{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "# [Comprehensions](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/Comprehensions.html) and [Generators](https://www.programiz.com/python-programming/generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Comprehensions](https://towardsdatascience.com/list-comprehensions-in-python-28d54c9286ca):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python offers some nice \"synctatic sugar\" constructions. They allow the creation of sequences in a clear in concise way  \n",
    "They are:  \n",
    "\n",
    "- List comprehension\n",
    "- Set comprehension\n",
    "- Dict comprehension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) List Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List comprehensions returns a list filled with elements derived from another sequence ot iterable, being modified or not. A common use is to build a new list where each element is the result of some expression applied to the original, or to build a sequence where the elements satisfy some conditions  \n",
    "\n",
    "The patterns are:  \n",
    "\n",
    "    [ <expression> for <variable> in <iterable or sequence> ]\n",
    "    [ <expression> for <variable> in <iterable or sequence> if <condition> ]\n",
    "    [ <expression> if <condition> else <expression> for <variable> in <iterable or sequence> ]\n",
    "    [ <expression> if <condition> else <expression> for <variable> in <iterable or sequence>  if <condition> ]\n",
    "    \n",
    "\n",
    "See the examples below:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "cubes = []\n",
    "for number in range(10):\n",
    "    cubes.append(number**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]\n"
     ]
    }
   ],
   "source": [
    "print(cubes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can rewrite it in a more concise way using list comprehension:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cubes2 = [number**3 for number in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]\n"
     ]
    }
   ],
   "source": [
    "print(cubes2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another example of the use of list comprehensions  \n",
    "Let's sum the square of the divisors of 3 and 5 below 1000:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155638903886\n",
      "CPU times: user 2.72 ms, sys: 0 ns, total: 2.72 ms\n",
      "Wall time: 2.72 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "my_list = []\n",
    "for x in range(10001):\n",
    "    if x%5 == 0 or x%3 == 0:\n",
    "        my_list.append(x**2)\n",
    "print(sum(my_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155638903886\n",
      "CPU times: user 1.72 ms, sys: 326 µs, total: 2.04 ms\n",
      "Wall time: 2.05 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "my_new_list = [x**2 for x in range(10001) if x%5 == 0 or x%3 == 0]\n",
    "print(sum(my_new_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(my_new_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More examples:\n",
    "\n",
    "+ #### Modifying a sequence  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = range(11)\n",
    "sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 16, 36, 64, 100]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[element**2 for element in sequence if element%2 == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### More than one variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 14, 11]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + y for x,y in [(9,4),(8,6),(2,9)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4, 9), (6, 8), (9, 2)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(y,x) for x,y in [(9,4),(8,6),(2,9)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 4]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x if x%2==0 else y for x,y in [(1,2),(2,3),(4,5)] if isinstance(x,int) or isinstance(y,float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sOMe oDd STRiNG'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def swapcase(string):\n",
    "    swapped = \"\".join([x.upper() if x.islower() else x.lower() for x in string])\n",
    "    return swapped\n",
    "\n",
    "swapcase(\"SomE OdD strIng\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### No variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 5, 5, 5]\n",
      "CPU times: user 82 µs, sys: 22 µs, total: 104 µs\n",
      "Wall time: 125 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print([3+2 for numero in range(4)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 5, 5, 5]\n",
      "CPU times: user 67 µs, sys: 0 ns, total: 67 µs\n",
      "Wall time: 71 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(4 * [5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Filtering by type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 9, 0, 4]\n"
     ]
    }
   ],
   "source": [
    "my_list = [1, \"4\", 9, \"a\", 0, 4]\n",
    "test_type = [elem for elem in my_list if isinstance(elem, int)]\n",
    "print(test_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Filtering by type, two conditions:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9, 0, 4]\n"
     ]
    }
   ],
   "source": [
    "my_list = [1, \"4\", 9, \"a\", 0, 4]\n",
    "test_type = [int(i) for i in my_list if isinstance(i, int) or (isinstance(i, str) and (i.isnumeric()))]\n",
    "print(test_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Multiple loops  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5)]\n",
      "CPU times: user 106 µs, sys: 0 ns, total: 106 µs\n",
      "Wall time: 111 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "points = []\n",
    "for x in [1,2,3]:\n",
    "    for y in [3,4,5]:\n",
    "        if x != y:\n",
    "            points.append((x,y))\n",
    "            \n",
    "print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5)]\n",
      "CPU times: user 115 µs, sys: 0 ns, total: 115 µs\n",
      "Wall time: 120 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "points = [(x,y) for x in [1,2,3] for y in [3,4,5] if x != y]\n",
    "print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 4, 8, 6, 2, 9]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[y for x in [(9,4),(8,6),(2,9)] for y in x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Using for filtering strings  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n",
      "0123456789\n",
      "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\n",
      "abcdefghijklmnopqrstuvwxyz\n",
      "ABCDEFGHIJKLMNOPQRSTUVWXYZ\n",
      "0123456789abcdefABCDEF\n"
     ]
    }
   ],
   "source": [
    "print(string.punctuation)\n",
    "print(string.digits)\n",
    "print(string.ascii_letters)\n",
    "print(string.ascii_lowercase)\n",
    "print(string.ascii_uppercase)\n",
    "print(string.hexdigits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know the string package, let's use it for cleaning strings with list comprehensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_string = 'This is a string. It is full of surprises! Would you believe it? I can clean it...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'a', 'string.', 'It', 'is', 'full', 'of', 'surprises!', 'Would', 'you', 'believe', 'it?', 'I', 'can', 'clean', 'it...']\n"
     ]
    }
   ],
   "source": [
    "tokens = [token for token in my_string.split()]\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['this', 'is', 'a', 'string.', 'it', 'is', 'full', 'of', 'surprises!', 'would', 'you', 'believe', 'it?', 'i', 'can', 'clean', 'it...']\n"
     ]
    }
   ],
   "source": [
    "lowercase_tokens = [token.lower() for token in my_string.split()]\n",
    "print(lowercase_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['this', 'is', 'a', 'string', 'it', 'is', 'full', 'of', 'surprises', 'would', 'you', 'believe', 'it', 'i', 'can', 'clean', 'it']\n"
     ]
    }
   ],
   "source": [
    "lower_strip_tokens = [token.strip(string.punctuation).lower() for token in my_string.split()]\n",
    "print(lower_strip_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Set Comprehensions:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set comprehensions returns a set filled with elements derived from a sequence or iterable, being modified or not. The only difference is that the results are a set instead of a list, that is, without repeated elements and not following a specific order  \n",
    "\n",
    "The patterns are:  \n",
    "\n",
    "    { <expression> for <variable> in <iterable or sequence> }\n",
    "    { <expression> for <variable> in <iterable or sequence> if <condition> }\n",
    "    { <expression> if <condition> else <expression> for <variable> in <iterable or sequence> }\n",
    "    { <expression> if <condition> else <expression> for <variable> in <iterable or sequence> if <condition> }\n",
    "\n",
    "See the examples below:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'e', 'o'}\n"
     ]
    }
   ],
   "source": [
    "vowels = {letter for letter in \"python lovers\" if letter in \"aeiou\"}\n",
    "print(vowels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(vowels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'s', 'c', 'p', 'r', 'm'}\n"
     ]
    }
   ],
   "source": [
    "consonants = {letter for letter in 'summer camp' if letter not in \"aeiou\" and letter != ' '}\n",
    "print(consonants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Dict Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dict comprehensions returns a dictionary filled with elements derived from sequences or iterable, being modified or not. The pattern is a varies a bit from list and set comprehensions, because we have keys and values deriving from a sequence.\n",
    "\n",
    "See the examples below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'first': 1, 'second': 2}\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "my_dict = {k:v for k,v in [('first',1),('second',2)]}\n",
    "print(my_dict)\n",
    "print(type(my_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 1: 1, 2: 4, 3: 9}\n"
     ]
    }
   ],
   "source": [
    "squares = {x:x**2 for x in range(4)}\n",
    "print(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Using _zip_ to merge two sequences in a list of tuples:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 5, 1: 6, 2: 7, 3: 8}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{x:y for x,y in zip(range(5), range(5,9))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Three ways to do the same thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3}\n",
      "CPU times: user 247 µs, sys: 64 µs, total: 311 µs\n",
      "Wall time: 202 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "new_dict = dict([('one',1),('two',2),('three',3)])\n",
    "print(new_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3}\n",
      "CPU times: user 87 µs, sys: 24 µs, total: 111 µs\n",
      "Wall time: 114 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "new_dict = {x:y for x,y in [('one',1),('two',2),('three',3)]}\n",
    "print(new_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3}\n",
      "CPU times: user 75 µs, sys: 21 µs, total: 96 µs\n",
      "Wall time: 99.4 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "new_dict = {x:y for x,y in zip(['one', 'two', 'three'],[1 ,2 ,3])}\n",
    "print(new_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Inverting key and value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2, 'three': 3}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'one', 2: 'two', 3: 'three'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_new = {value:key for key, value in new_dict.items()}\n",
    "dict_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Increasing a value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 2, 'two': 3, 'three': 4}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_one = {key:value + 1 for key, value in new_dict.items()}\n",
    "plus_one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Two ways for converting lists of tuples:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_tuples = [(2,3),(4,5),(1,7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 µs, sys: 1 µs, total: 6 µs\n",
      "Wall time: 9.78 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{2: 3, 4: 5, 1: 7}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "dict(list_of_tuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 µs, sys: 1 µs, total: 5 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{2: 3, 4: 5, 1: 7}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "{key:value for key, value in list_of_tuples}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [[x for x in range(12)],[x**2 for x in range(5)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [0, 1, 4, 9, 16]]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Generator Expressions](https://www.programiz.com/python-programming/generator) \n",
    "\n",
    "Generator expressions are similar to generator functions, but they are created like list comprehensions using simple brackets instead of square brackets   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generator"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator1 = (x**(0.5) for x in range(10) if x%5 == 0 or x%3 == 0)\n",
    "type(generator1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x7f5eac23ebd0>\n"
     ]
    }
   ],
   "source": [
    "print(generator1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### One way to materialise the generator is to convert it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.7320508075688772, 2.23606797749979, 2.449489742783178, 3.0]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(generator1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.7320508075688772, 2.23606797749979, 2.449489742783178, 3.0)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(generator1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### In order to access each generator element, we can use the command _next_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator2 = (x**x for x in range(2,99999999999999))  #A really big number. If it was a list, the memory would overflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### When a list command materialises the generator, it materializes all its elements and terminates the generator\n",
    "+ #### If we use _next_ to access it again, it will raise the _StopIteration_ error    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator3 = (x**2 for x in range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(generator3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-b768d348e2f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "next(generator3) #error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator4 = (letter for letter in \"abcd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(generator4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-f2f1aa0c597c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "next(generator4) #error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator4 = (letter for letter in \"abcd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n"
     ]
    },
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-4fbfd58e24ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    print(next(generator4))  #error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator4 = (letter for letter in \"abcd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for element in generator4:  # for iterations can handle stop iteration errors\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ #### Each Generators keeps its state, regardless of the iterators calling it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator4 = (letter for letter in \"abcd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ele1 in generator4:\n",
    "    print(\"first for\", ele1)\n",
    "    for ele2 in generator4:\n",
    "        print(\"second for\", ele2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special generators: _range*_, _zip_, _filter_, _map_ \n",
    "\n",
    "Obs:\n",
    "range is a class of immutable iterable objects. Their iteration behavior can be compared to tuples: you can't call next directly on them; you have to get an iterator by using iter. So no, range is not a proper generator.\n",
    "+ Ranges are immutable, so they can be used as dictionary keys.\n",
    "+ Ranges have the start, stop and step attributes (since Python 3.3), count and index methods and they support in, len and __getitem__ operations.\n",
    "* You can iterate over the same range multiple times.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = range(10)\n",
    "type(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__bool__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'count',\n",
       " 'index',\n",
       " 'start',\n",
       " 'step',\n",
       " 'stop']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'range' object is not an iterator",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-124-9f8729aff852>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'range' object is not an iterator"
     ]
    }
   ],
   "source": [
    "next(r) #error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = iter(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__length_hint__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__next__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = zip([1,2,3],[4,5,6],[6,7,8])\n",
    "type(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filter"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = filter(str.isupper,['a','A','E','r'])\n",
    "type(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "map"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = map(int,['1', '2', '3'])\n",
    "type(m)"
   ]
  }
 ],
 "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",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}