Sets and Dictionaries.ipynb

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   "source": [
    "# Sets\n",
    "- Set is an unordered collection of items, where every element is unique and immutable. However, the set itself is mutable.\n",
    "- There are two ways of creating a set\n",
    "    - Putting all the elements inside curly braces `{` and `}`\n",
    "    - Using `set()` method.\n",
    "- Adding elements to set - `add()`, `update()`\n",
    "- Removing elements from set - `remove()`, `discard()`\n",
    "- Python sets allow fast insertion, deletion and searching."
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "id": "2d183cb2",
   "metadata": {},
   "source": [
    "![1_WMiNIQ9THariDSJw47uU1w.png](attachment:1_WMiNIQ9THariDSJw47uU1w.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14b787f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {1, 4, 0, 2, 3, 100, 50, 22, 46, -9, -3, -2, 'hello', 'world'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d513facb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f506c8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2, -3, -9, 0, 1, 100, 2, 22, 3, 4, 46, 50, 'hello', 'world'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f92ab53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insertion, deletion, searching in a set - O(1) time\n",
    "# A constant number of operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4ee1764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 is in the set\n"
     ]
    }
   ],
   "source": [
    "if 4 in s:\n",
    "    print(\"4 is in the set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "126f86c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5260465a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2, -3, -9, 0, 1, 100, 2, 22, 3, 4, 46, 50, 'hello', 'world'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7edf6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "s.update([500, 600, 700, 800])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "562291cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2,\n",
       " -3,\n",
       " -9,\n",
       " 0,\n",
       " 1,\n",
       " 100,\n",
       " 2,\n",
       " 22,\n",
       " 3,\n",
       " 4,\n",
       " 46,\n",
       " 50,\n",
       " 500,\n",
       " 600,\n",
       " 700,\n",
       " 800,\n",
       " 'hello',\n",
       " 'world'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5521be0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "s.remove(600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91b501b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2, -3, -9, 0, 1, 100, 2, 22, 3, 4, 46, 50, 500, 700, 800, 'hello', 'world'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a74bf477",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "650",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jn/1bsrct314hv5xmfm7vrcdrzc0000gn/T/ipykernel_4163/1209813310.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m650\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 650"
     ]
    }
   ],
   "source": [
    "s.remove(650)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7811582c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s.discard(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df56b95d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2, -3, -9, 0, 1, 100, 2, 22, 3, 4, 46, 50, 700, 800, 'hello', 'world'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa4180d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "s.discard(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d7a4f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-2, -3, -9, 0, 1, 100, 2, 22, 3, 4, 46, 50, 700, 800, 'hello', 'world'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bfc812d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {\n",
    "    'Hello',\n",
    "    2,\n",
    "    5.6,\n",
    "    (2,5,8)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5dfa8e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(2, 5, 8), 2, 5.6, 'Hello'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3441c965",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee26c86d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fe41bd30",
   "metadata": {},
   "source": [
    "### Make a list of unique tv shows compiling from all your friends' favourite tv shows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3aa6e1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "friend1 = [\n",
    "    'How I Met Your Mother',\n",
    "    'Breaking Bad',\n",
    "    'The Wire'\n",
    "]\n",
    "\n",
    "friend2 = [\n",
    "    'Breaking Bad',\n",
    "    'Westworld',\n",
    "    'The Office'\n",
    "]\n",
    "\n",
    "friend3 = [\n",
    "    'Sopranos',\n",
    "    'Game of Thrones',\n",
    "    'Sherlock'\n",
    "]\n",
    "\n",
    "friend4 = [\n",
    "    'Friends',\n",
    "    'Sherlock',\n",
    "    'Daredevil'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d44d0147",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set()\n",
    "s.update(friend1, friend2, friend3, friend4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b3d39d7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Office, Sherlock, Friends, Sopranos, Breaking Bad, Daredevil, The Wire, How I Met Your Mother, Westworld, Game of Thrones, "
     ]
    }
   ],
   "source": [
    "for tv_series in s:\n",
    "    print(tv_series, end=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6335505",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2eedcba8",
   "metadata": {},
   "source": [
    "### A fifth friend comes along and produces his tv show list. He wants to check which of the shows you have in common with his list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b72e06fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "friend5 = [\n",
    "    'The Office',\n",
    "    'Doctor Who',\n",
    "    'Peaky Blinders'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5839c5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Office\n"
     ]
    }
   ],
   "source": [
    "for tv in friend5:\n",
    "#     searching for tv series in the set\n",
    "    if tv in s:\n",
    "        print(tv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1181ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c76f6eca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce77f340",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de55e687",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "083b2adf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d855e20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02d55af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a0e93cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77af28b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "768120b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "237430b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043c9945",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "05997ab8",
   "metadata": {},
   "source": [
    "# Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65cb9ac2",
   "metadata": {},
   "source": [
    "- They are used to store data in a key-value pair format.\n",
    "- The keys are always unique within a dictionary.\n",
    "- The values of the Python dictionary may or may not be unique.\n",
    "- The values within a dictionary can be of any data type but the thing to note is that the keys are immutable. Hence, the key can only be strings, numbers or tuples.\n",
    "- Dictionaries can be created using the `dict() `constructor or with curly brackets - `{}`\n",
    "- Dictionaries also use **Hashing** to store the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4833f24d",
   "metadata": {},
   "source": [
    "<img src=\"https://media.giphy.com/media/l2Je66zG6mAAZxgqI/giphy.gif\" width=\"280\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa851444",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a823bd6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "52b27a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency = {\n",
    "    'India': 'Rupee',\n",
    "    'USA': 'Dollar',\n",
    "    'Japan': 'Yen',\n",
    "    'Spain': 'Euro',\n",
    "    'Italy': 'Euro'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c812527e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'Dollar',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d39ff230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(currency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "64ce24d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Rupee'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency['India']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e5ed2c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency['USA'] = 'USD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aa549f15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'USD',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b100b68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = (1,2,3)\n",
    "\n",
    "currency[t] = 'ok'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "19558b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'USD',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro',\n",
       " (1, 2, 3): 'ok'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "acbb5bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "del currency[(1, 2, 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d76347d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'USD',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a326aa70",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency['El Salvador'] = set(['USD', 'Bitcoin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8c4ca108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'USD',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro',\n",
       " 'El Salvador': {'Bitcoin', 'USD'}}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e17b8511",
   "metadata": {},
   "outputs": [],
   "source": [
    "currency[None] = 'ok'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4d2448b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'India': 'Rupee',\n",
       " 'USA': 'USD',\n",
       " 'Japan': 'Yen',\n",
       " 'Spain': 'Euro',\n",
       " 'Italy': 'Euro',\n",
       " 'El Salvador': {'Bitcoin', 'USD'},\n",
       " None: 'ok'}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f14c35f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d3a467b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "menu = {\n",
    "    'Dal Makhani': 200,\n",
    "    'Shahi Paneer': 150,\n",
    "    'Naan': 45,\n",
    "    'Roti': 30,\n",
    "    'Rice': 100\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a0c2b0",
   "metadata": {},
   "source": [
    "### Order ?\n",
    "Is order maintained in Python Dictionaries ?\n",
    "\n",
    "All Python versions <= 3.6      -> unordered dictionaries\n",
    "All Python versions >= 3.7      -> ordered dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3d0ffc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5f964fb5",
   "metadata": {},
   "source": [
    "## Iterating over a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "45134464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dal Makhani': 200, 'Shahi Paneer': 150, 'Naan': 45, 'Roti': 30, 'Rice': 100}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "df704b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Dal Makhani', 'Shahi Paneer', 'Naan', 'Roti', 'Rice'])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c4272025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([200, 150, 45, 30, 100])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ba8128e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5e58612",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "211e66a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3227b09c",
   "metadata": {},
   "source": [
    "## Challenge - Check Palindrome!\n",
    "https://www.interviewbit.com/problems/check-palindrome/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "38fee609",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve(A):\n",
    "    # declare a frequency map (dictionary)\n",
    "    freq = {}\n",
    "\n",
    "    # O(n) time\n",
    "    for char in A:\n",
    "        if char in freq:\n",
    "            freq[char] += 1\n",
    "        else:\n",
    "            freq[char] = 1\n",
    "        print(freq)\n",
    "\n",
    "    # Freq map created\n",
    "\n",
    "\n",
    "    odd_frequencies = 0\n",
    "\n",
    "    # O(n)\n",
    "    for frequency in freq.values():\n",
    "        if frequency % 2 == 1:\n",
    "            odd_frequencies += 1\n",
    "\n",
    "\n",
    "    if odd_frequencies <= 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "    \n",
    "# time complexity - O(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "be6ad7c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 1}\n",
      "{'a': 1, 'b': 1}\n",
      "{'a': 2, 'b': 1}\n",
      "{'a': 2, 'b': 2}\n",
      "{'a': 2, 'b': 2, 'c': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(\"ababc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6497a202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(\"ab\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c144904",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1486c568",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bda9db2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcccc2fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55991262",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "46b7b9a5",
   "metadata": {},
   "source": [
    "# 2D Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1f7dd2",
   "metadata": {},
   "source": [
    "![](https://media.giphy.com/media/quEsMOrr3hmQ8/giphy.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "071f1fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1, 4, 9, 2, [1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8b460363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 2, [1, 2]]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "17a34ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9c0eb370",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8],\n",
    "    [9, 10, 11, 12]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "698713c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "641e9088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]  # <- first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "68611c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1]   # <- second row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8820abc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "365e02e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cc9a50bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "71e5e507",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8],\n",
    "    [9, 10, 11, 12]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f38e8955",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8],\n",
    "    [9, 10, 11, 12]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f63c4dc",
   "metadata": {},
   "source": [
    "## Iterating over 2D arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d657c4b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 4\n",
      "5 6 7 8\n",
      "9 10 11 12\n"
     ]
    }
   ],
   "source": [
    "for row in a:\n",
    "    print(*row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6895281a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4 9 16 \n",
      "25 36 49 64 \n",
      "81 100 121 144 \n"
     ]
    }
   ],
   "source": [
    "for row in a:\n",
    "    for x in row:\n",
    "        print(x ** 2, end=' ')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a4218e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee726380",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e8696d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7630a6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f3b9082",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ce33b029",
   "metadata": {},
   "source": [
    "## Reading 2D Input\n",
    "Let us say we are given an input as follows where the number of rows and columns is given first and then the 2D matrix:\n",
    "```\n",
    "2 4\n",
    "1 2 3 4\n",
    "5 6 7 8\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "736cd45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 4\n"
     ]
    }
   ],
   "source": [
    "r, c = map(int, input().split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "eb2ebe66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 4\n",
      "5 6 7 8\n"
     ]
    }
   ],
   "source": [
    "a = []\n",
    "\n",
    "# read the input row-by-row\n",
    "# run loop r times\n",
    "for i in range(r):\n",
    "    row = list(map(int, input().split()))\n",
    "    a.append(row)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e538ad0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3, 4], [5, 6, 7, 8]]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "abf89d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "a91f6d14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(3/4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1ca3552",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "24939cac",
   "metadata": {},
   "source": [
    "## Problems to Practice\n",
    "\n",
    "#### Easy\n",
    "1. Addition of two matrices\n",
    "2. Subtraction of two matrices\n",
    "\n",
    "\n",
    "#### Intermediate\n",
    "1. Compute determinant of a square matrix\n",
    "2. Compute product of two matrices (given they are product compatible)\n",
    "\n",
    "#### Hard\n",
    "1. Compute the inverse of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36542365",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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