Basics_of_Python.ipynb

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     "text": [
      " Volume in drive C has no label.\n",
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      "13-Nov-22  11:07 AM    <DIR>          .\n",
      "13-Nov-22  11:07 AM    <DIR>          ..\n",
      "09-Nov-22  10:19 PM    <DIR>          .ipynb_checkpoints\n",
      "27-Aug-22  10:56 PM           726,684 About_Data_Frames.ipynb\n",
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   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44600b2a",
   "metadata": {},
   "source": [
    "##### Navigating Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d60928f",
   "metadata": {},
   "source": [
    "+ ESC + B = Add a cell below current cell\n",
    "+ ESC + A = Add a cell above current cell\n",
    "+ ESC + X = Delete the current cell\n",
    "+ ESC + M = Convert cell to Markdown cell\n",
    "+ ESC + Y = Convert cell to code cell\n",
    "+ ESC + Z = Undo\n",
    "+ Shift + Tab = More Info\n",
    "+ .(dot / Period) + Tab = Functions List"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd85dc3",
   "metadata": {},
   "source": [
    "# This is just a Header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f78432",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datatypes in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e0370b",
   "metadata": {},
   "source": [
    "## Variables in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71db5075",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Variables in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4379db68",
   "metadata": {},
   "source": [
    "+ variables are like containers to hold data\n",
    "+ Or they can be thought of aliases for memory addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3db963ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# + Text above\n",
    "# + text above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7aa0d6a",
   "metadata": {},
   "source": [
    "+ <font color='blue'> variables are like containers to hold data </font>\n",
    "+ Or they can be thought of aliases for memory addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35270db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#+ <font color='blue'> variables are like containers to hold data </font>\n",
    "#+ Or they can be thought of aliases for memory addresses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7beb94fc",
   "metadata": {},
   "source": [
    "+ <font color='blue'> Variables are like containers to hold data </font>\n",
    "+ <b>Or they can be thought of aliases for memory addresses<b/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647d5f2f",
   "metadata": {},
   "source": [
    "#+ <font color='blue'> Variables are like containers to hold data </font>\n",
    "#+ <b>Or they can be thought of aliases for memory addresses<b/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c01cf7",
   "metadata": {},
   "source": [
    "### Header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7453b75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc1a8f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HELLO WORLD\n"
     ]
    }
   ],
   "source": [
    "print('HELLO WORLD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8c488f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOW ARE YOU?\n"
     ]
    }
   ],
   "source": [
    "print('HOW ARE YOU?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cffc351",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "056ce5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb89109a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b18e9c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2871071566416"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15442eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3bc51d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d114dde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x= 10\n",
      "y= 20\n"
     ]
    }
   ],
   "source": [
    "x=10\n",
    "y=20\n",
    "print('x=',x)\n",
    "print('y=',y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e1653b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__class_getitem__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'clear',\n",
       " 'copy',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "469dcb57",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x = -10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b1edd22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16c7562d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = -69.420"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "193a32b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "316884d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "35 % 7 #modulo division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8e5f882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 5\n",
    "y = 2\n",
    "x // y #floor division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8115e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.3333333333333335, -4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = -20\n",
    "y = 6\n",
    "x/y, x//y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27e7afd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x= 10\n",
      "y= 20\n"
     ]
    }
   ],
   "source": [
    "# Create a variable called x\n",
    "x=10\n",
    "y=20\n",
    "print('x=', x)\n",
    "print('y=', y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41a89e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2057249646992"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=256\n",
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc61608b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2055179130160"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=257\n",
    "id(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a2d7f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_int=10\n",
    "type(my_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fb0c68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_float=-69.420\n",
    "type(my_float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97f14984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "915b8a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=256\n",
    "b=256\n",
    "a == b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7badf97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = True\n",
    "b = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f44438f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bc76c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "795a8da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8523c7",
   "metadata": {},
   "source": [
    "# Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01feb7d9",
   "metadata": {},
   "source": [
    "+ positive indexing: left to right [0]\n",
    "+ neagtive indexing: right to left [-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7612309f",
   "metadata": {},
   "outputs": [],
   "source": [
    "string = 'abc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8334b016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f05a3b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30d9eccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c , c\n"
     ]
    }
   ],
   "source": [
    "print(string[2],',',string[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18dc6262",
   "metadata": {},
   "source": [
    "# Arithmetic Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c28800a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=64\n",
    "y=46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da503218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add two numbers\n",
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cde7c3fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Subtraction\n",
    "x-y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea78fa77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2944"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplication\n",
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8ed48f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total = x + y\n",
    "Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68f95d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.391304347826087"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / y # Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6df9a252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x % y #remainder of the division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "68c25b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 // 2 # floor division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53f0e67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.3333333333333335, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 20\n",
    "y = 6\n",
    "x/y, x // y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fa165da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.3333333333333335, -4)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = -20\n",
    "y = 6\n",
    "x/y, x // y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282bd5cb",
   "metadata": {},
   "source": [
    "# Boolean Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02e61b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e4b24d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e07c7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fd6ddf28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6f990d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 == 3 and 3 == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46de44a8",
   "metadata": {},
   "source": [
    "# Sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a423fbf",
   "metadata": {},
   "source": [
    "### List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7fad124f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = []\n",
    "type(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5dadfe74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[65, 76, 48, 93, 55]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks = [65, 76, 48, 93, 55]\n",
    "marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8a7103c",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=[100,'Tom','MySQL',78]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a89eb666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159f3030",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9375c86",
   "metadata": {},
   "source": [
    "+ Extraction of a substring\n",
    "+ syntax is start position and end position (end position excluded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe4d3ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst=[1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99e49bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[2:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6679b4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cfbe33c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "786fed0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 7]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b63c0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[2::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b95c61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ca26fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[8:2:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5219e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6453a36c",
   "metadata": {},
   "source": [
    "### Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "46b929c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tup = ()\n",
    "type(tup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a35bfbbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 76, 48, 93, 55)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup = (65, 76, 48, 93, 55)\n",
    "my_tup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a438f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38257d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_tup=(1,2,3,4,5,6,7,8,9,10,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6d046a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(my_tup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95b1f9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1),\n",
       " (1, 2),\n",
       " (2, 3),\n",
       " (3, 4),\n",
       " (4, 5),\n",
       " (5, 6),\n",
       " (6, 7),\n",
       " (7, 8),\n",
       " (8, 9),\n",
       " (9, 10),\n",
       " (10, 11)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(my_tup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e2b53d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tup=(1,2,3,4,[1,2,3],6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64746fd0",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'append'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_13152/2403767784.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
     ]
    }
   ],
   "source": [
    "tup.append(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af775244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1), (1, 2), (2, 3), (3, 4), (4, [1, 2, 3]), (5, 6)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(tup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d5971b5",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'insert'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_13152/1260704283.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'insert'"
     ]
    }
   ],
   "source": [
    "tup.insert[4]=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73861c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tup[4].append(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "400db017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, [1, 2, 3, 5], 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f662850",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a6a99884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 7, 8, 9, 10)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a16bd5b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5c783edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 10, 11)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2c486f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 5, 7, 9, 11)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f82b5fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 8, 9, 10, 11)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[6::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "43375f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d4bc0294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 9, 7, 5, 3, 1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[::-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "39420430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 8, 7, 6, 5, 4)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup[8:2:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa172ae4",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "tuple indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_9112/3381605885.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_tup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "print(my_tup[0:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad567bc",
   "metadata": {},
   "source": [
    "### String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afa5961c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefgh'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = \"abcd\"\n",
    "e = \"efgh\"\n",
    "a+e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69d56556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str = 'Python'\n",
    "type(my_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39fc1291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "aca024b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8d30c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'P'), (1, 'y'), (2, 't'), (3, 'h'), (4, 'o'), (5, 'n')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(my_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e54cc00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'o'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str[4]     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3f5dc5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'P'), (1, 'y'), (2, 't'), (3, 'h'), (4, 'o'), (5, 'n')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(my_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f875a3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1='python'\n",
    "str1.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8aa23efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'python'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "629e5533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " 'hELLO wORLD'.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7e4f98c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1.startswith('py'), str1.endswith('py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d7555e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1.endswith('on'), str1.startswith('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2839a87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_string='pythontista'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b31ce7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'p'),\n",
       " (1, 'y'),\n",
       " (2, 't'),\n",
       " (3, 'h'),\n",
       " (4, 'o'),\n",
       " (5, 'n'),\n",
       " (6, 't'),\n",
       " (7, 'i'),\n",
       " (8, 's'),\n",
       " (9, 't'),\n",
       " (10, 'a')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(my_string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61a36e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string.find('t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db083936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string.index('t',0,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ee63e832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string.index('t',3,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "46cb46c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string.index('t',7,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "508a2839",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha= 'python'\n",
    "alphanum= 'pyhton3'\n",
    "num= '1234'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "738f0b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha.isalpha(),alpha.isalnum(), alpha.isnumeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dc86fbdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True, False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphanum.isalpha(), alphanum.isalnum(), alphanum.isnumeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b61a899a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True, True)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num.isalpha(),num.isalnum(), num.isnumeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "762df51d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'python'.islower(), 'Python'.isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8f057d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'python'.isupper(), 'PYTHON'.isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e80570f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'pYTHON'.islower(),'pYTHON'.isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa8d72df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.isspace(), \" \".isspace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba4350c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Python', '  Python', 'Python  ')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'  Python   '.strip(), \"  Python   \".rstrip(),\"   Python  \".lstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d18e9508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'kiwi', 'banana', 'strawberry', 'orange']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'apple,kiwi,banana,strawberry,orange'.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2fd5c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple;kiwi', 'banana', 'strawberry:orange']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'apple;kiwi,banana,strawberry:orange'.split(',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd8c89e",
   "metadata": {},
   "source": [
    "# List Comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a72a85d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst=[1,2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "612184cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqr_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69a89328",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sqr_lst=[elem*elem for elem in lst]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b78d2945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_sqr_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ca304e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers=[6,7,8,9,10,11,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7edb0f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list=[number ** 2 for number in numbers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a27ab49c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36, 49, 64, 81, 100, 121, 144]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "76db731d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqr_numb=[2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "13517553",
   "metadata": {},
   "outputs": [],
   "source": [
    "numb=[sqr ** 3 for sqr in sqr_numb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7dd703a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 27, 64, 125, 216]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3c38925b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500]\n"
     ]
    }
   ],
   "source": [
    "range_func=[10,20,30,40,50]\n",
    "rang_f = [elem*10 for elem in range_func]\n",
    "print(rang_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55579eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 200, 300, 400, 500, 600]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_list=[1,2,3,4,5,6]\n",
    "ex_list=[numb*100 for numb in ex_list]\n",
    "ex_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9dd77b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 200, 300, 400, 500, 600]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = [n*100 for n in range(1,7)]\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c7dd560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 200, 300, 400, 500, 600]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ex = [final_exp for final_exp in range(100,601,100)]\n",
    "final_ex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9649107a",
   "metadata": {},
   "source": [
    "# For Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44209247",
   "metadata": {},
   "source": [
    "## Sequence are Iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0a80652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 6, 8, 10, 12]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,3,5,7,9,11]\n",
    "l[0] = l[0]+1\n",
    "l[1] = l[1]+1\n",
    "l[2] = l[2]+1\n",
    "l[3] = l[3]+1\n",
    "l[4] = l[4]+1\n",
    "l[5] = l[5]+1\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7e3875b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,10,23,25,7]\n",
    "total = 0\n",
    "for i in l:\n",
    "    total = total+i\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "427b676c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40250"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,10,23,25,7]\n",
    "total = 1\n",
    "for i in l:\n",
    "    total = total*i\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af96c227",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78d9fe70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "list_ = [1,3,5,7,9,11]\n",
    "for add_one in list_:\n",
    "    print(add_one+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "019bdda8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "[1, 8]\n",
      "[1, 8, 27]\n",
      "[1, 8, 27, 64]\n",
      "[1, 8, 27, 64, 125]\n",
      "[1, 8, 27, 64, 125, 216]\n",
      "[1, 8, 27, 64, 125, 216, 343]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096, 4913]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096, 4913, 5832]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096, 4913, 5832, 6859]\n",
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096, 4913, 5832, 6859, 8000]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
    "cube_root = []\n",
    "for i in l:\n",
    "    cube_root.append(i**3)\n",
    "    print(cube_root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5828bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744, 3375, 4096, 4913, 5832, 6859, 8000]\n"
     ]
    }
   ],
   "source": [
    "print(cube_root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5cd34cde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 14, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 15, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 16, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 17, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 18, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 19, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 20]\n",
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400]\n"
     ]
    }
   ],
   "source": [
    "l = list(range(1,21))\n",
    "for i in range(len(l)):\n",
    "    l[i]=l[i]**2\n",
    "    print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cae0af1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqr_lst=[]\n",
    "for elem in lst:\n",
    "    sqr_lst.append(elem*elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd44ad43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "for even in range(2,20,2):\n",
    "    print(even)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f8209832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HI\n",
      "Ram\n",
      "HI\n",
      "Shaam\n",
      "HI\n",
      "Rohan\n",
      "HI\n",
      "Mohan\n",
      "HI\n",
      "Jobu\n",
      "Hello Eeryone Hope you guys are doing great\n"
     ]
    }
   ],
   "source": [
    "Everyone=['Ram','Shaam','Rohan','Mohan','Jobu']\n",
    "for Members in Everyone:\n",
    "    print('HI')\n",
    "    print(Members)\n",
    "print('Hello Eeryone Hope you guys are doing great')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "189a9216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "for range_l in range(15):\n",
    "    print(range_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb263abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n"
     ]
    }
   ],
   "source": [
    "for comprehension in range(100,600,100):\n",
    "    print(comprehension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "95cfa3b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "for range_list in range(2,15):\n",
    "    print(range_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00faaab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_str='Python'\n",
    "for c in my_str:\n",
    "                print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65289e3",
   "metadata": {},
   "source": [
    "### if elif and else Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f653ea28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chutiya\n"
     ]
    }
   ],
   "source": [
    "# God while making humans\n",
    "human = \"\" \n",
    "facts = \"\"\n",
    "if human == \"Sajid\":\n",
    "    print('Chutiya')\n",
    "elif facts ==\"Nahi Yaar Sajid Mast Aadmi Hai\":\n",
    "    print('Chup Chutiye')\n",
    "else:\n",
    "    print('Mast Aadmi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a4a6b69c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7, 9] [2, 4, 6, 8, 14.0] [10.01, 12.02]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6,7,8,9,10.01,12.02,14.0]\n",
    "even = []\n",
    "odd = []\n",
    "fraction = []\n",
    "for i in l:\n",
    "    if i%2==0:\n",
    "        even.append(i)\n",
    "    elif i%2==1:\n",
    "        odd.append(i)\n",
    "    else:\n",
    "        fraction.append(i)\n",
    "print(odd,even,fraction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e915d5",
   "metadata": {},
   "source": [
    "# Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f9910b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# God while making humans\n",
    "def alexa(n):\n",
    "    if n==\"Sajid\":\n",
    "        print('Chutiya')\n",
    "    elif n==\"Nahi Yaar Sajid Mast Aadmi Hai\":\n",
    "        print('Chup Chutiye')\n",
    "    elif n==\"Sajid Khan\":\n",
    "        return \"According to history Sajid was the reason someone came up with the word 'Chutiya'\"\n",
    "    elif n==\"Maruf\":\n",
    "        return \"Tu Kisko chutiya banara hai jo pehle se chutiya hai\"\n",
    "    elif n==\"Pranav\":\n",
    "        return \"Sajid ko dekhke bas muh se bas shabdt chutiya nikalta hai\"\n",
    "    elif n==\"Yogesh Patil\":\n",
    "        return \"Ekdum Cheeel kawwa kabutar\"\n",
    "    elif n==\"Zoheb\":\n",
    "        return \"Insaan ki shakal badalji jaasakti hai lekin uski fitrat kabhi nahi badalti in context to recent Amaan's change of behavior\"\n",
    "    elif n==\"Amman\":\n",
    "        print('Mumbra ki saari ladkiya kharab hai','\\n''Bajaj ki gadi toh bas khadi rehti hai showroom se sidha garage')\n",
    "    elif n==\"Fazal\":\n",
    "        print('Fazal was the first Chutiya Scientiest to discover Sperm in knees','\\n''His Expertise are finding sperm at places were anybody can be suspicious','\\n'\"Loves to gang bang ahmm i mean loves to hang with 21 males friends gives them choco and calls it a day by kissing their dick a good bye and doesn't says no homo\")\n",
    "    else:\n",
    "        print('Mast Aadmi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "449a703a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow(5,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a483da36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Number(x):\n",
    "    if x%2==0:\n",
    "        return \"even\"\n",
    "    elif x%2==1:\n",
    "        return \"odd\"\n",
    "    else:\n",
    "        return \"fraction\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aedf8a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fraction', 'even', 'odd']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(Number,[2.20,12,13]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc01e67",
   "metadata": {},
   "source": [
    " # List Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1ec7b8",
   "metadata": {},
   "source": [
    "## Adding into the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e95dec21",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [0,1,2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9564c156",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.append(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "057a92cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "615762e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.extend([7,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb013ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e7bc246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "980dee44",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.extend(range(9, 14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "301239fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e639457",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.extend([14,15,16,17])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3d831d",
   "metadata": {},
   "source": [
    "### Insert is used to replace number of a particular index number "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3aee1cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.insert(18,19) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4f04e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540a10e6",
   "metadata": {},
   "source": [
    "### Removing elements from a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46b7a9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "095e5130",
   "metadata": {},
   "outputs": [],
   "source": [
    "del lst[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5d32c18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be1bc13",
   "metadata": {},
   "source": [
    "### Replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ffa5b095",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst[6] = 6 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ee2683",
   "metadata": {},
   "source": [
    "### Repitition Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee0a81c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst=[1,2,3]\n",
    "lst*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c831eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python', 'Python', 'Python']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml=[\"Python\"]\n",
    "ml*3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acac7274",
   "metadata": {},
   "source": [
    "### Sort a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d295695",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst=[67,34,42,12,98,56]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "efd2f707",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7da57ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 34, 42, 56, 67, 98]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bec56a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b889255d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[98, 67, 56, 42, 34, 12]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1d794c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Name=['Tom','Kate','George','Pete','Veronica','William','Adam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec5d2f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Name.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "43f7b3ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Adam', 'George', 'Kate', 'Pete', 'Tom', 'Veronica', 'William']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "314e4792",
   "metadata": {},
   "outputs": [],
   "source": [
    "Name.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "15aa98e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['William', 'Veronica', 'Tom', 'Pete', 'Kate', 'George', 'Adam']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a16271e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mumbra ki saari ladkiya kharab hai \n",
      "Bajaj ki gadi toh bas khadi rehti hai showroom se sidha garage\n"
     ]
    }
   ],
   "source": [
    "alexa('Aman Kalsekar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "416f0a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value of pi is 33.256'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'value of pi is {:.3f}' .format(33.256)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a7ee08",
   "metadata": {},
   "source": [
    "### Sets\n",
    "* A unique collection of IMMUTABLE items/objects\n",
    "* Syntax: ()\n",
    "* MUTABLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2e7378ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = set('Allahabad')\n",
    "b = set('Bangalore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1ce90db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A', 'a', 'b', 'd', 'h', 'l'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "84cb9761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B', 'a', 'e', 'g', 'l', 'n', 'o', 'r'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "62ed7948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A', 'B', 'a', 'b', 'd', 'e', 'g', 'h', 'l', 'n', 'o', 'r'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Union\n",
    "a | b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "999f7365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A', 'b', 'd', 'h'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "00c8521a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B', 'e', 'g', 'n', 'o', 'r'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b - a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "721f36aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'l'}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intersection\n",
    "a & b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38df5f07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa146b56",
   "metadata": {},
   "source": [
    "### string str = \"mumbai   is   the financial       capital of india\";\n",
    "### output str=\"mumbai is the financial capital of india\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6349de40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str = mumbai   is   the financial       capital of india\n",
      "output =  mumbai is the financial capital of india\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "print('str =', \"mumbai   is   the financial       capital of india\")\n",
    "str = \"mumbai   is   the financial       capital of india\"\n",
    "print('output = ',re.sub(' +', ' ', str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e38072a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-06T14:47:30.925882Z",
     "start_time": "2022-11-06T14:47:30.903884Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n",
      "Even\n",
      "Odd\n"
     ]
    }
   ],
   "source": [
    "for i in list(range(1,30)):\n",
    "    if i%2 == 0:\n",
    "        print('Even')\n",
    "    else:\n",
    "        print('Odd')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77b76d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n"
     ]
    }
   ],
   "source": [
    "lists = [ 1, 2, 3, 4, 5, 6 , 7 ,8, 9, 10, 11, 12, 13, 14, 15, 16]\n",
    "\n",
    "for i in lists:\n",
    "    if i%3 == 0:\n",
    "        print('Valid')\n",
    "    else:\n",
    "        print('Invalid')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6fbb8ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n",
      "Invalid\n",
      "Valid\n",
      "Invalid\n"
     ]
    }
   ],
   "source": [
    "lists = list(range(1, 17))\n",
    "\n",
    "for i in lists:\n",
    "    if i%3 == 0:\n",
    "        print('Valid')\n",
    "    else:\n",
    "        print('Invalid')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57483317",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [24]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.25\u001b[39;49m\u001b[43m)\u001b[49m)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "list(range(1, 3, 0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e631d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "818be39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.  , 1.25, 1.5 , 1.75, 2.  ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "891b0ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Welcome'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81705e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1894321250736"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ca0451b",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = s.replace('W','Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d488ee19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Zelcome'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04596107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1894321258160"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e4c2a4",
   "metadata": {},
   "source": [
    "# User Defined Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1aa97d74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:52:18.828504Z",
     "start_time": "2022-11-20T18:52:18.799505Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local Python Object\n",
      "Inside myFunction\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'a' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInside myFunction\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m myFunction()\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe value of a: \u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[43ma\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'a' is not defined"
     ]
    }
   ],
   "source": [
    "def myFunction():\n",
    "    a = 'Local Python Object'\n",
    "    print(a)\n",
    "    print('Inside myFunction')\n",
    "\n",
    "myFunction()\n",
    "\n",
    "print('The value of a: ', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba091af8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:51:45.508605Z",
     "start_time": "2022-11-20T18:51:45.496603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of Temp:  Global Python Object\n",
      "Inside of myFunction\n",
      "The value of temp in global scope:  Global Python Object\n"
     ]
    }
   ],
   "source": [
    "def myFunction():\n",
    "    global temp\n",
    "    temp = \"Global Python Object\"\n",
    "    print('Value of Temp: ', temp)\n",
    "    print(\"Inside of myFunction\")\n",
    "\n",
    "myFunction()\n",
    "print(\"The value of temp in global scope: \", temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a186fd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:52:20.938156Z",
     "start_time": "2022-11-20T18:52:20.924158Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of temp in global scope:  Global Python Object\n"
     ]
    }
   ],
   "source": [
    "def myFunction():\n",
    "    global temp\n",
    "    temp = \"Global Python Object\"\n",
    "    print('Value of Temp: ', temp)\n",
    "    print(\"Inside of myFunction\")\n",
    "\n",
    "# myFunction()\n",
    "print(\"The value of temp in global scope: \", temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6c71a00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:52:52.573584Z",
     "start_time": "2022-11-20T18:52:52.555580Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of temp in global scope:  Global Python Object\n",
      "Value of Temp:  Global Python Object\n",
      "Inside of myFunction\n"
     ]
    }
   ],
   "source": [
    "def myFunction():\n",
    "    global temp\n",
    "    temp = \"Global Python Object\"\n",
    "    print('Value of Temp: ', temp)\n",
    "    print(\"Inside of myFunction\")\n",
    "\n",
    "print(\"The value of temp in global scope: \", temp)\n",
    "\n",
    "myFunction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "003eeaad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:55:37.472367Z",
     "start_time": "2022-11-20T18:55:37.453364Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of runscored in main:  1\n"
     ]
    }
   ],
   "source": [
    "#global runscored\n",
    "runscored = 1\n",
    "\n",
    "def localFunction():\n",
    "    runscored = 2 # new local object\n",
    "    print('The value of runscored in localFunction: ', runscored)\n",
    "    \n",
    "print('The value of runscored in main: ', runscored)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "359c0d0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T18:56:16.563615Z",
     "start_time": "2022-11-20T18:56:16.544585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of runscored in main:  1\n",
      "The value of runscored in localFunction:  2\n",
      "After calling the function, The value of runscored in main:  1\n"
     ]
    }
   ],
   "source": [
    "#global runscored\n",
    "runscored = 1\n",
    "\n",
    "def localFunction():\n",
    "    runscored = 2 # new local object\n",
    "    print('The value of runscored in localFunction: ', runscored)\n",
    "    \n",
    "print('The value of runscored in main: ', runscored)\n",
    "localFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6505a6f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T14:41:23.085441Z",
     "start_time": "2022-11-21T14:41:23.065454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of runscored in main:  1\n",
      "The value of runscored in localFunction:  2\n",
      "After calling the local function, The value of runscored in main:  1\n",
      "The value of runscored in globalFunction():  3\n",
      "After calling the local function, The value of runscored in main:  3\n"
     ]
    }
   ],
   "source": [
    "#global runscored\n",
    "runscored = 1\n",
    "\n",
    "def localFunction():\n",
    "    runscored = 2 # new local object\n",
    "    print('The value of runscored in localFunction: ', runscored)\n",
    "    \n",
    "def globalFunction():\n",
    "    global runscored\n",
    "    runscored = 3\n",
    "    print('The value of runscored in globalFunction(): ', runscored)\n",
    "    \n",
    "print('The value of runscored in main: ', runscored)\n",
    "localFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)\n",
    "globalFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4da1282",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T14:42:18.934517Z",
     "start_time": "2022-11-21T14:42:18.914516Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of runscored in main:  1\n",
      "The value of runscored in localFunction:  2\n",
      "After calling the local function, The value of runscored in main:  1\n",
      "The value of runscored in globalFunction():  3\n",
      "After calling the local function, The value of runscored in main:  3\n",
      "The value of runscored in localFunction:  2\n",
      "After calling the local function, The value of runscored in main:  3\n"
     ]
    }
   ],
   "source": [
    "#global runscored\n",
    "runscored = 1\n",
    "\n",
    "def localFunction():\n",
    "    runscored = 2 # new local object\n",
    "    print('The value of runscored in localFunction: ', runscored)\n",
    "    \n",
    "def globalFunction():\n",
    "    global runscored\n",
    "    runscored = 3\n",
    "    print('The value of runscored in globalFunction(): ', runscored)\n",
    "    \n",
    "print('The value of runscored in main: ', runscored)\n",
    "localFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)\n",
    "globalFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)\n",
    "localFunction()\n",
    "print('After calling the local function, The value of runscored in main: ', runscored)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fbeb9a",
   "metadata": {},
   "source": [
    "## Write a program to calculate bmi - body mass indes\n",
    "### BMI = Weight (kg) / Height$^2$ (m$^2$) \n",
    "+ **If BMI <= 18.5 print 'Underweight'**\n",
    "+ **If BMI <= 24.9 print 'Normal'**\n",
    "+ **If BMI <= 29.9 print 'Overweight'**\n",
    "+ **If BMI >= 30.0 print 'Obese'**\n",
    "\n",
    "#### Inputs = 1. Weight and 2. Height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c0065d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T15:35:53.287293Z",
     "start_time": "2022-11-02T15:35:48.756872Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your Weight here: (kg)60\n",
      "Enter your Height here: (cm)177\n",
      "'You are Natural Weight'\n"
     ]
    }
   ],
   "source": [
    "weight = float(input('Enter your Weight here: (kg)'))\n",
    "height = float(input ('Enter your Height here: (cm)'))\n",
    "\n",
    "bmi = (weight)/(height/100)**2\n",
    "\n",
    "if bmi < 18.5:\n",
    "    print(\"'You are Under Weight'\")\n",
    "elif  18.5 < bmi < 24.9:\n",
    "    print(\"'You have a Natural Weight'\")\n",
    "elif  25.0 < bmi < 29.9:\n",
    "    print(\"'You are Over Weight'\")\n",
    "else:\n",
    "    print(\"'You are OBESE!'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d18ceae",
   "metadata": {},
   "source": [
    "## Factorial of Number"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9bb8fd",
   "metadata": {},
   "source": [
    "### Iterative Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b250da89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5! = 5 * 4 * 3 * 2 * 1\n",
    "# 1! = 1\n",
    "# 0! = 1\n",
    "# n! = n * (n-1) * (n-2) * (n-3) * .... * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bf009bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:28.355773Z",
     "start_time": "2022-11-12T09:58:28.344755Z"
    }
   },
   "outputs": [],
   "source": [
    "def Iterative_Factorial(num):\n",
    "    result = 1\n",
    "    for i in range(1, num + 1):\n",
    "        result  = result * i\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97083cc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:28.605112Z",
     "start_time": "2022-11-12T09:58:28.586075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iterative_Factorial(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "554e6e4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:28.871161Z",
     "start_time": "2022-11-12T09:58:28.858166Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iterative_Factorial(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c7a009cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:29.247036Z",
     "start_time": "2022-11-12T09:58:29.242037Z"
    }
   },
   "outputs": [],
   "source": [
    "def Iterative_Factorial(num):\n",
    "    result = 1\n",
    "    for i in range(num, 0, -1):\n",
    "        result  = result * i\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "36b405df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:29.682678Z",
     "start_time": "2022-11-12T09:58:29.673682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iterative_Factorial(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e812633c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T09:58:33.497068Z",
     "start_time": "2022-11-12T09:58:33.488035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iterative_Factorial(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e6f6a7",
   "metadata": {},
   "source": [
    "### Recursive Appraoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0e232db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5! = 5 * 4!\n",
    "# n! = n * (n-1)*\n",
    "# 0! = 1\n",
    "# 0! = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9983670",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T13:31:28.281330Z",
     "start_time": "2022-11-12T13:31:28.265300Z"
    }
   },
   "outputs": [],
   "source": [
    "def Recursive_Factorial(num):\n",
    "    if num == 0 or num == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return num * Recursive_Factorial(num - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35c2bbb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T13:31:28.517298Z",
     "start_time": "2022-11-12T13:31:28.488299Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recursive_Factorial(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "079996cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T13:31:28.688294Z",
     "start_time": "2022-11-12T13:31:28.667297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recursive_Factorial(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a43ca4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-12T13:37:28.635904Z",
     "start_time": "2022-11-12T13:37:28.624902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recursive_Factorial(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4393af08",
   "metadata": {},
   "source": [
    "## Default Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab630b98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T09:20:22.587197Z",
     "start_time": "2022-11-13T09:20:22.569026Z"
    }
   },
   "outputs": [],
   "source": [
    "def defaultargumentfunction(x=123):\n",
    "    print(\"The value of x is {}\".format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "472889e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T09:20:35.908068Z",
     "start_time": "2022-11-13T09:20:35.892032Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of x is 123\n"
     ]
    }
   ],
   "source": [
    "defaultargumentfunction()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc49f5e",
   "metadata": {},
   "source": [
    "## Lambda Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ab193de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lamda functions\n",
    "# Function with \"NO\" Name\n",
    "# Anonymous Function\n",
    "# One-Liner Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59936b0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:07:10.077280Z",
     "start_time": "2022-11-13T10:07:10.065248Z"
    }
   },
   "outputs": [],
   "source": [
    "f =  lambda num: num * num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9ff1d03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:07:15.696974Z",
     "start_time": "2022-11-13T10:07:15.652986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5044b34a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:07:58.781482Z",
     "start_time": "2022-11-13T10:07:58.763490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7edd0827",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:08:54.554707Z",
     "start_time": "2022-11-13T10:08:54.547741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda num: num * num)(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8587441",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:16:39.103354Z",
     "start_time": "2022-11-13T10:16:39.088286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y: x if x > y else y)(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b3116b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T10:18:11.735078Z",
     "start_time": "2022-11-13T10:18:11.715997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'100'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x: str(x))(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86036883",
   "metadata": {},
   "source": [
    "## args and kwargs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9682cef9",
   "metadata": {},
   "source": [
    "### Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d7a3c04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:32:55.364999Z",
     "start_time": "2022-11-13T13:32:55.353006Z"
    }
   },
   "outputs": [],
   "source": [
    "def SumofNumber(*args): # *args packing into a tuple\n",
    "    print('Args: ', args)\n",
    "    print('Type of args: ', type(args))\n",
    "    result = 0\n",
    "    for i in args:\n",
    "        result += i\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8b312d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:32:55.961346Z",
     "start_time": "2022-11-13T13:32:55.938312Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args:  (1, 2, 3, 4)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SumofNumber(1,2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4cae9692",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:43:12.896328Z",
     "start_time": "2022-11-13T13:43:12.890319Z"
    }
   },
   "outputs": [],
   "source": [
    "t = (5,4,2,3,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7785d477",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:43:28.126330Z",
     "start_time": "2022-11-13T13:43:28.106313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "4\n",
      "2\n",
      "3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in t:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a4d4153",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:44:17.061266Z",
     "start_time": "2022-11-13T13:44:17.037272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args:  (5, 4, 2, 3, 1)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SumofNumber(t[0], t[1], t[2], t[3], t[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "62b400dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:44:42.598595Z",
     "start_time": "2022-11-13T13:44:42.581559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args:  (5, 4, 2, 3, 1)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SumofNumber(*t) # uppacking the caller side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f3762628",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:03:57.825502Z",
     "start_time": "2022-11-13T14:03:57.810501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args:  (1, 2, 3, 4, 5)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = {2, 1, 3, 4, 5}\n",
    "SumofNumber(*s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "852c9fb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:51:04.055512Z",
     "start_time": "2022-11-13T13:50:58.019967Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter comma separated numbers to add: 1,2,4,5,3\n"
     ]
    }
   ],
   "source": [
    "userInput = input(\"Enter comma separated numbers to add: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2522154",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:51:32.825798Z",
     "start_time": "2022-11-13T13:51:32.812765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '2', '4', '5', '3']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userInput.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d6d8a27c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:53:42.278574Z",
     "start_time": "2022-11-13T13:53:42.267575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 4, 5, 3]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newNo = [int(item) for item in userInput.split(',')]\n",
    "newNo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ca8dd80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T13:54:27.904903Z",
     "start_time": "2022-11-13T13:54:27.883925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args:  (1, 2, 4, 5, 3)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SumofNumber(*newNo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7c7c8568",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:04:09.393500Z",
     "start_time": "2022-11-13T14:04:01.429501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter comma separated numbers to add: 1,2,3,4,5,6\n",
      "Args:  (1, 2, 3, 4, 5, 6)\n",
      "Type of args:  <class 'tuple'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SumofNumber(*[int(item) for item in input(\"Enter comma separated numbers to add: \").split(',')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102a0430",
   "metadata": {},
   "source": [
    "### Key word arguments (kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9b21c4be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:06:54.390773Z",
     "start_time": "2022-11-13T14:06:54.375775Z"
    }
   },
   "outputs": [],
   "source": [
    "# It is used as dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "63963ab7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:10:39.239492Z",
     "start_time": "2022-11-13T14:10:39.231492Z"
    }
   },
   "outputs": [],
   "source": [
    "def printplayersdetails(**kwargs): # packing + Dict\n",
    "    print(\"kwargs: \", kwargs)\n",
    "    print(\"Type of kwargs: \", type(kwargs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1fe32df7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:11:15.801565Z",
     "start_time": "2022-11-13T14:11:15.789559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB'}\n",
      "Type of kwargs:  <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "24e59e06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T14:11:43.598500Z",
     "start_time": "2022-11-13T14:11:43.577500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000}\n",
      "Type of kwargs:  <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84ec61e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:31:26.803969Z",
     "start_time": "2022-11-13T16:31:26.783969Z"
    }
   },
   "outputs": [],
   "source": [
    "def printplayersdetails(**kwargs): # packing + Dict\n",
    "    print(\"kwargs: \", kwargs)\n",
    "    print(\"Type of kwargs: \", type(kwargs))\n",
    "    if 'version' not in kwargs:\n",
    "        print(\"Not a valid client\")\n",
    "    elif kwargs['version'] == 'v1':\n",
    "        print(\"Printing for version V1\")\n",
    "    elif kwargs['version'] == 'v2':\n",
    "        print(\"Printing for version V2\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8f25d76",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:32:08.668658Z",
     "start_time": "2022-11-13T16:32:08.653657Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000, 'version': 'v1'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Printing for version V1\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000, version='v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ba2333c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:32:16.955755Z",
     "start_time": "2022-11-13T16:32:16.948787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000, 'version': 'v2'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Printing for version V2\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000, version='v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3792365",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:32:27.270097Z",
     "start_time": "2022-11-13T16:32:27.260101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Not a valid client\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6df4f6f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:34:18.856496Z",
     "start_time": "2022-11-13T16:34:18.844498Z"
    }
   },
   "outputs": [],
   "source": [
    "def printplayersdetails(**kwargs): # packing + Dict\n",
    "    print(\"kwargs: \", kwargs)\n",
    "    print(\"Type of kwargs: \", type(kwargs))\n",
    "    if 'version' not in kwargs:\n",
    "        print(\"Not a valid client\")\n",
    "        return\n",
    "    if kwargs['version'] == 'v1':\n",
    "        print(\"Printing for version V1\")\n",
    "    elif kwargs['version'] == 'v2':\n",
    "        print(\"Printing for version V2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6548a5eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:34:26.107824Z",
     "start_time": "2022-11-13T16:34:26.098792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Not a valid client\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db73b109",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:34:45.556251Z",
     "start_time": "2022-11-13T16:34:45.548246Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'Virat', 'age': 33, 'team': 'RCB', 'salary': 1000, 'version': 'v1'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Printing for version V1\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(name='Virat', age=33, team=\"RCB\", salary=1000, version='v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8e11617",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:38:40.362929Z",
     "start_time": "2022-11-13T16:38:40.350941Z"
    }
   },
   "outputs": [],
   "source": [
    "d = {\"name\": \"MSD\", 'age':39, \"team\": 'CSK', \"version\":'v2'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d71b35b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:38:50.769699Z",
     "start_time": "2022-11-13T16:38:50.748701Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'MSD', 'age': 39, 'team': 'CSK', 'version': 'v2'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Printing for version V2\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(**d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18f5221e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:39:09.090878Z",
     "start_time": "2022-11-13T16:39:09.085877Z"
    }
   },
   "outputs": [],
   "source": [
    "d = {\"name\": \"MSD\", 'age':39, \"team\": 'CSK', \"version\":'v1'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31d57605",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:39:10.925750Z",
     "start_time": "2022-11-13T16:39:10.915579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'MSD', 'age': 39, 'team': 'CSK', 'version': 'v1'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Printing for version V1\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(**d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b368f369",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:39:30.112074Z",
     "start_time": "2022-11-13T16:39:30.102965Z"
    }
   },
   "outputs": [],
   "source": [
    "d = {\"name\": \"MSD\", 'age':39, \"team\": 'CSK'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a8af8ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-13T16:39:30.583790Z",
     "start_time": "2022-11-13T16:39:30.567797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kwargs:  {'name': 'MSD', 'age': 39, 'team': 'CSK'}\n",
      "Type of kwargs:  <class 'dict'>\n",
      "Not a valid client\n"
     ]
    }
   ],
   "source": [
    "printplayersdetails(**d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227495eb",
   "metadata": {},
   "source": [
    "# Map Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e8947e",
   "metadata": {},
   "source": [
    "## Exercise 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cdbab81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take a sequence of input (numbers) (which are comma separated) and\n",
    "# Calculate the sum\n",
    "\n",
    "# Enter sequence of numbers: 1, 2, 3, 4, 5\n",
    "# Result 15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbdcaa0",
   "metadata": {},
   "source": [
    "### Approach 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da41db64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T13:21:04.415900Z",
     "start_time": "2022-11-20T13:20:57.193546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please Enter comma separated Numbers: 1, 2, 3, 4, 5\n",
      "Sum of Entered Numbers:  15\n"
     ]
    }
   ],
   "source": [
    "s = 0\n",
    "for i in input(\"Please Enter comma separated Numbers: \").split(','):\n",
    "    s = s + int(i)\n",
    "print('Sum of Entered Numbers: ',s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15492510",
   "metadata": {},
   "source": [
    "### Approach 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "213b08b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:11:44.575586Z",
     "start_time": "2022-11-20T14:11:28.071338Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please Enter comma separated Numbers: 4, 1, 3, 5, 2\n",
      "Sum of Entered Numbers:  15\n"
     ]
    }
   ],
   "source": [
    "print(\"Sum of Entered Numbers: \", sum(list(map(int, input(\"Please Enter comma separated Numbers: \").split(',')))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da4061f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:26:44.184901Z",
     "start_time": "2022-11-20T14:26:31.267992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please Enter comma separated Numbers: 1,2,3,4,5,6,7,8,9,10\n",
      "Sum of Entered Numbers:  55\n"
     ]
    }
   ],
   "source": [
    "print(\"Sum of Entered Numbers: \", sum(list(map(int, input(\"Please Enter comma separated Numbers: \").split(',')))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35183c1",
   "metadata": {},
   "source": [
    "# Filter Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26e0dec4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:15:00.294381Z",
     "start_time": "2022-11-20T14:15:00.277378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Find Even number\n",
    "even = lambda n: n % 2 == 0\n",
    "print(even(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42d0c1b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:17:42.490627Z",
     "start_time": "2022-11-20T14:17:42.468106Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "def isEven(n):\n",
    "    return n % 2 ==0\n",
    "print(isEven(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8718e731",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:22:18.944410Z",
     "start_time": "2022-11-20T14:22:18.926411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result using Filter - Even Numbers:  [2, 4, 6, 8, 10, 12, 14]\n"
     ]
    }
   ],
   "source": [
    "print(\"Result using Filter - Even Numbers: \", list(filter(lambda n: n % 2 == 0, range(1,16))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25f48dcd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:22:19.194408Z",
     "start_time": "2022-11-20T14:22:19.184412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result using Map - Even Numbers:  [False, True, False, True, False, True, False, True, False, True, False, True, False, True, False]\n"
     ]
    }
   ],
   "source": [
    "print(\"Result using Map - Even Numbers: \", list(map(lambda n: n % 2 == 0, range(1,16))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc784188",
   "metadata": {},
   "source": [
    "# Reduce Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa8e103e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:27:57.968937Z",
     "start_time": "2022-11-20T14:27:57.962936Z"
    }
   },
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e6255b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:28:48.203524Z",
     "start_time": "2022-11-20T14:28:48.183518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n"
     ]
    }
   ],
   "source": [
    "print(list(range(1,20)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f28aaa28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:25:14.430197Z",
     "start_time": "2022-11-20T14:25:14.422194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "myfunc = lambda x, y: x + y\n",
    "print(myfunc(7, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f587bde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:28:00.578603Z",
     "start_time": "2022-11-20T14:28:00.565606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result using Reduce Function:  55\n"
     ]
    }
   ],
   "source": [
    "print(\"Result using Reduce Function: \", reduce(lambda x, y: x + y, range(1, 11)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15f50b0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-20T14:30:17.500531Z",
     "start_time": "2022-11-20T14:30:17.491536Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result using Reduce Function:  120\n"
     ]
    }
   ],
   "source": [
    "print(\"Result using Reduce Function: \", reduce(lambda x, y: x * y, range(1, 6)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e44827c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hadoop\n",
    "# Map-Reduce\n",
    "# WordCount\n",
    "# 100 files OR 1 Files = 100GB\n",
    "# 100 parts - 100 * 1 GB => 100 Systems ->\n",
    "# {WordA: 1, WordB: 10, WordC: 35: ....}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d84e5e",
   "metadata": {},
   "source": [
    "# Exception Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf0af7d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:03:45.319483Z",
     "start_time": "2022-11-21T17:03:43.883531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 5\n",
      "Result:  20.0\n"
     ]
    }
   ],
   "source": [
    "userInput = input(\"Enter any number: \")\n",
    "result = 100 / int(userInput)\n",
    "print(\"Result: \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0da1beb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:50:43.653832Z",
     "start_time": "2022-11-21T17:50:42.695155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: a\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m userInput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnter any number: \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43muserInput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult: \u001b[39m\u001b[38;5;124m\"\u001b[39m, result)\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'"
     ]
    }
   ],
   "source": [
    "userInput = input(\"Enter any number: \")\n",
    "result = 100 / int(userInput)\n",
    "print(\"Result: \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5463968e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:53:23.228454Z",
     "start_time": "2022-11-21T17:53:23.211460Z"
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1545504434.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Input \u001b[1;32mIn [11]\u001b[1;36m\u001b[0m\n\u001b[1;33m    excetp ZeroDivisionError:\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb25516b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:52:44.270281Z",
     "start_time": "2022-11-21T17:52:42.582704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m      2\u001b[0m     userInput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnter any number: \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;241;43m100\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43muserInput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult: \u001b[39m\u001b[38;5;124m\"\u001b[39m, result)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13960b6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:54:07.306443Z",
     "start_time": "2022-11-21T17:54:06.253587Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n",
      "Please enter number greater than Zero.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')\n",
    "except ZeroDivisionError:\n",
    "    print(\"Please enter number greater than Zero.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22a10912",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:54:26.066424Z",
     "start_time": "2022-11-21T17:54:23.721857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 101\n",
      "Result:  0.9900990099009901\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')\n",
    "except ZeroDivisionError:\n",
    "    print(\"Please enter number greater than Zero.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc798aab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T17:54:44.930194Z",
     "start_time": "2022-11-21T17:54:42.215050Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: %\n",
      "Please enter only numbers!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')\n",
    "except ZeroDivisionError:\n",
    "    print(\"Please enter number greater than Zero.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ab42a06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T18:01:31.018227Z",
     "start_time": "2022-11-21T18:01:28.677232Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: i\n",
      "Invalid Input Error Occured\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except Exception:\n",
    "    print('Invalid Input Error Occured')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0fa5fe0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T18:02:23.817444Z",
     "start_time": "2022-11-21T18:02:21.454135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: *\n",
      "Exception Error occured:  invalid literal for int() with base 10: '*'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except Exception as e:\n",
    "    print('Exception Error occured: ', e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6e5bf65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T18:03:30.974959Z",
     "start_time": "2022-11-21T18:03:29.432015Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n",
      "Exception Error occured:  division by zero <class 'ZeroDivisionError'>\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except Exception as e:\n",
    "    print('Exception Error occured: ', e, e.__class__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6315cc10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T18:10:55.882825Z",
     "start_time": "2022-11-21T18:10:53.875075Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n",
      "Please enter number greater than Zero.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except ValueError:\n",
    "    print('Please enter only numbers!')\n",
    "except ZeroDivisionError:\n",
    "    print(\"Please enter number greater than Zero.\")\n",
    "except Exception as e:\n",
    "    print('Exception Error occured: ', e, e.__class__, str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b905198",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-21T18:11:23.921720Z",
     "start_time": "2022-11-21T18:11:20.709136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n",
      "Exception Error occured:  division by zero <class 'ZeroDivisionError'> division by zero\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except Exception as e:\n",
    "    print('Exception Error occured: ', e, e.__class__, str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae7c86fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T06:44:03.075319Z",
     "start_time": "2022-11-22T06:44:00.309464Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number: 0\n",
      "Exception Error occured:  division by zero <class 'ZeroDivisionError'> division by zero ZeroDivisionError\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    userInput = input(\"Enter any number: \")\n",
    "    result = 100 / int(userInput)\n",
    "    print(\"Result: \", result)\n",
    "except Exception as e:\n",
    "    print('Exception Error occured: ', e, e.__class__, str(e), type(e).__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc1b529",
   "metadata": {},
   "source": [
    "# REGEXP Funtions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7c64287",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T07:18:17.201403Z",
     "start_time": "2022-11-22T07:18:17.132406Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Welcome', 'to', 'Regexp', 'Programming', 'using', 'Python']\n"
     ]
    }
   ],
   "source": [
    "print(str(\"Welcome to   Regexp    Programming   using  Python\").split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f4ead6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T07:18:29.674179Z",
     "start_time": "2022-11-22T07:18:29.657878Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Welcome', 'to', '', '', 'Regexp', '', '', '', 'Programming', '', '', 'using', '', 'Python']\n"
     ]
    }
   ],
   "source": [
    "print(str(\"Welcome to   Regexp    Programming   using  Python\").split(' '))"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "8b2862b6",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "222d41c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:02:09.655795Z",
     "start_time": "2022-11-22T13:02:09.641799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of s       :    Welcome to   Regexp    Programming   using  Python\n",
      "Using regular Split  :  ['Welcome', 'to', '', '', 'Regexp', '', '', '', 'Programming', '', '', 'using', '', 'Python']\n",
      "Regex split using \\s :  ['Welcome', 'to', '', '', 'Regexp', '', '', '', 'Programming', '', '', 'using', '', 'Python']\n",
      "Regex split using \\s+:  ['Welcome', 'to', 'Regexp', 'Programming', 'using', 'Python']\n",
      "Regex split using s+ :  ['Welcome to   Regexp    Programming   u', 'ing  Python']\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "s = \"Welcome to   Regexp    Programming   using  Python\"\n",
    "print(\"The value of s       :   \", s)\n",
    "\n",
    "print('Using regular Split  : ', s.split(' '))\n",
    "\n",
    "print(\"Regex split using \\s : \", re.split(r\"\\s\", s))\n",
    "\n",
    "print(\"Regex split using \\s+: \", re.split(r\"\\s+\", s))\n",
    "\n",
    "print(\"Regex split using s+ : \", re.split(r\"s+\", s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "42c46cd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T07:56:16.011142Z",
     "start_time": "2022-11-22T07:56:15.990143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of s         :    Welcome to Regex Programming using Python\n",
      "Regex split using '[]' :  ['W', 'l', 'om', ' to R', 'g', 'x Progr', 'mming using Python']\n"
     ]
    }
   ],
   "source": [
    "s =  \"Welcome to Regex Programming using Python\"\n",
    "\n",
    "print(\"The value of s         :   \", s)\n",
    "\n",
    "print(\"Regex split using '[]' : \", re.split(r\"[a-f]\", s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1eb9a3b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T09:42:37.410756Z",
     "start_time": "2022-11-22T09:42:37.400724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of s         :    Welcome to Regex Programming using Python\n",
      "Regex split using '[]' :  ['W', 'come to Regex Progr', 'ming using Python']\n"
     ]
    }
   ],
   "source": [
    "s =  \"Welcome to Regex Programming using Python\"\n",
    "\n",
    "print(\"The value of s         :   \", s)\n",
    "\n",
    "print(\"Regex split using '[]' : \", re.split(r\"[a-f][l-n]\", s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba01f042",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T10:24:32.124471Z",
     "start_time": "2022-11-22T10:24:32.113507Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi\n",
      "11\n",
      "89\n",
      "Main,\n",
      "4th\n",
      "Cross,\n",
      "123\n",
      "Road,\n",
      "Marathalli,\n",
      "5678\n",
      "Bangalore,\n",
      "560023\n",
      "67893\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11 89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "for i in address.split():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b42bee1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T10:29:35.910815Z",
     "start_time": "2022-11-22T10:29:35.902821Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern:  ['11', '89', '4', '123', '5678', '560023', '67893']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11 89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern: \", re.findall(r\"\\d+\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b5e21014",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T10:43:15.444634Z",
     "start_time": "2022-11-22T10:43:15.426641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern:  ['11 ', '89 ', '4th', '23 ', '78 ', '23 ']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11 89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern: \", re.findall(r\"\\d{1}\\S\\D\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9208a283",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T10:48:43.485299Z",
     "start_time": "2022-11-22T10:48:43.473298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['11 ', '89 ', '4th', '23 ', '78 ', '23 ']\n",
      "11  89  4th 23  78  23 \n",
      "Finding Numbers using the Pattern:  None\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11 89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "lst_Values = re.findall(r\"\\d{1}\\S\\D\", address)\n",
    "\n",
    "print(lst_Values)\n",
    "\n",
    "# using list comprehension\n",
    "listToStr = ' '.join([str(elem) for elem in lst_Values])\n",
    "print(listToStr)\n",
    "\n",
    "print(\"Finding Numbers using the Pattern: \", re.match(r\"\\d$th\", listToStr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebc5776e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:06:02.919805Z",
     "start_time": "2022-11-22T13:06:02.898802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\d{6}':  ['560023']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\d{6}': \", re.findall(r\"\\d{6}\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6eac4af8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:07:04.541704Z",
     "start_time": "2022-11-22T13:07:04.519717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{2}\\s':  [' 11 ']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11 89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{2}\\s': \", re.findall(r\"\\s\\d{2}\\s\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3d30a54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:06:06.579281Z",
     "start_time": "2022-11-22T13:06:06.567289Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{2}\\s':  [' 11 ', ' 89 ']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{2}\\s': \", re.findall(r\"\\s\\d{2}\\s\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3a2e9bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:07:25.758718Z",
     "start_time": "2022-11-22T13:07:25.751716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\d{1,6}':  ['11', '89', '4', '123', '5678', '560023', '67893']\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\d{1,6}': \", re.findall(r\"\\d{1,6}\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a4a118b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:09:12.349427Z",
     "start_time": "2022-11-22T13:09:12.330426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{2}\\s':  [' 11 ', ' 89 ']\n",
      "Finding Numbers using the Pattern '\\s?\\d{2}\\s':  ['69 ', ' 11 ', ' 89 ', '23 ', '78 ', '23 ']\n"
     ]
    }
   ],
   "source": [
    "address = '69 Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{2}\\s': \", re.findall(r\"\\s\\d{2}\\s\", address))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s?\\d{2}\\s': \", re.findall(r\"\\s?\\d{2}\\s\", address))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f8f4916",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:16:44.371172Z",
     "start_time": "2022-11-22T17:16:44.347158Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{1}\\D{2}':  [' 4th']\n",
      "Finding Numbers using the Pattern '\\s\\d{1}\\D{2}':  4\n",
      "Finding Numbers using the Pattern '\\s\\d{1}\\D{2}':  4\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{1}\\D{2}': \", re.findall(r\"\\s\\d{1}\\D{2}\", address))\n",
    "\n",
    "lstValues = ''.join(re.findall(r\"\\s\\d{1}\\D{2}\", address))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{1}\\D{2}': \",  ''.join(re.findall(r\"\\d{1}\", lstValues)))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s(\\d{1})\\D{2}': \", ''.join(re.findall(r\"\\s(\\d{1})\\D{2}\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd49a99d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:17:20.541533Z",
     "start_time": "2022-11-22T17:17:20.531535Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}':  [' 123 Road']\n",
      "Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}':  123\n",
      "Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}':  123\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}': \", re.findall(r\"\\s\\d{3}\\s\\D{4}\", address))\n",
    "\n",
    "lstValues = ''.join(re.findall(r\"\\s\\d{3}\\s\\D{4}\", address))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}': \",  ''.join(re.findall(r\"\\d{3}\", lstValues)))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s(\\d{3})\\s\\D{4}': \", ''.join(re.findall(r\"\\s(\\d{3})\\s\\D{4}\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c27442bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:18:08.330835Z",
     "start_time": "2022-11-22T17:18:08.316836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}'   :  [' 123 Road']\n",
      "Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}'   :  123\n",
      "Finding Numbers using the Pattern '\\s(\\d{3})\\s\\D{4}' :  123\n"
     ]
    }
   ],
   "source": [
    "address = 'Hi 11  89 Main, 4th Cross, 123 Road, Marathalli, 5678 Bangalore, 560023 67893'\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}'   : \", re.findall(r\"\\s\\d{3}\\sRoad\", address))\n",
    "\n",
    "lstValues = ''.join(re.findall(r\"\\s\\d{3}\\sRoad\", address))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s\\d{3}\\s\\D{4}'   : \",  ''.join(re.findall(r\"\\d{3}\", lstValues)))\n",
    "\n",
    "print(\"Finding Numbers using the Pattern '\\s(\\d{3})\\s\\D{4}' : \", ''.join(re.findall(r\"\\s(\\d{3})\\sRoad\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75771f3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:15:08.862218Z",
     "start_time": "2022-11-22T13:15:08.842214Z"
    }
   },
   "outputs": [],
   "source": [
    "s = '''\n",
    "<html>\n",
    "<head>\n",
    "<title>Current IP Address Allocations\n",
    "</title>\n",
    "</head>\n",
    "<body>\n",
    "IP Address are 172.45.78.109\n",
    "LoopBack Address: 127.0.0.1\n",
    "Computer 1: 10.67.89.101\n",
    "Computer 2: 11.67.98.102\n",
    "Computer 2: 12.68.98.102\n",
    "</body>\n",
    "</html>\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e9ece32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:20:27.327946Z",
     "start_time": "2022-11-22T13:20:27.316919Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IP Address:  ['172.45.78.109', '127.0.0.1', '10.67.89.101', '11.67.98.102', '12.68.98.102']\n"
     ]
    }
   ],
   "source": [
    "# IP Address : 4 parts (octets)\n",
    "\n",
    "lstValues = re.findall(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", s)\n",
    "print(\"IP Address: \", lstValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e4a9dee3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:29:42.180322Z",
     "start_time": "2022-11-22T13:29:42.167324Z"
    }
   },
   "outputs": [],
   "source": [
    "s = '''\n",
    "<html>\n",
    "<head>\n",
    "<title>Current IP Address Allocations\n",
    "</title>\n",
    "</head>\n",
    "<body>\n",
    "IP Address are 172.45.78.109\n",
    "LoopBack Address: 127.0.0.1\n",
    "Computer 1: 10.67.89.101\n",
    "Computer 2: 11.67.98.102\n",
    "Computer 2: 10.68.98.102\n",
    "</body>\n",
    "</html>\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ab6d735",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:30:48.736435Z",
     "start_time": "2022-11-22T13:30:48.717466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IP Address:  ['10.67.89.101', '10.68.98.102']\n"
     ]
    }
   ],
   "source": [
    "# Find only those IP Address that starts with 10\n",
    "\n",
    "lstValues = re.findall(r\"10\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", s)\n",
    "print(\"IP Address: \", lstValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cfcc5177",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T13:53:31.385567Z",
     "start_time": "2022-11-22T13:53:31.366566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IP Address:  ['10.67.89.101', '11.67.98.102', '10.68.98.102']\n"
     ]
    }
   ],
   "source": [
    "# Find only those IP Address that starts with 10 or 11\n",
    "\n",
    "lstValues = re.findall(r\"1[0-1]\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", s)\n",
    "print(\"IP Address: \", lstValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "00e27d3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:52:04.935487Z",
     "start_time": "2022-11-22T17:52:04.916486Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  9\n",
      "Output:  9\n",
      "Output:  9\n"
     ]
    }
   ],
   "source": [
    "# Find the age from the address \n",
    "\n",
    "address = \"R.Kesavan aged 9, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]\\s?\\D{1,4}?\\s?(\\d{1,2}),|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs\", address)))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\s?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[yrs|years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6a4c600a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:52:05.550844Z",
     "start_time": "2022-11-22T17:52:05.540822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  63\n",
      "Output:  63\n",
      "Output:  63\n"
     ]
    }
   ],
   "source": [
    "address = \"R.Kesavan age63, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]\\s?\\D{1,4}?\\s?(\\d{1,2}),|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs\", address)))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\s?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[yrs|years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5f15baa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:52:13.083858Z",
     "start_time": "2022-11-22T17:52:13.062859Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  61\n",
      "Output:  61\n",
      "Output:  61\n"
     ]
    }
   ],
   "source": [
    "address = \"12-4-67 , R.Kesavan aged about 61, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]\\s?\\D{1,4}?\\s?(\\d{1,2}),|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs\", address)))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\s?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[yrs|years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "65b56f3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:51:30.376325Z",
     "start_time": "2022-11-22T17:51:30.364334Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  46\n",
      "Output:  \n"
     ]
    }
   ],
   "source": [
    "address = \"16th street R.Kesavan age 46yrs, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\s?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[yrs|years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3ee90bd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:48:13.214349Z",
     "start_time": "2022-11-22T17:48:13.197347Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  60\n",
      "Output:  \n"
     ]
    }
   ],
   "source": [
    "address = \"13/35 agedabout 60 years, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[$yrs|$years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cf76b00b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:49:15.578978Z",
     "start_time": "2022-11-22T17:49:15.563972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  37\n",
      "Output:  \n"
     ]
    }
   ],
   "source": [
    "address = \"Room no 43 R.Kesavan aged about 37 years, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[age|aged]?\\s?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[$yrs|$years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1475bdf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:57:41.242886Z",
     "start_time": "2022-11-22T17:57:41.227258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:  56\n",
      "Output:  \n"
     ]
    }
   ],
   "source": [
    "address = \"Door no 32 R.Kesavan 56yrs, residing at D.NO 59 Eswaran Koil St\"\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"\\d{1,2}\", ''.join(re.findall(r\"aged\\s\\d{1,2}|\\s\\d{1,2}\\syears|\\s\\d{1,2}yrs|aged\\s\\D+\\d{1,2}|age\\d{1,2}\", address)))))\n",
    "\n",
    "print(\"Output: \", ''.join(re.findall(r\"[^age|^aged]?\\D{4}?\\s?\\D{1,4}?\\s?(\\d{1,2}),?\\s?[$yrs|$years]?,\", address)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "964eae90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T14:00:01.624375Z",
     "start_time": "2022-11-22T14:00:01.615348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid Credit Card Number is:  3775 123456 78910\n"
     ]
    }
   ],
   "source": [
    "# Find the valid credit card number with pattern below\n",
    "\n",
    "empDetails = \"Hi I have a credit card with number 3775 123456 78910 and \" \\\n",
    "\"other card is\" \\\n",
    "\"3545 456789 12345\"\n",
    "\n",
    "#http://www.getcreditcardnumbers.com\n",
    "\n",
    "# AMEX Card\n",
    "# Always Start with 3\n",
    "# Second Digit is either 4 or 7\n",
    "# <4 Digit> <6 Digit> <5 Digit>\n",
    "\n",
    "print(\"Valid Credit Card Number is: \", ''.join(re.findall(r\"3[4|7]\\d{2}\\s\\d{6}\\s\\d{5}\", empDetails)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1abe31bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T15:00:49.123070Z",
     "start_time": "2022-11-22T15:00:49.103070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Names of players ['Rohit', 'Virat', 'Dhawan']\n",
      "Scores of players [' 122', ' 96', ' 46']\n",
      "Expected:  {'Rohit': ' 122', 'Virat': ' 96', 'Dhawan': ' 46'}\n",
      "Expected:  {'Rohit': ' 122', 'Virat': ' 96', 'Dhawan': ' 46'}\n"
     ]
    }
   ],
   "source": [
    "sampleString = '''\n",
    "Rohit made 122 runs, Virat made 96 runs, and Dhawan made 46 runs and we won the match.'''\n",
    "\n",
    "#Expected: {\"Rohit\": 122, \"Virat\": 96, \"Dhawan\": 46}\n",
    "\n",
    "keys =  re.findall(r\"[A-Z][a-z]+\", sampleString)\n",
    "values = re.findall(r\"\\s\\d+\", sampleString)\n",
    "\n",
    "print('Names of players', keys)\n",
    "print('Scores of players', values)\n",
    "print('Expected: ', {keys[i]: values[i] for i in range(len(keys))})\n",
    "print('Expected: ', dict(zip(keys, values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ed0f13a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:14:03.312268Z",
     "start_time": "2022-11-22T17:14:03.300272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find all matches for format Month day\n",
      "['June 24', 'August 9', 'Dec 12']\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "print(\"Find all matches for format Month day\") \n",
    "matches = re.findall(r\"[A-Z][a-z]+\\s\\d{1,2}\", \"These are the match dates June 24, August 9, Dec 12\") \n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "714f93a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:54:24.918898Z",
     "start_time": "2022-11-22T17:54:24.906765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find all matches for format Month day\n",
      "['June', 'August', 'Dec']\n"
     ]
    }
   ],
   "source": [
    "print(\"Find all matches for format Month day\") \n",
    "matches = re.findall(r\"([A-Z][a-z]+)\\s?\\d{1,2}?\", \"These are the match dates June 24, August 9, Dec 12\") \n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b24d7892",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:54:11.647942Z",
     "start_time": "2022-11-22T17:54:11.638943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find all matches for format Month day\n",
      "['24', '9', '12']\n"
     ]
    }
   ],
   "source": [
    "print(\"Find all matches for format Month day\") \n",
    "matches = re.findall(r\"[A-Z][a-z]+\\s?(\\d{1,2})\", \"These are the match dates June 24, August 9, Dec 12, and Nov\") \n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ef8f2a13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:55:40.404630Z",
     "start_time": "2022-11-22T17:55:40.394635Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find all matches for format Month day\n",
      "['June', 'August', 'Dec']\n"
     ]
    }
   ],
   "source": [
    "print(\"Find all matches for format Month day\") \n",
    "matches = re.findall(r\"\\s([A-Z][a-z]{2,6})\\s?\\d{1,2}?\", \"These are the match dates June 24, August 9, Dec 12, and Nov\") \n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "2de74462",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T17:56:31.835321Z",
     "start_time": "2022-11-22T17:56:31.820323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find all matches for format Month day\n",
      "[('June', '24'), ('August', '9'), ('Dec', '12')]\n"
     ]
    }
   ],
   "source": [
    "print(\"Find all matches for format Month day\") \n",
    "matches = re.findall(r\"([A-Z][a-z]+)\\s?(\\d{1,2})\", \"These are the match dates June 24, August 9, Dec 12, and Nov\") \n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "159ccba4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T18:09:17.239801Z",
     "start_time": "2022-11-22T18:09:17.227798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emails:  ['alice@google.com', 'helloab@abc.com', '23@gmail.com', 'my23@gmail.com']\n"
     ]
    }
   ],
   "source": [
    "S = \"purple alice@google.com abcde helloab@abc.com ---@gmail.com 23@gmail.com my23@gmail.com\"\n",
    "emails = re.findall(r\"[a-z0-9]+\\w?@[a-z]+.com\", S)\n",
    "print(\"emails: \",emails)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f9a01543",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T18:09:06.559962Z",
     "start_time": "2022-11-22T18:09:06.551956Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emails:  ['alice@google.com', 'helloab@abc.com', '23@gmail.com', 'my23@gmail.com']\n"
     ]
    }
   ],
   "source": [
    "print(\"emails: \", re.findall(r\"\\w+@\\w+\\.\\w+\", S))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "84b66a1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T18:11:33.165539Z",
     "start_time": "2022-11-22T18:11:33.144485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emails:  ['alice@google.com', 'helloab@abc.com', 'my23@gmail.com']\n"
     ]
    }
   ],
   "source": [
    "S = \"purple alice@google.com abcde helloab@abc.com ---@gmail.com 23@gmail.com my23@gmail.com\"\n",
    "emails = re.findall(r\"[A-Za-z]+\\w+?@[a-z]+.com\", S)\n",
    "print(\"emails: \",emails)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "2d215d5e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-22T18:11:14.335659Z",
     "start_time": "2022-11-22T18:11:14.319657Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emails:  ['alice@google.com', 'helloab@abc.com', 'my23@gmail.com']\n"
     ]
    }
   ],
   "source": [
    "print(\"emails: \", re.findall(r\"[A-Za-z]\\w+@\\w+\\.\\w+\", S))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f036055",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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