numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5e31153",
   "metadata": {},
   "source": [
    "# How to import NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b998e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1472043e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(a)\n",
    "a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa3d132",
   "metadata": {},
   "source": [
    "# More information about arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c755cf3",
   "metadata": {},
   "source": [
    "nothing should be noted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83daa04",
   "metadata": {},
   "source": [
    "# How to create a basic array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd2189b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2f8cf8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7307eefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 2., 1.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "839ecaba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "debe2478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,9,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eae94cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  2.5,  5. ,  7.5, 10. ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,10,num=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48f2b090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(2,dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba8a443",
   "metadata": {},
   "source": [
    "# Adding, removing, and sorting elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e8829f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])\n",
    "np.sort(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5e77d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "b = np.array([5, 6, 7, 8])\n",
    "np.concatenate((a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a60ecec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1, 2], [3, 4]])\n",
    "y = np.array([[5, 6]])\n",
    "np.concatenate((x, y), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4397a5a",
   "metadata": {},
   "source": [
    "# How do you know the shape and size of an array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4cb4f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_example = np.array([[[0, 1, 2, 3],\n",
    "                           [4, 5, 6, 7]],\n",
    "\n",
    "                          [[0, 1, 2, 3],\n",
    "                           [4, 5, 6, 7]],\n",
    "\n",
    "                          [[0 ,1 ,2, 3],\n",
    "                           [4, 5, 6, 7]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee01b415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_example.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a56f95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_example.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aba11cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_example.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0fd6ed",
   "metadata": {},
   "source": [
    "# Can you reshape an array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adfe4a75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4, 5]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(6)\n",
    "b = a.reshape(3, 2)\n",
    "print(b)\n",
    "\n",
    "np.reshape(a, newshape=(1, 6), order='C')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ee5579",
   "metadata": {},
   "source": [
    "# How to convert a 1D array into a 2D array (how to add a new axis to an array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fdcd4f62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6,)\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18f26ff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5 6]] 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = a[np.newaxis, :]\n",
    "print(a2,a2.ndim)\n",
    "a2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67ccded2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5 6]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_vector = a[np.newaxis, :]\n",
    "print(row_vector)\n",
    "row_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cde165e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_vector = a[:,np.newaxis]\n",
    "col_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1599954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.expand_dims(a, axis=1)\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78418ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.expand_dims(a, axis=0)\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8709e890",
   "metadata": {},
   "source": [
    "# Indexing and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a3f6926",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bf06faf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae2b2984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "29daa62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "760281e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fbcf58c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1 , 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ddc9bbfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0c770368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "five_up=(a>=5)\n",
    "a[five_up]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "652aa00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a%2==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "77079bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[(a>2)&(a<11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "015aae77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a>5)| (a==5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "37119618",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9d63a3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0]), array([0, 1, 2, 3]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.nonzero(a < 5)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f0085e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0)\n",
      "(0, 1)\n",
      "(0, 2)\n",
      "(0, 3)\n"
     ]
    }
   ],
   "source": [
    "list_of_coordinates = list(zip(b[0],b[1]))\n",
    "for coord in list_of_coordinates:\n",
    "    print(coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4734d497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67338a83",
   "metadata": {},
   "source": [
    "# How to create an array from existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "43a0bf21",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3ccdcea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = a[3:8]\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f6796830",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.array([[1, 1],\n",
    "               [2, 2]])\n",
    "\n",
    "a2 = np.array([[3, 3],\n",
    "               [4, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dfaf6e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2],\n",
       "       [3, 3],\n",
       "       [4, 4]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((a1,a2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8199da99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 3, 3],\n",
       "       [2, 2, 4, 4]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((a1, a2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8c20e34a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12],\n",
       "       [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(1, 25).reshape(2, 12)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fe7f999a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1,  2,  3,  4],\n",
       "        [13, 14, 15, 16]]),\n",
       " array([[ 5,  6,  7,  8],\n",
       "        [17, 18, 19, 20]]),\n",
       " array([[ 9, 10, 11, 12],\n",
       "        [21, 22, 23, 24]])]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hsplit(x, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "687f2739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1,  2,  3],\n",
       "        [13, 14, 15]]),\n",
       " array([[ 4],\n",
       "        [16]]),\n",
       " array([[ 5,  6,  7,  8,  9, 10, 11, 12],\n",
       "        [17, 18, 19, 20, 21, 22, 23, 24]])]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hsplit(x, (3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b606aeab",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0e65ec0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "[99  2  3  4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[99,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1 = a[0, :]\n",
    "print(b1)\n",
    "b1[0] = 99\n",
    "print(b1)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "23d86710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2 = a.copy()\n",
    "b2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06567445",
   "metadata": {},
   "source": [
    "# Basic array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f8a8d974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([1, 2])\n",
    "ones = np.ones(2, dtype=int)\n",
    "data+ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8d4eb2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data-ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "54a2c15f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data*data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e6b6030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c7800369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "76301138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[1, 1], [2, 2],[3,4]])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "205cd3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "014d1166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 7])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8dc3611",
   "metadata": {},
   "source": [
    "# Broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "028a359b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.6, 3.2])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([1.0, 2.0])\n",
    "data * 1.6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95ee8a8",
   "metadata": {},
   "source": [
    "# More useful array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "04233f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c2945000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "970e0b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e49283dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[0.45053314, 0.17296777, 0.34376245, 0.5510652],\n",
    "              [0.54627315, 0.05093587, 0.40067661, 0.55645993],\n",
    "              [0.12697628, 0.82485143, 0.26590556, 0.56917101]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "94b3b43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8595784"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a6c3d9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05093587"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "057bd817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12697628, 0.05093587, 0.26590556, 0.5510652 ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030cc51b",
   "metadata": {},
   "source": [
    "# Creating matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "02ff70cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4ec7dd8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "61835a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ef6dc322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:2,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4dd1c2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aed5ec05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "aebc1ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e1612737",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([[1, 2], [5, 3], [4, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "488b57c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "152c7b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 6])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "90d5f7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([[1, 2], [3, 4]])\n",
    "ones = np.array([[1, 1], [1, 1]])\n",
    "data+ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2138b7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "ones_row = np.array([[1, 1]])\n",
    "data + ones_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f85a591f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4, 3, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c1b71989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "58d5c57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a45c0d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58300632, 0.38702704, 0.94562436])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng()\n",
    "rng.random(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8e4e9c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "260f244d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0132d81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30231589, 0.15132945],\n",
       "       [0.14925073, 0.02461025],\n",
       "       [0.21117741, 0.91369485]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e9b1e5",
   "metadata": {},
   "source": [
    "# Generating random numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "82adf48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 0, 2],\n",
       "       [4, 4, 0, 1]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng.integers(5, size=(2, 4)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92cf694f",
   "metadata": {},
   "source": [
    "# How to get unique items and counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "292913fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18, 19, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9ed71993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 12 13 14 15 16 17 18 19 20]\n"
     ]
    }
   ],
   "source": [
    "unique_values = np.unique(a)\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "33d7e093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 12 13 14 15 16 17 18 19 20]\n",
      "[ 0  2  3  4  5  6  7 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "unique_values, indices_list = np.unique(a, return_index=True)\n",
    "print(unique_values)\n",
    "print(indices_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "016a92af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, 2, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values, occurrence_count = np.unique(a, return_counts=True)\n",
    "occurrence_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8af7c1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [1, 2, 3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d4a14fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "unique_values = np.unique(a_2d)\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "21e5e8a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "unique_rows = np.unique(a_2d, axis=0)\n",
    "print(unique_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ee5f94ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_rows, indices, occurrence_count = np.unique(\n",
    "     a_2d, axis=0, return_counts=True, return_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8456607f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "print(unique_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4e88f469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "print(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "70ed5a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(occurrence_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "027e79c8",
   "metadata": {},
   "source": [
    "# Transposing and reshaping a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e80008ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.arange(1,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c0dd396c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reshape(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8745a414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reshape(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ee805f18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(6).reshape(2,3)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "16a97201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3],\n",
       "       [1, 4],\n",
       "       [2, 5]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a476eae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3],\n",
       "       [1, 4],\n",
       "       [2, 5]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcc7fb0",
   "metadata": {},
   "source": [
    "# How to reverse an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a532195e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "319c6c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8 7 6 5 4 3 2 1]\n"
     ]
    }
   ],
   "source": [
    "reversed_arr = np.flip(arr)\n",
    "print(reversed_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "98c8f1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_2d = np.arange(1,13).reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b29979f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "be6db9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 11, 10,  9],\n",
       "       [ 8,  7,  6,  5],\n",
       "       [ 4,  3,  2,  1]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reversed_arr_2d =  np.flip(arr_2d)\n",
    "reversed_arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d35c0e68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9 10 11 12]\n",
      " [ 5  6  7  8]\n",
      " [ 1  2  3  4]]\n"
     ]
    }
   ],
   "source": [
    "reversed_arr_rows = np.flip(arr_2d, axis=0)\n",
    "print(reversed_arr_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5e9c0fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  3  2  1]\n",
      " [ 8  7  6  5]\n",
      " [12 11 10  9]]\n"
     ]
    }
   ],
   "source": [
    "reversed_arr_columns = np.flip(arr_2d, axis=1)\n",
    "print(reversed_arr_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "830b9555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 8,  7,  6,  5],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d[1] = np.flip(arr_2d[1])\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "26b56f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 10,  3,  4],\n",
       "       [ 8,  7,  6,  5],\n",
       "       [ 9,  2, 11, 12]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d[:,1] = np.flip(arr_2d[:,1])\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f4bc1f",
   "metadata": {},
   "source": [
    "# Reshaping and flattening multidimensional arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2ca5af32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1 , 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "x.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0c6ecf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "[99  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "a1 = x.flatten()\n",
    "a1[0] = 99\n",
    "print(x)\n",
    "print(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b899dedb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[98  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "[98  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "a2 = x.ravel()\n",
    "a2[0] = 98\n",
    "print(x)\n",
    "print(a2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2925edf4",
   "metadata": {},
   "source": [
    "# How to access the docstring for more informatiom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5396ea25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function max in module builtins:\n",
      "\n",
      "max(...)\n",
      "    max(iterable, *[, default=obj, key=func]) -> value\n",
      "    max(arg1, arg2, *args, *[, key=func]) -> value\n",
      "    \n",
      "    With a single iterable argument, return its biggest item. The\n",
      "    default keyword-only argument specifies an object to return if\n",
      "    the provided iterable is empty.\n",
      "    With two or more arguments, return the largest argument.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "20bfefd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "max?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ae84a439",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3, 4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f52a77dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "845bbf97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def double(a):\n",
    "  '''Return a * 2'''\n",
    "  return a * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "25a78996",
   "metadata": {},
   "outputs": [],
   "source": [
    "double?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "a9fba7f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "double??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "59daf00a",
   "metadata": {},
   "outputs": [],
   "source": [
    "len?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7378d834",
   "metadata": {},
   "outputs": [],
   "source": [
    "len??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36258a5",
   "metadata": {},
   "source": [
    "# Working with mathematical formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42695ba5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "95182590",
   "metadata": {},
   "source": [
    "# How to save and load NumPy objects\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f95fd0f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3, 4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "50bfd2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('filename', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "f048ba8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.load('filename.npy')\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "abc1f0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f3386a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('new_file.csv', csv_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "03136fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6., 7., 8.])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('new_file.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bee410",
   "metadata": {},
   "source": [
    "# Importing and exporting a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ad7fd328",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "a = np.array([[-2.58289208,  0.43014843, -1.24082018, 1.59572603],\n",
    "              [ 0.99027828, 1.17150989,  0.94125714, -0.14692469],\n",
    "              [ 0.76989341,  0.81299683, -0.95068423, 0.11769564],\n",
    "              [ 0.20484034,  0.34784527,  1.96979195, 0.51992837]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f0772dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0 -2.582892  0.430148 -1.240820  1.595726\n",
      "1  0.990278  1.171510  0.941257 -0.146925\n",
      "2  0.769893  0.812997 -0.950684  0.117696\n",
      "3  0.204840  0.347845  1.969792  0.519928\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(a)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d274e173",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('pd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "8c98e718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.582892</td>\n",
       "      <td>0.430148</td>\n",
       "      <td>-1.240820</td>\n",
       "      <td>1.595726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.990278</td>\n",
       "      <td>1.171510</td>\n",
       "      <td>0.941257</td>\n",
       "      <td>-0.146925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.769893</td>\n",
       "      <td>0.812997</td>\n",
       "      <td>-0.950684</td>\n",
       "      <td>0.117696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.204840</td>\n",
       "      <td>0.347845</td>\n",
       "      <td>1.969792</td>\n",
       "      <td>0.519928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         0         1         2         3\n",
       "0           0 -2.582892  0.430148 -1.240820  1.595726\n",
       "1           1  0.990278  1.171510  0.941257 -0.146925\n",
       "2           2  0.769893  0.812997 -0.950684  0.117696\n",
       "3           3  0.204840  0.347845  1.969792  0.519928"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('pd.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "fe52e7f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('np.csv', a, fmt='%.2f', delimiter=',', header='1,  2,  3,  4')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfeb5c8e",
   "metadata": {},
   "source": [
    "# Plotting arrays with Matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "68caa8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([2, 1, 5, 7, 4, 6, 8, 14, 10, 9, 18, 20, 22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "867d1f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd937b5ce80>]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "104587cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd93a484b50>]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 5, 20)\n",
    "y = np.linspace(0, 10, 20)\n",
    "plt.plot(x, y, 'purple') # line\n",
    "plt.plot(x, y, 'o')      # dots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "057fc261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7fd93a4fbd30>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "X = np.arange(-5, 5, 0.15)\n",
    "Y = np.arange(-5, 5, 0.15)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='viridis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}