{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the value of c is: 6\n"
     ]
    }
   ],
   "source": [
    "a = 2\n",
    "b = 3\n",
    "c = a * b\n",
    "print('the value of c is:', c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 3\n"
     ]
    }
   ],
   "source": [
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print the value of a is 2, and a / b equals: 6.6667e-01\n"
     ]
    }
   ],
   "source": [
    "d = a / b\n",
    "print(f'print the value of a is {a}, and a / b equals: {d:.4e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "x = 3\n",
    "y = (x - 1) * (x + 1)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5403023058681398"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-4, 4, 100)\n",
    "y = (x - 1) * (x + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1130698e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'g', label='a green line')\n",
    "plt.xlabel('this is the x-label')\n",
    "plt.ylabel('y-label goes here')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}