OSCon 2020 -- Live-coding Python and design patterns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design patterns in Python\n",
    "\n",
    "Reuven Lerner • https://lerner.co.il/ \n",
    "\n",
    "E-mail: reuven@lerner.co.il • Twitter: [@reuvenmlerner](https://Twitter.com/reuvenmlerner)\n",
    "\n",
    "This notebook is at https://github.com/reuven/oscon-2020, updated every 1-2 min!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Creational -- ways to create new objects\n",
    "2. Structural -- ways to arrange a number of objects for better, cleaner communication\n",
    "3. Behavioral -- divide up algorithms for easier maintenance and understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterator pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for one_letter in 'abcd':\n",
    "    print(one_letter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "for one_element in [10, 20, 30]:\n",
    "    print(one_element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n",
      "a\n",
      "b\n",
      "c\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "# create a new class that's iterable\n",
    "\n",
    "class MyIterIterator:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.index = 0\n",
    "\n",
    "    def __next__(self):\n",
    "        if self.index >= len(self.data):\n",
    "            raise StopIteration\n",
    "            \n",
    "        value = self.data[self.index]\n",
    "        self.index += 1\n",
    "        return value\n",
    "    \n",
    "\n",
    "class MyIter:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return MyIterIterator(self.data)\n",
    "    \n",
    "        \n",
    "m = MyIter('abcd')\n",
    "\n",
    "for one_item in m:\n",
    "    print(one_item)\n",
    "    \n",
    "for one_item in m:\n",
    "    print(one_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.MyIterIterator at 0x10c7b0790>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iter(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.MyIterIterator at 0x10c7b0be0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iter(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "e\n",
      "a\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "# create a new class that's iterable\n",
    "\n",
    "class VowelsOnlyIterator:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.index = 0\n",
    "\n",
    "    def __next__(self):\n",
    "        while True:            \n",
    "            if self.index >= len(self.data):\n",
    "                raise StopIteration\n",
    "\n",
    "            value = self.data[self.index]\n",
    "            self.index += 1\n",
    "            if value in 'aeiou':\n",
    "                return value\n",
    "    \n",
    "\n",
    "class VowelsOnly:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return VowelsOnlyIterator(self.data)\n",
    "    \n",
    "        \n",
    "m = VowelsOnly('abcde')\n",
    "\n",
    "for one_item in m:\n",
    "    print(one_item)\n",
    "    \n",
    "for one_item in m:\n",
    "    print(one_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuven\n",
      "[Logger]: Updated name to Someone else\n",
      "[NetworkLogger]: Updated name to Someone else\n",
      "Someone else\n"
     ]
    }
   ],
   "source": [
    "class Logger:\n",
    "    def print_log(self, message):\n",
    "        print(f'[Logger]: {message}')\n",
    "\n",
    "\n",
    "class NetworkLogger:\n",
    "    def print_log(self, message):\n",
    "        print(f'[NetworkLogger]: {message}')\n",
    "        \n",
    "class Person:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.observers = []\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self.name\n",
    "    \n",
    "    def set_name(self, new_name):\n",
    "        self.name = new_name\n",
    "        for one_observer in self.observers:\n",
    "            one_observer.print_log(f'Updated name to {new_name}')\n",
    "        \n",
    "    def register(self, new_observer):\n",
    "        self.observers.append(new_observer)\n",
    "        \n",
    "p = Person('Reuven')\n",
    "print(p.get_name())\n",
    "\n",
    "logger = Logger()\n",
    "nl = NetworkLogger()\n",
    "p.register(logger)\n",
    "p.register(nl)\n",
    "\n",
    "\n",
    "p.set_name('Someone else')\n",
    "print(p.get_name())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adapter\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'symbol': 'PYTHON', 'price': 86, 'time': 1592516293.736522}\n",
      "{'symbol': 'PYTHON', 'price': 23, 'time': 1592516293.73679}\n",
      "{'symbol': 'PYTHON', 'price': 78, 'time': 1592516293.73682}\n",
      "{'symbol': 'PYTHON', 'price': 7, 'time': 1592516293.73684}\n",
      "{'symbol': 'PYTHON', 'price': 45, 'time': 1592516293.7368581}\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import random\n",
    "\n",
    "class StockMarket:\n",
    "    def get_price(self, symbol):\n",
    "        return {'symbol':symbol,\n",
    "               'price':random.randint(0,100),\n",
    "               'time':time.time()}\n",
    "    \n",
    "class StockClient:\n",
    "    def __init__(self, service):\n",
    "        self.service = service\n",
    "        self.history = []\n",
    "        \n",
    "    def get_price(self, symbol):\n",
    "        price = self.service.get_price(symbol)\n",
    "        self.history.append(price)\n",
    "        return price\n",
    "    \n",
    "sm = StockMarket()\n",
    "c = StockClient(sm)\n",
    "\n",
    "for i in range(5):\n",
    "    print(c.get_price('PYTHON'))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quote(symbol='RUBY', price=23, time=1592516447.627787, delta=4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "Quote = namedtuple('Quote', ['symbol', 'price', 'time', 'delta'])\n",
    "\n",
    "class NewMarket:\n",
    "    def get_latest_price(self, symbol):\n",
    "        return Quote(symbol, \n",
    "                    random.randint(0, 100),\n",
    "                    time.time(),\n",
    "                    random.randint(0, 10))\n",
    "    \n",
    "    \n",
    "nm = NewMarket()\n",
    "nm.get_latest_price('RUBY')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StockAdapter:\n",
    "    def __init__(self, service):\n",
    "        self.service = service\n",
    "        \n",
    "    def get_price(self, symbol):\n",
    "        nt = self.service.get_latest_price(symbol)\n",
    "        \n",
    "        return {'symbol': nt.symbol,\n",
    "                'price': nt.price,\n",
    "                'time': nt.time}\n",
    "    \n",
    "c = StockClient(StockAdapter(NewMarket()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'symbol': 'RUBY', 'price': 60, 'time': 1592516565.062718}\n",
      "{'symbol': 'RUBY', 'price': 55, 'time': 1592516565.062887}\n",
      "{'symbol': 'RUBY', 'price': 85, 'time': 1592516565.062912}\n",
      "{'symbol': 'RUBY', 'price': 100, 'time': 1592516565.062933}\n",
      "{'symbol': 'RUBY', 'price': 99, 'time': 1592516565.062953}\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(c.get_price('RUBY'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Singleton\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Singleton:\n",
    "    _instance = None  # class attribute\n",
    "    \n",
    "    def __new__(cls, *args, **kwargs):\n",
    "        if Singleton._instance is None:\n",
    "            Singleton._instance = object.__new__(cls)\n",
    "        return Singleton._instance\n",
    "        \n",
    "\n",
    "s1 = Singleton()\n",
    "s2 = Singleton()\n",
    "\n",
    "s1 is s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.x = 10\n",
    "s2.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.x = 20\n",
    "s1.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(None)() is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a choice: a\n",
      "I'm in a!\n",
      "Enter a choice: b\n",
      "I'm in b!\n",
      "Enter a choice: a\n",
      "I'm in a!\n",
      "Enter a choice: b\n",
      "I'm in b!\n",
      "Enter a choice: c\n",
      "No such function c\n",
      "Enter a choice: \n"
     ]
    }
   ],
   "source": [
    "def a():\n",
    "    return \"I'm in a!\"\n",
    "\n",
    "def b():\n",
    "    return \"I'm in b!\"\n",
    "\n",
    "funcs = {'a':a, \n",
    "        'b':b}\n",
    "\n",
    "while True:\n",
    "    choice = input(\"Enter a choice: \")\n",
    "    \n",
    "    if not choice:\n",
    "        break\n",
    "        \n",
    "    if choice in funcs:\n",
    "        print(funcs[choice]())\n",
    "        \n",
    "    else:\n",
    "        print(f'No such function {choice}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1251249d0>"
      ]
     },
     "execution_count": 35,
     "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": [
    "s = Series([10, 20, 30, 40, 50])\n",
    "s.plot.line()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}