Lambda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d590ed05",
   "metadata": {},
   "source": [
    "In Python, an anonymous function is a function that is defined without a name.\n",
    "\n",
    "\n",
    "While normal functions are defined using the def keyword in Python, anonymous functions are defined using the lambda keyword.\n",
    "\n",
    "\n",
    "Hence, anonymous functions are also called lambda functions."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "cdfdec53",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "22b77a7c",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd02597c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# Program to show the use of lambda functions\n",
    "double = lambda x: x * 2\n",
    "\n",
    "print(double(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3960fe78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x: x + 1)\n",
    "\n",
    "(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffe4b7bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 6, 8, 12]\n"
     ]
    }
   ],
   "source": [
    "# Program to filter out only the even items from a list\n",
    "my_list = [1, 5, 4, 6, 8, 11, 3, 12]\n",
    "new_list = list(filter(lambda x: (x%2 == 0) , my_list))\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "680e4571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 10, 8, 12, 16, 22, 6, 24]\n"
     ]
    }
   ],
   "source": [
    "# Program to double each item in a list using map()\n",
    "\n",
    "my_list = [1, 5, 4, 6, 8, 11, 3, 12]\n",
    "\n",
    "new_list = list(map(lambda x: x * 2 , my_list))\n",
    "\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15c11be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x00000219D48DCAF0>\n",
      "Debasish Sahoo\n"
     ]
    }
   ],
   "source": [
    "# Python program to demonstrate\n",
    "# lambda functions\n",
    "\n",
    "\n",
    "string ='Debasish Sahoo'\n",
    "\n",
    "# lambda returns a function object\n",
    "print(lambda string : string)\n",
    "\n",
    "(lambda string : print(string))(string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81efed5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125\n",
      "125\n"
     ]
    }
   ],
   "source": [
    "# Python code to illustrate cube of a number\n",
    "# showing difference between def() and lambda().\n",
    "def cube(y):return y*y*y\n",
    "\n",
    "lambda_cube = lambda y: y*y*y\n",
    "\n",
    "# using the normally\n",
    "# defined function\n",
    "print(cube(5))\n",
    "\n",
    "# using the lambda function\n",
    "print(lambda_cube(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3161521a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "tables = [lambda x=x: x*10 for x in range(1, 11)]\n",
    "\n",
    "for table in tables:\n",
    "\tprint(table())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d96555a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# Example of lambda function using if-else\n",
    "Max = lambda a, b : a if(a > b) else b\n",
    "\n",
    "print(Max(1, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "249ad6f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 16, 9]\n"
     ]
    }
   ],
   "source": [
    "List = [[2,3,4],[1, 4, 16, 64],[3, 6, 9, 12]]\n",
    "\n",
    "# Sort each sublist\n",
    "sortList = lambda x: (sorted(i) for i in x)\n",
    "\n",
    "# Get the second largest element\n",
    "secondLargest = lambda x, f : [y[len(y)-2] for y in f(x)]\n",
    "res = secondLargest(List, sortList)\n",
    "\n",
    "print(res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bfcea90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 97, 77, 23, 73, 61]\n"
     ]
    }
   ],
   "source": [
    "# Python code to illustrate\n",
    "# filter() with lambda()\n",
    "li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]\n",
    "\n",
    "final_list = list(filter(lambda x: (x%2 != 0) , li))\n",
    "print(final_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d64df57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[90, 59, 21, 60]\n"
     ]
    }
   ],
   "source": [
    "# Python 3 code to people above 18 yrs\n",
    "ages = [13, 90, 17, 59, 21, 60, 5]\n",
    "\n",
    "adults = list(filter(lambda age: age>18, ages))\n",
    "\n",
    "print(adults)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a5e7910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 14, 44, 194, 108, 124, 154, 46, 146, 122]\n"
     ]
    }
   ],
   "source": [
    "# Python code to illustrate\n",
    "# map() with lambda()\n",
    "# to get double of a list.\n",
    "li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]\n",
    "\n",
    "final_list = list(map(lambda x: x*2, li))\n",
    "print(final_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aad2f4fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DOG', 'CAT', 'PARROT', 'RABBIT']\n"
     ]
    }
   ],
   "source": [
    "# Python program to demonstrate\n",
    "# use of lambda() function\n",
    "# with map() function\n",
    "animals = ['dog', 'cat', 'parrot', 'rabbit']\n",
    "\n",
    "# here we intend to change all animal names\n",
    "# to upper case and return the same\n",
    "uppered_animals = list(map(lambda animal: str.upper(animal), animals))\n",
    "\n",
    "print(uppered_animals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50ddfa89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193\n"
     ]
    }
   ],
   "source": [
    "# Python code to illustrate\n",
    "# reduce() with lambda()\n",
    "# to get sum of a list\n",
    "\n",
    "from functools import reduce\n",
    "li = [5, 8, 10, 20, 50, 100]\n",
    "sum = reduce((lambda x, y: x + y), li)\n",
    "print (sum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b9d767b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum element of the list is : 6\n"
     ]
    }
   ],
   "source": [
    "# python code to demonstrate working of reduce()\n",
    "# with a lambda function\n",
    "\n",
    "# importing functools for reduce()\n",
    "import functools\n",
    "\n",
    "# initializing list\n",
    "lis = [ 1 , 3, 5, 6, 2, ]\n",
    "\n",
    "# using reduce to compute maximum element from list\n",
    "print (\"The maximum element of the list is : \",end=\"\")\n",
    "print (functools.reduce(lambda a,b : a if a > b else b,lis))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab98fc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Full name: Debasish Sahoo'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_name = lambda first, last: f'Full name: {first.title()} {last.title()}'\n",
    "full_name('Debasish', 'Sahoo')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7deec241",
   "metadata": {},
   "source": [
    "### Lambda functions     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5ca17ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.<lambda>(x, y)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda x, y: x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7cf8ef75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d3a9cc",
   "metadata": {},
   "source": [
    "## Immediately Invoked Function Expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a112fa79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y: x + y)(2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a9440c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "high_ord_func = lambda x, func: x + func(x)\n",
    "\n",
    "print(high_ord_func(2, lambda x: x * x))\n",
    "print(high_ord_func(2, lambda x: x + 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd6d2117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'function'>\n",
      "------------------------\n",
      "  2           0 LOAD_FAST                0 (x)\n",
      "              2 LOAD_FAST                1 (y)\n",
      "              4 BINARY_ADD\n",
      "              6 RETURN_VALUE\n",
      "None\n",
      "------------------------\n",
      "<function <lambda> at 0x00000219D48DCDC0>\n"
     ]
    }
   ],
   "source": [
    "import dis\n",
    "add = lambda x, y: x + y\n",
    "print(type(add))\n",
    "print('------------------------')\n",
    "print(dis.dis(add))\n",
    "print('------------------------')        \n",
    "print(add)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "62bbf0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'odd'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Single Expression\n",
    "(lambda x:(x % 2 and 'odd' or 'even'))(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb5429e",
   "metadata": {},
   "source": [
    "# Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d500516e",
   "metadata": {},
   "source": [
    "###  Positional arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b2165b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y, z: x + y + z)(1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692ab8df",
   "metadata": {},
   "source": [
    "### Named arguments (sometimes called keyword arguments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32736bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y, z=3: x + y + z)(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e06f07e",
   "metadata": {},
   "source": [
    "### Variable list of arguments (often referred to as varargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74b3504f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y, z=3: x + y + z)(1, y=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbbf0e02",
   "metadata": {},
   "source": [
    "### Variable list of keyword arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c5daab0d",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4060/2597040428.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[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\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;32m~\\AppData\\Local\\Temp/ipykernel_4060/2597040428.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(*args)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\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: 'int' object is not callable"
     ]
    }
   ],
   "source": [
    "print((lambda *args : sum(args)/len(args))(1,2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6475bfd6",
   "metadata": {},
   "source": [
    "### Keyword-only arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bded8c5a",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4060/1015547791.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[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtwo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4060/1015547791.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtwo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable"
     ]
    }
   ],
   "source": [
    "print((lambda **kwargs : sum(kwargs.values())/len(kwargs))(one = 1, two = 2, three = 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f1f56e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, *, y=0, z=0: x + y + z)(1, y=2, z=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f63b0eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def some_decorator(f):\n",
    "    def wraps(*args):\n",
    "        print(f\"Calling function '{f.__name__}'\")\n",
    "        return f(args)\n",
    "    return wraps\n",
    "\n",
    "@some_decorator\n",
    "def decorated_function(x):\n",
    "    print(f\"With argument '{x}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8be7485f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling function 'decorated_function'\n",
      "With argument '('Python',)'\n"
     ]
    }
   ],
   "source": [
    "decorated_function(\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "85b44203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRACE] func: add_two, args: (3,), kwargs: {}\n",
      "[TRACE] func: <lambda>, args: (3,), kwargs: {}\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# Defining a decorator\n",
    "def trace(f):\n",
    "    def wrap(*args, **kwargs):\n",
    "        print(f\"[TRACE] func: {f.__name__}, args: {args}, kwargs: {kwargs}\")\n",
    "        return f(*args, **kwargs)\n",
    "\n",
    "    return wrap\n",
    "\n",
    "# Applying decorator to a function\n",
    "@trace\n",
    "def add_two(x):\n",
    "    return x + 2\n",
    "\n",
    "# Calling the decorated function\n",
    "add_two(3)\n",
    "\n",
    "# Applying decorator to a lambda\n",
    "print((trace(lambda x: x ** 2))(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eb913420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRACE] func: <lambda>, args: (0,), kwargs: {}\n",
      "[TRACE] func: <lambda>, args: (1,), kwargs: {}\n",
      "[TRACE] func: <lambda>, args: (2,), kwargs: {}\n",
      "[0, 2, 4]\n"
     ]
    }
   ],
   "source": [
    "print(list(map(trace(lambda x: x*2), range(3))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e1a4495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id1', 'id100', 'id2', 'id22', 'id3', 'id30']\n"
     ]
    }
   ],
   "source": [
    "ids = ['id1', 'id2', 'id30', 'id3', 'id22', 'id100']\n",
    "print(sorted(ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c32f9508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id1', 'id2', 'id3', 'id22', 'id30', 'id100']\n"
     ]
    }
   ],
   "source": [
    "sorted_ids = sorted(ids, key=lambda x: int(x[2:]))\n",
    "print(sorted_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "143a9f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cat', 'Dog', 'Cow']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(lambda x: x.capitalize(), ['cat', 'dog', 'cow']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dcb2ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cat', 'Dog', 'Cow']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x.capitalize() for x in ['cat', 'dog', 'cow']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1b3e299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "even = lambda x: x%2 == 0\n",
    "list(filter(even, range(11)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03ee1f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in range(11) if x%2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900e4348",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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