Learn Python in Y Minutes

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   # => 2
- 1   # => 7
* 2  # => 20
/ 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 ( the capitalization)

True   # => True
False  # => False

negate with not

not True   # => False
not False  # => True

Boolean Operators. 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

== False  # => True
== True   # => True
== True   # => False
-5 != False # => 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(4)     # => True
bool(-6)    # => True
0 and 2     # => 0
-5 or 0     # => -5

Equality is ==

1 == 1  # => True
2 == 1  # => False

Inequality is !=

1 != 1  # => False
2 != 1  # => True

More comparisons

< 10  # => True
> 10  # => False
<= 2  # => True
>= 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

You can also format using f-strings or formatted string literals (in Python 3.6+)

name = "Reiko"
f"She said her name is {name}." # => "She said her name is Reiko"

You can basically put any Python expression inside the braces and it will be output in 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

None, 0, and empty strings/lists/dicts/tuples all evaluate to False. All other values are True

bool(0)   # => False
bool("")  # => False
bool([])  # => False
bool({})  # => False
bool(())  # => False

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 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

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}

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"}  # => Raises 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.

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. - 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. Yeah, it looks a bit like a dict. Sorry.

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:

for animal in ["dog", "cat", "mouse"]:
    # You can use format() to interpolate formatted strings
    print("{} is a mammal".format(animal))

dog is a mammal
cat is a mammal
mouse is a mammal

range(number) returns an iterable of numbers from zero to the given number

for i in range(4):
    print(i)
 # =>   0
 # =>   1
 # =>   2
 # =>   3

range(lower, upper) returns an iterable of numbers from the lower number to the upper number

for i in range(4, 8):
    print(i)
  # => 4
  # => 5
  # => 6
  # => 7

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.

for i in range(4, 8, 2):
    print(i)
  # => 4
  # => 6

To loop over a list, and retrieve both the index and the value of each item in the list

animals = ["dog", "cat", "mouse"]
for i, value in enumerate(animals):
    print(i, value)
  # => 0 dog
  # => 1 cat
  # => 2 mouse

While loops go until a condition is no longer met.

x = 0
while x < 4:
    print(x)
    x += 1  # Shorthand for x = x + 1
  # => 0
  # => 1
  # => 2
  # => 3

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                 # Pass is just a no-op. Usually you would do recovery here.
except (TypeError, NameError):
    pass                 # Multiple exceptions can be handled together, if required.
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

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 calling list() on it.

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 to all_the_args(1, 2, 3, 4)
all_the_args(**kwargs)         # equivalent to all_the_args(a=3, b=4)
all_the_args(*args, **kwargs)  # equivalent to 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.
                 # (:bulb: 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 parenthesis have been excluded but can be included.

Function Scope

x = 5

def set_x(num):
    # Local var x not the same as global variable x
    x = num    # => 43
    print(x)   # => 43

def set_global_x(num):
    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)

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 can itself be a nested list

[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. 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.

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.

Assign the argument to the instance's name attribute

    def __init__(self, name):
        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

Reading a file

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, or in other words: 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

this would raise an AttributeError

i.age                         

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
```python

# Define Batman as a child that inherits from both Superhero and Bat
```python
class Batman(Superhero, Bat):

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".

    def __init__(self, *args, **kwargs):
        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.

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 :(