{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "1) Create a list with sales considering the following assumptions:\n",
    "\n",
    "* inicial valueSales =1000\n",
    "* growth rate = 10%\n",
    "* number of years = 6\n",
    "\n",
    "use compressed list:\n",
    "\n",
    "[x for x in range(min,max)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Create a list with cost, considering a margin of 70%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) create a list with the profit (profit = sales - cost)\n",
    "\n",
    "sugestions:\n",
    "[x+y for (x,y) in zip (listX,listY)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Calculate a newProfit suposing 500 of fixed costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Create a newSales with random generated values between 200 and 2000.\n",
    "some ints:\n",
    "\n",
    "* Import module, writing import random \n",
    "* Use the method random.randint(a, b). \n",
    "\n",
    "This method is used to generate values between a and b ( Return a random integer N such that a <= N <= b.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Create a new list with profits (newProfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) What is the percentage of years having profits in the total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8) Compare sales and newSales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'years')"
      ]
     },
     "execution_count": 41,
     "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": [
    "import matplotlib.pyplot as plt \n",
    "plt.plot(sales)\n",
    "plt.plot(newSales)\n",
    "plt.ylabel('sales')\n",
    "plt.xlabel('years')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}