{"id":931966,"date":"2024-12-26T17:48:45","date_gmt":"2024-12-26T09:48:45","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/931966.html"},"modified":"2024-12-26T17:48:47","modified_gmt":"2024-12-26T09:48:47","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8numpy","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/931966.html","title":{"rendered":"python\u5982\u4f55\u8c03\u7528numpy"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25070033\/4d7f3975-22a0-4f9f-b218-115cc261f2fe.webp\" alt=\"python\u5982\u4f55\u8c03\u7528numpy\" \/><\/p>\n<p><p> <strong>Python\u8c03\u7528Numpy\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u4f7f\u7528<code>import<\/code>\u8bed\u53e5\u5bfc\u5165Numpy\u5e93\u3001\u901a\u8fc7<code>np<\/code>\u522b\u540d\u4f7f\u7528Numpy\u51fd\u6570\u3001\u786e\u4fdd\u5728\u9879\u76ee\u73af\u5883\u4e2d\u5b89\u88c5Numpy\u5e93\u3002<\/strong>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fddNumpy\u5e93\u5df2\u6b63\u786e\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4\u884c\u4f7f\u7528<code>pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u5728Python\u4ee3\u7801\u4e2d\u901a\u8fc7<code>import numpy as np<\/code>\u8bed\u53e5\u5bfc\u5165Numpy\u5e93\uff0c\u8fd9\u6837\u53ef\u4ee5\u65b9\u4fbf\u5730\u8c03\u7528Numpy\u7684\u5404\u79cd\u51fd\u6570\u548c\u65b9\u6cd5\u3002\u6700\u540e\uff0c\u901a\u8fc7<code>np<\/code>\u8fd9\u4e2a\u522b\u540d\u6765\u8c03\u7528Numpy\u63d0\u4f9b\u7684\u5404\u79cd\u529f\u80fd\uff0c\u4f8b\u5982\u6570\u7ec4\u64cd\u4f5c\u3001\u6570\u5b66\u8fd0\u7b97\u3001\u7edf\u8ba1\u5206\u6790\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001Numpy\u5e93\u7684\u5b89\u88c5\u4e0e\u5bfc\u5165<\/p>\n<\/p>\n<p><p>\u5728\u4f7f\u7528Numpy\u4e4b\u524d\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u786e\u4fdd\u5728\u6211\u4eec\u7684Python\u73af\u5883\u4e2d\u5df2\u7ecf\u5b89\u88c5\u4e86Numpy\u5e93\u3002Numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002\u5b89\u88c5Numpy\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b8c\u6210\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u5b89\u88c5Numpy<\/strong><\/p>\n<\/p>\n<p><p>\u8981\u5b89\u88c5Numpy\uff0c\u53ef\u4ee5\u6253\u5f00\u547d\u4ee4\u884c\u6216\u7ec8\u7aef\uff0c\u5e76\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8be5\u547d\u4ee4\u5c06\u4ecePython\u5305\u7d22\u5f15\uff08PyPI\uff09\u4e2d\u4e0b\u8f7d\u5e76\u5b89\u88c5Numpy\u5e93\u3002\u5982\u679c\u5df2\u7ecf\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528<code>pip show numpy<\/code>\u547d\u4ee4\u6765\u68c0\u67e5\u5b89\u88c5\u7684\u7248\u672c\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5bfc\u5165Numpy\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u9700\u8981\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Numpy\u5e93\uff0c\u4ee5\u4fbf\u4f7f\u7528\u5176\u529f\u80fd\u3002\u901a\u5e38\uff0c\u6211\u4eec\u4f1a\u4f7f\u7528<code>np<\/code>\u4f5c\u4e3aNumpy\u7684\u522b\u540d\uff0c\u4ee5\u7b80\u5316\u4ee3\u7801\u4e66\u5199\u3002\u5bfc\u5165Numpy\u7684\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<code>np<\/code>\u6765\u8c03\u7528Numpy\u7684\u5404\u79cd\u51fd\u6570\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e8c\u3001\u521b\u5efa\u548c\u64cd\u4f5cNumpy\u6570\u7ec4<\/p>\n<\/p>\n<p><p>Numpy\u7684\u6838\u5fc3\u529f\u80fd\u4e4b\u4e00\u662f\u5176\u5f3a\u5927\u7684\u6570\u7ec4\u5bf9\u8c61<code>ndarray<\/code>\uff0c\u5b83\u652f\u6301\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u8fd0\u7b97\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Numpy\u7684\u6570\u7ec4\u521b\u5efa\u51fd\u6570\u6765\u751f\u6210\u6570\u7ec4\uff0c\u5e76\u5bf9\u5176\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u521b\u5efaNumpy\u6570\u7ec4<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u521b\u5efa\u6570\u7ec4\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<ul>\n<li>\n<p>\u4ecePython\u5217\u8868\u521b\u5efa\u6570\u7ec4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>list_data = [1, 2, 3, 4, 5]<\/p>\n<p>array_data = np.array(list_data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p>\u521b\u5efa\u5168\u96f6\u6570\u7ec4\u6216\u5168\u4e00\u6570\u7ec4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">zeros_array = np.zeros((3, 3))  # \u521b\u5efa3x3\u7684\u5168\u96f6\u6570\u7ec4<\/p>\n<p>ones_array = np.ones((2, 2))    # \u521b\u5efa2x2\u7684\u5168\u4e00\u6570\u7ec4<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p>\u521b\u5efa\u7b49\u5dee\u6570\u7ec4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">arange_array = np.arange(0, 10, 2)  # \u751f\u6210\u4ece0\u523010\uff0c\u4ee52\u4e3a\u6b65\u957f\u7684\u6570\u7ec4<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p>\u521b\u5efa\u968f\u673a\u6570\u7ec4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">random_array = np.random.rand(3, 3)  # \u521b\u5efa3x3\u7684\u968f\u673a\u6570\u7ec4<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>Numpy\u6570\u7ec4\u7684\u64cd\u4f5c<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u6570\u7ec4\u652f\u6301\u5404\u79cd\u8fd0\u7b97\u548c\u64cd\u4f5c\uff0c\u5305\u62ec\u57fa\u672c\u7684\u7b97\u672f\u8fd0\u7b97\u3001\u77e9\u9635\u8fd0\u7b97\u3001\u6570\u7ec4\u53d8\u5f62\u7b49\uff1a<\/p>\n<\/p>\n<ul>\n<li>\n<p>\u57fa\u672c\u7b97\u672f\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">array1 = np.array([1, 2, 3])<\/p>\n<p>array2 = np.array([4, 5, 6])<\/p>\n<p>sum_array = array1 + array2  # \u6570\u7ec4\u52a0\u6cd5<\/p>\n<p>diff_array = array1 - array2  # \u6570\u7ec4\u51cf\u6cd5<\/p>\n<p>product_array = array1 * array2  # \u5143\u7d20\u4e58\u6cd5<\/p>\n<p>quotient_array = array1 \/ array2  # \u5143\u7d20\u9664\u6cd5<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p>\u6570\u7ec4\u53d8\u5f62\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">reshaped_array = np.reshape(array_data, (5, 1))  # \u5c06\u6570\u7ec4\u91cd\u5851\u4e3a5\u884c1\u5217<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p>\u6570\u7ec4\u5408\u5e76\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">vertical_stack = np.vstack((array1, array2))  # \u5782\u76f4\u5806\u53e0\u6570\u7ec4<\/p>\n<p>horizontal_stack = np.hstack((array1, array2))  # \u6c34\u5e73\u5806\u53e0\u6570\u7ec4<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p><p>\u4e09\u3001Numpy\u4e2d\u7684\u6570\u5b66\u51fd\u6570<\/p>\n<\/p>\n<p><p>Numpy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\uff0c\u7528\u4e8e\u6267\u884c\u5404\u79cd\u6570\u5b66\u8ba1\u7b97\u548c\u5206\u6790\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5bf9\u6570\u7ec4\u8fdb\u884c\u9010\u5143\u7d20\u8ba1\u7b97\uff0c\u7b80\u5316\u4e86\u8bb8\u591a\u590d\u6742\u7684\u6570\u5b66\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u57fa\u672c\u6570\u5b66\u51fd\u6570<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u4e2d\u7684\u57fa\u672c\u6570\u5b66\u51fd\u6570\u5305\u62ec\u4e09\u89d2\u51fd\u6570\u3001\u6307\u6570\u51fd\u6570\u3001\u5bf9\u6570\u51fd\u6570\u7b49\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">angles = np.array([0, np.pi\/2, np.pi])<\/p>\n<p>sin_values = np.sin(angles)  # \u8ba1\u7b97\u6b63\u5f26\u503c<\/p>\n<p>cos_values = np.cos(angles)  # \u8ba1\u7b97\u4f59\u5f26\u503c<\/p>\n<p>exp_values = np.exp(angles)  # \u8ba1\u7b97\u6307\u6570\u503c<\/p>\n<p>log_values = np.log(exp_values)  # \u8ba1\u7b97\u5bf9\u6570\u503c<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u7edf\u8ba1\u51fd\u6570<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u51fd\u6570\uff0c\u7528\u4e8e\u8ba1\u7b97\u6570\u7ec4\u7684\u7edf\u8ba1\u91cf\uff0c\u5982\u5e73\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = np.array([1, 2, 3, 4, 5])<\/p>\n<p>mean_value = np.mean(data)  # \u8ba1\u7b97\u5e73\u5747\u503c<\/p>\n<p>std_value = np.std(data)  # \u8ba1\u7b97\u6807\u51c6\u5dee<\/p>\n<p>max_value = np.max(data)  # \u8ba1\u7b97\u6700\u5927\u503c<\/p>\n<p>min_value = np.min(data)  # \u8ba1\u7b97\u6700\u5c0f\u503c<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u7ebf\u6027\u4ee3\u6570\u51fd\u6570<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u8fd8\u5305\u542b\u8bb8\u591a\u7ebf\u6027\u4ee3\u6570\u51fd\u6570\uff0c\u652f\u6301\u77e9\u9635\u8fd0\u7b97\u3001\u7279\u5f81\u503c\u5206\u89e3\u3001\u5947\u5f02\u503c\u5206\u89e3\u7b49\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">matrix = np.array([[1, 2], [3, 4]])<\/p>\n<p>determinant = np.linalg.det(matrix)  # \u8ba1\u7b97\u77e9\u9635\u884c\u5217\u5f0f<\/p>\n<p>inverse_matrix = np.linalg.inv(matrix)  # \u8ba1\u7b97\u77e9\u9635\u7684\u9006<\/p>\n<p>eigenvalues, eigenvectors = np.linalg.eig(matrix)  # \u8ba1\u7b97\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u56db\u3001Numpy\u7684\u9ad8\u7ea7\u529f\u80fd<\/p>\n<\/p>\n<p><p>\u9664\u4e86\u57fa\u672c\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u6570\u5b66\u51fd\u6570\u5916\uff0cNumpy\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u5e7f\u64ad\u673a\u5236\u3001\u6570\u7ec4\u7d22\u5f15\u3001\u6570\u7ec4\u5207\u7247\u7b49\uff0c\u8fd9\u4e9b\u529f\u80fd\u5927\u5927\u589e\u5f3a\u4e86Numpy\u7684\u7075\u6d3b\u6027\u548c\u529f\u80fd\u6027\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u5e7f\u64ad\u673a\u5236<\/strong><\/p>\n<\/p>\n<p><p>\u5e7f\u64ad\u673a\u5236\u4f7f\u5f97\u4e0d\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u80fd\u591f\u5728\u7b97\u672f\u8fd0\u7b97\u4e2d\u8fdb\u884c\u517c\u5bb9\u64cd\u4f5c\u3002\u901a\u8fc7\u5e7f\u64ad\u673a\u5236\uff0cNumpy\u53ef\u4ee5\u81ea\u52a8\u6269\u5c55\u8f83\u5c0f\u7684\u6570\u7ec4\u4ee5\u5339\u914d\u8f83\u5927\u7684\u6570\u7ec4\u7684\u5f62\u72b6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">array1 = np.array([1, 2, 3])<\/p>\n<p>array2 = np.array([[1], [2], [3]])<\/p>\n<p>broadcasted_sum = array1 + array2  # \u901a\u8fc7\u5e7f\u64ad\u673a\u5236\u8fdb\u884c\u5143\u7d20\u76f8\u52a0<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u7ec4\u7d22\u5f15\u548c\u5207\u7247<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u652f\u6301\u7075\u6d3b\u7684\u6570\u7ec4\u7d22\u5f15\u548c\u5207\u7247\u64cd\u4f5c\uff0c\u53ef\u4ee5\u5bf9\u6570\u7ec4\u7684\u7279\u5b9a\u90e8\u5206\u8fdb\u884c\u8bbf\u95ee\u548c\u4fee\u6539\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">array = np.array([1, 2, 3, 4, 5])<\/p>\n<p>element = array[2]  # \u8bbf\u95ee\u7d22\u5f15\u4e3a2\u7684\u5143\u7d20<\/p>\n<p>sub_array = array[1:4]  # \u5207\u7247\uff0c\u83b7\u53d6\u7d22\u5f151\u52303\u7684\u5143\u7d20<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5e03\u5c14\u7d22\u5f15<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u5141\u8bb8\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\uff0c\u6839\u636e\u6761\u4ef6\u9009\u62e9\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = np.array([1, 2, 3, 4, 5])<\/p>\n<p>condition = data &gt; 3<\/p>\n<p>filtered_data = data[condition]  # \u83b7\u53d6\u5927\u4e8e3\u7684\u5143\u7d20<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u9ad8\u7ea7\u7d22\u5f15<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u652f\u6301\u591a\u79cd\u9ad8\u7ea7\u7d22\u5f15\u65b9\u5f0f\uff0c\u5982\u6574\u6570\u6570\u7ec4\u7d22\u5f15\u548c\u63a9\u7801\u7d22\u5f15\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">array = np.array([10, 20, 30, 40, 50])<\/p>\n<p>indices = [0, 2, 4]<\/p>\n<p>selected_elements = array[indices]  # \u4f7f\u7528\u6574\u6570\u6570\u7ec4\u8fdb\u884c\u7d22\u5f15<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e94\u3001Numpy\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n<p><p>Numpy\u56e0\u5176\u5f3a\u5927\u7684\u529f\u80fd\u548c\u9ad8\u6548\u7684\u6027\u80fd\uff0c\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u591a\u4e2a\u9886\u57df\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5178\u578b\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u5206\u6790<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cNumpy\u5e38\u7528\u4e8e\u6570\u636e\u9884\u5904\u7406\u3001\u7279\u5f81\u63d0\u53d6\u548c\u6570\u636e\u6e05\u6d17\u3002\u5176\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u80fd\u529b\u4f7f\u5176\u6210\u4e3a\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u7684\u7406\u60f3\u5de5\u5177\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u79d1\u5b66\u8ba1\u7b97<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u662f\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5de5\u5177\u4e4b\u4e00\uff0c\u5176\u5f3a\u5927\u7684\u6570\u5b66\u51fd\u6570\u548c\u7ebf\u6027\u4ee3\u6570\u529f\u80fd\u4f7f\u5176\u5728\u79d1\u5b66\u7814\u7a76\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong><a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a><\/strong><\/p>\n<\/p>\n<p><p>\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0cNumpy\u5e38\u7528\u4e8e\u5b9e\u73b0\u5404\u79cd\u7b97\u6cd5\u7684\u5e95\u5c42\u64cd\u4f5c\uff0c\u5982\u68af\u5ea6\u8ba1\u7b97\u3001\u77e9\u9635\u8fd0\u7b97\u7b49\u3002\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u5e93\uff08\u5982Scikit-learn\u3001TensorFlow\uff09\u90fd\u4f9d\u8d56\u4e8eNumpy\u8fdb\u884c\u5e95\u5c42\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u50cf\u5904\u7406<\/strong><\/p>\n<\/p>\n<p><p>Numpy\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u540c\u6837\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\uff0c\u5176\u6570\u7ec4\u64cd\u4f5c\u80fd\u529b\u4f7f\u5f97\u56fe\u50cf\u7684\u8bfb\u53d6\u3001\u4fee\u6539\u548c\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cNumpy\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\u3002\u901a\u8fc7\u638c\u63e1Numpy\u7684\u57fa\u672c\u7528\u6cd5\u548c\u9ad8\u7ea7\u529f\u80fd\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u6570\u636e\u5904\u7406\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u66f4\u9ad8\u6548\u5730\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5NumPy\u5e93\uff1f<\/strong><br \/>\u5728Python\u4e2d\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u5148\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b89\u88c5\u3002\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<code>pip install numpy<\/code>\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u5c31\u53ef\u4ee5\u5728\u4efb\u4f55Python\u811a\u672c\u4e2d\u5bfc\u5165\u5e76\u4f7f\u7528NumPy\u5e93\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5982\u4f55\u5bfc\u5165NumPy\u5e93\uff1f<\/strong><br \/>\u5bfc\u5165NumPy\u5e93\u975e\u5e38\u7b80\u5355\u3002\u53ea\u9700\u5728\u60a8\u7684Python\u811a\u672c\u5f00\u5934\u6dfb\u52a0\u4ee5\u4e0b\u4ee3\u7801\uff1a<code>import numpy as np<\/code>\u3002\u8fd9\u6837\uff0c\u60a8\u5c31\u53ef\u4ee5\u4f7f\u7528<code>np<\/code>\u4f5c\u4e3aNumPy\u7684\u522b\u540d\u6765\u8c03\u7528\u5e93\u4e2d\u7684\u5404\u79cd\u51fd\u6570\u548c\u65b9\u6cd5\u3002<\/p>\n<p><strong>NumPy\u5e93\u7684\u4e3b\u8981\u529f\u80fd\u548c\u5e94\u7528\u573a\u666f\u6709\u54ea\u4e9b\uff1f<\/strong><br \/>NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u5b66\u5e93\uff0c\u63d0\u4f9b\u4e86\u652f\u6301\u5927\u89c4\u6a21\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u7684\u5bf9\u8c61\uff0c\u6b64\u5916\uff0c\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u6570\u7ec4\u64cd\u4f5c\u7684\u51fd\u6570\u3002\u5b83\u5e38\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3001\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u7b49\u9886\u57df\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u8fdb\u884c\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97\u3001\u5085\u91cc\u53f6\u53d8\u6362\u3001\u968f\u673a\u6570\u751f\u6210\u7b49\u64cd\u4f5c\uff0c\u6781\u5927\u5730\u63d0\u9ad8\u4e86\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8c03\u7528Numpy\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u4f7f\u7528import\u8bed\u53e5\u5bfc\u5165Numpy\u5e93\u3001\u901a\u8fc7np\u522b\u540d\u4f7f\u7528Numpy\u51fd\u6570\u3001 [&hellip;]","protected":false},"author":3,"featured_media":931967,"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\/931966"}],"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=931966"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/931966\/revisions"}],"predecessor-version":[{"id":931968,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/931966\/revisions\/931968"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/931967"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=931966"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=931966"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=931966"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}