\n| np.dstack()<\/td>\n | \u6df1\u5ea6\u62fc\u63a5（\u6309\u7b2c\u4e09\u4e2a\u7ef4\u5ea6）<\/td>\n | np.concatenate(…, axis=2)<\/td>\n | \u4e09\u7ef4\u6570\u7ec4\u62fc\u63a5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u793a\u4f8b：vstack\/hstack \u7b80\u5316\u64cd\u4f5c<\/h6>\npython<\/p>\n import numpy as np<\/p>\n # \u4e8c\u7ef4\u6570\u7ec4
\narr1 = np.array([[1,2],[3,4]])
\narr2 = np.array([[5,6],[7,8]])<\/p>\n # \u5782\u76f4\u62fc\u63a5（\u7b49\u4ef7\u4e8e axis=0）
\narr_v = np.vstack((arr1, arr2))
\nprint("vstack\u7ed3\u679c：\\\\n", arr_v) # 4\u884c2\u5217<\/p>\n # \u6c34\u5e73\u62fc\u63a5（\u7b49\u4ef7\u4e8e axis=1）
\narr_h = np.hstack((arr1, arr2))
\nprint("hstack\u7ed3\u679c：\\\\n", arr_h) # 2\u884c4\u5217<\/p>\n \u793a\u4f8b：\u4e00\u7ef4\u6570\u7ec4\u7684 hstack\/vstack \u7279\u6b8a\u5904\u7406<\/h6>\n\u4e00\u7ef4\u6570\u7ec4\u7528 hstack\/vstack \u4f1a\u81ea\u52a8\u8c03\u6574\u7ef4\u5ea6：<\/p>\n python<\/p>\n import numpy as np<\/p>\n a = np.array([1,2,3]) # \u5f62\u72b6(3,)
\nb = np.array([4,5,6]) # \u5f62\u72b6(3,)<\/p>\n # vstack：\u4e00\u7ef4\u6570\u7ec4\u5408\u5e76\u4e3a\u4e8c\u7ef4（\u6309\u884c）\u2192 (2,3)
\nv_arr = np.vstack((a, b))
\nprint("vstack\u4e00\u7ef4\u6570\u7ec4：\\\\n", v_arr)
\nprint("\u5f62\u72b6：", v_arr.shape) # (2,3)<\/p>\n # hstack：\u4e00\u7ef4\u6570\u7ec4\u5408\u5e76\u4e3a\u66f4\u957f\u7684\u4e00\u7ef4 \u2192 (6,)
\nh_arr = np.hstack((a, b))
\nprint("hstack\u4e00\u7ef4\u6570\u7ec4：", h_arr)
\nprint("\u5f62\u72b6：", h_arr.shape) # (6,)<\/p>\n 3. \u6df1\u5ea6\u62fc\u63a5：np.dstack ()（\u4e09\u7ef4\u6570\u7ec4）<\/h5>\n\u6309\u7b2c\u4e09\u4e2a\u7ef4\u5ea6（\u6df1\u5ea6）\u62fc\u63a5，\u9002\u7528\u4e8e\u56fe\u50cf\u3001\u4e09\u7ef4\u6570\u636e：<\/p>\n python<\/p>\n import numpy as np<\/p>\n # \u4e24\u4e2a\u4e8c\u7ef4\u6570\u7ec4（\u5f62\u72b6\u5747\u4e3a(2,3)）
\narr1 = np.array([[1,2,3],[4,5,6]])
\narr2 = np.array([[7,8,9],[10,11,12]])<\/p>\n # \u6df1\u5ea6\u62fc\u63a5 \u2192 \u5f62\u72b6(2,3,2)
\nd_arr = np.dstack((arr1, arr2))
\nprint("dstack\u7ed3\u679c\u5f62\u72b6：", d_arr.shape) # (2,3,2)
\nprint("dstack\u7ed3\u679c[0,0,:]：", d_arr[0,0,:]) # [1,7]（\u6df1\u5ea6\u7ef4\u5ea6\u7684\u4e24\u4e2a\u503c）<\/p>\n \u4e09\u3001\u9ad8\u7ef4\u6570\u7ec4\u62fc\u63a5（\u8fdb\u9636）<\/h4>\n\u9ad8\u7ef4\u6570\u7ec4\u62fc\u63a5\u7684\u6838\u5fc3\u662f\u660e\u786e\u62fc\u63a5\u8f74\u7684\u7f16\u53f7，\u7ef4\u5ea6\u7f16\u53f7\u4ece 0 \u5f00\u59cb：<\/p>\n \n- \u4e09\u7ef4\u6570\u7ec4\u5f62\u72b6 (a,b,c) \u2192 \u8f74 0=a\u3001\u8f74 1=b\u3001\u8f74 2=c；<\/li>\n
- \u62fc\u63a5\u65f6，\u9664\u62fc\u63a5\u8f74\u5916，\u5176\u4ed6\u7ef4\u5ea6\u5927\u5c0f\u5fc5\u987b\u4e00\u81f4\u3002<\/li>\n<\/ul>\n
\u793a\u4f8b：\u4e09\u7ef4\u6570\u7ec4\u62fc\u63a5<\/h5>\npython<\/p>\n import numpy as np<\/p>\n # \u4e24\u4e2a\u4e09\u7ef4\u6570\u7ec4：\u5f62\u72b6(2,3,4)（\u8f740=2，\u8f741=3，\u8f742=4）
\narr1 = np.arange(24).reshape(2,3,4)
\narr2 = np.arange(24,48).reshape(2,3,4)<\/p>\n # \u6309\u8f740\u62fc\u63a5 \u2192 \u5f62\u72b6(4,3,4)
\nconcat0 = np.concatenate((arr1, arr2), axis=0)
\nprint("\u6309\u8f740\u62fc\u63a5\u5f62\u72b6：", concat0.shape) # (4,3,4)<\/p>\n # \u6309\u8f741\u62fc\u63a5 \u2192 \u5f62\u72b6(2,6,4)
\nconcat1 = np.concatenate((arr1, arr2), axis=1)
\nprint("\u6309\u8f741\u62fc\u63a5\u5f62\u72b6：", concat1.shape) # (2,6,4)<\/p>\n # \u6309\u8f742\u62fc\u63a5 \u2192 \u5f62\u72b6(2,3,8)
\nconcat2 = np.concatenate((arr1, arr2), axis=2)
\nprint("\u6309\u8f742\u62fc\u63a5\u5f62\u72b6：", concat2.shape) # (2,3,8)<\/p>\n \u56db\u3001\u62fc\u63a5\u7684\u517c\u5bb9\u6027\u89c4\u5219（\u6838\u5fc3\u907f\u5751！）<\/h4>\n\u62fc\u63a5\u5931\u8d25\u7684\u552f\u4e00\u539f\u56e0\u662f\u5f62\u72b6\u4e0d\u517c\u5bb9，\u9700\u7262\u8bb0：<\/p>\n \u9664\u62fc\u63a5\u8f74\u5916，\u5176\u4ed6\u6240\u6709\u7ef4\u5ea6\u7684\u5927\u5c0f\u5fc5\u987b\u5b8c\u5168\u4e00\u81f4\u3002<\/p>\n \u793a\u4f8b：\u4e0d\u517c\u5bb9\u7684\u62fc\u63a5（\u62a5\u9519）<\/h5>\npython<\/p>\n import numpy as np<\/p>\n # \u9519\u8bef1：\u6309\u884c\u62fc\u63a5（axis=0）\u4f46\u5217\u6570\u4e0d\u540c
\narr1 = np.array([[1,2],[3,4]]) # 2\u884c2\u5217
\narr2 = np.array([[5,6,7],[8,9,10]]) # 2\u884c3\u5217
\ntry:
\n np.concatenate((arr1, arr2), axis=0)
\nexcept ValueError as e:
\n print("\u62a5\u95191：", e) # all the input array dimensions except for the concatenation axis must match exactly<\/p>\n # \u9519\u8bef2：\u6309\u5217\u62fc\u63a5（axis=1）\u4f46\u884c\u6570\u4e0d\u540c
\narr3 = np.array([[1,2],[3,4]]) # 2\u884c2\u5217
\narr4 = np.array([[5,6]]) # 1\u884c2\u5217
\ntry:
\n np.concatenate((arr3, arr4), axis=1)
\nexcept ValueError as e:
\n print("\u62a5\u95192：", e)<\/p>\n \u89e3\u51b3\u65b9\u6cd5：\u8c03\u6574\u6570\u7ec4\u5f62\u72b6（reshape\/expand_dims）<\/h5>\n\u901a\u8fc7 reshape \u6216 np.expand_dims \u8ba9\u6570\u7ec4\u5f62\u72b6\u517c\u5bb9：<\/p>\n python<\/p>\n import numpy as np<\/p>\n # \u539f\u6570\u7ec4：arr4\u662f(1,2)，\u884c\u6570\u4e3a1（arr3\u884c\u6570\u4e3a2，\u4e0d\u517c\u5bb9）
\narr3 = np.array([[1,2],[3,4]])
\narr4 = np.array([[5,6]])<\/p>\n # \u65b9\u6cd51：\u7ed9arr4\u6dfb\u52a0\u4e00\u884c（\u88650）
\narr4_exp = np.vstack((arr4, [[0,0]])) # \u53d8\u4e3a2\u884c2\u5217
\nconcat_ok = np.concatenate((arr3, arr4_exp), axis=1)
\nprint("\u8c03\u6574\u540e\u62fc\u63a5\u7ed3\u679c：\\\\n", concat_ok)<\/p>\n # \u65b9\u6cd52：\u6269\u5c55\u7ef4\u5ea6（\u4e00\u7ef4\u2192\u4e8c\u7ef4）
\na = np.array([1,2,3]) # (3,)
\nb = np.array([[4,5,6]]) # (1,3)
\n# \u5148\u5c06a\u8f6c\u4e3a(1,3)，\u518d\u6309\u884c\u62fc\u63a5
\na_2d = np.expand_dims(a, axis=0) # (1,3)
\nconcat_a_b = np.concatenate((a_2d, b), axis=0)
\nprint("\u4e00\u7ef4\u8f6c\u4e8c\u7ef4\u540e\u62fc\u63a5：\\\\n", concat_a_b)<\/p>\n \u4e94\u3001\u5e38\u89c1\u5e94\u7528\u573a\u666f<\/h4>\n\u573a\u666f 1：\u5408\u5e76\u591a\u7ec4\u6570\u636e（\u673a\u5668\u5b66\u4e60\u6570\u636e\u96c6）<\/h5>\npython<\/p>\n import numpy as np<\/p>\n # \u6a21\u62df3\u7ec4\u7279\u5f81\u6570\u636e（\u6bcf\u7ec410\u4e2a\u6837\u672c\u00d75\u4e2a\u7279\u5f81）
\nfeat1 = np.random.rand(10,5)
\nfeat2 = np.random.rand(10,5)
\nfeat3 = np.random.rand(10,5)<\/p>\n # \u5408\u5e76\u4e3a30\u4e2a\u6837\u672c\u7684\u6570\u636e\u96c6（\u6309\u884c\u62fc\u63a5）
\nall_feat = np.concatenate((feat1, feat2, feat3), axis=0)
\nprint("\u5408\u5e76\u540e\u6570\u636e\u96c6\u5f62\u72b6：", all_feat.shape) # (30,5)<\/p>\n # \u5408\u5e76\u7279\u5f81\u5217（\u6bd4\u5982\u6dfb\u52a0\u65b0\u7279\u5f81）
\nnew_feat = np.random.rand(30,1) # 30\u4e2a\u6837\u672c\u00d71\u4e2a\u65b0\u7279\u5f81
\nall_feat_new = np.hstack((all_feat, new_feat))
\nprint("\u6dfb\u52a0\u65b0\u7279\u5f81\u540e\u5f62\u72b6：", all_feat_new.shape) # (30,6)<\/p>\n \u573a\u666f 2：\u5408\u5e76\u65f6\u95f4\u5e8f\u5217\u6570\u636e（\u6e29\u5ea6\u6570\u636e）<\/h5>\npython<\/p>\n import numpy as np<\/p>\n # \u6a21\u62df3\u6708\u548c4\u6708\u7684\u6e29\u5ea6\u6570\u636e（\u540431\u5929\u00d724\u5c0f\u65f6）
\nmarch_temp = np.random.normal(5,6,(31,24)).round(1)
\napril_temp = np.random.normal(15,5,(30,24)).round(1)<\/p>\n # \u5408\u5e76\u4e3a2\u4e2a\u6708\u7684\u6e29\u5ea6\u6570\u636e（\u6309\u884c\u62fc\u63a5，\u884c\u657031+30=61）
\nspring_temp = np.vstack((march_temp, april_temp))
\nprint("\u6625\u5b63\u6e29\u5ea6\u6570\u636e\u5f62\u72b6：", spring_temp.shape) # (61,24)<\/p>\n \u573a\u666f 3：\u6279\u91cf\u62fc\u63a5\u591a\u4e2a\u6570\u7ec4<\/h5>\npython<\/p>\n import numpy as np<\/p>\n # \u751f\u621010\u4e2a\u4e00\u7ef4\u6570\u7ec4，\u6bcf\u4e2a\u6570\u7ec45\u4e2a\u5143\u7d20
\narr_list = [np.random.randint(0,10,5) for _ in range(10)]<\/p>\n # \u62fc\u63a5\u4e3a\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4
\nall_arr = np.concatenate(arr_list, axis=0)
\nprint("\u6279\u91cf\u62fc\u63a5\u540e\u5f62\u72b6：", all_arr.shape) # (50,)<\/p>\n \u516d\u3001\u907f\u5751\u70b9\u4e0e\u6700\u4f73\u5b9e\u8df5<\/h4>\n1. \u6838\u5fc3\u907f\u5751\u70b9<\/h5>\n\n- \u274c \u8bef\u533a：\u76f4\u63a5\u4f20\u591a\u4e2a\u6570\u7ec4\u7ed9 concatenate（\u5fc5\u987b\u7528\u5143\u7ec4 \/ \u5217\u8868\u5305\u88f9，\u5982 (arr1,arr2)）；<\/li>\n
- \u274c \u9519\u8bef：\u4e00\u7ef4\u6570\u7ec4\u7528 axis=1 \u62fc\u63a5（\u4e00\u7ef4\u6570\u7ec4\u53ea\u6709 axis=0，\u4f1a\u62a5\u9519）；<\/li>\n
- \u274c \u5ffd\u7565：\u62fc\u63a5\u8f74\u7684\u65b9\u5411（\u4e8c\u7ef4\u6570\u7ec4 axis=0 \u662f\u884c，axis=1 \u662f\u5217，\u6613\u8bb0\u53cd）；<\/li>\n
- \u274c \u6df7\u6dc6：vstack \u5bf9\u4e00\u7ef4\u6570\u7ec4\u7684\u5904\u7406（\u4f1a\u8f6c\u4e3a\u4e8c\u7ef4，\u800c concatenate \u4fdd\u6301\u4e00\u7ef4）\u3002<\/li>\n<\/ul>\n
2. \u6700\u4f73\u5b9e\u8df5<\/h5>\n\n- \u2705 \u7b80\u5355\u62fc\u63a5\u7528 vstack\/hstack（\u8bed\u4e49\u6e05\u6670，\u65e0\u9700\u8bb0 axis）；<\/li>\n
- \u2705 \u9ad8\u7ef4\u6570\u7ec4\u7528 np.concatenate（\u660e\u786e\u6307\u5b9a axis）；<\/li>\n
- \u2705 \u62fc\u63a5\u524d\u68c0\u67e5\u5f62\u72b6：\u7528 arr.shape \u786e\u8ba4\u9664\u62fc\u63a5\u8f74\u5916\u5176\u4ed6\u7ef4\u5ea6\u4e00\u81f4；<\/li>\n
- \u2705 \u6279\u91cf\u62fc\u63a5\u591a\u4e2a\u6570\u7ec4\u65f6，\u5148\u653e\u5165\u5217\u8868，\u518d\u4f20\u7ed9 concatenate；<\/li>\n
- \u2705 \u9700\u9891\u7e41\u62fc\u63a5\u65f6，\u5148\u9884\u5206\u914d\u6570\u7ec4（np.empty）\u518d\u586b\u5145，\u6bd4\u591a\u6b21\u62fc\u63a5\u66f4\u9ad8\u6548\u3002<\/li>\n<\/ul>\n
\u603b\u7ed3<\/h4>\n - NumPy \u6570\u7ec4\u62fc\u63a5\u7684\u6838\u5fc3\u662f np.concatenate()，vstack\/hstack \u662f\u7b80\u5316\u7248（\u5206\u522b\u5bf9\u5e94 axis=0\/1）；<\/li>\n
- \u62fc\u63a5\u89c4\u5219：\u9664\u62fc\u63a5\u8f74\u5916，\u5176\u4ed6\u7ef4\u5ea6\u5927\u5c0f\u5fc5\u987b\u5b8c\u5168\u4e00\u81f4，\u5426\u5219\u62a5\u9519；<\/li>\n
- \u7ef4\u5ea6\u8bf4\u660e：\n
\n- \u4e00\u7ef4\u6570\u7ec4：\u4ec5\u80fd\u6309 axis=0 \u62fc\u63a5（hstack \u5408\u5e76\u4e3a\u66f4\u957f\u4e00\u7ef4，vstack \u8f6c\u4e3a\u4e8c\u7ef4）；<\/li>\n
- \u4e8c\u7ef4\u6570\u7ec4：axis=0 \u6309\u884c\u62fc\u63a5（\u5217\u6570\u4e00\u81f4），axis=1 \u6309\u5217\u62fc\u63a5（\u884c\u6570\u4e00\u81f4）；<\/li>\n<\/ul>\n<\/li>\n
- \u907f\u5751\u5173\u952e：\u62fc\u63a5\u524d\u68c0\u67e5\u5f62\u72b6\u517c\u5bb9\u6027，\u4e00\u7ef4\u6570\u7ec4\u9700\u8f6c\u4e8c\u7ef4\u65f6\u7528 np.expand_dims；<\/li>\n
- \u5e38\u7528\u573a\u666f：\u5408\u5e76\u6570\u636e\u96c6\u3001\u6dfb\u52a0\u7279\u5f81\u5217\u3001\u5408\u5e76\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/li>\n","protected":false},"excerpt":{"rendered":"
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