4\u5207\u7247\u662f\u6307\u4ece\u4e00\u4e2a\u6570\u7ec4\u4e2d\u9009\u53d6\u7279\u5b9a\u7684\u5143\u7d20\u7ec4\u6210\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4\u3002\u5728 numpy \u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u6765\u5b9e\u73b0\u6570\u7ec4\u7684\u5207\u7247\u3002\u5207\u7247\u64cd\u4f5c\u4f7f\u7528\u5192\u53f7\uff08:\uff09\u6765\u8868\u793a\u8303\u56f4\u3002\u4f8b\u5982\uff0c\u8981\u4ece\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4\u4e2d\u9009\u53d6\u524d5\u4e2a\u5143\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528\u5207\u7247\u64cd\u4f5c array[:5]<\/code>\u3002\u5982\u679c\u8981\u9009\u53d6\u7279\u5b9a\u8303\u56f4\u5185\u7684\u5143\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528\u4f8b\u5982 array[2:6]<\/code> \u7684\u5207\u7247\u64cd\u4f5c\u3002<\/p>\n
    2. numpy \u6570\u7ec4\u5207\u7247\u7684\u7528\u9014\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n
    \u6570\u7ec4\u5207\u7247\u5728 numpy \u4e2d\u975e\u5e38\u6709\u7528\uff0c\u5b83\u53ef\u4ee5\u7528\u6765\u9009\u53d6\u6570\u7ec4\u4e2d\u7684\u7279\u5b9a\u5143\u7d20\u6216\u8303\u56f4\uff0c\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002\u901a\u8fc7\u5207\u7247\u64cd\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u6570\u7ec4\u7684\u5b50\u96c6\uff0c\u4ece\u800c\u8fdb\u884c\u6570\u636e\u7b5b\u9009\u3001\u64cd\u4f5c\u6216\u5206\u6790\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u7ec4\u5207\u7247\u6765\u63d0\u53d6\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u67d0\u4e2a\u65f6\u95f4\u6bb5\u7684\u6570\u636e\uff0c\u6216\u8005\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u67d0\u4e2a\u533a\u57df\u8fdb\u884c\u5904\u7406\u3002<\/p>\n
    3. \u5982\u4f55\u8fdb\u884c\u591a\u7ef4\u6570\u7ec4\u7684\u5207\u7247\u64cd\u4f5c\uff1f<\/strong><\/p>\n
    numpy \u8fd8\u652f\u6301\u5bf9\u591a\u7ef4\u6570\u7ec4\u8fdb\u884c\u5207\u7247\u64cd\u4f5c\u3002\u5728\u591a\u7ef4\u6570\u7ec4\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u9017\u53f7\u6765\u5206\u9694\u4e0d\u540c\u7ef4\u5ea6\u7684\u5207\u7247\u8303\u56f4\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u4f7f\u7528 array[:, 2:5]<\/code> \u6765\u9009\u53d6\u7b2c\u4e8c\u5230\u7b2c\u56db\u5217\u7684\u5143\u7d20\u3002\u5982\u679c\u60f3\u8981\u9009\u53d6\u7b2c\u4e00\u884c\u548c\u7b2c\u4e8c\u884c\uff0c\u53ef\u4ee5\u4f7f\u7528 array[:2, :]<\/code>\u3002\u901a\u8fc7\u7075\u6d3b\u7684\u591a\u7ef4\u5207\u7247\u64cd\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u591a\u7ef4\u6570\u7ec4\u4e2d\u7684\u7279\u5b9a\u7ef4\u5ea6\u8fdb\u884c\u5207\u7247\uff0c\u5b9e\u73b0\u66f4\u7cbe\u786e\u7684\u6570\u636e\u9009\u53d6\u548c\u64cd\u4f5c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u6570\u7ec4\u5207\u7247\u5728NumPy\u4e2d\u662f\u5904\u7406\u6570\u636e\u7684\u57fa\u7840\u64cd\u4f5c\u4e4b\u4e00\u3002\u901a\u8fc7\u5207\u7247\uff0c\u53ef\u4ee5\u4ece\u5927\u578b\u6570\u7ec4\u4e2d\u63d0\u53d6\u51fa\u6570\u636e\u5b50\u96c6\u3002NumPy\u6570\u7ec4\u7684\u5207\u7247 […]","protected":false},"author":3,"featured_media":294844,"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\/294827"}],"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=294827"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/294827\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/294844"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=294827"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=294827"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=294827"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}