8-3_Pythonic_Features.ipynb

{
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   "source": [
    "# Lecture 8-3\n",
    "\n",
    "# Pythonic Features\n",
    "\n",
    "## Week 8 Friday\n",
    "\n",
    "## Miles Chen, PhD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Named Tuples\n",
    "\n",
    "Named tuples are a quick and simple way to define a new class if the Class definition only contains values and does not require its own methods.\n",
    "\n",
    "Recall we defined a class Point with the following definition.\n",
    "\n",
    "```\n",
    "class Point:\n",
    "    def __init__(self, x=0, y=0):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "    def __str__(self):\n",
    "        return '(%g, %g)' % (self.x, self.y)\n",
    "```\n",
    "\n",
    "A named tuple can be created that functions in a nearly identical fashion. You will need to import `namedtuple` from the `collections` module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Once we have imported namedtuple, we can create a named tuple.\n",
    "\n",
    "We'll create a Point Class named tuple that contains two values, `x` and `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "Point = namedtuple('Point', ['x', 'y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Point"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "With our namedtuple defined, we can create instances of it like we would any other class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "p = Point(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Point(x=1, y=2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "Now that we have created an instance of the named tuple, we can access the values using dot notation. We can also access values using indexed square-bracket notation as well because it is a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "data": {
      "text/plain": [
       "1"
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     "execution_count": 6,
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   "source": [
    "p.x"
   ]
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   "execution_count": 7,
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    {
     "data": {
      "text/plain": [
       "2"
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   "source": [
    "p.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "data": {
      "text/plain": [
       "1"
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     },
     "execution_count": 8,
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   ],
   "source": [
    "p[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   },
   "source": [
    "A named tuple will inherit all of the methods associated with tuples such as comparison and \"addition\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "p1 = Point(0, 1)\n",
    "p2 = Point(3, 4)\n",
    "p3 = Point(2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 > p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 3, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 + p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Point(x=0, y=1), Point(x=3, y=4), Point(x=2, y=2)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [p1, p2, p3]\n",
    "l "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Point(x=0, y=1), Point(x=2, y=2), Point(x=3, y=4)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "If the class definition needs to become more complicated you can define a new class that inherits from the namedtuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vector(Point):\n",
    "    \"\"\"A class based on the named tuple Point\"\"\"\n",
    "    def __add__(self, other):\n",
    "        return Vector(x = self.x + other.x, y = self.y + other.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1 = Vector(0, 1)\n",
    "v2 = Vector(3, 4)\n",
    "v3 = v1 + v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vector(x=3, y=5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counters\n",
    "\n",
    "Counters are like dictionaries and are useful for quickly tallying elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordlist = ['red', 'blue', 'red', 'green', 'blue', 'blue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tally = Counter(wordlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'blue': 3, 'red': 2, 'green': 1})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('blue', 3), ('red', 2)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tally.most_common(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tally['blue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'a': 3,\n",
       "         'b': 3,\n",
       "         'c': 3,\n",
       "         'd': 3,\n",
       "         'e': 1,\n",
       "         'f': 1,\n",
       "         'g': 1,\n",
       "         'j': 1,\n",
       "         'i': 1,\n",
       "         'l': 1,\n",
       "         'k': 1,\n",
       "         'h': 1})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(\"abcddefgabcdjilkhabc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'sea': 2,\n",
       "         'she': 1,\n",
       "         'sells': 1,\n",
       "         'shells': 1,\n",
       "         'by': 1,\n",
       "         'the': 1,\n",
       "         'shore': 1})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(\"she sells sea shells by the sea shore\".split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## List comprehensions\n",
    "\n",
    "List comprehensions allow us to create new lists concisely based on an existing collection\n",
    "\n",
    "They take the form:\n",
    "\n",
    "`[expr for val in collection if condition]`\n",
    "\n",
    "This is basically equivalent to the following loop:\n",
    "\n",
    "```\n",
    "result = []\n",
    "for val in collection:\n",
    "    if condition:\n",
    "        result.append(expr)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list of the squares \n",
    "[x ** 2 for x in range(1, 11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array([x**2 for x in range(1, 11)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 9, 25, 49, 81]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# square only the odd numbers\n",
    "[x**2 for x in range(1, 11) if x % 2 == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BAT', 'CAR', 'DOVE', 'PYTHON']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a list of strings, and write the words that are over 2 characters long in uppercase.\n",
    "strings = ['a', 'as', 'bat', 'car', 'dove', 'python']\n",
    "[x.upper() for x in strings if len(x) > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SHE', 'SELLS', 'SEA', 'SHELLS', 'BY', 'THE', 'SEA', 'SHORE']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x.upper() for x in \"she sells sea shells by the sea shore\".split()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "You can create a list comprehension from any iterable (list, tuple, string, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 6, 3, 2, 5, 7]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the digits from a string\n",
    "string = \"Hello 963257 World\"\n",
    "[int(x) for x in string if x.isdigit()]\n",
    "# for x in string, will look at each character individually\n",
    "# if x is a digit, then convert it using int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# iterate over a dictionary's items\n",
    "d = {'a':'apple', 'b':'banana', 'c':'carrots', 'd':'donut', 'e':'eggs'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 'apple'),\n",
       " ('b', 'banana'),\n",
       " ('c', 'carrots'),\n",
       " ('d', 'donut'),\n",
       " ('e', 'eggs')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(d.items())  # recall what dict.items() returns: a list of tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a is for apple', 'c is for carrots', 'e is for eggs']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['{} is for {}'.format(key, value) for key, value in d.items() if key not in ('b', 'd') ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cartesian Products with List Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardranks = [\"A\",\"K\",\"Q\",\"J\"]\n",
    "cardsuits = [\"clubs\",\"diamonds\",\"hearts\",\"spades\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('A', 'clubs'),\n",
       " ('A', 'diamonds'),\n",
       " ('A', 'hearts'),\n",
       " ('A', 'spades'),\n",
       " ('K', 'clubs'),\n",
       " ('K', 'diamonds'),\n",
       " ('K', 'hearts'),\n",
       " ('K', 'spades'),\n",
       " ('Q', 'clubs'),\n",
       " ('Q', 'diamonds'),\n",
       " ('Q', 'hearts'),\n",
       " ('Q', 'spades'),\n",
       " ('J', 'clubs'),\n",
       " ('J', 'diamonds'),\n",
       " ('J', 'hearts'),\n",
       " ('J', 'spades')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cartesian product\n",
    "[(rank, suit) for rank in cardranks for suit in cardsuits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('A', 'clubs'),\n",
       " ('K', 'clubs'),\n",
       " ('Q', 'clubs'),\n",
       " ('J', 'clubs'),\n",
       " ('A', 'diamonds'),\n",
       " ('K', 'diamonds'),\n",
       " ('Q', 'diamonds'),\n",
       " ('J', 'diamonds'),\n",
       " ('A', 'hearts'),\n",
       " ('K', 'hearts'),\n",
       " ('Q', 'hearts'),\n",
       " ('J', 'hearts'),\n",
       " ('A', 'spades'),\n",
       " ('K', 'spades'),\n",
       " ('Q', 'spades'),\n",
       " ('J', 'spades')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cartesian product\n",
    "[(rank, suit) for suit in cardsuits for rank in cardranks ] # notice the change in order of for clauses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dictionary Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dict comprehension looks like this:\n",
    "\n",
    "`dict_comp = {key-expr : value-expr for value in collection if condition}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Apple': 5, 'Mango': 5, 'Banana': 6, 'Cherry': 6}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dictionary, where the key is the word capitalized, and the value is the length of the word\n",
    "fruits = ['apple', 'mango', 'banana', 'cherry']\n",
    "{f.capitalize():len(f) for f in fruits}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# create a dictionary where the key is the index, and the value is the string in the strings list.\n",
    "strings = ['a', 'as', 'bat', 'car', 'dove', 'python']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'a'), (1, 'as'), (2, 'bat'), (3, 'car'), (4, 'dove'), (5, 'python')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(strings))  # enumerate produces a collection of tuples, with index and value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'a', 1: 'as', 2: 'bat', 3: 'car', 4: 'dove', 5: 'python'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_map = {index:val for index, val in enumerate(strings)}\n",
    "index_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0, 'as': 1, 'bat': 2, 'car': 3, 'dove': 4, 'python': 5}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note that enumerate returns tuples in the order (index, val)\n",
    "# in the creation of a dictionary, you can swap those positions\n",
    "# and even apply functions to them\n",
    "\n",
    "# We create a dictionary where the key is the string, and the value is the index in the strings list.\n",
    "loc_mapping = {val : index for index, val in enumerate(strings)}\n",
    "loc_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[55], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m index_map[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "\u001b[1;31mKeyError\u001b[0m: 'a'"
     ]
    }
   ],
   "source": [
    "index_map['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_mapping['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 0, 'as': 1, 'bat': 2, 'car': 3, 'dove': 4, 'python': 5, 0: 'a', 1: 'as', 2: 'bat', 3: 'car', 4: 'dove', 5: 'python'}\n"
     ]
    }
   ],
   "source": [
    "# combine dictionaries with kwargs \n",
    "dd = {**loc_mapping, **index_map}\n",
    "print(dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0,\n",
       " 'as': 1,\n",
       " 'bat': 2,\n",
       " 'car': 3,\n",
       " 'dove': 4,\n",
       " 'python': 5,\n",
       " 0: 'a',\n",
       " 1: 'as',\n",
       " 2: 'bat',\n",
       " 3: 'car',\n",
       " 4: 'dove',\n",
       " 5: 'python'}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# even better... use dict.update(). This modifies the dictionary in place\n",
    "loc_mapping.update(index_map)\n",
    "loc_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generator Expressions\n",
    "\n",
    "Generator Expressions are similar to List comprehensions, with the key difference being that they are *lazy*. \n",
    "\n",
    "You create them with parentheses instead of square brackets.\n",
    "\n",
    "The result is a generator object. You can access values in the generator using `next()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "g = (n**2 for n in range(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x0000021D4B29CE10>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "25\n",
      "36\n",
      "49\n",
      "64\n",
      "81\n",
      "100\n",
      "121\n"
     ]
    }
   ],
   "source": [
    "for val in g:\n",
    "    print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[66], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mnext\u001b[39m(g)\n",
      "\u001b[1;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "next(g) # calling next after it has run out of iterations will result in an error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## List Comprehension vs Generator Expressions in Python\n",
    "\n",
    "The big difference between a list comprehension and a generator is that the generator is **lazy**.\n",
    "\n",
    "The list comprehension will evaluate the entire sequence of iterations. The generator will only generate the next value when it is asked to do so.\n",
    "\n",
    "Depending on the expression that needs to be evaluated, you may prefer to use a generator over the list comprehension.\n",
    "\n",
    "The following examples are from: https://code-maven.com/list-comprehension-vs-generator-expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "l = [n*2 for n in range(1000)] # List comprehension\n",
    "g = (n*2 for n in range(1000))  # Generator expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'generator'>\n"
     ]
    }
   ],
   "source": [
    "print(type(l))  # 'list'\n",
    "print(type(g))  # 'generator'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8856\n",
      "208\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.getsizeof(l))  # more space in memory\n",
    "print(sys.getsizeof(g))  # less space in memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'generator' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[70], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# cannot access values in a generator by index\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(l[\u001b[38;5;241m4\u001b[39m])   \u001b[38;5;66;03m# 8\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(g[\u001b[38;5;241m4\u001b[39m])\n",
      "\u001b[1;31mTypeError\u001b[0m: 'generator' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "# cannot access values in a generator by index\n",
    "print(l[4])   # 8\n",
    "print(g[4])   # TypeError: 'generator' object is not subscriptable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "# you can interate over lists and generators\n",
    "for item in l:\n",
    "    print(item)\n",
    "    if item > 12:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "for item in g:\n",
    "    print(item)\n",
    "    if item > 12:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x0000021D4B1B5CB0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998944"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum demands that all elements of g be calculated so the generator evaluates them and provides the sum\n",
    "# note that the first 8 values have already been evaluated, so the sum is the sum begins at n = 8\n",
    "sum(g) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(l) # the list has all of the values in memory ready to be summed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998944"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(l[8:]) # to get the equivalent sum, we can start it at 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that the generator has finished running, there are no more values left to evaluate\n",
    "sum(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the list is unaffected by calling sum on it.\n",
    "sum(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# map and lambda functions\n",
    "\n",
    "The `map(function, iterable)` function takes a particular function and maps it to each element of an iterable. The object it returns is a map object which itself is iterable.\n",
    "\n",
    "A lambda function allows you to create and use a new short function without having to formally define it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# the module re is used for regular expressions\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# re.sub substitutes one pattern of text with another.\n",
    "# Here we define a function that replaces multiple instances of white space (\\s+) with one space:\n",
    "def replace_space(x):\n",
    "    return(re.sub('\\s+', ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Alabama '"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace_space('Hello     Alabama ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = ['Hello     Alabama', \n",
    "        'Georgia!',\n",
    "        'Georgia',\n",
    "        'georgia', \n",
    "        'FlOrIda',\n",
    "        'south  carolina##',\n",
    "        'West virginia?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<map at 0x21d47e33ee0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map(replace_space, text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = map(replace_space, text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Alabama'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Georgia!'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Alabama\n",
      "Georgia!\n",
      "Georgia\n",
      "georgia\n",
      "FlOrIda\n",
      "south carolina##\n",
      "West virginia?\n"
     ]
    }
   ],
   "source": [
    "# we can use the map function to map the replace_space() function to each element of the list text\n",
    "for item in map(replace_space, text):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello Alabama',\n",
       " 'Georgia!',\n",
       " 'Georgia',\n",
       " 'georgia',\n",
       " 'FlOrIda',\n",
       " 'south carolina##',\n",
       " 'West virginia?']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also put the map results inside a list\n",
    "list(map(replace_space, text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello Alabama',\n",
       " 'Georgia!',\n",
       " 'Georgia',\n",
       " 'georgia',\n",
       " 'FlOrIda',\n",
       " 'south carolina##',\n",
       " 'West virginia?']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# however, because the code for the function is so short, it might be easier to just create\n",
    "# a quick function without a formal name. These 'anonymous' functions are also known as lambda functions\n",
    "list(map(lambda x: re.sub('\\\\s+',' ', x), text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello     Alabama',\n",
       " 'Georgia!',\n",
       " 'Georgia',\n",
       " 'Georgia',\n",
       " 'Florida',\n",
       " 'South  Carolina##',\n",
       " 'West Virginia?']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here's a similar function that turns the text into title case.\n",
    "list(map(lambda string: string.title(), text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "lambda functions are written in the form:\n",
    "\n",
    "`lambda argument1, argument2, etc: expression to return`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101, 202, 303]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lambda functions can also accept multiple arguments\n",
    "# if you use it with map, you'll need to provide a list for each argument\n",
    "list(map(lambda x, y: x + y, [1, 2, 3], [100, 200, 300]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = set(\"abcdefgabc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'f', 'g'}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = set(\"fghij\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'f', 'g', 'h', 'i', 'j'}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'f', 'g'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 & s2 # intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 | s2 # union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e'}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 - s2 # set difference (in s1 but not s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'h', 'i', 'j'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 - s1 # set difference (in s2 but not s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'h', 'i', 'j'}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 ^ s2 # xor = (in s1 but not s2) union (in s2 but not s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"b\" in s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"x\" in s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = set(\"abc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3 < s1  # is s3 a subset of s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'f', 'g'}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.add(\"z\") # add an element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'f', 'g', 'z'}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.discard(\"z\") # remove an element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e', 'f', 'g'}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### use cases of sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering values:\n",
    "data = [1, 2, 3, 4, 5]\n",
    "exclude = {2, 4}\n",
    "filtered_data = [x for x in data if x not in exclude]\n",
    "filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 6, 7}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique items across multiple lists\n",
    "list1 = [1, 2, 3]\n",
    "list2 = [3, 4, 5]\n",
    "list3 = [5, 6, 7]\n",
    "\n",
    "unique_items = set(list1) | set(list2) | set(list3)\n",
    "unique_items"
   ]
  }
 ],
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