3장_넘파이_배열_프로그래밍_(3.2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7768470e",
   "metadata": {},
   "source": [
    "# 배열의 생성과 변형"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1f8552c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1, 2, 3])\n",
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d85e86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int32"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6a9aa4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_22472\\2068585620.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  np.array([0, 1, -1, 0]) / np.array([1, 0, 0, 0])\n",
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_22472\\2068585620.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  np.array([0, 1, -1, 0]) / np.array([1, 0, 0, 0])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  0.,  inf, -inf,  nan])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([0, 1, -1, 0]) / np.array([1, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fbf4ce",
   "metadata": {},
   "source": [
    "NumPy는 몇가지 단순한 배열을 생성하는 명령을 제공한다.\n",
    "\n",
    "* zeros, ones\n",
    "\n",
    "* zeros_like, ones_like\n",
    "\n",
    "* empty\n",
    "\n",
    "* arange\n",
    "\n",
    "* linspace, logspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63b6f597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros(5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4121b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.zeros((2,3))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11618adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0]], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.zeros((5, 2), dtype=\"i\")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84ace3bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.ones((2, 3, 4), dtype = \"i8\")\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "321aa514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.ones_like(b, dtype=\"f\")\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f68d968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.05224250e-311, 2.81617418e-322, 0.00000000e+000],\n",
       "       [0.00000000e+000, 1.20953760e-312, 5.74084271e+169],\n",
       "       [7.42982691e-091, 2.95066457e+179, 3.61199946e+174],\n",
       "       [1.59922593e+160, 6.48224659e+170, 4.93432906e+257]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = np.empty((4, 3))\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "544e0974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  5,  7,  9, 11, 13, 15, 17, 19])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(3, 21, 2)  # 시작, 끝(포함하지 않음), 단계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc85b1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  25.,  50.,  75., 100.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 100, 5)  # 시작, 끝(포함), 갯수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "913e4303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22c3e591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [2, 5],\n",
       "       [3, 6]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7f8ca30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1cdd313b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(4, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "238b1cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3],\n",
       "        [ 4,  5]],\n",
       "\n",
       "       [[ 6,  7],\n",
       "        [ 8,  9],\n",
       "        [10, 11]]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(2, -1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0c3e333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.ones((2, 3))\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2698918b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.zeros((2, 2))\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0bfc131d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 0., 0.],\n",
       "       [1., 1., 1., 0., 0.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([a1, a2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c1e0d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.zeros((2, 3))\n",
    "np.vstack([a1, a2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a3a7ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = np.ones((3, 4))\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dafddbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = np.zeros((3, 4))\n",
    "c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dfbeb79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]],\n",
       "\n",
       "       [[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]],\n",
       "\n",
       "       [[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dstack([c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "43f3d196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.dstack([c1, c2])).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a29cef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.stack([c1, c2], axis = 0)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "de88c13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.stack([c1, c2])).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7ccfbca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0.]]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.stack([c1, c2], axis=1)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e46d3605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1865b37f",
   "metadata": {},
   "source": [
    "r_ 메서드는 hstack 명령과 비슷하게 배열을 좌우로 연결한다. 다만 메서드임에도 불구하고 소괄호(parenthesis, ())를 사용하지 않고 인덱싱과 같이 대괄호(bracket, [])를 사용한다. 이런 특수 메서드를 인덱서(indexer)라고 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4b5c7729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.r_[np.array([1, 2, 3]), np.array([4, 5, 6])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2c7728ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [2, 5],\n",
       "       [3, 6]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.c_[np.array([1, 2, 3]), np.array([4, 5, 6])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dcc31f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.zeros(3)\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fad67305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.ones(2)\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "57160403",
   "metadata": {},
   "outputs": [],
   "source": [
    "a3 = np.hstack([a1, a2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "860db49f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 1.],\n",
       "       [0., 0., 0., 1., 1.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4 = np.vstack([a3, a3])\n",
    "a4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "db902a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 1.],\n",
       "       [0., 0., 0., 1., 1.],\n",
       "       [0., 0., 0., 1., 1.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5 = np.vstack([a3, a4])\n",
    "a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b37ee7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120, 130,\n",
       "       140, 150])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1 = np.arange(10,151,10)\n",
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c2500899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10,  20,  30,  40,  50],\n",
       "       [ 60,  70,  80,  90, 100],\n",
       "       [110, 120, 130, 140, 150]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2 = b1.reshape(-1,5)\n",
    "b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "72c038fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [ 10.,  20.,  30.,  40.,  50.],\n",
       "       [ 60.,  70.,  80.,  90., 100.],\n",
       "       [110., 120., 130., 140., 150.]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = np.vstack([a5,b2])\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc1bf852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [ 10.,  20.,  30.,  40.,  50.],\n",
       "       [ 60.,  70.,  80.,  90., 100.],\n",
       "       [110., 120., 130., 140., 150.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [  0.,   0.,   0.,   1.,   1.],\n",
       "       [ 10.,  20.,  30.,  40.,  50.],\n",
       "       [ 60.,  70.,  80.,  90., 100.],\n",
       "       [110., 120., 130., 140., 150.]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.vstack([c1, c1])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "da83e3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(3)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9c43c993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.arange(5)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b107055f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = np.meshgrid(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "54585b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [0, 1, 2],\n",
       "       [0, 1, 2],\n",
       "       [0, 1, 2],\n",
       "       [0, 1, 2]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a99b85c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [1, 1, 1],\n",
       "       [2, 2, 2],\n",
       "       [3, 3, 3],\n",
       "       [4, 4, 4]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "57ea74cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(0, 0), (1, 0), (2, 0)],\n",
       " [(0, 1), (1, 1), (2, 1)],\n",
       " [(0, 2), (1, 2), (2, 2)],\n",
       " [(0, 3), (1, 3), (2, 3)],\n",
       " [(0, 4), (1, 4), (2, 4)]]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[list(zip(x, y)) for x, y in zip(X,Y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56cd27a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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