{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4fcc94e-6e57-450e-8de7-b757834b6d9f",
   "metadata": {},
   "source": [
    "### Here's a class with an `__init__` thingy and a method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2c48975-7736-4f72-9e47-7c9df5b534df",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is an oldtimer.\n"
     ]
    }
   ],
   "source": [
    "class Car:\n",
    "    def __init__(self, brand, year):\n",
    "        self.brand = brand\n",
    "        self.year = year\n",
    "\n",
    "    currentYear = 2025\n",
    "\n",
    "    def isOldTimer(self):\n",
    "        age = 2025 - self.year\n",
    "        if(age > 30):\n",
    "            print(\"This is an oldtimer.\")\n",
    "        else:\n",
    "            print(\"This isn't an oldtimer yet.\")\n",
    "\n",
    "myCar = Car(\"Bentley\", 1967)\n",
    "\n",
    "myCar.isOldTimer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc40be0-a3af-49cf-93e4-14134c75325a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Here's what I learned today about `yield`, _comprehension_, and _sets_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f726fc34-8b87-482a-9100-05d26e2853db",
   "metadata": {},
   "outputs": [],
   "source": [
    "bentley = {\"brand\": \"Bentley\", \"category\": \"sporty luxury vehicles\"}\n",
    "volkswagen = {\"brand\": \"Volkswagen\", \"category\": \"lackluster utilitarian vehicles\"}\n",
    "jaguar = {\"brand\": \"Jaguar\", \"category\": \"sporty luxury vehicles\"}\n",
    "koenig = {\"brand\": \"Koenigsegg\"}\n",
    "default = {\"category\": \"default vehicle\"}\n",
    "\n",
    "cars = [bentley, volkswagen, jaguar, koenig]\n",
    "\n",
    "#A 'comprehension' is a shorthand for defining lists, sets, dictionaries, and tuples\n",
    "brands = [car.get(\"brand\") for car in cars if car.get(\"brand\")]\n",
    "\n",
    "#Here's a comprehension for a set. A set is like a list, but unordered, and it can only have unique values\n",
    "categories = {car.get(\"category\") for car in cars if car.get(\"category\")}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c94a579a-7229-4d19-b445-d70b20dbc731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bentley', 'Volkswagen', 'Jaguar', 'Koenigsegg']\n"
     ]
    }
   ],
   "source": [
    "print(brands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d99825f-4e1c-4846-bc44-3001ea85df75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'lackluster utilitarian vehicles', 'sporty luxury vehicles'}\n"
     ]
    }
   ],
   "source": [
    "print(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59fd4d0b-c9de-44a9-8205-8b8353940481",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bentley\n",
      "Jaguar\n",
      "Koenigsegg\n"
     ]
    }
   ],
   "source": [
    "#'yield' is comparable to 'return' with the difference that it doesn't load entire lists to memory\n",
    "#btw, 'from' allows for a more condensed way of a 'for x in y' statement\n",
    "import time\n",
    "\n",
    "def listBrands():\n",
    "    yield from [brand for brand in brands if not brand.startswith('V')]\n",
    "\n",
    "for brand in listBrands():\n",
    "    print(brand)\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c52158e-1786-4638-a7c7-add61d932459",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}