numpy-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b694508",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fec7f7e",
   "metadata": {},
   "source": [
    "## the basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54e095ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3], dtype='int16')\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa4a9ae8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6. 7. 8.]\n",
      " [2. 5. 9.]]\n"
     ]
    }
   ],
   "source": [
    "b= np.array([[6.0,7.0, 8.0],[2.0, 5.0,9.0]])\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83726d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get dimension\n",
    "b.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c881471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get shape\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5655e253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf439134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get size\n",
    "b.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b8fed27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get total size\n",
    "a.nbytes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2132dcd4",
   "metadata": {},
   "source": [
    "## accessing/changing specific elements, rows, columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a1311584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3,4,5,6,7],[8,9,10,11,12,13,14]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "582e6da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a specific element\n",
    "a[1,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13d78b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a specific row\n",
    "a[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1400b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 10])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get column\n",
    "a[:, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad003dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more fancy [startindex:endindex:stepsize]  \n",
    "a[0,0:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b684bfd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  5  4  5  6  7]\n",
      " [ 8  9  5 11 12 20 14]]\n",
      "[[ 1  2  5  4  5  6  7]\n",
      " [ 8  9  5 11 12 20 14]]\n"
     ]
    }
   ],
   "source": [
    "a[1,5]=20\n",
    "print(a)\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b20f756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  5  4  5  6  7]\n",
      " [ 8  9  5 11 12 20 14]]\n"
     ]
    }
   ],
   "source": [
    "a[:,2]=5\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246d57d2",
   "metadata": {},
   "source": [
    "# 3d example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c852072e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2]\n",
      "  [3 4]]\n",
      "\n",
      " [[5 6]\n",
      "  [7 8]]]\n"
     ]
    }
   ],
   "source": [
    "b= np.array([[[1,2],[3,4]],[[5,6],[7,8]]])\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "844db73b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get specific element\n",
    "b[0,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08530f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace\n",
    "b[:,1,:]=[[8,9],[8,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "162dd2a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2]\n",
      "  [8 9]]\n",
      "\n",
      " [[5 6]\n",
      "  [8 8]]]\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4391935e",
   "metadata": {},
   "source": [
    "## initializing different types of arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ef11846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all 0s matrix\n",
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3a33683d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c7d1023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4,3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8dfa562c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99, 99],\n",
       "       [99, 99]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any other no\n",
    "np.full((2,2),99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5daedb61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4, 4, 4, 4, 4],\n",
       "       [4, 4, 4, 4, 4, 4, 4]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any other no(full like)\n",
    "np.full_like(a,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3a9673cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56247693, 0.77548323],\n",
       "       [0.49745286, 0.23234716],\n",
       "       [0.89547001, 0.88433452],\n",
       "       [0.29727526, 0.66152474]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#random decimal nos\n",
    "np.random.rand(4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ad182be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 3],\n",
       "       [0, 6, 1],\n",
       "       [3, 6, 2]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random integer\n",
    "np.random.randint(7,size=(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "02988d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identity matrix\n",
    "np.identity(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "73d0e1e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "#repeat an array\n",
    "arr=np.array([[1,2,3]])\n",
    "r1 = np.repeat(arr, 3, axis=0)\n",
    "print(r1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e9151bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]]\n",
      "[[0. 0. 0.]\n",
      " [0. 9. 0.]\n",
      " [0. 0. 0.]]\n",
      "[[1. 1. 1. 1. 1.]\n",
      " [1. 0. 0. 0. 1.]\n",
      " [1. 0. 9. 0. 1.]\n",
      " [1. 0. 0. 0. 1.]\n",
      " [1. 1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "output = np.ones((5,5))\n",
    "print(output)\n",
    "\n",
    "z=np.zeros((3,3))\n",
    "z[1,1] = 9\n",
    "print(z)\n",
    "\n",
    "output[1:4,1:4]=z\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b44c0ce",
   "metadata": {},
   "source": [
    "## be careful while copying array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c5d4567f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "a= np.array([1,2,3])\n",
    "b = a.copy()\n",
    "b[0] = 100\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461ad1be",
   "metadata": {},
   "source": [
    "## mathematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5d2524ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "a= np.array([1,2,3,4])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "581dcb11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7af3d50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1,  2])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "58b0e226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a1ddc2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 1.5, 2. ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "01d24261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, 4])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b= np.array([1,0,1,0])\n",
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bf93b7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a **2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e9a0cda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.84147098,  0.90929743,  0.14112001, -0.7568025 ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take sin \n",
    "np.sin(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aacc8743",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for more (https://docs.spicy.org/doc/numpy/reference/routines.math.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca7abf5",
   "metadata": {},
   "source": [
    "## linear algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "552253f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [1 1 1]]\n",
      "[[2 2]\n",
      " [2 2]\n",
      " [2 2]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[6, 6],\n",
       "       [6, 6]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a= np.full((2,3),1)\n",
    "print(a)\n",
    "\n",
    "b= np.full((3,2),2)\n",
    "print(b)\n",
    "\n",
    "np.matmul(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "43e8f5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c= np.identity(3)\n",
    "np.linalg.det(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "14936341",
   "metadata": {},
   "outputs": [],
   "source": [
    "## reference docs(https://numpy.org/doc/stable/reference/routines.linalg.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94f5306",
   "metadata": {},
   "source": [
    "## statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a1308753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = np.array([[1,2,3],[4,5,6]])\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "14471e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7ef68b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e0dc7e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(stats, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9c00bfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(stats ,axis=1) ##min of first and second row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "154169c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b3e2a8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 9])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(stats, axis= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827eada9",
   "metadata": {},
   "source": [
    "## reorganizing arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c1e3f2f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [5 6 7 8]]\n",
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "before = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "print(before)\n",
    "after = before.reshape((4,2))\n",
    "print (after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "135ed24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8],\n",
       "       [5, 6, 7, 8],\n",
       "       [1, 2, 3, 4]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vertically stacking vectors\n",
    "v1 = np.array([1,2,3,4])\n",
    "v2 = np.array([5,6,7,8])\n",
    "\n",
    "np.vstack([v1,v2,v2,v1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ef761388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 0., 0.],\n",
       "       [1., 1., 1., 1., 0., 0.]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# horizontal stacking\n",
    "h1= np.ones((2,4))\n",
    "h2= np.zeros((2,2))\n",
    "\n",
    "np.hstack((h1,h2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a85989",
   "metadata": {},
   "source": [
    "## miscellaneous"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8102f116",
   "metadata": {},
   "source": [
    "# load data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c10b57d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  13,  21,  11, 196,  75,   4,   3,  34,   6,   7,   8,   0,\n",
       "          1,   2,   3,   4,   5],\n",
       "       [  3,  42,  12,  33, 766,  75,   4,  55,   6,   4,   3,   4,   5,\n",
       "          6,   7,   0,  11,  12],\n",
       "       [  1,  22,  33,  11, 999,  11,   2,   1,  78,   0,   1,   2,   9,\n",
       "          8,   7,   1,  76,  88]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata=np.genfromtxt('data.txt', delimiter=',')\n",
    "filedata.astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984c3d6b",
   "metadata": {},
   "source": [
    "#### boolean masking and aadvance indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c96949a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False,  True,  True, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False,  True,  True, False,  True, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False,  True, False, False, False,  True,\n",
       "        False, False, False, False, False, False, False,  True,  True]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cbef130e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([196.,  75., 766.,  75.,  55., 999.,  78.,  76.,  88.])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata[filedata > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a13a8126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 7])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## you can index with a list in numpy\n",
    "a=np.array([1,2,3,4,5,6,7,8,9])\n",
    "a[[1,3,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1d1df3b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True, False,  True,  True,\n",
       "       False, False, False, False, False, False, False,  True,  True])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(filedata > 50, axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d7913bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(filedata > 50, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a3bed997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True, False, False,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True, False, False,  True, False,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True, False,  True,  True,  True, False,\n",
       "         True,  True,  True,  True,  True,  True,  True, False, False]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~(filedata>50 )&(filedata<100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ae70968",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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