dictionary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBJECTS AND CLASSES.\n",
    "OBJECTS: PEOPLE\n",
    "ATTRIBUTES: NAME, AGE, RACE, GENDER, WEIGHT, HEIGHT\n",
    "METHODS: INTRODUCE SELF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class People:\n",
    "    def __init__(self, name, age, race, gender, weight, height):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.race = race\n",
    "        self.gender = gender\n",
    "        self.weight = weight\n",
    "        self.height = height\n",
    "\n",
    "    def introduce_self(self):\n",
    "        print(f'Hey there, my name is {self.name}, {self.age} years old, {self.race} in color, {self.gender}, {self.weight} kgs heavy and {self.height} cm tall.')\n",
    "        \n",
    "\n",
    "class Robots:\n",
    "    def __init__(self, name, color, weight):\n",
    "        self.name = name\n",
    "        self.color = color\n",
    "        self.weight = weight\n",
    "    def introduce_self(self):\n",
    "        print('My name is ', self.name)\n",
    "        print('I am ', self.color)\n",
    "        print('I weigh '+ str(self.weight)+ ' KGS')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = People('Peter Muhumuki', 18, 'Black', 'Male', 55, 119)\n",
    "p2 = People('Alice', 20, 'White', 'Female', 70, 150)\n",
    "\n",
    "r1 = Robots('Tom', 'Red', 40)\n",
    "r2 = Robots('Jerry', 'Blue', 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hey there, my name is Peter Muhumuki, 18 years old, Black in color, Male, 55 kgs heavy and 119 cm tall.\n"
     ]
    }
   ],
   "source": [
    "p1.introduce_self()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CAR:\n",
    "    def __init__(self, color, doors, tyres):\n",
    "        self.color = color\n",
    "        self.doors = doors\n",
    "        self.tyres = tyres\n",
    "\n",
    "    def brake(self):\n",
    "        return'BRAKING'\n",
    "    \n",
    "    def drive(self):\n",
    "        return'Am driving'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BRAKING'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = CAR('Green', 'Blue', 'New')\n",
    "\n",
    "c1.brake()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Peter and I am 18 years old\n"
     ]
    }
   ],
   "source": [
    "class Person:\n",
    "  def __init__(self, name, age):\n",
    "    self.age = age\n",
    "    self.name = name\n",
    "        \n",
    "        \n",
    "  def introduceself(self):\n",
    "    intro = 'My name is {} and I am {} years old'\n",
    "    print(intro.format(self.name, self.age))\n",
    "        \n",
    "        \n",
    "p1 = Person('Peter', 18)\n",
    "p1.introduceself()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Spencer Retail\n",
      "136, Garia Station Road,\n",
      "Kolkata:700084\n",
      "--------------------------------------------------\n",
      "Product(s)\t\tPrice\n",
      "Apricot\t\t\t300\n",
      "Dates\t\t\t400\n",
      "Almond\t\t\t500\n",
      "Combo_1\t\t\t630.0\n",
      "Combo_2\t\t\t810.0\n",
      "Combo_3\t\t\t720.0\n",
      "GiftBox\t\t\t900.0\n",
      "**************************************************\n",
      "For free delivery, contact 123-456-789\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "saleperson = 'Spencer Retail'\n",
    "address = '136, Garia Station Road,\\nKolkata:700084'\n",
    "\n",
    "apricot = 300\n",
    "dates = 400\n",
    "almond = 500\n",
    "apricot_dates_combo = (apricot+dates)*0.9 #10% discount\n",
    "apricot_almond_combo = (apricot+almond)*0.9\n",
    "almond_dates_combo = (almond+dates)*0.9\n",
    "gift_pack = (apricot + almond + dates) *0.75\n",
    "\n",
    "\n",
    "print('-'*50)\n",
    "print(f'{saleperson}')\n",
    "print(f'{address}')\n",
    "print('-'*50)\n",
    "print('Product(s)\\t\\tPrice')\n",
    "print(f'Apricot\\t\\t\\t{apricot}')\n",
    "print(f'Dates\\t\\t\\t{dates}')\n",
    "print(f'Almond\\t\\t\\t{almond}')\n",
    "print(f'Combo_1\\t\\t\\t{apricot_dates_combo}')\n",
    "print(f'Combo_2\\t\\t\\t{almond_dates_combo}')\n",
    "print(f'Combo_3\\t\\t\\t{apricot_almond_combo}')\n",
    "print(f'GiftBox\\t\\t\\t{gift_pack}')\n",
    "print('*'*50)\n",
    "print('For free delivery, contact 123-456-789')\n",
    "print('*'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x =np.array(['Bananas', 'Apple', 'Oranges'])\n",
    "y = np.array([23, 14, 18])\n",
    "\n",
    "plt.bar(x, y, color = 'Cyan', width= 0.3 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.array([23,45, 67, 12, 34, 67])\n",
    "labels = ('Alice', 'Ben', 'Catherine', 'Dominic', 'Everlyne', 'Faith')\n",
    "mycolors = ('Red', 'Green', 'Yellow', 'Blue', 'Orange', 'Cyan')\n",
    "\n",
    "plt.pie(z, labels = labels, startangle=90, colors = mycolors)\n",
    "plt.legend(title = 'Students names')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2711a45d590>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting subplots.\n",
    "\n",
    "#plot 1\n",
    "\n",
    "x = np.array([1, 2, 3, 4])\n",
    "y = np.array([6, 7, 8, 9])\n",
    "\n",
    "#first argument shows the number of rows, second shows columns and third shows the index of the subplot.\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Income')\n",
    "plt.plot(x, y, 'D-.g')\n",
    "\n",
    "#plot 2\n",
    "x = np.array([6,7,8,9])\n",
    "y = np.array([1, 2, 3, 4])\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('Sales')\n",
    "plt.plot(x, y, 'o:r')\n",
    "plt.show()\n",
    "\n",
    "#plot 3\n",
    "x = np.array([10, 11, 12, 13])\n",
    "y = np.array([6, 10, 6, 19])\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title('Employees')\n",
    "plt.plot(x, y, 'H--y' )\n",
    "\n",
    "#plot 4\n",
    "x = np.array([40, 30, 10, 20])\n",
    "y =np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title('Guess')\n",
    "plt.plot(x, y, 'p-c' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Phone:\n",
    "    def __init__(self, Brand, Cost):\n",
    "        self.Brand = Brand\n",
    "        self.Cost = Cost\n",
    "\n",
    "    def  __str__(self):\n",
    "        return(f'The phone is a/an {self.Brand}, and it\\'s worth is ${self.Cost}')\n",
    "        \n",
    "    def introduce(self):\n",
    "        return(f'The phone is a/an {self.Brand}, and it\\'s worth is ${self.Cost}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The phone is a/an Tecno, and it's worth is $100\n"
     ]
    }
   ],
   "source": [
    "p1 = Phone('Tecno', 100)\n",
    "\n",
    "p1.introduce()\n",
    "\n",
    "print(p1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.77703582 10.44771288  2.93398477  0.32514974  0.57765296  5.00893849\n",
      "  6.36374633  0.28055463  7.22601908  2.13123565  8.61316489  2.51674927\n",
      "  9.72869555 10.03537899  5.24786374  0.72008958  8.94816211  2.0086595\n",
      "  9.99801927  1.55188395  5.16018016  0.42114967  9.29586314  5.49435027\n",
      "  4.26401366  3.32003728  1.23638533  1.79035432  9.04290932 10.05145353\n",
      "  9.03288688  4.26147712  0.11247297  8.99027117  3.47671622  1.91410102\n",
      "  6.83877966  6.94682517  5.61602683  4.13898049  0.65862033  3.35331356\n",
      "  4.78149106  8.85417901  4.38036359  0.59470331  6.47676088  4.01545837\n",
      "  0.25717395  8.01789854  4.08323286  9.38949085  9.60746537 10.00811565\n",
      "  5.58298601  6.9382732   9.24684019  7.27966858  0.34486726  7.95316226\n",
      "  4.88001984  5.31900546  6.64682559  2.00326963  6.65081664  5.26277396\n",
      "  9.36306623  8.06602556  5.65078143  9.4314486   4.97298037  9.52056027\n",
      "  2.99809139 10.48019213  1.96553332  5.39787977  6.60325326  2.18987635\n",
      "  0.6595981   6.70990392  3.32546312  2.64962599  9.85764688  5.67594007\n",
      "  1.88511632  4.37079031  8.69424264  0.76958383  4.15901561  1.51799473\n",
      "  0.23459685  4.57260332  6.33756787  6.32906301  5.24282563  1.25729156\n",
      "  9.34882746  0.56944506  0.88141805  5.51638833  6.67163592  4.4588706\n",
      "  8.15565195  2.02385156  0.95507728  3.23997159 10.28950949  8.44722748\n",
      "  8.30207817  5.03043173  6.0683905   1.02838107  7.50135716  8.8775505\n",
      "  2.02542957  2.51342287  5.43005639  5.37848379  1.77672039  5.77619501\n",
      "  8.34353247  3.80253897  6.66863863  6.39987402  8.27103001  7.91107266\n",
      "  3.42648464  5.47193398  6.20499379  3.5984883   9.28667493  1.02406226\n",
      "  8.1888047   5.36225677  2.03865231  9.54086185  2.09728327  4.07913419\n",
      "  4.51679191 10.33755971  9.92336772  8.2512145   2.27258775  5.24028107\n",
      "  0.89066812  4.95602372  1.9858901   8.486038    7.90421191  1.06168519\n",
      "  8.45194636  4.77477477 10.31252461  4.98255836 10.23160907  5.65622039\n",
      "  8.84465391  4.54152095  2.12293157  3.03361978  4.7303296   5.00498003\n",
      "  0.35959294  0.62931072  3.58251504  0.49019597  7.55497672  6.78486599\n",
      "  2.16760078 10.22487703  1.94100272  3.48677619  1.1312212   5.7303343\n",
      "  7.48078403  8.96577726  2.60702473  1.12211279  2.69023978  7.04528685\n",
      "  5.43374606  8.8849127   8.50925739  5.40972856  5.90210222 10.07416986\n",
      "  1.93048548  2.04312967  7.99150957  7.02549845  2.30521962  9.70069846\n",
      " 10.36189742  6.06073391  4.62841641  7.97120583  1.01696188  0.39254271\n",
      "  8.69309106  3.31434013  4.10161033  8.97805078  4.67418972  4.61208215\n",
      "  9.80353064  7.80882401  3.92525387  6.42024339  8.25752089  7.86963387\n",
      "  4.92415426  0.6833275   9.07737405  7.5674658   6.55449551  7.29172504\n",
      "  4.20344948  0.42911254  2.30348032  9.62606164  6.12012449  6.4464593\n",
      "  6.38448467  5.63260798  5.15858192  4.96424704  3.55864398  3.93242631\n",
      "  8.71445636  4.52587939  0.82888961  1.19564999  9.0368637   9.83993282\n",
      "  3.40474894  6.79727451  2.94950713  5.64968833  7.75962007  7.18885444\n",
      " 10.17039875  6.64538076  6.75096259  2.86795972  1.88740624  9.38873203\n",
      " 10.42278952  5.9328901   7.72188571  9.23512522]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.uniform(0.0, 10.5, 250)\n",
    "print (x)\n",
    "\n",
    "plt.hist(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please Enter a Number: 10\n",
      " *\n",
      " * *\n",
      " * * *\n",
      " * * * *\n",
      " * * * * *\n",
      " * * * * * *\n",
      " * * * * * * *\n",
      " * * * * * * * *\n",
      " * * * * * * * * *\n",
      " * * * * * * * * * *\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "x = int(input('Please Enter a Number: '))\n",
    "\n",
    "while i<=x:\n",
    "    print(f\" *\"*i)\n",
    "    i+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please answer this question: 12*8%3\n",
      "\n",
      "Your answer is wrong!!\n",
      "The correct answer is: 0\n"
     ]
    }
   ],
   "source": [
    "name = '12*8%3'\n",
    "answer = 0\n",
    "\n",
    "print(f'Please answer this question: {name}')\n",
    "usr = int(input('Please Enter your answer: '))\n",
    "\n",
    "if answer==usr:\n",
    "    print('Correct answer')\n",
    "else:\n",
    "    print('\\nYour answer is wrong!!')\n",
    "    print(f'The correct answer is: {answer}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The computer has picked a random number from 1-20(both included.)\n",
      "**********\n",
      "GAME STARTS NOW:::\n",
      "**********\n",
      "Please Guess the nimber the computer has picked for you: 10\n",
      "The number is higher! Try again\n",
      "Please Guess the nimber the computer has picked for you: 18\n",
      "The number is higher! Try again\n",
      "Please Guess the nimber the computer has picked for you: 10\n",
      "Well done😘😘! You did it in 3 attempts!!\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "\n",
    "i = 1\n",
    "no = randint(1,20)\n",
    "print('The computer has picked a random number from 1-20(both included.)')\n",
    "print('*'*10)\n",
    "print('GAME STARTS NOW:::')\n",
    "print('*'*10)\n",
    "\n",
    "while i<=3:\n",
    "    user = int(input('Please Guess the nimber the computer has picked for you: '))\n",
    "\n",
    "    if user != no:\n",
    "        if user>no:\n",
    "            print('The number is higher! Try again')\n",
    "        else:\n",
    "            print('The number is lower! Try again!')\n",
    "        if i==3:\n",
    "            print(f'The computer chose {no}')\n",
    "            print('You loose😢😢, please try again!!')\n",
    "    else:\n",
    "        print(f'Well done😘😘! You did it in {i} attempts!!')\n",
    "        break\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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