{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "solar-edinburgh",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 12, -3, 3.4, 3.141592653589793, 19, -12]\n"
     ]
    }
   ],
   "source": [
    "# Lists\n",
    "\n",
    "# Lists are created using square brackets.\n",
    "import numpy as np\n",
    "xData = [1, 2, 5, 12, -3, 3.4, np.pi, 19, -12]\n",
    "print(xData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "homeless-stanford",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can use 'len' to check the number of elements in a list.\n",
    "len(xData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "accompanied-edmonton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's another list of the same length.\n",
    "yData = [2, 3, 4, 5, 5, 4, 3, 2, 4]\n",
    "len(yData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "norman-fireplace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\n"
     ]
    }
   ],
   "source": [
    "# You can also automatically generate a list.\n",
    "x = list(range(11, 22))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "competitive-insulation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 13, 15, 17, 19, 21]\n",
      "[22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12]\n"
     ]
    }
   ],
   "source": [
    "# Using a third option in range, you can specify the increment of the list.\n",
    "x = list(range(11, 22, 2))\n",
    "print(x)\n",
    "y = list(range(22, 11,-1))\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "federal-philippines",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is possible select specific elements of a list using square brackets.\n",
    "# Note that the first element is indexed as zero and the nth element is indexed\n",
    "# as n-1.\n",
    "print(xData[0])\n",
    "xData[len(xData) - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intensive-aspect",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 12, -3, 3.4]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also extract a range of values from the list.\n",
    "xData[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "naval-capital",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.           2.           5.          12.          -3.\n",
      "   3.4          3.14159265  19.         -12.        ]\n"
     ]
    }
   ],
   "source": [
    "# The 'NumPy' module can be used to do algebraic operations on lists and to convert the lists to arrays.\n",
    "xArray = np.array(xData)\n",
    "print(xArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "educated-raise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.        ,   4.        ,  10.        ,  24.        ,\n",
       "        -6.        ,   6.8       ,   6.28318531,  38.        ,\n",
       "       -24.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now you can scale the array...\n",
    "2*xArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "married-agency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.       ,   4.       ,  25.       , 144.       ,   9.       ,\n",
       "        11.56     ,   9.8696044, 361.       , 144.       ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# square each element in the array...\n",
    "xArray**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "northern-clerk",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.          -1.           1.           7.          -8.\n",
      "  -0.6          0.14159265  17.         -16.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  2.        ,   6.        ,  20.        ,  60.        ,\n",
       "       -15.        ,  13.6       ,   9.42477796,  38.        ,\n",
       "       -48.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do element by element addition and products...\n",
    "yArray = np.array(yData)\n",
    "print(xArray - yArray)\n",
    "xArray*yArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "retained-database",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.02477796076937"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate dot products...\n",
    "np.dot(xArray, yArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "functioning-optimum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-7, 14, -7])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and evaluate cross products of 3-elemnet arrays (among other things)\n",
    "x3 = np.array([1, 2, 3])\n",
    "y3 = np.array([6, 5, 4])\n",
    "np.cross(x3, y3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}