{"id":930247,"date":"2024-12-26T17:12:01","date_gmt":"2024-12-26T09:12:01","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/930247.html"},"modified":"2024-12-26T17:12:03","modified_gmt":"2024-12-26T09:12:03","slug":"python-%e5%a6%82%e4%bd%95%e4%bf%9d%e5%ad%98%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/930247.html","title":{"rendered":"python \u5982\u4f55\u4fdd\u5b58\u77e9\u9635"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25065046\/b82e6909-9226-42f4-b52f-6cfe3d13ccb7.webp\" alt=\"python \u5982\u4f55\u4fdd\u5b58\u77e9\u9635\" \/><\/p>\n<p><p> \u5f00\u5934\u6bb5\u843d\uff1a<br \/><strong>\u5728Python\u4e2d\u4fdd\u5b58\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528NumPy\u5e93\u4fdd\u5b58\u4e3a\u4e8c\u8fdb\u5236\u6587\u4ef6\u3001\u4f7f\u7528pandas\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\u3001\u4f7f\u7528pickle\u6a21\u5757\u4fdd\u5b58\u4e3a\u5e8f\u5217\u5316\u5bf9\u8c61\u3001\u4ee5\u53ca\u4f7f\u7528h5py\u4fdd\u5b58\u4e3aHDF5\u683c\u5f0f<\/strong>\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u4f7f\u7528\u573a\u666f\u3002\u4f8b\u5982\uff0cNumPy\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u683c\u5f0f\u9002\u5408\u5b58\u50a8\u5927\u89c4\u6a21\u7684\u6570\u503c\u6570\u636e\u5e76\u4e14\u8bfb\u53d6\u901f\u5ea6\u5feb\uff0c\u800cCSV\u6587\u4ef6\u683c\u5f0f\u5219\u9002\u5408\u9700\u8981\u4e0e\u5176\u4ed6\u8f6f\u4ef6\uff08\u5982Excel\uff09\u8fdb\u884c\u6570\u636e\u4ea4\u6362\u7684\u573a\u5408\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e2e\u52a9\u60a8\u9009\u62e9\u6700\u5408\u9002\u7684\u65b9\u5f0f\u6765\u4fdd\u5b58\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001NUMPY\u4fdd\u5b58\u77e9\u9635\u4e3a\u4e8c\u8fdb\u5236\u6587\u4ef6<\/p>\n<\/p>\n<p><p>\u4f7f\u7528NumPy\u4fdd\u5b58\u548c\u8bfb\u53d6\u77e9\u9635\u662f\u4e00\u79cd\u975e\u5e38\u9ad8\u6548\u7684\u65b9\u6cd5\u3002NumPy\u63d0\u4f9b\u4e86<code>np.save<\/code>\u548c<code>np.load<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u77e9\u9635\u4fdd\u5b58\u4e3a\u4e8c\u8fdb\u5236\u6587\u4ef6<code>.npy<\/code>\u683c\u5f0f\uff0c\u8fd9\u79cd\u683c\u5f0f\u4e13\u4e3a\u5b58\u50a8NumPy\u6570\u7ec4\u8bbe\u8ba1\uff0c\u652f\u6301\u9ad8\u6548\u7684\u5b58\u50a8\u548c\u8bfb\u53d6\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u4fdd\u5b58\u77e9\u9635\u4e3a\u4e8c\u8fdb\u5236\u6587\u4ef6<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528<code>np.save<\/code>\u51fd\u6570\uff0c\u4fdd\u5b58\u65f6\u53ea\u9700\u8981\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u77e9\u9635\u5373\u53ef\u3002\u6b64\u683c\u5f0f\u4e0d\u4ec5\u5b58\u50a8\u901f\u5ea6\u5feb\uff0c\u800c\u4e14\u5360\u7528\u7a7a\u95f4\u5c0f\uff0c\u975e\u5e38\u9002\u5408\u4fdd\u5b58\u5927\u89c4\u6a21\u7684\u6570\u503c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u77e9\u9635<\/strong><\/h2>\n<p>matrix = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n<h2><strong>\u4fdd\u5b58\u77e9\u9635\u5230\u6587\u4ef6<\/strong><\/h2>\n<p>np.save(&#39;matrix.npy&#39;, matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u8bfb\u53d6\u4e8c\u8fdb\u5236\u6587\u4ef6\u4e2d\u7684\u77e9\u9635<\/strong>\uff1a\u4f7f\u7528<code>np.load<\/code>\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bfb\u53d6\u4fdd\u5b58\u7684\u77e9\u9635\uff0c\u8bfb\u53d6\u901f\u5ea6\u4e5f\u975e\u5e38\u5feb\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u77e9\u9635<\/p>\n<p>loaded_matrix = np.load(&#39;matrix.npy&#39;)<\/p>\n<p>print(loaded_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><p>\u8fd9\u79cd\u65b9\u6cd5\u7531\u4e8e\u5176\u9ad8\u6548\u6027\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u9700\u8981\u5927\u91cf\u6570\u503c\u8ba1\u7b97\u7684\u9886\u57df\uff0c\u5982<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u548c\u6570\u636e\u79d1\u5b66\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001PANDAS\u4fdd\u5b58\u77e9\u9635\u4e3aCSV\u6587\u4ef6<\/p>\n<\/p>\n<p><p>Pandas\u63d0\u4f9b\u4e86\u975e\u5e38\u4fbf\u6377\u7684\u65b9\u6cd5\u6765\u4fdd\u5b58\u548c\u8bfb\u53d6\u77e9\u9635\u6570\u636e\u3002CSV\u683c\u5f0f\u662f\u6700\u901a\u7528\u7684\u6570\u636e\u4ea4\u6362\u683c\u5f0f\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4e0e\u5176\u4ed6\u8f6f\u4ef6\u4ea4\u6362\u6570\u636e\u7684\u573a\u5408\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u4fdd\u5b58\u77e9\u9635\u4e3aCSV\u6587\u4ef6<\/strong>\uff1a\u9996\u5148\u5c06\u77e9\u9635\u8f6c\u6362\u4e3a<code>pandas.DataFrame<\/code>\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528<code>to_csv<\/code>\u65b9\u6cd5\u5373\u53ef\u5c06\u77e9\u9635\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u77e9\u9635<\/strong><\/h2>\n<p>matrix = [[1, 2, 3], [4, 5, 6]]<\/p>\n<h2><strong>\u5c06\u77e9\u9635\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame(matrix)<\/p>\n<h2><strong>\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6<\/strong><\/h2>\n<p>df.to_csv(&#39;matrix.csv&#39;, index=False)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u77e9\u9635<\/strong>\uff1a\u4f7f\u7528<code>pandas.read_csv<\/code>\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u77e9\u9635<\/p>\n<p>df_loaded = pd.read_csv(&#39;matrix.csv&#39;)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n<p>loaded_matrix = df_loaded.to_numpy()<\/p>\n<p>print(loaded_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><p>CSV\u683c\u5f0f\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u901a\u7528\u6027\uff0c\u51e0\u4e4e\u6240\u6709\u6570\u636e\u5904\u7406\u8f6f\u4ef6\u90fd\u652f\u6301\u8fd9\u79cd\u683c\u5f0f\uff0c\u4f46\u7f3a\u70b9\u662f\u5bf9\u5b58\u50a8\u7a7a\u95f4\u548c\u5b58\u53d6\u901f\u5ea6\u7684\u4f18\u5316\u4e0d\u5982\u4e8c\u8fdb\u5236\u683c\u5f0f\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001PICKLE\u6a21\u5757\u4fdd\u5b58\u4e3a\u5e8f\u5217\u5316\u5bf9\u8c61<\/p>\n<\/p>\n<p><p>Python\u7684<code>pickle<\/code>\u6a21\u5757\u53ef\u4ee5\u5c06\u4efb\u4f55Python\u5bf9\u8c61\u5e8f\u5217\u5316\uff0c\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u5e76\u5728\u9700\u8981\u65f6\u53cd\u5e8f\u5217\u5316\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5b58\u50a8Python\u5bf9\u8c61\u7684\u573a\u5408\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u5e8f\u5217\u5316\u77e9\u9635<\/strong>\uff1a\u4f7f\u7528<code>pickle.dump<\/code>\u53ef\u4ee5\u5c06\u77e9\u9635\u5bf9\u8c61\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pickle<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u77e9\u9635<\/strong><\/h2>\n<p>matrix = [[1, 2, 3], [4, 5, 6]]<\/p>\n<h2><strong>\u5e8f\u5217\u5316\u77e9\u9635<\/strong><\/h2>\n<p>with open(&#39;matrix.pkl&#39;, &#39;wb&#39;) as f:<\/p>\n<p>    pickle.dump(matrix, f)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u53cd\u5e8f\u5217\u5316\u77e9\u9635<\/strong>\uff1a\u4f7f\u7528<code>pickle.load<\/code>\u53ef\u4ee5\u5c06\u6587\u4ef6\u4e2d\u7684\u77e9\u9635\u5bf9\u8c61\u8bfb\u53d6\u56de\u6765\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u53cd\u5e8f\u5217\u5316\u77e9\u9635<\/p>\n<p>with open(&#39;matrix.pkl&#39;, &#39;rb&#39;) as f:<\/p>\n<p>    loaded_matrix = pickle.load(f)<\/p>\n<p>print(loaded_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><p>\u4f7f\u7528<code>pickle<\/code>\u7684\u4f18\u70b9\u662f\u53ef\u4ee5\u4fdd\u5b58\u4efb\u610fPython\u5bf9\u8c61\uff0c\u800c\u4e0d\u4ec5\u9650\u4e8eNumPy\u6570\u7ec4\uff0c\u4f46\u7f3a\u70b9\u662f\u751f\u6210\u7684\u6587\u4ef6\u4e0d\u6613\u4e0e\u5176\u4ed6\u8bed\u8a00\u548c\u5de5\u5177\u517c\u5bb9\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001H5PY\u4fdd\u5b58\u4e3aHDF5\u683c\u5f0f<\/p>\n<\/p>\n<p><p>HDF5\u662f\u4e00\u79cd\u7528\u4e8e\u5b58\u50a8\u548c\u7ec4\u7ec7\u5927\u89c4\u6a21\u6570\u636e\u7684\u6587\u4ef6\u683c\u5f0f\u3002<code>h5py<\/code>\u662fPython\u4e2d\u7528\u4e8e\u5904\u7406HDF5\u6587\u4ef6\u7684\u5e93\uff0c\u9002\u5408\u9700\u8981\u5b58\u50a8\u5927\u89c4\u6a21\u3001\u591a\u7ef4\u6570\u636e\u96c6\u7684\u573a\u5408\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u4fdd\u5b58\u77e9\u9635\u4e3aHDF5\u6587\u4ef6<\/strong>\uff1a\u4f7f\u7528<code>h5py.File<\/code>\u5bf9\u8c61\u7684<code>create_dataset<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5c06\u77e9\u9635\u4fdd\u5b58\u4e3aHDF5\u683c\u5f0f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import h5py<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u77e9\u9635<\/strong><\/h2>\n<p>matrix = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n<h2><strong>\u4fdd\u5b58\u4e3aHDF5\u6587\u4ef6<\/strong><\/h2>\n<p>with h5py.File(&#39;matrix.h5&#39;, &#39;w&#39;) as f:<\/p>\n<p>    f.create_dataset(&#39;dataset_name&#39;, data=matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u8bfb\u53d6HDF5\u6587\u4ef6\u4e2d\u7684\u77e9\u9635<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528<code>h5py.File<\/code>\u5bf9\u8c61\u7684<code>__getitem__<\/code>\u65b9\u6cd5\u8bfb\u53d6\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4eceHDF5\u6587\u4ef6\u4e2d\u8bfb\u53d6\u77e9\u9635<\/p>\n<p>with h5py.File(&#39;matrix.h5&#39;, &#39;r&#39;) as f:<\/p>\n<p>    loaded_matrix = f[&#39;dataset_name&#39;][:]<\/p>\n<p>print(loaded_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><p>HDF5\u683c\u5f0f\u7684\u4f18\u70b9\u662f\u5176\u53ef\u4ee5\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u8bfb\u53d6\u5927\u89c4\u6a21\u6570\u636e\uff0c\u5e76\u652f\u6301\u6570\u636e\u96c6\u7684\u5206\u5c42\u5b58\u50a8\u548c\u538b\u7f29\uff0c\u4f46\u5176\u7f3a\u70b9\u662f\u9700\u8981\u5b89\u88c5\u989d\u5916\u7684\u5e93\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u9009\u62e9\u5408\u9002\u7684\u4fdd\u5b58\u65b9\u6cd5<\/p>\n<\/p>\n<p><p>\u5728\u9009\u62e9\u5982\u4f55\u4fdd\u5b58\u77e9\u9635\u65f6\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u8fdb\u884c\u6743\u8861\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5efa\u8bae\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u6570\u636e\u89c4\u6a21<\/strong>\uff1a\u5982\u679c\u6570\u636e\u89c4\u6a21\u8f83\u5927\uff0c\u4e14\u9700\u8981\u9891\u7e41\u8bfb\u53d6\uff0c\u63a8\u8350\u4f7f\u7528NumPy\u7684\u4e8c\u8fdb\u5236\u683c\u5f0f\u6216HDF5\u683c\u5f0f\u3002<\/li>\n<li><strong>\u6570\u636e\u4ea4\u6362<\/strong>\uff1a\u5982\u679c\u9700\u8981\u4e0e\u5176\u4ed6\u8f6f\u4ef6\u8fdb\u884c\u6570\u636e\u4ea4\u6362\uff0cCSV\u683c\u5f0f\u662f\u6700\u901a\u7528\u7684\u9009\u62e9\u3002<\/li>\n<li><strong>\u5bf9\u8c61\u590d\u6742\u6027<\/strong>\uff1a\u5982\u679c\u9700\u8981\u4fdd\u5b58\u590d\u6742\u7684Python\u5bf9\u8c61\u6216\u81ea\u5b9a\u4e49\u5bf9\u8c61\uff0c\u4f7f\u7528<code>pickle<\/code>\u6a21\u5757\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/li>\n<li><strong>\u8de8\u5e73\u53f0\u4f7f\u7528<\/strong>\uff1a\u5982\u679c\u9700\u8981\u8de8\u5e73\u53f0\u6216\u8de8\u8bed\u8a00\u4f7f\u7528\u6570\u636e\uff0cHDF5\u683c\u5f0f\u63d0\u4f9b\u4e86\u826f\u597d\u7684\u652f\u6301\u3002<\/li>\n<\/ol>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\uff0c\u751a\u81f3\u5f00\u53d1\u81ea\u5b9a\u4e49\u7684\u4fdd\u5b58\u65b9\u6848\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002\u901a\u8fc7\u5bf9\u6bd4\u4e0d\u540c\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u9009\u62e9\u9002\u5408\u60a8\u9879\u76ee\u7684\u4fdd\u5b58\u65b9\u5f0f\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u4fdd\u5b58\u4e00\u4e2a\u77e9\u9635\u5230\u6587\u4ef6\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u5f0f\u5c06\u77e9\u9635\u4fdd\u5b58\u5230\u6587\u4ef6\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u5229\u7528NumPy\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86<code>numpy.save()<\/code>\u548c<code>numpy.savetxt()<\/code>\u51fd\u6570\u3002<code>numpy.save()<\/code>\u5c06\u77e9\u9635\u4fdd\u5b58\u4e3a\u4e8c\u8fdb\u5236\u683c\u5f0f\uff0c\u800c<code>numpy.savetxt()<\/code>\u53ef\u4ee5\u5c06\u77e9\u9635\u4fdd\u5b58\u4e3a\u6587\u672c\u6587\u4ef6\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u4f60\u5bf9\u6570\u636e\u683c\u5f0f\u7684\u9700\u6c42\u3002<\/p>\n<p><strong>\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u6587\u4ef6\u683c\u5f0f\u6765\u4fdd\u5b58\u77e9\u9635\u6570\u636e\uff1f<\/strong><br \/>\u5e38\u89c1\u7684\u6587\u4ef6\u683c\u5f0f\u5305\u62ecCSV\u3001TXT\u548c\u4e8c\u8fdb\u5236\u683c\u5f0f\u3002CSV\u6587\u4ef6\u9002\u5408\u5b58\u50a8\u8868\u683c\u6570\u636e\uff0c\u4fbf\u4e8e\u5728Excel\u7b49\u8f6f\u4ef6\u4e2d\u6253\u5f00\uff1bTXT\u6587\u4ef6\u53ef\u4ee5\u4ee5\u6587\u672c\u5f62\u5f0f\u5b58\u50a8\uff0c\u4fbf\u4e8e\u9605\u8bfb\u548c\u7f16\u8f91\uff1b\u4e8c\u8fdb\u5236\u683c\u5f0f\uff08\u5982<code>.npy<\/code>\uff09\u5219\u9002\u5408\u5728Python\u4e2d\u5feb\u901f\u52a0\u8f7d\u548c\u4fdd\u5b58\uff0c\u5c24\u5176\u662f\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u3002<\/p>\n<p><strong>\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u4fdd\u5b58\u7684\u77e9\u9635\uff1f<\/strong><br \/>\u8bfb\u53d6\u4fdd\u5b58\u7684\u77e9\u9635\u540c\u6837\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u3002\u5bf9\u4e8e\u6587\u672c\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528<code>numpy.loadtxt()<\/code>\u51fd\u6570\uff0c\u800c\u5bf9\u4e8e\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528<code>numpy.load()<\/code>\u51fd\u6570\u3002\u8fd9\u4e9b\u51fd\u6570\u80fd\u591f\u65b9\u4fbf\u5730\u5c06\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u56dePython\u73af\u5883\uff0c\u4f9b\u8fdb\u4e00\u6b65\u5904\u7406\u548c\u5206\u6790\u3002\u786e\u4fdd\u5728\u8bfb\u53d6\u65f6\u4f7f\u7528\u4e0e\u4fdd\u5b58\u65f6\u76f8\u540c\u7684\u683c\u5f0f\uff0c\u4ee5\u907f\u514d\u6570\u636e\u8bfb\u53d6\u9519\u8bef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a\u5728Python\u4e2d\u4fdd\u5b58\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528NumPy\u5e93\u4fdd\u5b58\u4e3a\u4e8c\u8fdb\u5236\u6587\u4ef6\u3001\u4f7f\u7528pandas\u4fdd\u5b58 [&hellip;]","protected":false},"author":3,"featured_media":930250,"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\/930247"}],"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=930247"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/930247\/revisions"}],"predecessor-version":[{"id":930253,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/930247\/revisions\/930253"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/930250"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=930247"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=930247"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=930247"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}