Common Data Structures in Python - Dictionaries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each data structure provides a particular way of organizing data so it can be accessed effificently, depending on your use case.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries, Maps, and Hashtables\n",
    "\n",
    "- Dictionaries are also often called maps, hashmaps, lookup tables, or associative arrays. They allow for the efficient lookup, insertion and deletion of any object associated with a given key.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4321\n",
      "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}\n"
     ]
    }
   ],
   "source": [
    "# Python provides some userful \"Syntactic Sugar\" for working with dictionaries in your programs. For example, \n",
    "# the curly-braces dictionary expression syntax and dictionary comprehensions allow you to conveniently define\n",
    "# new dictionary objects\n",
    "\n",
    "phonebook = {\n",
    "    'bob'   : 1234,\n",
    "    'alice' : 4321,\n",
    "    \n",
    "}\n",
    "\n",
    "squares = {x: x * x for x in range(6)}\n",
    "print(phonebook['alice'])\n",
    "print(squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3789705017596477050)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hash(3), hash((2,3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are some restrictions on which objects can be used as valid keys.\n",
    "- Python's dictionaries are indexed by keys that can be of any hashable type. A hashable object has a hash value which never changes during its lifetime, and it can be compared to other objects. In addition, hashable objects which compare as equal must have the same value.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hashable\n",
    "\n",
    "\n",
    "An object is hashable if it has a hash value which never changes during its lifetime (it needs a [\\__hash\\__()](https://docs.python.org/3/reference/datamodel.html#object.__hash__) method), and can be compared to other objects (it needs an [\\__eq\\__()](https://docs.python.org/3/reference/datamodel.html#object.__eq__) method). Hashable objects which compare equal must have the same hash value.\n",
    "\n",
    "_**Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally**_.\n",
    "\n",
    "_**All of Python’s immutable built-in objects are hashable; mutable containers (such as lists or dictionaries) are not**_.\n",
    "\n",
    "_**Objects which are instances of user-defined classes are hashable by default. They all compare unequal (except with themselves), and their hash value is derived from their [id()](https://docs.python.org/3/library/functions.html#id)**_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### id(object)\n",
    "\n",
    "- Return the \"identity\" of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.\n",
    "\n",
    "- _**CPython implementation detail: This is the address of the object in memory**_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### hash(object)\n",
    "\n",
    "- Returns the hash value of the object(if it has one). Hash values are integers. They are used to quickly compare dictionary keys during a dicionary lookup. Numerical values that compare equal have the same hash value ( even if they are of different types, as in the case for 1 and 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({1}, {1: 13})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([1,1.0]), {1:12, 1.0:13}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-a85459af7b3f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtset\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "tset = set([[1],2,3])\n",
    "tset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107472427647 2 1995989184\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "class AnInteger:\n",
    "    val = None\n",
    "    def __init__( self, x ):\n",
    "        self.val = x\n",
    "print(hash( AnInteger(2)), hash(2), id(2))\n",
    "tobj = AnInteger(2)\n",
    "print(tobj.val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9223371900877922132, 2175629658824)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class tClass:\n",
    "    pass\n",
    "tobj = tClass()\n",
    "hash(tobj), id(tobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2175628124744, 2175628124744)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id([1,2,3]), id([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 3\n",
      "1719558841680 1719558841792\n",
      "107472427605 107472427612\n"
     ]
    }
   ],
   "source": [
    "tobj1 = AnInteger(2)\n",
    "tobj2 = AnInteger(3)\n",
    "print(tobj1.val, tobj2.val)\n",
    "print(id(tobj1), id(tobj2))\n",
    "print(hash(tobj1), hash(tobj2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 2\n",
      "1719558841232 1719558841008\n",
      "107472427577 107472427563\n"
     ]
    }
   ],
   "source": [
    "tobj1 = AnInteger(2)\n",
    "tobj2 = AnInteger(2)\n",
    "print(tobj1.val, tobj2.val)\n",
    "print(id(tobj1), id(tobj2))\n",
    "print(hash(tobj1), hash(tobj2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hash(\"viswanath\") == hash(\"viswanath\"), id(\"viswanath\") == id(\"viswanath\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Immtable types like strings and numbers are hashable and work well as dictionary keys. You can also use tuple objects as dictionary keys, as long as they contain only hashable types themselves.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'key': 2}\n",
      "{2: 2}\n",
      "{(1, 2): [1, 2]}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'set'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-f84cd07b92bb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# So set members need to hashable but not set\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'set'"
     ]
    }
   ],
   "source": [
    "print({'key': 2})\n",
    "print({2:2})\n",
    "print({(1,2): [1,2]})\n",
    "print({set([1,2]): [1,2]}) # So set members need to hashable but not set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False True\n",
      "False True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2528502973977326415, 2528502973977326415)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print([1,2,3] is [1,2,3], [1,2,3] == [1,2,3])\n",
    "print((1,2,3) is (1,2,3), (1,2,3) == (1,2,3))\n",
    "hash((1,2,3)), hash((1,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "import sys; print(sys.hash_info.width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For most use cases, Python's built-in dictionary implementation will do everything you need. Dictionaries are highly optimized and underlie many parts of the language, for example class attributes and variables in a stack frame are both stored internally in dictionaries.\n",
    "\n",
    "- Python dictionaries are based on well-tested and finely tuned hash table implementations that provides the performance charcterstics you would expect: O(1) time complexity for lookup, insert, update, and delete operations in the average case.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### collections.OrderedDict - Remembers the Insertion Order of Keys\n",
    "\n",
    "- While standard dict instances preserve the insertion order of keys in CPython 3.6 and above, this is just a side effect of the CPython implementation and is not defined in the language spec. So, if key order is important for your algorithm to work, it's best to communicate this clearly by explicitly using the OrderDict class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('one', 1), ('two', 2), ('three', 3), ('four', 4)])\n",
      "odict_keys(['one', 'two', 'three', 'four'])\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "d2 = collections.OrderedDict(one=1, two=2, three=3)\n",
    "d2['four'] = 4\n",
    "print(d2)\n",
    "print(d2.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {'dogs': ['1234']})\n",
      "['1234']\n"
     ]
    }
   ],
   "source": [
    "# collections.defaultdict\n",
    "\n",
    "from collections import defaultdict\n",
    "dd = defaultdict(list)\n",
    "\n",
    "# Access a missing key creates it and initializes it using the default factory, i.e. list() in this example\n",
    "dd['dogs'].append('1234')\n",
    "print(dd)\n",
    "print(dd['dogs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChainMap({'one': 1, 'two': 2}, {'three': 3, 'four': 4})\n",
      "3\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'missing'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-6b94af880429>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'three'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'missing'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\collections\\__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    881\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 883\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m            \u001b[1;31m# support subclasses that define __missing__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    884\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\collections\\__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'missing'"
     ]
    }
   ],
   "source": [
    "# collections.ChainMap - Search Multiple Dictionaries as a Single Mapping\n",
    "\n",
    "from collections import ChainMap\n",
    "\n",
    "dict1 = {'one':1, 'two':2}\n",
    "dict2 = {'three':3, 'four': 4}\n",
    "chain = ChainMap(dict1, dict2)\n",
    "print(chain)\n",
    "print(chain['three'])\n",
    "print(chain['missing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2}\n",
      "{'one': 1, 'two': 2}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'mappingproxy' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-78-636197f27ac3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mread_only\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMappingProxyType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwritable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mread_only\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mread_only\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'one'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m11\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'mappingproxy' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# types.MappingProxyType - A wrapper for Making Read-only dictionaries\n",
    "\n",
    "# MappingProxyType is a wrapper around a standard dictionary that provides a read-only view into read-only view into the \n",
    "# wrapped dictionary's data. This class is added in Python 3.3 and it can be used to create immutable proxy versions\n",
    "# of dictionaries.\n",
    "\n",
    "from types import MappingProxyType\n",
    "\n",
    "writable = {'one':1, 'two':2}\n",
    "print(writable)\n",
    "\n",
    "read_only = MappingProxyType(writable)\n",
    "print(read_only)\n",
    "read_only['one'] = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2} {'one': 1, 'two': 2}\n"
     ]
    }
   ],
   "source": [
    "print(read_only, writable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3} {'one': 1, 'two': 2, 'three': 3}\n"
     ]
    }
   ],
   "source": [
    "writable['three'] = 3\n",
    "print(writable, read_only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 2, 'two': 2, 'three': 3} {'one': 2, 'two': 2, 'three': 3}\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "writable['one'] = 1 + 1\n",
    "print(writable, read_only)\n",
    "print(read_only['three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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