Python Data Types .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4561bcc4",
   "metadata": {},
   "source": [
    "# Python Data Types"
   ]
  },
  {
   "cell_type": "raw",
   "id": "47e95a1f",
   "metadata": {},
   "source": [
    "1) Numeric \n",
    "    i) integer\n",
    "    ii) floating point\n",
    "    iii) complex numbers\n",
    "2) Strings\n",
    "    i) Concatenation\n",
    "    ii) Slicing\n",
    "    iii) Repetition\n",
    "3) Tuple\n",
    "4) List\n",
    "5) Set\n",
    "6) Dictionary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb739485",
   "metadata": {},
   "source": [
    "# 1) Numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efa6e4a",
   "metadata": {},
   "source": [
    "   ### i) Integer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adc5ee3",
   "metadata": {},
   "source": [
    "* **An integer is a non fraction number that can be positve, negative and zero like, 1,2,3,100,1000,0,-1,-100,-1199, -9999**\n",
    "* **In python language integer is represented as a data type 'int'**\n",
    "* **there is no limit on how long the integer value is**\n",
    "\n",
    "* **int class tells the program that the given information/data is in integer from and act accordingly**\n",
    "* **FOR EXAMPLE 'int' SHOWS THAT WE CAN PERFOM NUMERIC OPERATIONS ON THE GIVEN DATA**\n",
    "* **NUMERIC OPERATION COULD BE +, -, x, OR /**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "6182d1a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124\n"
     ]
    }
   ],
   "source": [
    "print(124)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "2f79d93e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193898938493974\n"
     ]
    }
   ],
   "source": [
    "print(193898938493974)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "a2cc17b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000000000124\n"
     ]
    }
   ],
   "source": [
    "print(10000000000000 + 124)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "b4a2b84e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000000000000000000000000000000000000000000000000000000000000000000000000001\n"
     ]
    }
   ],
   "source": [
    "print(1000000000000000000000000000000000000000000000000000000000000000000000000000000 + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "0dd17f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afd0ea5",
   "metadata": {},
   "source": [
    "* In Python Integers are represented by **'int'** class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e3f7a2",
   "metadata": {},
   "source": [
    "* Python can handle a platheora of integers (ofcourse it depends on the system memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29a39d5",
   "metadata": {},
   "source": [
    "### ii) Floating Point Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee72b49",
   "metadata": {},
   "source": [
    "****A float or floating point is a number with a decimal point****\n",
    "   * **Examples include 1.22,0.100,-9.8, -.19.98, 0.000001345 etc**\n",
    "   * **float tells the program that a given piece of information belongs to 'float' class and it has values with decimal point**\n",
    "   \n",
    "**Very small or very large values are often represented in scientific notation (e or E)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "eefa880a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.29\n"
     ]
    }
   ],
   "source": [
    "print(1.29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "bbacef02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.98\n"
     ]
    }
   ],
   "source": [
    "print(-11.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "036789ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.99924\n"
     ]
    }
   ],
   "source": [
    "print(-0.99924)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "0ecc86f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1096\n"
     ]
    }
   ],
   "source": [
    "print(0.123 - 0.0134)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "aa39608a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0898493248109808\n"
     ]
    }
   ],
   "source": [
    "print(0.08984932481098091858798259138401924097859822034839418388798798347242439847 + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "231c32ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120000000.0\n"
     ]
    }
   ],
   "source": [
    "print(1.2e8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "42b4c16b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.01920981821e-13\n"
     ]
    }
   ],
   "source": [
    "print(0.000000000000901920981821)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "8f61093f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0012\n"
     ]
    }
   ],
   "source": [
    "print(1.2e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "de91f511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.67e-19\n"
     ]
    }
   ],
   "source": [
    "print(0.000000000000000000167)  # Charge on an electron/proton "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "833cbd77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300000000.0\n"
     ]
    }
   ],
   "source": [
    "print(3e8)                     # Speed of light"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "a825c453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(3.1415)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462eb6dd",
   "metadata": {},
   "source": [
    "**In Python floating point numbers are represented by the class 'float'**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca23bc",
   "metadata": {},
   "source": [
    "### iii) Complex Number"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1756c7e0",
   "metadata": {},
   "source": [
    "****Complex numbers are numbers that contain both real part and imaginary part****\n",
    "* **Examples include 8+3j where 8 is a real part and 3j is an imaginary part**\n",
    "* **Other examples include 1-5j, -1-2j, -23+33j etc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f1e011e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8+3j)\n"
     ]
    }
   ],
   "source": [
    "print(8+3j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "1e953022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1-5j)\n"
     ]
    }
   ],
   "source": [
    "print(1-5j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "bba94e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1-2j)\n"
     ]
    }
   ],
   "source": [
    "print(-1-2j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "4386a306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-23+33j)\n"
     ]
    }
   ],
   "source": [
    "print(-23+33j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "fac636ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "complex"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(8+3j)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a287dc",
   "metadata": {},
   "source": [
    "* In python complex numbers are represented by the class 'complex' as shown above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad9fb30",
   "metadata": {},
   "source": [
    "## 2) String"
   ]
  },
  {
   "attachments": {
    "string_example.webp": {
     "image/webp": 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"
    }
   },
   "cell_type": "markdown",
   "id": "6be2ae92",
   "metadata": {},
   "source": [
    "![string_example.webp](attachment:string_example.webp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bec3520",
   "metadata": {},
   "source": [
    "****String is a data type used to represent text data****\n",
    "                    * OR *\n",
    "* **We can say it is a sequence of characters**\n",
    "\n",
    "\n",
    "* ***There can be one or more characters in a string with signle quotation, double quotation and even triple quotation marks***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "1f41baa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "d3ed374e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "d20e962a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('''a''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "24e271d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Class!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello Class!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "ba7f330c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Sahibzada Salman, and I am your Artificial Intelligence instructor\n"
     ]
    }
   ],
   "source": [
    "print('My name is Sahibzada Salman, and I am your Artificial Intelligence instructor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "6ec6b716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So this is what a string is, it is a sequence of characters under the quotation marks. The characters can be anything ranging from simple letters to some strange looking charaters like / or  * or & or ^ or % etc\n"
     ]
    }
   ],
   "source": [
    "print('''So this is what a string is, it is a sequence of characters under the quotation marks. The characters can be anything ranging from simple letters to some strange looking charaters like / or  * or & or ^ or % etc''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "46cfc9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('This is a string class :)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451d7c8e",
   "metadata": {},
   "source": [
    "* **In Python, the string is repsented by a class 'str'**\n",
    "\n",
    "* **Strings are IMMUTABLE means we cannot change the items in a string**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85f9b48",
   "metadata": {},
   "source": [
    "#### Operations in String"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89cc23fe",
   "metadata": {},
   "source": [
    "##### We can Perform Concatenation, Slicing and Repetition in Strings "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf9812d",
   "metadata": {},
   "source": [
    "#### i) Concatenation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3793a8",
   "metadata": {},
   "source": [
    "* **Concatenation means joing two strings together**\n",
    "\n",
    "* *let's conocatenate two different strings together* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "423cc652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Class, this is an example of concatenation in strings!\n"
     ]
    }
   ],
   "source": [
    "print('Hello Class, ' + 'this is an example of concatenation in strings!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "157ac282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So, basically we are just adding two different strings here using a \"+\" operator\n"
     ]
    }
   ],
   "source": [
    "print('So, basically we are ' + 'just adding two different strings here using a \"+\" operator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "02239dac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print('Hello '  +  'World!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6fc43f",
   "metadata": {},
   "source": [
    "**This is how you can join two different strings**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309b5164",
   "metadata": {},
   "source": [
    "#### ii) Slicing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7043b6d",
   "metadata": {},
   "source": [
    "**Slicing means cutting a string into different parts**\n",
    "\n",
    "   **OR**\n",
    "     \n",
    "**Extracting different parts of a string**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "fb524bfe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LANGUAGE\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e42e11",
   "metadata": {},
   "source": [
    "* *There are 8 different characters in this word*\n",
    "* *So let's extract the 3rd, 4th and the 5th letter from the string which are N, G and U*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "26094a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NGU\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[2:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d188e5e2",
   "metadata": {},
   "source": [
    "#####  You Must Be Wondering Why Did We Selected The Range From [2:5], While We Wanted To Select The 3rd, 4th And The 5th Letter? It Should Have Been From [3:5]?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3470dfda",
   "metadata": {},
   "source": [
    "**ANS:** **Because in Python the indexing starts from 0**\n",
    "\n",
    "*or*\n",
    "\n",
    "**We Can Say that Python Starts its counting from '0' not '1' :)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "e522caee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "c2cd69d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "ca4d1c3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "1dacd7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "4bd271fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "3e2b970e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "d7585c65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "6d8fbb0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E\n"
     ]
    }
   ],
   "source": [
    "print('LANGUAGE'[7])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1edc59",
   "metadata": {},
   "source": [
    "**There are 8 characters in this string, but the last character is on no 7 position, becuase python starts counting from 0**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3212eddf",
   "metadata": {},
   "source": [
    "#####  iii) Repetition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157638fc",
   "metadata": {},
   "source": [
    "**Repetition means repeating a sequence or a string a certain number of times**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "0b8bccc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GO GO \n"
     ]
    }
   ],
   "source": [
    "print('GO '*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "9705e8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GO GO GO GO \n"
     ]
    }
   ],
   "source": [
    "print('GO '*4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "77049b3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hey Hey Hey Hey Hey \n"
     ]
    }
   ],
   "source": [
    "print('Hey '*5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "912f574e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "etc. etc. etc. etc. etc. etc. etc. etc. etc. etc. \n"
     ]
    }
   ],
   "source": [
    "print('etc. '*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ddb4482",
   "metadata": {},
   "source": [
    "#### Including a special character as a part of string ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753b75fd",
   "metadata": {},
   "source": [
    "##### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7103cd6",
   "metadata": {},
   "source": [
    "let's try this one example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "87067cbc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-309-72027c80da4f>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-309-72027c80da4f>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    print('including a special character i.e single quote (') in this example.')\u001b[0m\n\u001b[1;37m                                                                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "print('including a special character i.e single quote (') in this example.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e800a54",
   "metadata": {},
   "source": [
    "****Can any one explain why Python gave an error when we tried to include a quotation mark in our string?****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740c3210",
   "metadata": {},
   "source": [
    "**There are 2 methods to tackle this error** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c442bc",
   "metadata": {},
   "source": [
    "#### a) Delimit the string with the other type, for example if you want to include a signle quote in your string, delimit it with double quotes and vice versa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21d40d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"including a special character i.e signle quote (') in this example.  :p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ddb36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e double character (\") in this example. :p')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9a7f63",
   "metadata": {},
   "source": [
    "#### b) Escape Sequences in the strings "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4509c48",
   "metadata": {},
   "source": [
    "****Escape sequences are used to suppress the meaning of special characters****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e619d3fc",
   "metadata": {},
   "source": [
    "* **For example, signle quote (') has a special meaning in python as they are used to define strings, In previous example we wanted to include a single quote as a part of our string but the Python gave error why? because in that sentence signle quote has a special meaning which is 'it terminates the string'**\n",
    "\n",
    "* That's why we use escape sequences as they suppress/bypass the special meaning of that character\n",
    "\n",
    "**In Python we use (\\\\) backslash in front of the quote character in a string to supress its special meaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76d04d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e single quote (') in this example.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8007697",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e single quote (\\') in this example.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a23a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e single quote (\\') in this example.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545e31b7",
   "metadata": {},
   "source": [
    "*****Other escape sequences are*****\n",
    "* **\\\\' ----> suppress signle quote and considers it as a part of the string**\n",
    "* **\\\\\" ----> suppress double quote and considers it as a part of the string**\n",
    "* **\\ ----> Ignores the new line**\n",
    "* **\\\\t ----> used to add tab space**\n",
    "* **\\\\\\ ----> for single backlash**\n",
    "* **\\\\n ----> for new line"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add5c86e",
   "metadata": {},
   "source": [
    "****let's see an example of each****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "934c92cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e single quote (\\') in this example.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edacca45",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('including a special character i.e single quote (\\') in this example.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6227d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('This \\\n",
    "is \\\n",
    "an \\\n",
    "example \\\n",
    "of \\\n",
    "using \\\n",
    "escape \\\n",
    "sequence \\\n",
    "for \\\n",
    "ignoring \\\n",
    "new \\\n",
    "lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82c476e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('In order to ignore a escape character we have add another escape character :p Like in this example I used double backlashes (\\\\) in order to add single backlash')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c73a8639",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('a\\\\b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31303441",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('break\\\\fast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4565064",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84791e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('break\\tfast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93bce6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[:5])\n",
    "\n",
    "c = \"Hello, World!\"\n",
    "print(c[6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8618db1",
   "metadata": {},
   "source": [
    "****Task No1****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce95813f",
   "metadata": {},
   "source": [
    "## 3) List  "
   ]
  },
  {
   "attachments": {
    "python-list-index.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "81cca296",
   "metadata": {},
   "source": [
    "![python-list-index.png](attachment:python-list-index.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c8d5c7",
   "metadata": {},
   "source": [
    "**List is a collection of different elements or items inside square brackets '[This, is, a, list, :)]' separated by commas**\n",
    "\n",
    "**A list can have elements of the same data type or even elemets of different types for example, a signle list can have integers, float and string items**\n",
    "\n",
    "**List  **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2718ca0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,3,4,5,6,7,8,9,10])  # a List of Integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4934b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10]) # a list of floating point numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc740ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1+2j, -1+2j, -1-2j, 1-2j, -3-3j, 5-6j]) # a list of complex numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14cdcddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(['This', 'is', 'a', 'string', 'list']) # a list of strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65a63892",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1, 10, 100, 1.2, 2.3, 3.4, 1+1j, 2+2j, 3+3j, 'A list can have a combination of all the data types that we discussed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380ebdaf",
   "metadata": {},
   "source": [
    "\n",
    "* **A list is MUTABLE, means we can change items in the list by simply using the slicing method (indexing)**\n",
    "\n",
    "* *Let's see an example below*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf8b2b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70a2626",
   "metadata": {},
   "source": [
    "**There are 8 items in this list, let's access each element one by one**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f549775",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5cb4058",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939fa5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c03f9fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d98ec5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60766a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840b36a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a246cffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][7])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a7eb95",
   "metadata": {},
   "source": [
    "**Let's access first 3 items at the same time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ef8ac46",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][0:3]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94639605",
   "metadata": {},
   "source": [
    "**Now, let's access the last 3 items from the list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8cdb09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print([1,2,-3, -4, 2.3, -4.5, 1+3j, 'string'][4:7])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31c4043",
   "metadata": {},
   "source": [
    "***We can also replace an element in the list***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79eb3cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "listt = [1,2,-3, -4, 2.3, -4.5, 1+3j, 'string']\n",
    "print(listt[0:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20161d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "listt[3] = \"replacing the previously placed element (-4) with this sentence :p\"\n",
    "print(listt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13af9f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(listt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41b7e2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = ['Physics', 'Chemistry', 'Boiology', 'Mathematics']\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e18c8ec",
   "metadata": {},
   "source": [
    "**Negative Indexing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a9c068",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11f5b385",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b057ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dca7007",
   "metadata": {},
   "source": [
    "## Adding an element in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e61ac741",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = ['Physics', 'Chemistry', 'Boiology', 'Mathematics']\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f555868c",
   "metadata": {},
   "source": [
    "****If you want to add another item at the end of list we can do that using \".append\" function****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1e957ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.append('English')\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f7b9d8",
   "metadata": {},
   "source": [
    "****What if we want to add an item at a perticular position, what will we do then? We'll use \".insert\" function****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cef792d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.insert(2, 'Astronomy')\n",
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cabd781",
   "metadata": {},
   "source": [
    "**What if we join two lists together**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d27d113",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Science = ['Physics', 'Chemistry', 'Biology', 'Mathematics']\n",
    "Compuls = ['English', 'Urdu', 'Pak-Studies', 'Islamiyat']\n",
    "print(Science)\n",
    "print(Compuls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fca39f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subjects = Science + Compuls\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38cd3ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Science = ['Physics', 'Chemistry', 'Biology', 'Mathematics']\n",
    "Compuls = ['English', 'Urdu', 'Pak Studies', 'Islamiyat','Social Studies']\n",
    "print(Science)\n",
    "print(Compuls)\n",
    "subjects = Science + Compuls\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77df824a",
   "metadata": {},
   "source": [
    "## Removing an element from a list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b0b3ab",
   "metadata": {},
   "source": [
    "****To remove an item from a list we use pop() fucntion or del keyword****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d662de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7ff6c9",
   "metadata": {},
   "source": [
    "#### pop() function\n",
    "***To remove an item from a list we use the index number of that item***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5815c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "subjects.pop(3)\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f7b077",
   "metadata": {},
   "source": [
    "****If we don't specify the position of the item, pop() funciton automatically removes the last element from the list****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd0fb15",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(subjects)\n",
    "subjects.pop()\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b2b7b0",
   "metadata": {},
   "source": [
    "#### del keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ec9a9c",
   "metadata": {},
   "source": [
    "****del keyword also removes an item from a list using specified index number****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c85093d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(subjects)\n",
    "del subjects[2]\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7fe4b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(subjects)\n",
    "del subjects\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f81b13",
   "metadata": {},
   "source": [
    "**To find how many items we have in the list**\n",
    "**We use ( len() ) function to determine the length of a list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77c82d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(Science))\n",
    "print(len(Compuls))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001da3e7",
   "metadata": {},
   "source": [
    "##### How to find an item in a list?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cee5809",
   "metadata": {},
   "source": [
    "***We use item in list method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b728a2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "'Mathematics' in Science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c60b7b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "'Islamiyat' in Compuls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7806f491",
   "metadata": {},
   "outputs": [],
   "source": [
    "'Astronomy' in Science "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2cbf5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "'Social-Studies' in Compuls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b20d355",
   "metadata": {},
   "source": [
    "## Reversing a list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb80e7d1",
   "metadata": {},
   "source": [
    "****We can use slicing operator to reverse a list****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d76a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "newList = [1,2,2.2,4.5,2-5j,'reverse','list']\n",
    "print('Our list: ',newList)\n",
    "reverseList = newList[::-1]\n",
    "print('Reversed list: ', reverseList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a18dc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type(newList))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ce8f6b",
   "metadata": {},
   "source": [
    "****In Python a list is represented by a \"list\" class****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd35a7f3",
   "metadata": {},
   "source": [
    "## 4) Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed273a1",
   "metadata": {},
   "source": [
    "**A tuple is a data type used to store multiple items in a single variable**\n",
    "\n",
    "***A tuple is a collection of items which are ordered and unchangable***\n",
    "\n",
    "****The only differnece between a tuple and a list is that lists are mutable while tuples are not****\n",
    "\n",
    "\n",
    "**Tuples are identified by paranthesis ()**"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "a7a91053",
   "metadata": {},
   "source": [
    "![python-tuple.jpg](attachment:python-tuple.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21459015",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e6f7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "type((1,2.3,1-3j,'This is an example of tuple'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb40b056",
   "metadata": {},
   "source": [
    "**We can only access the items in a tuple but not change/replace**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6729897c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8d8450e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "209d5b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bc97f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db3a8d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54ce0a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((1,2.3,1-3j,'This is an example of tuple')[2:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc1b8a3",
   "metadata": {},
   "source": [
    "## 5) Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfe2155",
   "metadata": {},
   "source": [
    "* **A set is an unordered collection of items**\n",
    "\n",
    "* **A set can contain only UNIQUE items means no repetition of the same element**\n",
    "\n",
    "* **There is no indexing in Sets as all the items are unordered and are unique**\n",
    "\n",
    "* **A set is MUTABLE but since there is no order we cannot use slicing/indexing to change the items**\n",
    "\n",
    "* **A Set is identified/represented by curly braceks or braces { }**"
   ]
  },
  {
   "attachments": {
    "python-sets.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "id": "1d73d038",
   "metadata": {},
   "source": [
    "![python-sets.jpg](attachment:python-sets.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1734cf69",
   "metadata": {},
   "source": [
    "### No Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa43ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_1 = {3,1.22,2.3,3+5j, \"this is a set\"}\n",
    "print(set_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb1eee8",
   "metadata": {},
   "source": [
    "****Note that the order of our items in the set is changed the moment we executed the code, meaning 'A Set does not maintain the order of the data'****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5cae797",
   "metadata": {},
   "source": [
    "### No Repetition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0961cbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_2 = {1,1,1,3,3,-6,-7,-8,-8,9,-9,10,11.11,12.2,12.3,15,13.9,'This is an example of a Set'}\n",
    "print(set_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4580266",
   "metadata": {},
   "source": [
    "**Note that we had repeated items in the above example but Sets do not allow repetition**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61ac628",
   "metadata": {},
   "source": [
    "### No Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04ee15e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_3 = {1,2.3,1-3j,'There is no indexing in the SETS'}\n",
    "print(set_3[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c23b52",
   "metadata": {},
   "source": [
    "### Multiple Data types as Elements in a Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d09534",
   "metadata": {},
   "source": [
    "****Set allows multiple datatypes as elements like in the example below we have have added a tuple as an element****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80e1e6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_4 = {1,2.3,1-3j,'This is a set', (1, 2.3, 3-5j, 'tuple')}\n",
    "print(set_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ead4812",
   "metadata": {},
   "source": [
    "### No Mutable Data types as Elements!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de06635",
   "metadata": {},
   "source": [
    "****Set does not allow mutable datatypes like \"lists\"****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60b1b1ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "set_5 = {1,2.3,1-3j,'This is a set', [1, 2.3, 3-5j, 'list']}\n",
    "print(set_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71de00a",
   "metadata": {},
   "source": [
    "### Sets are Mutable!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce0b6b0",
   "metadata": {},
   "source": [
    "****Sets are MUTABLE! but we cannot use slicing or indexing method to access or change the items****\n",
    "\n",
    "****To add a single item in a set we use add() function****\n",
    "\n",
    "****To add multiple items in a set we use update() function****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "c071811e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2.2, 'God Zilla', (3-1j)}\n",
      "{1, 2.2, 'God Zilla', 14, (3-1j)}\n"
     ]
    }
   ],
   "source": [
    "m_set = {1,2.2,3-1j,'God Zilla'}\n",
    "print(m_set)\n",
    "\n",
    "m_set.add(14)\n",
    "print(m_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "11a717e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'GodZilla', 1, 2.2, (3-1j)}\n",
      "{'GodZilla', 1, 2.2, 'Doraemon', 14, 'Nobita', 23, (3-1j)}\n"
     ]
    }
   ],
   "source": [
    "m_set1 = {1,2.2,3-1j,'GodZilla'}\n",
    "print(m_set1)\n",
    "\n",
    "m_set1.update([23,14,'Doraemon', 'Nobita'])\n",
    "print(m_set1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9793f85",
   "metadata": {},
   "source": [
    "****We can use all datatypes whether mutable or immutable while updating a set****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "33ca4437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'GodZilla', 1, 2.2, (3-1j)}\n",
      "{'GodZilla', 1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian', (3-1j)}\n"
     ]
    }
   ],
   "source": [
    "m_set2 = {1,2.2,3-1j,'GodZilla'}\n",
    "print(m_set2)\n",
    "m_list = [12,13,'Doraemon', 'Gian']\n",
    "m_tuple = ('Shizuka','Nobita', 14, 15)\n",
    "m_set = {12,13,14,16,'Sonyo'}\n",
    "m_set2.update(m_list, m_tuple, m_set)\n",
    "print(m_set2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da951342",
   "metadata": {},
   "source": [
    "### Removing Items from A Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cc5a26",
   "metadata": {},
   "source": [
    "****We can use either discard() function or remove() fucntion to remove an item from a list****\n",
    "\n",
    "****The only difference remove() function will give an error if an item is not present in a set while discard() function will not****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905b05fa",
   "metadata": {},
   "source": [
    "#### discard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "213ce7bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'GodZilla', 1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian', (3-1j)}\n",
      "{1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian', (3-1j)}\n"
     ]
    }
   ],
   "source": [
    "print(m_set2)\n",
    "m_set2.discard('GodZilla')\n",
    "\n",
    "print(m_set2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "d1b14e4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian', (3-1j)}\n",
      "{1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian'}\n"
     ]
    }
   ],
   "source": [
    "print(m_set2)\n",
    "m_set2.discard(3-1j)\n",
    "\n",
    "print(m_set2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4f5767",
   "metadata": {},
   "source": [
    "#### remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "5520662f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2.2, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian'}\n",
      "{1, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian'}\n"
     ]
    }
   ],
   "source": [
    "print(m_set2)\n",
    "m_set2.remove(2.2)\n",
    "\n",
    "print(m_set2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "7d48adc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 'Doraemon', 'Sonyo', 12, 13, 'Shizuka', 14, 15, 16, 'Nobita', 'Gian'}\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'GodZilla'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-316-18714e504b60>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_set2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mm_set2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'GodZilla'\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      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_set2\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: 'GodZilla'"
     ]
    }
   ],
   "source": [
    "print(m_set2)\n",
    "m_set2.remove('GodZilla')\n",
    "\n",
    "print(m_set2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b7b924",
   "metadata": {},
   "source": [
    "#### clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05202eed",
   "metadata": {},
   "source": [
    "****clear() function is used to remove all the items from a given set****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26879d2",
   "metadata": {},
   "source": [
    "### Operations in Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4412792",
   "metadata": {},
   "source": [
    "### i) AUB"
   ]
  },
  {
   "attachments": {
    "AUB_1.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "c2db4746",
   "metadata": {},
   "source": [
    "![AUB_1.jpg](attachment:AUB_1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0616ac6",
   "metadata": {},
   "source": [
    "****AUB, A Union B is basically a merger of two different sets****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c5c3ba",
   "metadata": {},
   "source": [
    "****We can either use \" | \" operator or union() function to merge two different sets****"
   ]
  },
  {
   "cell_type": "raw",
   "id": "bfa5898b",
   "metadata": {},
   "source": [
    "A = {1, 2.22, 1-2j, 'Sets'}\n",
    "B = {2, 3.3, -1+2j, 'Union'}\n",
    "C = A | B\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c75b8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(A.union(B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa157d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(B.union(A))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d801b3fb",
   "metadata": {},
   "source": [
    "### ii) A∩B"
   ]
  },
  {
   "attachments": {
    "A%E2%88%A9B.gif": {
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    }
   },
   "cell_type": "markdown",
   "id": "65073426",
   "metadata": {},
   "source": [
    "![A%E2%88%A9B.gif](attachment:A%E2%88%A9B.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65c8c6a",
   "metadata": {},
   "source": [
    "****A∩B, A Intersection B is a set of items that are common in two different sets****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb51afbb",
   "metadata": {},
   "source": [
    "****We either use \" & \" operator or intersection() function to find out the common elements in different sets****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9748635e",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = {1,2,3.22,'Common elements', 3-2j}\n",
    "B = {1.1, 3, 3.22, 3-2j, 'Intersection'}\n",
    "print(A)\n",
    "print(B)\n",
    "D = A & B\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23933a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(A.intersection(B))\n",
    "print(B.intersection(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a9a7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7973398d",
   "metadata": {},
   "source": [
    "**In Python sets are represented by the class \"set\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859fec03",
   "metadata": {},
   "source": [
    "## 6) Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7476706",
   "metadata": {},
   "source": [
    "****Dictionary is a data type used to store values in \" key:value\" pairs****\n",
    "\n",
    "****Dictionaries are ordered, changable and do not allow duplicates****\n",
    "\n",
    "* **Dictionaries are optimized to retrieve values when a key is known**\n",
    "* ***In key:value, value is the actual information associated with a key, think of it as, items in your room are values, your door is the key and together we call it a room (dic)***\n",
    "\n",
    "    * ****things in your room are values****\n",
    "    * ****door of your room is the key****\n",
    "    * ****together they make a room which is a dictionary****\n",
    "    \n",
    "    \n",
    "*****Each key-value pair is separated by a 'colon :', whereas each key is separated by a 'comma ,'*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09cee7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dictionary = {1:'Look', 2:'closely', 3:'You see the difference?'}\n",
    "print(Dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c1f5e4",
   "metadata": {},
   "source": [
    "**In above example 1,2, and 3 are the keys, while 'look', 'closely' and 'You see the difference?' are the values assosciated with these keys**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f99104",
   "metadata": {},
   "source": [
    "****To access an item in the list we look at their keys****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6280f85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dictt = {1:'Look', 2:'closely', 3:'You see the difference?', 4:'This is an example', 5:'of', 6:'Dictionaries'}\n",
    "print(dictt[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "924d166c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197fbce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebb7c49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e945dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232d09b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d258f5",
   "metadata": {},
   "source": [
    "**We cannot use values to access the key :p**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de2c86e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt['Look'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991ae59c",
   "metadata": {},
   "source": [
    "*****We can also update a dictionary with new items*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb398a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dictt[7] = 'New item'\n",
    "print(dictt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a207653",
   "metadata": {},
   "source": [
    "****We can also replace an item from a dictionary with another item****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1038322e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt, '\\tThis is before replacement')\n",
    "dictt[7] = 'Replaced item'\n",
    "print(dictt, '\\tThis is after replacement')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48d3413",
   "metadata": {},
   "source": [
    "*****We can also use mixed keys in the dictionaries, In the previous examples we have used only number keys*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d4f419",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dictt[1:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3e36b4",
   "metadata": {},
   "source": [
    "#### Dictionaries with Mixed Keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "31df5cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'Artifical Intelligence', 'Instructor': 'Sahibzada Salman', 2: 'Week 3', 'Duration': '6 months'}\n"
     ]
    }
   ],
   "source": [
    "dictionary = {1:'Artifical Intelligence', 'Instructor': 'Sahibzada Salman', 2:'Week 3', 'Duration': '6 months'}\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854d9897",
   "metadata": {},
   "source": [
    "****How will you access the values in this case?****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "3cc461d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Artifical Intelligence'"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "c361a405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sahibzada Salman'"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary['Instructor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "9d3b89ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Week 3'"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "e240419d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6 months'"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary['Duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "66eb19cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((1, 'creating'), (2, 'a'), (3, 'dictionary'), (4, 'from'), (5, 'tuples'))\n"
     ]
    }
   ],
   "source": [
    "tup = ((1,'creating'),(2,'a'),(3,'dictionary'),(4,'from'),(5,'tuples'))\n",
    "print(tup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "92f0c80b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'creating', 2: 'a', 3: 'dictionary', 4: 'from', 5: 'tuples'}\n"
     ]
    }
   ],
   "source": [
    "tup_dict = dict(tup)\n",
    "print(tup_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "260a6def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Name': 'Henry Cavil', 'Age': 33, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man'}\n"
     ]
    }
   ],
   "source": [
    "dictionary = {'Name': 'Henry Cavil', 'Age': 33, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man' }\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b13778",
   "metadata": {},
   "source": [
    "****1) Updating Values****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "eb02ebe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Name': 'Henry Cavil', 'Age': 33, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man'}\n",
      "{'Name': 'Henry Cavil', 'Age': 36, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man'}\n"
     ]
    }
   ],
   "source": [
    "print(dictionary)\n",
    "dictionary['Age'] = 36\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48debb21",
   "metadata": {},
   "source": [
    "****2) Adding an Element****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "3b608d24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Name': 'Henry Cavil', 'Age': 36, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man'}\n",
      "{'Name': 'Henry Cavil', 'Age': 36, 'Movie': 'Man of Steel', 'Fictional Name': 'Super Man', 'Location': 'Hollywood'}\n"
     ]
    }
   ],
   "source": [
    "print(dictionary)\n",
    "dictionary['Location'] = 'Hollywood'\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7443b070",
   "metadata": {},
   "source": [
    "****3) Removing an Element****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e75b68",
   "metadata": {},
   "source": [
    "**We can use pop() function to remove an item from a list using it's key**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "f84e284a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3, 2: 2020, 'Movie': 'End Game', 'Favourite Character': 'Captain America'}\n"
     ]
    }
   ],
   "source": [
    "pop_dict = {1:3, 2: 2020, 'Movie':'End Game', 'Favourite Character':'Captain America'}\n",
    "print(pop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "29a8af82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3, 2: 2020, 'Movie': 'End Game', 'Favourite Character': 'Captain America'}\n",
      "{1: 3, 'Movie': 'End Game', 'Favourite Character': 'Captain America'}\n"
     ]
    }
   ],
   "source": [
    "print(pop_dict)\n",
    "pop_dict.pop(2)\n",
    "print(pop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "0d7cdc14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3, 'Movie': 'End Game', 'Favourite Character': 'Captain America'}\n",
      "{1: 3, 'Movie': 'End Game'}\n"
     ]
    }
   ],
   "source": [
    "print(pop_dict)\n",
    "pop_dict.pop('Favourite Character')\n",
    "print(pop_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289dfc12",
   "metadata": {},
   "source": [
    "**To delete an entire dictionary we can use del keyword**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "4527ffcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3, 'Movie': 'End Game'}\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'pop_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-330-824f78a60e56>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpop_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mpop_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpop_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pop_dict' is not defined"
     ]
    }
   ],
   "source": [
    "print(pop_dict)\n",
    "del pop_dict\n",
    "print(pop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b94584",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377475bd",
   "metadata": {},
   "source": [
    "****In Python the dictionaries are represented by the class 'dict'**** **as shown above**"
   ]
  }
 ],
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