{"id":88679,"date":"2026-08-01T15:49:01","date_gmt":"2026-08-01T07:49:01","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/88679.html"},"modified":"2026-08-01T15:49:01","modified_gmt":"2026-08-01T07:49:01","slug":"python-numpy-%e6%96%87%e4%bb%b6%e6%93%8d%e4%bd%9c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/88679.html","title":{"rendered":"Python-numpy-\u6587\u4ef6\u64cd\u4f5c"},"content":{"rendered":"

NumPy \u6587\u4ef6\u64cd\u4f5c<\/h2>\n

💾 \u6982\u8ff0<\/h3>\n

NumPy \u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u683c\u5f0f（.npy \/ .npz），\u8bfb\u5199\u901f\u5ea6\u8fdc\u5feb\u4e8e CSV，\u4e14\u4fdd\u7559 dtype \u548c shape \u7b49\u5143\u4fe1\u606f\u3002<\/p>\n\n\u683c\u5f0f\u9002\u7528\u573a\u666f\u901f\u5ea6\u538b\u7f29<\/tr>\n\n\n\n\n\n
.npy<\/td>\n\u5355\u4e2a\u6570\u7ec4<\/td>\n\u6781\u5feb<\/td>\n\u5426<\/td>\n<\/tr>\n
.npz<\/td>\n\u591a\u4e2a\u6570\u7ec4<\/td>\n\u6781\u5feb<\/td>\n\u53ef\u538b\u7f29<\/td>\n<\/tr>\n
.csv \/ .txt<\/td>\n\u4e0e\u5176\u4ed6\u5de5\u5177\u4ea4\u4e92<\/td>\n\u6162<\/td>\n\u5426<\/td>\n<\/tr>\n
memmap<\/td>\n\u8d85\u5927\u6587\u4ef6（>\u5185\u5b58）<\/td>\n\u6309\u9700<\/td>\n\u5426<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

📥 .npy \u2014 \u5355\u6570\u7ec4\u4fdd\u5b58\u4e0e\u52a0\u8f7d \u2b50<\/h3>\n

import<\/span> numpy as<\/span> np<\/p>\n

arr =<\/span> np.<\/span>arange(<\/span>1000000<\/span>)<\/span>.<\/span>reshape(<\/span>1000<\/span>,<\/span> 1000<\/span>)<\/span><\/p>\n

# \u4fdd\u5b58<\/span>
\nnp.<\/span>save(<\/span>'data.npy'<\/span>,<\/span> arr)<\/span>
\n# \u6587\u4ef6\u5927\u5c0f \u2248 arr.nbytes + 128 \u5b57\u8282（\u5934\u4fe1\u606f）<\/span><\/p>\n

# \u52a0\u8f7d<\/span>
\nloaded =<\/span> np.<\/span>load(<\/span>'data.npy'<\/span>)<\/span>
\nprint<\/span>(<\/span>loaded.<\/span>shape)<\/span> # (1000, 1000)<\/span>
\nprint<\/span>(<\/span>loaded.<\/span>dtype)<\/span> # int32（\u4fdd\u7559\u539f\u59cb\u7c7b\u578b）<\/span><\/p>\n

# \u4e5f\u53ef\u4ee5\u7528 savez \u7684 save \u683c\u5f0f\u5b58\u50a8<\/span>
\n# \u6ce8\u610f: np.load \u8fd4\u56de\u7684\u662f ndarray（\u5bf9 .npy）\u6216 NpzFile（\u5bf9 .npz）<\/span><\/p>\n

\u4f18\u52bf：\u4fdd\u7559 shape\u3001dtype\u3001\u5b57\u8282\u5e8f\u7b49\u6240\u6709\u5143\u4fe1\u606f，\u52a0\u8f7d\u5373\u539f\u6837\u3002<\/p>\n

\n

📦 .npz \u2014 \u591a\u6570\u7ec4\u4fdd\u5b58\u4e0e\u52a0\u8f7d<\/h3>\n

\u672a\u538b\u7f29\u7248\u672c<\/h4>\n

train_data =<\/span> np.<\/span>random.<\/span>rand(<\/span>10000<\/span>,<\/span> 28<\/span>,<\/span> 28<\/span>)<\/span>
\ntrain_labels =<\/span> np.<\/span>random.<\/span>randint(<\/span>0<\/span>,<\/span> 10<\/span>,<\/span> 10000<\/span>)<\/span>
\ntest_data =<\/span> np.<\/span>random.<\/span>rand(<\/span>2000<\/span>,<\/span> 28<\/span>,<\/span> 28<\/span>)<\/span>
\ntest_labels =<\/span> np.<\/span>random.<\/span>randint(<\/span>0<\/span>,<\/span> 10<\/span>,<\/span> 2000<\/span>)<\/span><\/p>\n

# \u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4（\u952e\u503c\u5bf9）<\/span>
\nnp.<\/span>savez(<\/span>'dataset.npz'<\/span>,<\/span>
\n X_train=<\/span>train_data,<\/span> y_train=<\/span>train_labels,<\/span>
\n X_test=<\/span>test_data,<\/span> y_test=<\/span>test_labels)<\/span><\/p>\n

# \u52a0\u8f7d<\/span>
\ndata =<\/span> np.<\/span>load(<\/span>'dataset.npz'<\/span>)<\/span>
\n# data \u662f NpzFile \u5bf9\u8c61（\u7c7b\u4f3c\u5b57\u5178，\u61d2\u52a0\u8f7d）<\/span>
\nprint<\/span>(<\/span>data.<\/span>files)<\/span> # ['X_train', 'y_train', 'X_test', 'y_test']<\/span>
\nprint<\/span>(<\/span>data[<\/span>'X_train'<\/span>]<\/span>.<\/span>shape)<\/span> # (10000, 28, 28)<\/span>
\nprint<\/span>(<\/span>data[<\/span>'y_train'<\/span>]<\/span>.<\/span>shape)<\/span> # (10000,)<\/span><\/p>\n

\u538b\u7f29\u7248\u672c（\u63a8\u8350）<\/h4>\n

# savez_compressed: \u538b\u7f29\u5b58\u50a8（zip \u538b\u7f29）<\/span>
\nnp.<\/span>savez_compressed(<\/span>'dataset_compressed.npz'<\/span>,<\/span>
\n X_train=<\/span>train_data,<\/span> y_train=<\/span>train_labels,<\/span>
\n X_test=<\/span>test_data,<\/span> y_test=<\/span>test_labels)<\/span><\/p>\n

# \u52a0\u8f7d\u65b9\u5f0f\u76f8\u540c<\/span>
\ndata =<\/span> np.<\/span>load(<\/span>'dataset_compressed.npz'<\/span>)<\/span><\/p>\n

\n

📄 \u6587\u672c\u683c\u5f0f（CSV \/ TXT）<\/h3>\n

np.loadtxt() \u2014 \u52a0\u8f7d\u7b80\u5355\u6587\u672c<\/h4>\n

# \u6570\u636e\u6587\u4ef6 data.txt:<\/span>
\n# 1.0 2.0 3.0<\/span>
\n# 4.0 5.0 6.0<\/span>
\n# 7.0 8.0 9.0<\/span><\/p>\n

data =<\/span> np.<\/span>loadtxt(<\/span>'data.txt'<\/span>)<\/span>
\nprint<\/span>(<\/span>data)<\/span>
\n# [[1. 2. 3.]<\/span>
\n# [4. 5. 6.]<\/span>
\n# [7. 8. 9.]]<\/span><\/p>\n

# \u5e38\u7528\u53c2\u6570<\/span>
\ndata =<\/span> np.<\/span>loadtxt(<\/span>'data.csv'<\/span>,<\/span>
\n delimiter=<\/span>','<\/span>,<\/span> # \u5206\u9694\u7b26<\/span>
\n skiprows=<\/span>1<\/span>,<\/span> # \u8df3\u8fc7\u8868\u5934<\/span>
\n dtype=<\/span>np.<\/span>float32,<\/span> # \u6307\u5b9a\u7c7b\u578b<\/span>
\n usecols=<\/span>(<\/span>0<\/span>,<\/span> 2<\/span>)<\/span>,<\/span> # \u53ea\u8bfb\u7b2c 0\u30012 \u5217<\/span>
\n max_rows=<\/span>100<\/span>)<\/span> # \u6700\u591a\u8bfb\u53d6\u884c\u6570<\/span><\/p>\n

np.savetxt() \u2014 \u4fdd\u5b58\u6587\u672c<\/h4>\n

arr =<\/span> np.<\/span>random.<\/span>rand(<\/span>100<\/span>,<\/span> 5<\/span>)<\/span><\/p>\n

np.<\/span>savetxt(<\/span>'output.csv'<\/span>,<\/span> arr,<\/span>
\n delimiter=<\/span>','<\/span>,<\/span>
\n fmt=<\/span>'%.4f'<\/span>,<\/span> # \u683c\u5f0f: 4 \u4f4d\u5c0f\u6570<\/span>
\n header=<\/span>'col1,col2,col3,col4,col5'<\/span>,<\/span>
\n comments=<\/span>''<\/span>)<\/span> # \u8868\u5934\u524d\u4e0d\u52a0 #<\/span><\/p>\n

np.genfromtxt() \u2014 \u5904\u7406\u590d\u6742\u6587\u672c \u2b50<\/h4>\n

\u6bd4 loadtxt \u66f4\u5f3a\u5927：\u5904\u7406\u7f3a\u5931\u503c\u3001\u6df7\u5408\u7c7b\u578b\u3001\u81ea\u52a8\u63a8\u65ad\u3002<\/p>\n

# \u6570\u636e\u542b\u7f3a\u5931\u503c（\u7528 ? \u6216\u7a7a\u767d\u8868\u793a）<\/span>
\ndata =<\/span> np.<\/span>genfromtxt(<\/span>'messy.csv'<\/span>,<\/span>
\n delimiter=<\/span>','<\/span>,<\/span>
\n dtype=<\/span>None<\/span>,<\/span> # \u81ea\u52a8\u63a8\u65ad\u7c7b\u578b<\/span>
\n names=<\/span>True<\/span>,<\/span> # \u7b2c 1 \u884c\u4f5c\u4e3a\u5217\u540d<\/span>
\n missing_values=<\/span>'?'<\/span>,<\/span> # \u7f3a\u5931\u503c\u6807\u8bb0<\/span>
\n filling_values=<\/span>0<\/span>,<\/span> # \u7f3a\u5931\u503c\u586b\u5145\u4e3a 0<\/span>
\n encoding=<\/span>'utf-8'<\/span>)<\/span><\/p>\n

# \u8bbf\u95ee\u7ed3\u6784\u5316\u6570\u7ec4\u7684\u5217<\/span>
\nprint<\/span>(<\/span>data[<\/span>'column_name'<\/span>]<\/span>)<\/span><\/p>\n

# \u5e38\u7528\u53c2\u6570<\/span>
\ndata =<\/span> np.<\/span>genfromtxt(<\/span>'data.csv'<\/span>,<\/span>
\n delimiter=<\/span>','<\/span>,<\/span>
\n skip_header=<\/span>1<\/span>,<\/span> # \u8df3\u8fc7\u7684\u8868\u5934\u884c\u6570<\/span>
\n skip_footer=<\/span>1<\/span>,<\/span> # \u8df3\u8fc7\u7684\u5c3e\u90e8\u884c\u6570<\/span>
\n usecols=<\/span>(<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 3<\/span>)<\/span>,<\/span> # \u8bfb\u53d6\u7684\u5217\u7d22\u5f15<\/span>
\n dtype=<\/span>[<\/span>(<\/span>'name'<\/span>,<\/span> 'U10'<\/span>)<\/span>,<\/span> (<\/span>'age'<\/span>,<\/span> 'i4'<\/span>)<\/span>,<\/span> (<\/span>'score'<\/span>,<\/span> 'f8'<\/span>)<\/span>]<\/span>)<\/span><\/p>\n

\n

🗂\ufe0f \u5185\u5b58\u6620\u5c04（Memory-Mapped Files）<\/h3>\n

\u5bf9\u4e8e\u8d85\u5927\u6587\u4ef6（>\u5185\u5b58\u5bb9\u91cf），\u4f7f\u7528 memmap \u6309\u9700\u52a0\u8f7d，\u4e0d\u4f1a\u4e00\u6b21\u6027\u8bfb\u5165\u5185\u5b58\u3002<\/p>\n

# 1. \u521b\u5efa\u5185\u5b58\u6620\u5c04\u6587\u4ef6<\/span>
\nfp =<\/span> np.<\/span>memmap(<\/span>'large.dat'<\/span>,<\/span> dtype=<\/span>'float32'<\/span>,<\/span> mode=<\/span>'w+'<\/span>,<\/span> shape=<\/span>(<\/span>100000<\/span>,<\/span> 1000<\/span>)<\/span>)<\/span>
\n# \u6b64\u65f6\u53ea\u5206\u914d\u4e86\u865a\u62df\u5730\u5740\u7a7a\u95f4，\u4e0d\u5360\u7269\u7406\u5185\u5b58<\/span><\/p>\n

# \u50cf\u666e\u901a\u6570\u7ec4\u4e00\u6837\u64cd\u4f5c<\/span>
\nfp[<\/span>0<\/span>,<\/span> :<\/span>]<\/span> =<\/span> np.<\/span>random.<\/span>rand(<\/span>1000<\/span>)<\/span>.<\/span>astype(<\/span>'float32'<\/span>)<\/span>
\nfp[<\/span>1<\/span>,<\/span> :<\/span>]<\/span> =<\/span> np.<\/span>random.<\/span>rand(<\/span>1000<\/span>)<\/span>.<\/span>astype(<\/span>'float32'<\/span>)<\/span><\/p>\n

# \u5199\u5165\u78c1\u76d8<\/span>
\nfp.<\/span>flush(<\/span>)<\/span> # \u5f3a\u5236\u5237\u5230\u78c1\u76d8<\/span>
\ndel<\/span> fp # \u91ca\u653e\u6620\u5c04<\/span><\/p>\n

# 2. \u52a0\u8f7d\u5df2\u6709\u7684\u5185\u5b58\u6620\u5c04<\/span>
\nfp =<\/span> np.<\/span>memmap(<\/span>'large.dat'<\/span>,<\/span> dtype=<\/span>'float32'<\/span>,<\/span> mode=<\/span>'r'<\/span>,<\/span> shape=<\/span>(<\/span>100000<\/span>,<\/span> 1000<\/span>)<\/span>)<\/span>
\n# \u53ea\u52a0\u8f7d\u8bbf\u95ee\u5230\u7684\u90e8\u5206，\u4e0d\u4f1a\u628a\u6574\u4e2a\u6587\u4ef6\u8bfb\u5165\u5185\u5b58<\/span>
\nprint<\/span>(<\/span>fp[<\/span>0<\/span>:<\/span>10<\/span>,<\/span> :<\/span>5<\/span>]<\/span>)<\/span> # \u786c\u76d8\u53ea\u8bfb\u8fd9 10\u00d75 \u7684\u6570\u636e<\/span><\/p>\n

# 3. \u5c31\u5730\u4fee\u6539<\/span>
\nfp =<\/span> np.<\/span>memmap(<\/span>'large.dat'<\/span>,<\/span> dtype=<\/span>'float32'<\/span>,<\/span> mode=<\/span>'r+'<\/span>)<\/span>
\nfp[<\/span>0<\/span>,<\/span> 0<\/span>]<\/span> =<\/span> 3.14<\/span> # \u76f4\u63a5\u5199\u5165\u78c1\u76d8\u6587\u4ef6<\/span>
\nfp.<\/span>flush(<\/span>)<\/span><\/p>\n

memmap \u6a21\u5f0f<\/h4>\n\nmode\u8bf4\u660e<\/tr>\n\n\n\n\n
'r'<\/td>\n\u53ea\u8bfb<\/td>\n<\/tr>\n
'r+'<\/td>\n\u8bfb\u5199（\u6587\u4ef6\u5fc5\u987b\u5b58\u5728）<\/td>\n<\/tr>\n
'w+'<\/td>\n\u521b\u5efa\u6216\u8986\u76d6，\u8bfb\u5199<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

memmap vs \u666e\u901a\u52a0\u8f7d<\/h4>\n

# 10GB \u7684\u6570\u636e\u96c6<\/span>
\n# \u274c \u666e\u901a\u52a0\u8f7d：\u9700\u8981 10GB \u5185\u5b58<\/span>
\n# arr = np.load('huge.npy')<\/span><\/p>\n

# \u2705 memmap：\u5185\u5b58\u5360\u7528\u6781\u5c0f<\/span>
\narr =<\/span> np.<\/span>load(<\/span>'huge.npy'<\/span>,<\/span> mmap_mode=<\/span>'r'<\/span>)<\/span> # \u4ee5 memmap \u65b9\u5f0f\u6253\u5f00<\/span>
\nprint<\/span>(<\/span>arr[<\/span>10000<\/span>:<\/span>10050<\/span>]<\/span>)<\/span> # \u53ea\u52a0\u8f7d\u8fd9\u51e0\u884c<\/span><\/p>\n

\n

📥 \u4ece\u6807\u51c6 IO \u8bfb\u53d6<\/h3>\n

# \u4ece\u5b57\u7b26\u4e32\u8bfb\u53d6<\/span>
\nimport<\/span> io<\/p>\n

text =<\/span> "1,2,3\\\\n4,5,6\\\\n7,8,9"<\/span>
\ndata =<\/span> np.<\/span>genfromtxt(<\/span>io.<\/span>StringIO(<\/span>text)<\/span>,<\/span> delimiter=<\/span>','<\/span>)<\/span>
\nprint<\/span>(<\/span>data)<\/span><\/p>\n

# \u4ece\u5b57\u8282\u8bfb\u53d6<\/span>
\nbinary_data =<\/span> b"1.0 2.0\\\\n3.0 4.0"<\/span>
\ndata =<\/span> np.<\/span>loadtxt(<\/span>io.<\/span>BytesIO(<\/span>binary_data)<\/span>)<\/span><\/p>\n

\n

📂 np.savez \u7684\u61d2\u52a0\u8f7d<\/h3>\n

# npz \u6587\u4ef6\u53ea\u5728\u8bbf\u95ee\u65f6\u624d\u52a0\u8f7d\u5bf9\u5e94\u7684\u6570\u7ec4（\u7701\u5185\u5b58）<\/span>
\ndata =<\/span> np.<\/span>load(<\/span>'dataset.npz'<\/span>)<\/span><\/p>\n

# \u6b64\u65f6\u53ea\u6709\u5143\u6570\u636e\u52a0\u8f7d，\u5b9e\u9645\u6570\u636e\u8fd8\u5728\u78c1\u76d8<\/span>
\nprint<\/span>(<\/span>data.<\/span>files)<\/span> # \u5217\u51fa\u952e\u540d<\/span><\/p>\n

# \u8bbf\u95ee\u65f6\u624d\u52a0\u8f7d<\/span>
\nX_train =<\/span> data[<\/span>'X_train'<\/span>]<\/span> # \u73b0\u5728\u624d\u4ece\u78c1\u76d8\u8bfb\u53d6<\/span><\/p>\n

# \u7528\u5b8c\u540e\u5173\u95ed（\u91ca\u653e\u6587\u4ef6\u53e5\u67c4）<\/span>
\n# data.close()<\/span><\/p>\n

\n

🔄 HDF5 \u683c\u5f0f（\u5927\u6570\u636e\u63a8\u8350）<\/h3>\n

\u5bf9\u4e8e GB \u4ee5\u4e0a\u7684\u6570\u636e\u96c6，\u63a8\u8350\u4f7f\u7528 HDF5 \u683c\u5f0f（\u66f4\u7075\u6d3b\u7684\u5206\u5c42\u5b58\u50a8）：<\/p>\n

# pip install h5py<\/span>
\nimport<\/span> h5py<\/p>\n

# \u4fdd\u5b58<\/span>
\nwith<\/span> h5py.<\/span>File(<\/span>'dataset.h5'<\/span>,<\/span> 'w'<\/span>)<\/span> as<\/span> f:<\/span>
\n f.<\/span>create_dataset(<\/span>'images'<\/span>,<\/span> data=<\/span>images_array,<\/span> compression=<\/span>'gzip'<\/span>)<\/span>
\n f.<\/span>create_dataset(<\/span>'labels'<\/span>,<\/span> data=<\/span>labels_array,<\/span> compression=<\/span>'gzip'<\/span>)<\/span><\/p>\n

# \u52a0\u8f7d（\u652f\u6301\u5207\u7247，\u4e0d\u4f1a\u5168\u8bfb\u5165\u5185\u5b58）<\/span>
\nwith<\/span> h5py.<\/span>File(<\/span>'dataset.h5'<\/span>,<\/span> 'r'<\/span>)<\/span> as<\/span> f:<\/span>
\n images =<\/span> f[<\/span>'images'<\/span>]<\/span>
\n print<\/span>(<\/span>images.<\/span>shape)<\/span>
\n batch =<\/span> images[<\/span>0<\/span>:<\/span>32<\/span>]<\/span> # \u53ea\u52a0\u8f7d 32 \u5f20\u56fe<\/span><\/p>\n

\n

📊 \u683c\u5f0f\u9009\u62e9\u5efa\u8bae<\/h3>\n\n\u573a\u666f\u63a8\u8350\u683c\u5f0f<\/tr>\n\n\n\n\n\n\n\n
\u5355\u4e2a NumPy \u6570\u7ec4<\/td>\n.npy<\/td>\n<\/tr>\n
\u591a\u4e2a NumPy \u6570\u7ec4<\/td>\n.npz（\u538b\u7f29\u7528 .npz zipped）<\/td>\n<\/tr>\n
\u4e0e\u5176\u4ed6\u5de5\u5177（Excel\/R）\u5171\u4eab<\/td>\n.csv \/ .txt<\/td>\n<\/tr>\n
\u8d85\u5927\u6587\u4ef6（>\u5185\u5b58）<\/td>\nmemmap \u6216 HDF5<\/td>\n<\/tr>\n
\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3<\/td>\n.npy \/ .npz \/ HDF5<\/td>\n<\/tr>\n
\u4e2d\u7b49\u6570\u636e + \u53ef\u8bfb\u6027<\/td>\n.csv<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

📝 \u901f\u67e5\u8868<\/h3>\n\n\u9700\u6c42\u4ee3\u7801<\/tr>\n\n\n\n\n\n\n\n\n\n\n\n\n
\u4fdd\u5b58\u5355\u4e2a\u6570\u7ec4<\/td>\nnp.save('a.npy', arr)<\/td>\n<\/tr>\n
\u52a0\u8f7d\u5355\u4e2a\u6570\u7ec4<\/td>\nnp.load('a.npy')<\/td>\n<\/tr>\n
\u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4<\/td>\nnp.savez('a.npz', x=x, y=y)<\/td>\n<\/tr>\n
\u538b\u7f29\u4fdd\u5b58<\/td>\nnp.savez_compressed('a.npz', x=x, y=y)<\/td>\n<\/tr>\n
\u7b80\u5355\u6587\u672c\u52a0\u8f7d<\/td>\nnp.loadtxt('a.txt')<\/td>\n<\/tr>\n
\u590d\u6742\u6587\u672c\u52a0\u8f7d<\/td>\nnp.genfromtxt('a.csv', delimiter=',')<\/td>\n<\/tr>\n
\u4fdd\u5b58\u6587\u672c<\/td>\nnp.savetxt('a.csv', arr, delimiter=',')<\/td>\n<\/tr>\n
\u5185\u5b58\u6620\u5c04（\u521b\u5efa）<\/td>\nnp.memmap('f.dat', dtype='f4', mode='w+', shape=s)<\/td>\n<\/tr>\n
\u5185\u5b58\u6620\u5c04（\u52a0\u8f7d）<\/td>\nnp.load('f.npy', mmap_mode='r')<\/td>\n<\/tr>\n
\u5f3a\u5236\u5237\u76d8<\/td>\nfp.flush()<\/td>\n<\/tr>\n
\u5217\u51fa npz \u952e<\/td>\ndata.files<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

[[numpy-\u603b\u89c8|\u2190 \u8fd4\u56de\u603b\u89c8]]<\/p>\n","protected":false},"excerpt":{"rendered":"

NumPy \u6587\u4ef6\u64cd\u4f5c
\n💾 \u6982\u8ff0
\nNumPy \u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u683c\u5f0f（.npy \/ .npz），\u8bfb\u5199\u901f\u5ea6\u8fdc\u5feb\u4e8e CSV，\u4e14\u4fdd\u7559 dtype \u548c shape \u7b49\u5143\u4fe1\u606f\u3002\u683c\u5f0f\u9002\u7528\u573a\u666f\u901f\u5ea6\u538b\u7f29.npy\u5355\u4e2a\u6570\u7ec4\u6781\u5feb\u5426.npz\u591a\u4e2a\u6570\u7ec4\u6781\u5feb\u53ef\u538b\u7f29.csv \/ .txt\u4e0e\u5176\u4ed6\u5de5\u5177\u4ea4\u4e92\u6162\u5426memmap\u8d85\u5927\u6587\u4ef6（>\u5185\u5b58）\u6309\u9700\u5426
\n📥 .npy \u2014 \u5355\u6570\u7ec4\u4fdd\u5b58\u4e0e\u52a0\u8f7d \u2b50
\nimpor<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[1818,81,190,125,305],"topic":[],"class_list":["post-88679","post","type-post","status-publish","format-standard","hentry","category-server","tag-numpy","tag-python","tag-190","tag-125","tag-305"],"yoast_head":"\nPython-numpy-\u6587\u4ef6\u64cd\u4f5c - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/88679.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python-numpy-\u6587\u4ef6\u64cd\u4f5c - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"NumPy \u6587\u4ef6\u64cd\u4f5c 💾 \u6982\u8ff0 NumPy \u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u683c\u5f0f（.npy \/ .npz），\u8bfb\u5199\u901f\u5ea6\u8fdc\u5feb\u4e8e CSV，\u4e14\u4fdd\u7559 dtype \u548c shape \u7b49\u5143\u4fe1\u606f\u3002\u683c\u5f0f\u9002\u7528\u573a\u666f\u901f\u5ea6\u538b\u7f29.npy\u5355\u4e2a\u6570\u7ec4\u6781\u5feb\u5426.npz\u591a\u4e2a\u6570\u7ec4\u6781\u5feb\u53ef\u538b\u7f29.csv \/ .txt\u4e0e\u5176\u4ed6\u5de5\u5177\u4ea4\u4e92\u6162\u5426memmap\u8d85\u5927\u6587\u4ef6（>\u5185\u5b58）\u6309\u9700\u5426 📥 .npy \u2014 \u5355\u6570\u7ec4\u4fdd\u5b58\u4e0e\u52a0\u8f7d \u2b50 impor\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/88679.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-01T07:49:01+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/88679.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/88679.html\",\"name\":\"Python-numpy-\u6587\u4ef6\u64cd\u4f5c - 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