{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据拼接\n",
    "\n",
    "1. np.concatenate 是numpy中对array进行拼接的函数\n",
    "axis参数为指定按照哪个维度进行拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.62434536  0.38824359  0.47182825 -0.07296862]\n",
      " [ 1.86540763 -1.3015387   2.74481176  0.2387931 ]\n",
      " [ 1.3190391   0.75062962  2.46210794 -1.06014071]\n",
      " [ 0.6775828   0.61594565  2.13376944 -0.09989127]\n",
      " [ 0.82757179  0.12214158  1.04221375  1.58281521]] \n",
      " (5, 4) \n",
      "\n",
      "[[-0.10061918  2.14472371  1.90159072  1.50249434]\n",
      " [ 1.90085595  0.31627214  0.87710977  0.06423057]\n",
      " [ 0.73211192  1.53035547  0.30833925  0.60324647]] \n",
      " (3, 4) \n",
      "\n",
      "[[ 0.3128273   0.15479436]\n",
      " [ 0.32875387  0.9873354 ]\n",
      " [-0.11731035  1.2344157 ]\n",
      " [ 2.65980218  1.74204416]\n",
      " [ 0.80816445  0.11237104]] \n",
      " (5, 2) \n",
      "\n",
      "[[ 2.62434536  0.38824359  0.47182825 -0.07296862]\n",
      " [ 1.86540763 -1.3015387   2.74481176  0.2387931 ]\n",
      " [ 1.3190391   0.75062962  2.46210794 -1.06014071]\n",
      " [ 0.6775828   0.61594565  2.13376944 -0.09989127]\n",
      " [ 0.82757179  0.12214158  1.04221375  1.58281521]\n",
      " [-0.10061918  2.14472371  1.90159072  1.50249434]\n",
      " [ 1.90085595  0.31627214  0.87710977  0.06423057]\n",
      " [ 0.73211192  1.53035547  0.30833925  0.60324647]] \n",
      " (8, 4) \n",
      "\n",
      "[[ 2.62434536  0.38824359  0.47182825 -0.07296862  0.3128273   0.15479436]\n",
      " [ 1.86540763 -1.3015387   2.74481176  0.2387931   0.32875387  0.9873354 ]\n",
      " [ 1.3190391   0.75062962  2.46210794 -1.06014071 -0.11731035  1.2344157 ]\n",
      " [ 0.6775828   0.61594565  2.13376944 -0.09989127  2.65980218  1.74204416]\n",
      " [ 0.82757179  0.12214158  1.04221375  1.58281521  0.80816445  0.11237104]] \n",
      " (5, 6) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "rdm = np.random.RandomState(1)\n",
    "x1 = rdm.normal(1,1,(5,4))\n",
    "x2 = rdm.normal(1,1,(3,4))\n",
    "x3 = rdm.normal(1,1,(5,2))\n",
    "print(x1,\"\\n\",x1.shape,\"\\n\")\n",
    "print(x2,\"\\n\",x2.shape,\"\\n\")\n",
    "print(x3,\"\\n\",x3.shape,\"\\n\")\n",
    "\n",
    "con1 = np.concatenate([x1,x2],axis=0)\n",
    "print(con1,\"\\n\",con1.shape,\"\\n\")\n",
    "\n",
    "con2 = np.concatenate([x1,x3],axis=1)\n",
    "print(con2,\"\\n\",con2.shape,\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('gym')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "eb62451c918ef6a4174992a3510c06ea27ba0bc2fc5ee7a9f470b6f52b0b170f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}