10_Lambda functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Lambda\n",
    "A lambda function is a small anonymous function.\n",
    "\n",
    "A lambda function can take any number of arguments, but can only have one expression.\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "12.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Syntax\n",
    "![12.png](attachment:12.png)\n",
    "The expression is executed and the result is returned:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.<lambda>(a)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda a : a + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x0000020F126759D8>\n"
     ]
    }
   ],
   "source": [
    "x = lambda a : a + 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "x = lambda a : a + 10\n",
    "print(x(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lambda functions can take any number of arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "x = lambda a, b : a * b\n",
    "print(x(5, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "x = lambda a, b, c : a + b + c\n",
    "print(x(5, 6, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's see the power of lambda :\n",
    "1. Using it in other functions\n",
    "2. Use of lambda() with map()\n",
    "3. Use of lambda() with filter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using it in other functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 5), (2, 5), (3, 2), (4, 0), (9, 3)]\n"
     ]
    }
   ],
   "source": [
    "a = [(3, 2), (1, 5), (9, 3), (2, 5), (4, 0)]\n",
    "a.sort()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(4, 0), (3, 2), (9, 3), (1, 5), (2, 5)]\n"
     ]
    }
   ],
   "source": [
    "# passing the lambda function into a funtion to change some works in it.\n",
    "a.sort(key = lambda x : x[1])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use of lambda() with map()\n",
    "\n",
    "The map() function in Python takes in a function and a list as argument. The function is called with a lambda function and a list and a new list is returned which contains all the lambda modified items returned by that function for each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<map at 0x20f13fc3488>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1, 3, 4, 2, 0.5]\n",
    "map(lambda x: x * 2, my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 6, 8, 4, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print(list(map(lambda x: x * 2, my_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['small', 'big', 'small', 'small', 'big', 'small', 'small', 'big']\n"
     ]
    }
   ],
   "source": [
    "numbers = [10 ,11, 8, 6 , 100, 7, 9, 21]\n",
    "print(list(map(lambda x: 'big' if x > 10 else 'small', numbers)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use of lambda() with filter()\n",
    "\n",
    "The filter() function in Python takes in a function and a list as arguments. This offers an elegant way to filter out all the elements of a sequence “sequence”, for which the function returns True. Here is a small program that returns the odd numbers from an input list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "my_list = [1, 5, 6, 8, 10 , 11]\n",
    "print(list(filter(lambda x : x % 2 == 0, my_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 97, 77, 23, 73, 61]\n"
     ]
    }
   ],
   "source": [
    "li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61] \n",
    "final_list = list(filter(lambda x: x%2 != 0 , li)) \n",
    "print(final_list) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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