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    "## lambda(λ) function in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `Python` supports to make anonymous functions which are not bound to a name(@ runtime).\n",
    "* It uses a construct called `\"lambda\"`.\n",
    "* **This is not exactly the same as lambda in functional programming languages.**"
   ]
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   "source": [
    "# func(x) returns the power of given parameter\n",
    "def func(x):\n",
    "    return x**2\n",
    "print(func(5))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# this will do the same thing with out using functions\n",
    "lam = lambda x:x**2\n",
    "print(lam(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* lambda function does not have `return` statement.\n",
    "* As well as we can use \"lambda\" function inside of the regular function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# implementing lambda function inside of the function\n",
    "def increment(num):\n",
    "    return lambda x:x+num\n",
    "\n",
    "print(increment(20)(25)) # 20+25=45\n",
    "\n",
    "a = increment(15)\n",
    "b = increment(25)\n",
    "print(a(5), b(5)) # 15+5=20 , 25+5=30\n",
    "\n",
    "print(a(a(10))) # 15 + 15 + 10 = 40"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* That increment function generates an anonymous function(lambda) at the runtime and returns that lambda function.\n",
    "\n",
    "* beauty of the lambda function is that, we can use function pipeline {Ex: a(b(c(512))) } to get a answer.\n",
    "___\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Go into deep\n",
    "\n",
    "* Let's use lambda functions with a python list.\n",
    "* For this we can use several library functions such as `filter()` , `map()` , `reduce()`.\n",
    "\n",
    "filter() :  creates a list of elements for which a function returns true.\n",
    "\n",
    "map() :  applies a function to all the items in an input list.\n",
    "\n",
    "reduce() : performing some computation on a list and returning the result.\n",
    "* You have to import reduce function _`from functools import reduce`_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inList = [6, 20, 14, 2, 5, 18, 10, -7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_than_ten = list(filter(lambda x : x < 10, inList))\n",
    "print(less_than_ten)\n",
    "\n",
    "even_numbers = list(filter(lambda y : y % 2 == 0, inList))\n",
    "print(even_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remainder_list = list(map(lambda x : x%4, inList))\n",
    "print(remainder_list)\n",
    "\n",
    "modified_list = list(map(lambda y : y + 100, inList))\n",
    "print(modified_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "sum_of_inList = reduce(lambda x, y : x + y, inList)\n",
    "print(sum_of_inList)\n",
    "\n",
    "print(sum(inList)) # by built-in function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sieve of Eratosthenes (Finding prime numbers)\n",
    "![algo](./Sieve_of_Eratosthenes_animation.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113]\n"
     ]
    }
   ],
   "source": [
    "numbers = list(range(2,120))\n",
    "for i in range(2, 8):\n",
    "    numbers = list(filter(lambda x:x==i or x%i, numbers))\n",
    "print(numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use in NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']\n",
      "[3, 5, 5, 3, 5, 4, 3, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "sentence = \"The quick brown fox jumps over the lazy dog\"\n",
    "words = sentence.split()\n",
    "print(words)\n",
    "lengths = list(map(lambda x:len(x), words))\n",
    "print(lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "+ http://www.secnetix.de/olli/Python/lambda_functions.hawk\n",
    "+ http://book.pythontips.com/en/latest/map_filter.html\n",
    "+ https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes\n"
   ]
  }
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