main.py

def main():
    # Single line comments start with a number symbol.

    """ Multiline strings can be written
        using three "s, and are often used
        as documentation.
    """

    ####################################################
    # 1. Primitive Datatypes and Operators
    ####################################################

    # You have numbers
    print(3)  # => 3

    # Math is what you would expect
    print(1 + 1)  # => 2
    print(8 - 1)  # => 7
    print(10 * 2)  # => 20
    print(35 / 5)  # => 7.0

#     # Integer division rounds down for both positive and negative numbers.
#     5 // 3  # => 1
#     -5 // 3  # => -2
#     5.0 // 3.0  # => 1.0 # works on floats too
#     -5.0 // 3.0  # => -2.0
#
#     # The result of division is always a float
#     10.0 / 3  # => 3.3333333333333335
#
#     # Modulo operation
#     7 % 3  # => 1
#     # i % j have the same sign as j, unlike C
#     -7 % 3  # => 2
#
#     # Exponentiation (x**y, x to the yth power)
#     2 ** 3  # => 8
#
#     # Enforce precedence with parentheses
#     1 + 3 * 2  # => 7
#     (1 + 3) * 2  # => 8
#
#     # Boolean values are primitives (Note: the capitalization)
#     True  # => True
#     False  # => False
#
#     # negate with not
#     not True  # => False
#     not False  # => True
#
#     # Boolean Operators
#     # Note "and" and "or" are case-sensitive
#     True and False  # => False
#     False or True  # => True
#
#     # True and False are actually 1 and 0 but with different keywords
#     True + True  # => 2
#     True * 8  # => 8
#     False - 5  # => -5
#
#     # Comparison operators look at the numerical value of True and False
#     0 == False  # => True
#     2 > True  # => True
#     2 == True  # => False
#     -5 != False  # => True
#
#     # None, 0, and empty strings/lists/dicts/tuples/sets all evaluate to False.
#     # All other values are True
#     bool(0)  # => False
#     bool("")  # => False
#     bool([])  # => False
#     bool({})  # => False
#     bool(())  # => False
#     bool(set())  # => False
#     bool(4)  # => True
#     bool(-6)  # => True
#
#     # Using boolean logical operators on ints casts them to booleans for evaluation,
#     # but their non-cast value is returned. Don't mix up with bool(ints) and bitwise
#     # and/or (&,|)
#     bool(0)  # => False
#     bool(2)  # => True
#     0 and 2  # => 0
#     bool(-5)  # => True
#     bool(2)  # => True
#     -5 or 0  # => -5
#
#     # Equality is ==
#     1 == 1  # => True
#     2 == 1  # => False
#
#     # Inequality is !=
#     1 != 1  # => False
#     2 != 1  # => True
#
#     # More comparisons
#     1 < 10  # => True
#     1 > 10  # => False
#     2 <= 2  # => True
#     2 >= 2  # => True
#
#     # Seeing whether a value is in a range
#     1 < 2 and 2 < 3  # => True
#     2 < 3 and 3 < 2  # => False
#     # Chaining makes this look nicer
#     1 < 2 < 3  # => True
#     2 < 3 < 2  # => False
#
#     # (is vs. ==) is checks if two variables refer to the same object, but == checks
#     # if the objects pointed to have the same values.
#     a = [1, 2, 3, 4]  # Point a at a new list, [1, 2, 3, 4]
#     b = a  # Point b at what a is pointing to
#     b is a  # => True, a and b refer to the same object
#     b == a  # => True, a's and b's objects are equal
#     b = [1, 2, 3, 4]  # Point b at a new list, [1, 2, 3, 4]
#     b is a  # => False, a and b do not refer to the same object
#     b == a  # => True, a's and b's objects are equal
#
#     # Strings are created with " or '
#     "This is a string."
#     'This is also a string.'
#
#     # Strings can be added too
#     "Hello " + "world!"  # => "Hello world!"
#     # String literals (but not variables) can be concatenated without using '+'
#     "Hello " "world!"  # => "Hello world!"
#
#     # A string can be treated like a list of characters
#     "Hello world!"[0]  # => 'H'
#
#     # You can find the length of a string
#     len("This is a string")  # => 16
#
#     # Since Python 3.6, you can use f-strings or formatted string literals.
#     name = "Reiko"
#     f"She said her name is {name}."  # => "She said her name is Reiko"
#     # Any valid Python expression inside these braces is returned to the string.
#     f"{name} is {len(name)} characters long."  # => "Reiko is 5 characters long."
#
#     # None is an object
#     None  # => None
#
#     # Don't use the equality "==" symbol to compare objects to None
#     # Use "is" instead. This checks for equality of object identity.
#     "etc" is None  # => False
#     None is None  # => True
#
#     ####################################################
#     ## 2. Variables and Collections
#     ####################################################
#
#     # Python has a print function
#     print("I'm Python. Nice to meet you!")  # => I'm Python. Nice to meet you!
#
#     # By default the print function also prints out a newline at the end.
#     # Use the optional argument end to change the end string.
#     print("Hello, World", end="!")  # => Hello, World!
#
#     # Simple way to get input data from console
#     input_string_var = input("Enter some data: ")  # Returns the data as a string
#
#     # There are no declarations, only assignments.
#     # Convention is to use lower_case_with_underscores
#     some_var = 5
#     some_var  # => 5
#
#     # Accessing a previously unassigned variable is an exception.
#     # See Control Flow to learn more about exception handling.
#     some_unknown_var  # Raises a NameError
#
#     # if can be used as an expression
#     # Equivalent of C's '?:' ternary operator
#     "yay!" if 0 > 1 else "nay!"  # => "nay!"
#
#     # Lists store sequences
#     li = []
#     # You can start with a prefilled list
#     other_li = [4, 5, 6]
#
#     # Add stuff to the end of a list with append
#     li.append(1)  # li is now [1]
#     li.append(2)  # li is now [1, 2]
#     li.append(4)  # li is now [1, 2, 4]
#     li.append(3)  # li is now [1, 2, 4, 3]
#     # Remove from the end with pop
#     li.pop()  # => 3 and li is now [1, 2, 4]
#     # Let's put it back
#     li.append(3)  # li is now [1, 2, 4, 3] again.
#
#     # Access a list like you would any array
#     li[0]  # => 1
#     # Look at the last element
#     li[-1]  # => 3
#
#     # Looking out of bounds is an IndexError
#     li[4]  # Raises an IndexError
#
#     # You can look at ranges with slice syntax.
#     # The start index is included, the end index is not
#     # (It's a closed/open range for you mathy types.)
#     li[1:3]  # Return list from index 1 to 3 => [2, 4]
#     li[2:]  # Return list starting from index 2 => [4, 3]
#     li[:3]  # Return list from beginning until index 3  => [1, 2, 4]
#     li[::2]  # Return list selecting every second entry => [1, 4]
#     li[::-1]  # Return list in reverse order => [3, 4, 2, 1]
#     # Use any combination of these to make advanced slices
#     # li[start:end:step]
#
#     # Make a one layer deep copy using slices
#     li2 = li[:]  # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false.
#
#     # Remove arbitrary elements from a list with "del"
#     del li[2]  # li is now [1, 2, 3]
#
#     # Remove first occurrence of a value
#     li.remove(2)  # li is now [1, 3]
#     li.remove(2)  # Raises a ValueError as 2 is not in the list
#
#     # Insert an element at a specific index
#     li.insert(1, 2)  # li is now [1, 2, 3] again
#
#     # Get the index of the first item found matching the argument
#     li.index(2)  # => 1
#     li.index(4)  # Raises a ValueError as 4 is not in the list
#
#     # You can add lists
#     # Note: values for li and for other_li are not modified.
#     li + other_li  # => [1, 2, 3, 4, 5, 6]
#
#     # Concatenate lists with "extend()"
#     li.extend(other_li)  # Now li is [1, 2, 3, 4, 5, 6]
#
#     # Check for existence in a list with "in"
#     1 in li  # => True
#
#     # Examine the length with "len()"
#     len(li)  # => 6
#
#     # Tuples are like lists but are immutable.
#     tup = (1, 2, 3)
#     tup[0]  # => 1
#     tup[0] = 3  # Raises a TypeError
#
#     # Note that a tuple of length one has to have a comma after the last element but
#     # tuples of other lengths, even zero, do not.
#     type((1))  # => <class 'int'>
#     type((1,))  # => <class 'tuple'>
#     type(())  # => <class 'tuple'>
#
#     # You can do most of the list operations on tuples too
#     len(tup)  # => 3
#     tup + (4, 5, 6)  # => (1, 2, 3, 4, 5, 6)
#     tup[:2]  # => (1, 2)
#     2 in tup  # => True
#
#     # You can unpack tuples (or lists) into variables
#     a, b, c = (1, 2, 3)  # a is now 1, b is now 2 and c is now 3
#     # You can also do extended unpacking
#     a, *b, c = (1, 2, 3, 4)  # a is now 1, b is now [2, 3] and c is now 4
#     # Tuples are created by default if you leave out the parentheses
#     d, e, f = 4, 5, 6  # tuple 4, 5, 6 is unpacked into variables d, e and f
#     # respectively such that d = 4, e = 5 and f = 6
#     # Now look how easy it is to swap two values
#     e, d = d, e  # d is now 5 and e is now 4
#
#     # Dictionaries store mappings from keys to values
#     empty_dict = {}
#     # Here is a prefilled dictionary
#     filled_dict = {"one": 1, "two": 2, "three": 3}
#
#     # Note keys for dictionaries have to be immutable types. This is to ensure that
#     # the key can be converted to a constant hash value for quick look-ups.
#     # Immutable types include ints, floats, strings, tuples.
#     invalid_dict = {[1, 2, 3]: "123"}  # => Yield a TypeError: unhashable type: 'list'
#     valid_dict = {(1, 2, 3): [1, 2, 3]}  # Values can be of any type, however.
#
#     # Look up values with []
#     filled_dict["one"]  # => 1
#
#     # Get all keys as an iterable with "keys()". We need to wrap the call in list()
#     # to turn it into a list. We'll talk about those later.  Note - for Python
#     # versions <3.7, dictionary key ordering is not guaranteed. Your results might
#     # not match the example below exactly. However, as of Python 3.7, dictionary
#     # items maintain the order at which they are inserted into the dictionary.
#     list(filled_dict.keys())  # => ["three", "two", "one"] in Python <3.7
#     list(filled_dict.keys())  # => ["one", "two", "three"] in Python 3.7+
#
#     # Get all values as an iterable with "values()". Once again we need to wrap it
#     # in list() to get it out of the iterable. Note - Same as above regarding key
#     # ordering.
#     list(filled_dict.values())  # => [3, 2, 1]  in Python <3.7
#     list(filled_dict.values())  # => [1, 2, 3] in Python 3.7+
#
#     # Check for existence of keys in a dictionary with "in"
#     "one" in filled_dict  # => True
#     1 in filled_dict  # => False
#
#     # Looking up a non-existing key is a KeyError
#     filled_dict["four"]  # KeyError
#
#     # Use "get()" method to avoid the KeyError
#     filled_dict.get("one")  # => 1
#     filled_dict.get("four")  # => None
#     # The get method supports a default argument when the value is missing
#     filled_dict.get("one", 4)  # => 1
#     filled_dict.get("four", 4)  # => 4
#
#     # "setdefault()" inserts into a dictionary only if the given key isn't present
#     filled_dict.setdefault("five", 5)  # filled_dict["five"] is set to 5
#     filled_dict.setdefault("five", 6)  # filled_dict["five"] is still 5
#
#     # Adding to a dictionary
#     filled_dict.update({"four": 4})  # => {"one": 1, "two": 2, "three": 3, "four": 4}
#     filled_dict["four"] = 4  # another way to add to dict
#
#     # Remove keys from a dictionary with del
#     del filled_dict["one"]  # Removes the key "one" from filled dict
#
#     # From Python 3.5 you can also use the additional unpacking options
#     {'a': 1, **{'b': 2}}  # => {'a': 1, 'b': 2}
#     {'a': 1, **{'a': 2}}  # => {'a': 2}
#
#     # Sets store ... well sets
#     empty_set = set()
#     # Initialize a set with a bunch of values.
#     some_set = {1, 1, 2, 2, 3, 4}  # some_set is now {1, 2, 3, 4}
#
#     # Similar to keys of a dictionary, elements of a set have to be immutable.
#     invalid_set = {[1], 1}  # => Raises a TypeError: unhashable type: 'list'
#     valid_set = {(1,), 1}
#
#     # Add one more item to the set
#     filled_set = some_set
#     filled_set.add(5)  # filled_set is now {1, 2, 3, 4, 5}
#     # Sets do not have duplicate elements
#     filled_set.add(5)  # it remains as before {1, 2, 3, 4, 5}
#
#     # Do set intersection with &
#     other_set = {3, 4, 5, 6}
#     filled_set & other_set  # => {3, 4, 5}
#
#     # Do set union with |
#     filled_set | other_set  # => {1, 2, 3, 4, 5, 6}
#
#     # Do set difference with -
#     {1, 2, 3, 4} - {2, 3, 5}  # => {1, 4}
#
#     # Do set symmetric difference with ^
#     {1, 2, 3, 4} ^ {2, 3, 5}  # => {1, 4, 5}
#
#     # Check if set on the left is a superset of set on the right
#     {1, 2} >= {1, 2, 3}  # => False
#
#     # Check if set on the left is a subset of set on the right
#     {1, 2} <= {1, 2, 3}  # => True
#
#     # Check for existence in a set with in
#     2 in filled_set  # => True
#     10 in filled_set  # => False
#
#     # Make a one layer deep copy
#     filled_set = some_set.copy()  # filled_set is {1, 2, 3, 4, 5}
#     filled_set is some_set  # => False
#
#     ####################################################
#     ## 3. Control Flow and Iterables
#     ####################################################
#
#     # Let's just make a variable
#     some_var = 5
#
#     # Here is an if statement. Indentation is significant in Python!
#     # Convention is to use four spaces, not tabs.
#     # This prints "some_var is smaller than 10"
#     if some_var > 10:
#         print("some_var is totally bigger than 10.")
#     elif some_var < 10:  # This elif clause is optional.
#         print("some_var is smaller than 10.")
#     else:  # This is optional too.
#         print("some_var is indeed 10.")
#
#     """
#     For loops iterate over lists
#     prints:
#         dog is a mammal
#         cat is a mammal
#         mouse is a mammal
#     """
#     for animal in ["dog", "cat", "mouse"]:
#         # You can use format() to interpolate formatted strings
#         print("{} is a mammal".format(animal))
#
#     """
#     "range(number)" returns an iterable of numbers
#     from zero up to (but excluding) the given number
#     prints:
#         0
#         1
#         2
#         3
#     """
#     for i in range(4):
#         print(i)
#
#     """
#     "range(lower, upper)" returns an iterable of numbers
#     from the lower number to the upper number
#     prints:
#         4
#         5
#         6
#         7
#     """
#     for i in range(4, 8):
#         print(i)
#
#     """
#     "range(lower, upper, step)" returns an iterable of numbers
#     from the lower number to the upper number, while incrementing
#     by step. If step is not indicated, the default value is 1.
#     prints:
#         4
#         6
#     """
#     for i in range(4, 8, 2):
#         print(i)
#
#     """
#     Loop over a list to retrieve both the index and the value of each list item:
#         0 dog
#         1 cat
#         2 mouse
#     """
#     animals = ["dog", "cat", "mouse"]
#     for i, value in enumerate(animals):
#         print(i, value)
#
#     """
#     While loops go until a condition is no longer met.
#     prints:
#         0
#         1
#         2
#         3
#     """
#     x = 0
#     while x < 4:
#         print(x)
#         x += 1  # Shorthand for x = x + 1
#
#     # Handle exceptions with a try/except block
#     try:
#         # Use "raise" to raise an error
#         raise IndexError("This is an index error")
#     except IndexError as e:
#         pass  # Refrain from this, provide a recovery (next example).
#     except (TypeError, NameError):
#         pass  # Multiple exceptions can be processed jointly.
#     else:  # Optional clause to the try/except block. Must follow
#         # all except blocks.
#         print("All good!")  # Runs only if the code in try raises no exceptions
#     finally:  # Execute under all circumstances
#         print("We can clean up resources here")
#
#     # Instead of try/finally to cleanup resources you can use a with statement
#     with open("myfile.txt") as f:
#         for line in f:
#             print(line)
#
#     # Writing to a file
#     contents = {"aa": 12, "bb": 21}
#     with open("myfile1.txt", "w+") as file:
#         file.write(str(contents))  # writes a string to a file
#
#     import json
#     with open("myfile2.txt", "w+") as file:
#         file.write(json.dumps(contents))  # writes an object to a file
#
#     # Reading from a file
#     with open('myfile1.txt', "r+") as file:
#         contents = file.read()  # reads a string from a file
#     print(contents)
#     # print: {"aa": 12, "bb": 21}
#
#     with open('myfile2.txt', "r+") as file:
#         contents = json.load(file)  # reads a json object from a file
#     print(contents)
#     # print: {"aa": 12, "bb": 21}
#
#     # Python offers a fundamental abstraction called the Iterable.
#     # An iterable is an object that can be treated as a sequence.
#     # The object returned by the range function, is an iterable.
#
#     filled_dict = {"one": 1, "two": 2, "three": 3}
#     our_iterable = filled_dict.keys()
#     print(our_iterable)  # => dict_keys(['one', 'two', 'three']). This is an object
#     # that implements our Iterable interface.
#
#     # We can loop over it.
#     for i in our_iterable:
#         print(i)  # Prints one, two, three
#
#     # However we cannot address elements by index.
#     our_iterable[1]  # Raises a TypeError
#
#     # An iterable is an object that knows how to create an iterator.
#     our_iterator = iter(our_iterable)
#
#     # Our iterator is an object that can remember the state as we traverse through
#     # it. We get the next object with "next()".
#     next(our_iterator)  # => "one"
#
#     # It maintains state as we iterate.
#     next(our_iterator)  # => "two"
#     next(our_iterator)  # => "three"
#
#     # After the iterator has returned all of its data, it raises a
#     # StopIteration exception
#     next(our_iterator)  # Raises StopIteration
#
#     # We can also loop over it, in fact, "for" does this implicitly!
#     our_iterator = iter(our_iterable)
#     for i in our_iterator:
#         print(i)  # Prints one, two, three
#
#     # You can grab all the elements of an iterable or iterator by call of list().
#     list(our_iterable)  # => Returns ["one", "two", "three"]
#     list(our_iterator)  # => Returns [] because state is saved
#
#     ####################################################
#     ## 4. Functions
#     ####################################################
#
#     # Use "def" to create new functions
#     def add(x, y):
#         print("x is {} and y is {}".format(x, y))
#         return x + y  # Return values with a return statement
#
#     # Calling functions with parameters
#     add(5, 6)  # => prints out "x is 5 and y is 6" and returns 11
#
#     # Another way to call functions is with keyword arguments
#     add(y=6, x=5)  # Keyword arguments can arrive in any order.
#
#     # You can define functions that take a variable number of
#     # positional arguments
#     def varargs(*args):
#         return args
#
#     varargs(1, 2, 3)  # => (1, 2, 3)
#
#     # You can define functions that take a variable number of
#     # keyword arguments, as well
#     def keyword_args(**kwargs):
#         return kwargs
#
#     # Let's call it to see what happens
#     keyword_args(big="foot", loch="ness")  # => {"big": "foot", "loch": "ness"}
#
#     # You can do both at once, if you like
#     def all_the_args(*args, **kwargs):
#         print(args)
#         print(kwargs)
#
#     """
#     all_the_args(1, 2, a=3, b=4) prints:
#         (1, 2)
#         {"a": 3, "b": 4}
#     """
#
#     # When calling functions, you can do the opposite of args/kwargs!
#     # Use * to expand tuples and use ** to expand kwargs.
#     args = (1, 2, 3, 4)
#     kwargs = {"a": 3, "b": 4}
#     all_the_args(*args)  # equivalent: all_the_args(1, 2, 3, 4)
#     all_the_args(**kwargs)  # equivalent: all_the_args(a=3, b=4)
#     all_the_args(*args, **kwargs)  # equivalent: all_the_args(1, 2, 3, 4, a=3, b=4)
#
#     # Returning multiple values (with tuple assignments)
#     def swap(x, y):
#         return y, x  # Return multiple values as a tuple without the parenthesis.
#         # (Note: parenthesis have been excluded but can be included)
#
#     x = 1
#     y = 2
#     x, y = swap(x, y)  # => x = 2, y = 1
#     # (x, y) = swap(x,y)  # Again the use of parenthesis is optional.
#
#     # global scope
#     x = 5
#
#     def set_x(num):
#         # local scope begins here
#         # local var x not the same as global var x
#         x = num  # => 43
#         print(x)  # => 43
#
#     def set_global_x(num):
#         # global indicates that particular var lives in the global scope
#         global x
#         print(x)  # => 5
#         x = num  # global var x is now set to 6
#         print(x)  # => 6
#
#     set_x(43)
#     set_global_x(6)
#     """
#     prints:
#         43
#         5
#         6
#     """
#
#     # Python has first class functions
#     def create_adder(x):
#         def adder(y):
#             return x + y
#
#         return adder
#
#     add_10 = create_adder(10)
#     add_10(3)  # => 13
#
#     # There are also anonymous functions
#     (lambda x: x > 2)(3)  # => True
#     (lambda x, y: x ** 2 + y ** 2)(2, 1)  # => 5
#
#     # There are built-in higher order functions
#     list(map(add_10, [1, 2, 3]))  # => [11, 12, 13]
#     list(map(max, [1, 2, 3], [4, 2, 1]))  # => [4, 2, 3]
#
#     list(filter(lambda x: x > 5, [3, 4, 5, 6, 7]))  # => [6, 7]
#
#     # We can use list comprehensions for nice maps and filters
#     # List comprehension stores the output as a list (which itself may be nested).
#     [add_10(i) for i in [1, 2, 3]]  # => [11, 12, 13]
#     [x for x in [3, 4, 5, 6, 7] if x > 5]  # => [6, 7]
#
#     # You can construct set and dict comprehensions as well.
#     {x for x in 'abcddeef' if x not in 'abc'}  # => {'d', 'e', 'f'}
#     {x: x ** 2 for x in range(5)}  # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
#
#     ####################################################
#     ## 5. Modules
#     ####################################################
#
#     # You can import modules
#     import math
#     print(math.sqrt(16))  # => 4.0
#
#     # You can get specific functions from a module
#     from math import ceil, floor
#     print(ceil(3.7))  # => 4.0
#     print(floor(3.7))  # => 3.0
#
#     # You can import all functions from a module.
#     # Warning: this is not recommended
#     from math import *
#
#     # You can shorten module names
#     import math as m
#     math.sqrt(16) == m.sqrt(16)  # => True
#
#     # Python modules are just ordinary Python files. You
#     # can write your own, and import them. The name of the
#     # module is the same as the name of the file.
#
#     # You can find out which functions and attributes
#     # are defined in a module.
#     import math
#     dir(math)
#
#     # If you have a Python script named math.py in the same
#     # folder as your current script, the file math.py will
#     # be loaded instead of the built-in Python module.
#     # This happens because the local folder has priority
#     # over Python's built-in libraries.
#
#     ####################################################
#     ## 6. Classes
#     ####################################################
#
#     # We use the "class" statement to create a class
#     class Human:
#
#         # A class attribute. It is shared by all instances of this class
#         species = "H. sapiens"
#
#         # Basic initializer, this is called when this class is instantiated.
#         # Note that the double leading and trailing underscores denote objects
#         # or attributes that are used by Python but that live in user-controlled
#         # namespaces. Methods(or objects or attributes) like: __init__, __str__,
#         # __repr__ etc. are called special methods (or sometimes called dunder
#         # methods). You should not invent such names on your own.
#         def __init__(self, name):
#             # Assign the argument to the instance's name attribute
#             self.name = name
#
#             # Initialize property
#             self._age = 0
#
#         # An instance method. All methods take "self" as the first argument
#         def say(self, msg):
#             print("{name}: {message}".format(name=self.name, message=msg))
#
#         # Another instance method
#         def sing(self):
#             return 'yo... yo... microphone check... one two... one two...'
#
#         # A class method is shared among all instances
#         # They are called with the calling class as the first argument
#         @classmethod
#         def get_species(cls):
#             return cls.species
#
#         # A static method is called without a class or instance reference
#         @staticmethod
#         def grunt():
#             return "*grunt*"
#
#         # A property is just like a getter.
#         # It turns the method age() into a read-only attribute of the same name.
#         # There's no need to write trivial getters and setters in Python, though.
#         @property
#         def age(self):
#             return self._age
#
#         # This allows the property to be set
#         @age.setter
#         def age(self, age):
#             self._age = age
#
#         # This allows the property to be deleted
#         @age.deleter
#         def age(self):
#             del self._age
#
#     # When a Python interpreter reads a source file it executes all its code.
#     # This __name__ check makes sure this code block is only executed when this
#     # module is the main program.
#     if __name__ == '__main__':
#         # Instantiate a class
#         i = Human(name="Ian")
#         i.say("hi")  # "Ian: hi"
#         j = Human("Joel")
#         j.say("hello")  # "Joel: hello"
#         # i and j are instances of type Human; i.e., they are Human objects.
#
#         # Call our class method
#         i.say(i.get_species())  # "Ian: H. sapiens"
#         # Change the shared attribute
#         Human.species = "H. neanderthalensis"
#         i.say(i.get_species())  # => "Ian: H. neanderthalensis"
#         j.say(j.get_species())  # => "Joel: H. neanderthalensis"
#
#         # Call the static method
#         print(Human.grunt())  # => "*grunt*"
#
#         # Static methods can be called by instances too
#         print(i.grunt())  # => "*grunt*"
#
#         # Update the property for this instance
#         i.age = 42
#         # Get the property
#         i.say(i.age)  # => "Ian: 42"
#         j.say(j.age)  # => "Joel: 0"
#         # Delete the property
#         del i.age
#         # i.age                         # => this would raise an AttributeError
#
#     ####################################################
#     ## 6.1 Inheritance
#     ####################################################
#
#     # Inheritance allows new child classes to be defined that inherit methods and
#     # variables from their parent class.
#
#     # Using the Human class defined above as the base or parent class, we can
#     # define a child class, Superhero, which inherits the class variables like
#     # "species", "name", and "age", as well as methods, like "sing" and "grunt"
#     # from the Human class, but can also have its own unique properties.
#
#     # To take advantage of modularization by file you could place the classes above
#     # in their own files, say, human.py
#
#     # To import functions from other files use the following format
#     # from "filename-without-extension" import "function-or-class"
#
#     from human import Human
#
#     # Specify the parent class(es) as parameters to the class definition
#     class Superhero(Human):
#
#         # If the child class should inherit all of the parent's definitions without
#         # any modifications, you can just use the "pass" keyword (and nothing else)
#         # but in this case it is commented out to allow for a unique child class:
#         # pass
#
#         # Child classes can override their parents' attributes
#         species = 'Superhuman'
#
#         # Children automatically inherit their parent class's constructor including
#         # its arguments, but can also define additional arguments or definitions
#         # and override its methods such as the class constructor.
#         # This constructor inherits the "name" argument from the "Human" class and
#         # adds the "superpower" and "movie" arguments:
#         def __init__(self, name, movie=False,
#                      superpowers=["super strength", "bulletproofing"]):
#             # add additional class attributes:
#             self.fictional = True
#             self.movie = movie
#             # be aware of mutable default values, since defaults are shared
#             self.superpowers = superpowers
#
#             # The "super" function lets you access the parent class's methods
#             # that are overridden by the child, in this case, the __init__ method.
#             # This calls the parent class constructor:
#             super().__init__(name)
#
#         # override the sing method
#         def sing(self):
#             return 'Dun, dun, DUN!'
#
#         # add an additional instance method
#         def boast(self):
#             for power in self.superpowers:
#                 print("I wield the power of {pow}!".format(pow=power))
#
#     if __name__ == '__main__':
#         sup = Superhero(name="Tick")
#
#         # Instance type checks
#         if isinstance(sup, Human):
#             print('I am human')
#         if type(sup) is Superhero:
#             print('I am a superhero')
#
#         # Get the Method Resolution search Order used by both getattr() and super()
#         # This attribute is dynamic and can be updated
#         print(Superhero.__mro__)  # => (<class '__main__.Superhero'>,
#         # => <class 'human.Human'>, <class 'object'>)
#
#         # Calls parent method but uses its own class attribute
#         print(sup.get_species())  # => Superhuman
#
#         # Calls overridden method
#         print(sup.sing())  # => Dun, dun, DUN!
#
#         # Calls method from Human
#         sup.say('Spoon')  # => Tick: Spoon
#
#         # Call method that exists only in Superhero
#         sup.boast()  # => I wield the power of super strength!
#         # => I wield the power of bulletproofing!
#
#         # Inherited class attribute
#         sup.age = 31
#         print(sup.age)  # => 31
#
#         # Attribute that only exists within Superhero
#         print('Am I Oscar eligible? ' + str(sup.movie))
#
#     ####################################################
#     ## 6.2 Multiple Inheritance
#     ####################################################
#
#     # Another class definition
#     # bat.py
#     class Bat:
#
#         species = 'Baty'
#
#         def __init__(self, can_fly=True):
#             self.fly = can_fly
#
#         # This class also has a say method
#         def say(self, msg):
#             msg = '... ... ...'
#             return msg
#
#         # And its own method as well
#         def sonar(self):
#             return '))) ... ((('
#
#     if __name__ == '__main__':
#         b = Bat()
#         print(b.say('hello'))
#         print(b.fly)
#
#     # And yet another class definition that inherits from Superhero and Bat
#     # superhero.py
#     from superhero import Superhero
#     from bat import Bat
#
#     # Define Batman as a child that inherits from both Superhero and Bat
#     class Batman(Superhero, Bat):
#
#         def __init__(self, *args, **kwargs):
#             # Typically to inherit attributes you have to call super:
#             # super(Batman, self).__init__(*args, **kwargs)
#             # However we are dealing with multiple inheritance here, and super()
#             # only works with the next base class in the MRO list.
#             # So instead we explicitly call __init__ for all ancestors.
#             # The use of *args and **kwargs allows for a clean way to pass
#             # arguments, with each parent "peeling a layer of the onion".
#             Superhero.__init__(self, 'anonymous', movie=True,
#                                superpowers=['Wealthy'], *args, **kwargs)
#             Bat.__init__(self, *args, can_fly=False, **kwargs)
#             # override the value for the name attribute
#             self.name = 'Sad Affleck'
#
#         def sing(self):
#             return 'nan nan nan nan nan batman!'
#
#     if __name__ == '__main__':
#         sup = Batman()
#
#         # Get the Method Resolution search Order used by both getattr() and super().
#         # This attribute is dynamic and can be updated
#         print(Batman.__mro__)  # => (<class '__main__.Batman'>,
#         # => <class 'superhero.Superhero'>,
#         # => <class 'human.Human'>,
#         # => <class 'bat.Bat'>, <class 'object'>)
#
#         # Calls parent method but uses its own class attribute
#         print(sup.get_species())  # => Superhuman
#
#         # Calls overridden method
#         print(sup.sing())  # => nan nan nan nan nan batman!
#
#         # Calls method from Human, because inheritance order matters
#         sup.say('I agree')  # => Sad Affleck: I agree
#
#         # Call method that exists only in 2nd ancestor
#         print(sup.sonar())  # => ))) ... (((
#
#         # Inherited class attribute
#         sup.age = 100
#         print(sup.age)  # => 100
#
#         # Inherited attribute from 2nd ancestor whose default value was overridden.
#         print('Can I fly? ' + str(sup.fly))  # => Can I fly? False
#
#     ####################################################
#     ## 7. Advanced
#     ####################################################
#
#     # Generators help you make lazy code.
#     def double_numbers(iterable):
#         for i in iterable:
#             yield i + i
#
#     # Generators are memory-efficient because they only load the data needed to
#     # process the next value in the iterable. This allows them to perform
#     # operations on otherwise prohibitively large value ranges.
#     # NOTE: `range` replaces `xrange` in Python 3.
#     for i in double_numbers(range(1, 900000000)):  # `range` is a generator.
#         print(i)
#         if i >= 30:
#             break
#
#     # Just as you can create a list comprehension, you can create generator
#     # comprehensions as well.
#     values = (-x for x in [1, 2, 3, 4, 5])
#     for x in values:
#         print(x)  # prints -1 -2 -3 -4 -5 to console/terminal
#
#     # You can also cast a generator comprehension directly to a list.
#     values = (-x for x in [1, 2, 3, 4, 5])
#     gen_to_list = list(values)
#     print(gen_to_list)  # => [-1, -2, -3, -4, -5]
#
#     # Decorators
#     # In this example `beg` wraps `say`. If say_please is True then it
#     # will change the returned message.
#     from functools import wraps
#
#     def beg(target_function):
#         @wraps(target_function)
#         def wrapper(*args, **kwargs):
#             msg, say_please = target_function(*args, **kwargs)
#             if say_please:
#                 return "{} {}".format(msg, "Please! I am poor :(")
#             return msg
#
#         return wrapper
#
#     @beg
#     def say(say_please=False):
#         msg = "Can you buy me a beer?"
#         return msg, say_please
#
#     print(say())  # Can you buy me a beer?
#     print(say(say_please=True))  # Can you buy me a beer? Please! I am poor :(


if __name__ == '__main__':
    main()