--- name: Python contributors: - ["Louie Dinh", "http://pythonpracticeprojects.com"] - ["Steven Basart", "http://github.com/xksteven"] - ["Andre Polykanine", "https://github.com/Oire"] - ["Zachary Ferguson", "http://github.com/zfergus2"] - ["evuez", "http://github.com/evuez"] - ["Rommel Martinez", "https://ebzzry.io"] - ["Roberto Fernandez Diaz", "https://github.com/robertofd1995"] - ["caminsha", "https://github.com/caminsha"] - ["Stanislav Modrak", "https://stanislav.gq"] - ["John Paul Wohlscheid", "https://gitpi.us"] filename: learnpython.py --- Python was created by Guido van Rossum in the early 90s. It is now one of the most popular languages in existence. I fell in love with Python for its syntactic clarity. It's basically executable pseudocode. ```python # 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 3 # => 3 # Math is what you would expect 1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7.0 # Floor division rounds towards negative infinity 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 in naming variables is snake_case style 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 elements with a step size of 2 => [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)) # => type((1,)) # => type(()) # => # 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") 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 args (tuples) and use ** to expand kwargs (dictionaries). 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 # Closures in nested functions: # We can use the nonlocal keyword to work with variables in nested scope which shouldn't be declared in the inner functions. def create_avg(): total = 0 count = 0 def avg(n): nonlocal total, count total += n count += 1 return total/count return avg avg = create_avg() avg(3) # => 3.0 avg(5) # (3+5)/2 => 4.0 avg(7) # (8+7)/3 => 5.0 # 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 print(floor(3.7)) # => 3 # 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 # the leading underscore indicates the "age" property is # intended to be used internally # do not rely on this to be enforced: it's a hint to other devs # 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 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 Order" used by both getattr() and super() # (the order in which classes are searched for an attribute or method) # This attribute is dynamic and can be updated print(Superhero.__mro__) # => (, # => , ) # 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() # The Method Resolution Order print(Batman.__mro__) # => (, # => , # => , # => , ) # 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 are a form of syntactic sugar. # They make code easier to read while accomplishing clunky syntax. # Wrappers are one type of decorator. # They're really useful for adding logging to existing functions without needing to modify them. def log_function(func): def wrapper(*args, **kwargs): print("Entering function", func.__name__) result = func(*args, **kwargs) print("Exiting function", func.__name__) return result return wrapper @log_function # equivalent: def my_function(x,y): # def my_function(x,y): return x+y # return x+y # my_function = log_function(my_function) # The decorator @log_function tells us as we begin reading the function definition # for my_function that this function will be wrapped with log_function. # When function definitions are long, it can be hard to parse the non-decorated # assignment at the end of the definition. my_function(1,2) # => "Entering function my_function" # => "3" # => "Exiting function my_function" # But there's a problem. # What happens if we try to get some information about my_function? print(my_function.__name__) # => 'wrapper' print(my_function.__code__.co_argcount) # => 0. The argcount is 0 because both arguments in wrapper()'s signature are optional. # Because our decorator is equivalent to my_function = log_function(my_function) # we've replaced information about my_function with information from wrapper # Fix this using functools from functools import wraps def log_function(func): @wraps(func) # this ensures docstring, function name, arguments list, etc. are all copied # to the wrapped function - instead of being replaced with wrapper's info def wrapper(*args, **kwargs): print("Entering function", func.__name__) result = func(*args, **kwargs) print("Exiting function", func.__name__) return result return wrapper @log_function def my_function(x,y): return x+y my_function(1,2) # => "Entering function my_function" # => "3" # => "Exiting function my_function" print(my_function.__name__) # => 'my_function' print(my_function.__code__.co_argcount) # => 2 ``` ### Free Online * [Automate the Boring Stuff with Python](https://automatetheboringstuff.com) * [The Official Docs](https://docs.python.org/3/) * [Hitchhiker's Guide to Python](https://docs.python-guide.org/) * [Python Course](https://www.python-course.eu) * [First Steps With Python](https://realpython.com/learn/python-first-steps/) * [A curated list of awesome Python frameworks, libraries and software](https://github.com/vinta/awesome-python) * [Official Style Guide for Python](https://peps.python.org/pep-0008/) * [Python 3 Computer Science Circles](https://cscircles.cemc.uwaterloo.ca/) * [Dive Into Python 3](https://www.diveintopython3.net/) * [Python Tutorial for Intermediates](https://pythonbasics.org/) * [Build a Desktop App with Python](https://pythonpyqt.com/)