MAP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "918d8df0",
   "metadata": {},
   "source": [
    "### map() function returns a map object(which is an iterator) of the results after applying the given function to each item of a given iterable (list, tuple etc.)\n",
    "\n",
    "##### Syntax:\n",
    "map(function(), iterator[])\n",
    "\n",
    "**function : It is a function to which map passes each element of given iterable.\n",
    "\n",
    "**iterator : It is a iterable which is to be mapped.\n",
    "\n",
    "**an iterable like sets, lists, tuples, etc"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "4a1ae947",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "274c270f",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53970846",
   "metadata": {},
   "source": [
    "# Using Loop  (TRADITIONAL CODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a440c550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, 3, 4, 5]\n",
    "squared = []\n",
    "\n",
    "for num in numbers:\n",
    "    squared.append(num ** 2)\n",
    "\n",
    "\n",
    "squared"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc6a0ff",
   "metadata": {},
   "source": [
    "# Using Map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8bfa815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(number):\n",
    "     return number ** 2\n",
    "    \n",
    "numbers = [1, 2, 3, 4, 5]\n",
    "squared = map(square, numbers)\n",
    "list(squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "759d0faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6, 8]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Return double of n\n",
    "def addition(n):\n",
    "\treturn n + n\n",
    "\n",
    "\n",
    "# We double all numbers using map()\n",
    "numbers = (1, 2, 3, 4)\n",
    "result = map(addition, numbers)\n",
    "print(list(result))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "420fff3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6, 8]\n"
     ]
    }
   ],
   "source": [
    "# Double all numbers using map and lambda\n",
    "\n",
    "numbers = (1, 2, 3, 4)\n",
    "result = map(lambda x: x + x, numbers)\n",
    "print(list(result))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3f069df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 9]\n"
     ]
    }
   ],
   "source": [
    "# Add two lists using map and lambda\n",
    "\n",
    "numbers1 = [1, 2, 3]\n",
    "numbers2 = [4, 5, 6]\n",
    "\n",
    "result = map(lambda x, y: x + y, numbers1, numbers2)\n",
    "print(list(result))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "749f8071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['s', 'a', 't'], ['b', 'a', 't'], ['c', 'a', 't'], ['m', 'a', 't']]\n"
     ]
    }
   ],
   "source": [
    "# List of strings\n",
    "l = ['sat', 'bat', 'cat', 'mat']\n",
    "\n",
    "# map() can listify the list of strings individually\n",
    "test = list(map(list, l))\n",
    "print(test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65884d2",
   "metadata": {},
   "source": [
    "map() Return Value\n",
    "The map() function returns an object of map class. The returned value can be passed to functions like\n",
    "\n",
    "**list() - to convert to list\n",
    "\n",
    "**set() - to convert to a set, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0415d838",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<map object at 0x000001F7170CE940>\n"
     ]
    }
   ],
   "source": [
    "# Return double of n\n",
    "def addition(n):\n",
    "\treturn n + n\n",
    "\n",
    "\n",
    "# We double all numbers using map()\n",
    "numbers = (1, 2, 3, 4)\n",
    "result = map(addition, numbers)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b227744",
   "metadata": {},
   "source": [
    "# Type Casting using Map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16a1dde6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only Map Object: <map object at 0x000001F717167BB0>\n",
      "Map object convertlist: [4, 8, 6, 5, 3, 2, 8, 9, 2, 5]\n",
      "only str Object: ['4', '8', '6', '5', '3', '2', '8', '9', '2', '5']\n"
     ]
    }
   ],
   "source": [
    "str_nums = [\"4\", \"8\", \"6\", \"5\", \"3\", \"2\", \"8\", \"9\", \"2\", \"5\"]\n",
    "\n",
    "int_nums = map(int, str_nums)\n",
    "\n",
    "print('Only Map Object:',int_nums)\n",
    "\n",
    "print('Map object convertlist:',list(int_nums))\n",
    "\n",
    "print('only str Object:',str_nums)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93a7f5b",
   "metadata": {},
   "source": [
    "# map() using Built-in Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ee0ff95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 1, 0, 1, 2]\n",
      "[-2.0, -1.0, 0.0, 1.0, 2.0]\n",
      "[7, 2, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "numbers = [-2, -1, 0, 1, 2]\n",
    "\n",
    "abs_values = list(map(abs, numbers))\n",
    "print(abs_values)\n",
    "\n",
    "print(list(map(float, numbers)))\n",
    "\n",
    "\n",
    "words = [\"Welcome\", \"to\", \"Real\", \"Python\"]\n",
    "\n",
    "print(list(map(len, words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb277a09",
   "metadata": {},
   "source": [
    "# math operations on several input iterables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "241fc016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1]\n",
      "[10, 15]\n"
     ]
    }
   ],
   "source": [
    "print(list(map(lambda x, y: x - y, [2, 4, 6], [1, 3, 5])))\n",
    "\n",
    "\n",
    "print(list(map(lambda x, y, z: x + y + z, [2, 4], [1, 3], [7, 8])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb4ef85",
   "metadata": {},
   "source": [
    "# map using string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a9f8466",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capital: ['Processing', 'Strings', 'With', 'Map']\n",
      "Upper: ['PROCESSING', 'STRINGS', 'WITH', 'MAP']\n",
      "Lower: ['processing', 'strings', 'with', 'map']\n",
      "SwapCase: ['PROCESSING', 'STRINGS', 'WITH', 'MAP']\n",
      "Title: ['Processing', 'Strings', 'With', 'Map']\n"
     ]
    }
   ],
   "source": [
    "string_it = [\"processing\", \"strings\", \"with\", \"map\"]\n",
    "print(\"Capital:\",list(map(str.capitalize, string_it)))\n",
    "\n",
    "print(\"Upper:\",list(map(str.upper, string_it)))\n",
    "\n",
    "print(\"Lower:\",list(map(str.lower, string_it)))\n",
    "\n",
    "print(\"SwapCase:\",list(map(str.swapcase, string_it)))\n",
    "\n",
    "print(\"Title:\",list(map(str.title, string_it)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8993c465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['processing', 'strings', 'with', 'map']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove Space\n",
    "with_spaces = [\"processing \", \"  strings\", \"with   \", \" map   \"]\n",
    "list(map(str.strip, with_spaces))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0719172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['processing', 'strings', 'with', 'map']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove Dots(.)\n",
    "with_dots = [\"processing..\", \"...strings\", \"with....\", \"..map..\"]\n",
    "list(map(lambda s: s.strip(\".\"), with_dots))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b464820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "def remove_punctuation(word):\n",
    "     return re.sub(r'[!?.:;,\"()-=]', \"\", word)\n",
    "\n",
    "remove_punctuation(\"...Python!()--=--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e946c78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Some',\n",
       " 'people',\n",
       " 'when',\n",
       " 'confronted',\n",
       " 'with',\n",
       " 'a',\n",
       " 'problem',\n",
       " 'think',\n",
       " 'I',\n",
       " 'know',\n",
       " \"I'll\",\n",
       " 'use',\n",
       " 'regular',\n",
       " 'expressions',\n",
       " 'Now',\n",
       " 'they',\n",
       " 'have',\n",
       " 'two',\n",
       " 'problems',\n",
       " 'Jamie',\n",
       " 'Zawinski']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"\"\"Some people, when confronted with a problem, think\n",
    "... \"I know, I'll use regular expressions.\"\n",
    "... Now they have two problems. Jamie Zawinski\"\"\"\n",
    "\n",
    "\n",
    "words = text.split()\n",
    "words\n",
    "['Some', 'people,', 'when', 'confronted', 'with', 'a', 'problem,', 'think'\n",
    ", '\"I', 'know,', \"I'll\", 'use', 'regular', 'expressions.\"', 'Now', 'they',\n",
    " 'have', 'two', 'problems.', 'Jamie', 'Zawinski']\n",
    "\n",
    "list(map(remove_punctuation, words))"
   ]
  }
 ],
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