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Fred Baptiste
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Added Descriptors section
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‎Part 4/Section 08 - Descriptors/01 - Descriptors.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Descriptors"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Python **descriptors** are simply objects that implement the **descriptor protocol**.\n",
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"\n",
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"The protocol is comprised of the following special methods - not all are required.\n",
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"- `__get__`: used to retrieve the property value\n",
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"- `__set__`: used to store the property value (we'll see where we can do this in a bit)\n",
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"- `__del__`: delete a property from the instance\n",
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"- `__set_name__`: new to Python 3.6, we can use this to capture the property name as it is being defined in the owner class (the class where the property is defined).\n",
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"\n",
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"There are two types of descriptors we need to distingush as I explain in the video lecture:\n",
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"- non-data descriptors: these are descriptors that only implement `__get__` (and optionally `__set_name__`)\n",
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"- data descriptors: these implement the `__set__` method, and normally, also the `__get__` method."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's create a simple non-data descriptor:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"\n",
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"class TimeUTC:\n",
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" def __get__(self, instance, owner_class):\n",
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" return datetime.utcnow().isoformat()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"So `TimeUTC` is a class that implements the `__get__` method only, and is therefore considered a non-data descriptor."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now use it to create properties in other classes:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Logger:\n",
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" current_time = TimeUTC()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note that `current_time` is a class attribute:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"mappingproxy({'__module__': '__main__',\n",
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" 'current_time': <__main__.TimeUTC at 0x7fdcd84bbd68>,\n",
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" '__dict__': <attribute '__dict__' of 'Logger' objects>,\n",
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" '__weakref__': <attribute '__weakref__' of 'Logger' objects>,\n",
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" '__doc__': None})"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Logger.__dict__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can access that attribute from an instance of the `Logger` class:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"l = Logger()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'2019-07-13T20:47:06.391770'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"l.current_time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also access it from the class itself, and for now it behaves the same (we'll come back to that later):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'2019-07-13T20:47:06.405059'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Logger.current_time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's consider another example."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Suppose we want to create class that allows us to select a random suit and random card from that suit from a deck of cards (with replacement, i.e. the same card can be picked more than once)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We could approach it this way:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from random import choice, seed\n",
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"\n",
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"class Deck:\n",
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" @property\n",
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" def suit(self):\n",
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" return choice(('Spade', 'Heart', 'Diamond', 'Club'))\n",
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" \n",
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" @property\n",
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" def card(self):\n",
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" return choice(tuple('23456789JQKA') + ('10',))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = Deck()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8 Club\n",
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"2 Diamond\n",
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"J Club\n",
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"8 Diamond\n",
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"9 Diamond\n",
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"Q Heart\n",
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"J Heart\n",
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"6 Heart\n",
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"10 Spade\n",
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"Q Diamond\n"
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]
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}
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],
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"source": [
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"seed(0)\n",
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"\n",
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"for _ in range(10):\n",
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" print(d.card, d.suit)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This was pretty easy, but as you can see both properties essentially did the same thing - they picked a random choice from some iterable.\n",
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"\n",
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"Let's rewrite this using a custom descriptor:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Choice:\n",
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" def __init__(self, *choices):\n",
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" self.choices = choices\n",
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" \n",
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" def __get__(self, instance, owner_class):\n",
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" return choice(self.choices)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And now we can rewrite our `Deck` class this way:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Deck:\n",
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" suit = Choice('Spade', 'Heart', 'Diamond', 'Club')\n",
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" card = Choice(*'23456789JQKA', '10')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8 Club\n",
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"2 Diamond\n",
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"J Club\n",
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"8 Diamond\n",
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"9 Diamond\n",
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"Q Heart\n",
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"J Heart\n",
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"6 Heart\n",
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"10 Spade\n",
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"Q Diamond\n"
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]
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}
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],
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"source": [
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"seed(0)\n",
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"\n",
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"d = Deck()\n",
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"\n",
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"for _ in range(10):\n",
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" print(d.card, d.suit)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Of course we are not limited to just cards, we could use it in other classes too:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Dice:\n",
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" die_1 = Choice(1,2,3,4,5,6)\n",
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" die_2 = Choice(1,2,3,4,5,6)\n",
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" die_3 = Choice(1,2,3,4,5,6)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4 4 1\n",
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"3 5 4\n",
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"4 3 4\n",
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"3 5 2\n",
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"5 2 3\n",
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"2 1 5\n",
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"3 5 6\n",
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"5 2 3\n",
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"1 6 1\n",
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"6 3 4\n"
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]
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}
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],
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"source": [
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"seed(0)\n",
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"\n",
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"dice = Dice()\n",
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"for _ in range(10):\n",
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" print(dice.die_1, dice.die_2, dice.die_3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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"nbformat": 4,
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"nbformat_minor": 2
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