Python 3.6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Mesdames, Messieurs, voici ``python 3.6``\n",
    "\n",
    "Mais ne me croyez pas sur parole, on va vérifier\n",
    "\n",
    "(présentation en avant-première !)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.0b4 (default, Nov 25 2016, 08:08:08) \n",
      "[GCC 5.4.0 20160609]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Yay\n",
    "\n",
    "Bon quoi de neuf, du coup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Les Strings formattées encore améliorées"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Vous connaissez déjà la syntaxe suivante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonjour le dernier Meetup Python de l'année 2016\n"
     ]
    }
   ],
   "source": [
    "language = \"Python\"\n",
    "year = 2016\n",
    "\n",
    "template = \"Bonjour le dernier Meetup %(language)s de l'année %(year)d\"\n",
    "\n",
    "print(template % {\"language\": language, \"year\": year})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Vous avez peut-être l'habitude d'utiliser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonjour le dernier Meetup Python de l'année 2016\n"
     ]
    }
   ],
   "source": [
    "template = \"Bonjour le dernier Meetup %s de l'année %d\"\n",
    "print(template % (language, year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais c'est long à taper.\n",
    "\n",
    "Ça c'était en Python 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Vous connaissez aussi la nouvelle syntaxe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonjour le dernier Meetup Python de l'année 2016\n"
     ]
    }
   ],
   "source": [
    "template = \"Bonjour le dernier Meetup {language} de l'année {year}\"\n",
    "\n",
    "print(template.format(language=language, year=year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais c'est toujours long à taper..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Et peut-etre même savez-vous que ca permet de faire des choses merveilleuses comme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~ Bonjour Python ! ~~~~~~\n"
     ]
    }
   ],
   "source": [
    "print(\"{:~^30}\".format(\" Bonjour Python ! \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-11-28\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(\"{:%Y-%m-%d}\".format(datetime.now()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(protip : plongez vous dans les arcanes du ``.format`` sur http://pyformat.info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Et maintenant vous avez... (roulement de tambours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bonjour le dernier Meetup Python de 2016'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Bonjour le dernier Meetup {language} de {year}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bonjour le dernier Meetup Python de 2016'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Bonjour le dernier Meetup {language} de {datetime.now():%Y}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Les annotations de types pour les variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python, circa 2014 (a.k.a Python 3.4-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average grade: 3.6\n"
     ]
    }
   ],
   "source": [
    "first_grade = 3\n",
    "grades = [first_grade, 2, 5.7]\n",
    "\n",
    "class Student:\n",
    "    notes = []\n",
    "    \n",
    "    def average(self, multiplier=1):\n",
    "        if not self.grades:\n",
    "            return \"Aucune note\"\n",
    "        \n",
    "        average = sum(self.grades) / len(self.grades)\n",
    "        average *= multiplier\n",
    "        return \"Average grade: {:.1f}\".format(average)\n",
    "    \n",
    "s = Student()\n",
    "s.grades = grades\n",
    "print(s.average())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python, circa 2015 (a.k.a Python 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average grade: 3.6\n"
     ]
    }
   ],
   "source": [
    "first_grade = 3\n",
    "grades = [first_grade, 2, 5.7]\n",
    "\n",
    "class Student:\n",
    "    notes = []\n",
    "    \n",
    "    def average(self, multiplier: float=1) -> str:\n",
    "        if not self.grades:\n",
    "            return \"Aucune note\"\n",
    "        \n",
    "        average = sum(self.grades) / len(self.grades)\n",
    "        average *= multiplier\n",
    "        return \"Average grade: {:.1f}\".format(average)\n",
    "    \n",
    "s = Student()\n",
    "s.grades = grades\n",
    "print(s.average())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python, circa 2016 (a.k.a Python 3.6+)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average grade: 3.6\n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "\n",
    "first_grade: int = 3\n",
    "grades: List[float] = [first_grade, 2, 5.7]\n",
    "\n",
    "class Student:\n",
    "    notes: List[float] = []\n",
    "    \n",
    "    def average(self, multiplier: float=1) -> str:\n",
    "        if not self.grades:\n",
    "            return \"Aucune note\"\n",
    "        \n",
    "        average: float = sum(self.grades) / len(self.grades)\n",
    "        average *= multiplier\n",
    "        return f\"Average grade: {average:.1f}\"\n",
    "    \n",
    "s:Student = Student()\n",
    "s.grades = grades\n",
    "print(s.average())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Des grands nombres enfin lisibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000000000000\n",
      "FFFFFFFF\n"
     ]
    }
   ],
   "source": [
    "print(1_000_000_000_000_000)\n",
    "\n",
    "print(f\"{0x_FF_FF_FF_FF:X}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Des nouvelles méthodes magiques "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Pour l'héritage..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<class '__main__.Plugin1'>, <class '__main__.Plugin2'>]\n"
     ]
    }
   ],
   "source": [
    "class PluginBase:\n",
    "    subclasses = []\n",
    "\n",
    "    def __init_subclass__(cls, **kwargs):\n",
    "        super().__init_subclass__(**kwargs)\n",
    "        cls.subclasses.append(cls)\n",
    "\n",
    "class Plugin1(PluginBase):\n",
    "    pass\n",
    "\n",
    "class Plugin2(PluginBase):\n",
    "    pass\n",
    "\n",
    "print(PluginBase.subclasses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "... et pour la composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class IntField:\n",
    "    # this is the new initializer:\n",
    "    def __set_name__(self, owner, name):\n",
    "        self.model = owner\n",
    "        self.name = name\n",
    "        \n",
    "    def whats_your_name(self):\n",
    "        print(f\"{self.model} m'a nommé {self.name}\")\n",
    "\n",
    "class ModelA:\n",
    "    value_a = IntField()\n",
    "    \n",
    "class ModelB:\n",
    "    value_b = IntField()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.ModelA'> m'a nommé value_a\n",
      "<class '__main__.ModelB'> m'a nommé value_b\n"
     ]
    }
   ],
   "source": [
    "ModelA().value_a.whats_your_name()\n",
    "ModelB().value_b.whats_your_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Toujours plus d'asynchrone\n",
    "\n",
    "L'API d'asyncio est stabilisée et on peut maintenant créer des générateurs asynchrones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ``pathlib`` amélioré !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Spotify-AdKiller', 'beautifuldjango.github.io', 'agency-jekyll-theme', 'django', 'deps', 'theano', 'test', 'req.txt', 'jupyter']\n"
     ]
    }
   ],
   "source": [
    "import os, pathlib\n",
    "\n",
    "path = pathlib.Path() / \"..\"\n",
    "\n",
    "print(os.listdir(path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.TextIOWrapper name='../jupyter/Python 3.6.ipynb' mode='r' encoding='UTF-8'>\n"
     ]
    }
   ],
   "source": [
    "path = path / \"jupyter\" / \"Python 3.6.ipynb\"\n",
    "\n",
    "print(open(path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../jupyter\n"
     ]
    }
   ],
   "source": [
    "print(path.parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".ipynb\n"
     ]
    }
   ],
   "source": [
    "print(path.suffix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.6\n"
     ]
    }
   ],
   "source": [
    "print(path.stem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python connait ``tau`` !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "1.00, -0.00\n"
     ]
    }
   ],
   "source": [
    "from math import tau, pi, cos, sin\n",
    "\n",
    "print(tau == 2 * pi)\n",
    "print(f\"{cos(tau):.2f}, {sin(tau):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Les dictionnaires sont maintenant ordonnés "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bonus : ils sont plus compacts en mémoire !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list({i: i for i in range(100)}) == list(range(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Le module ``secrets`` pour générer des clés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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n"
     ]
    }
   ],
   "source": [
    "import secrets\n",
    "print(f\"{secrets.randbits(2048):X}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# En route pour la 3.7 !\n",
    "## Merci de votre attention. Des questions ?\n",
    "\n",
    "Novembre 2016 - Joachim Jablon (@ewjoachim) - PeopleDoc (on recrute !)\n",
    "![](http://cdn2.hubspot.net/hubfs/468550/assets/logo.png?t=1453755414564)\n",
    "\n",
    "_Réalisation technique des slides: Jupyter Notebook et Reveal.js_"
   ]
  }
 ],
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   "display_name": "Python 3",
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