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.
Feedback would be highly appreciated! You can reach me at @louiedinh or louiedinh [at] [google’s email service]
Note: This article applies to Python 3 specifically. Check out here if you want to learn the old Python 2.7
# 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
# Result of integer division truncated down both for positive and negative.
5 // 3 # => 1
5.0 // 3.0 # => 1.0 # works on floats too
-5 // 3 # => -2
-5.0 // 3.0 # => -2.0
# The result of division is always a float
10.0 / 3 # => 3.3333333333333335
# Modulo operation
7 % 3 # => 1
# Exponentiation (x**y, x to the yth power)
2**3 # => 8
# Enforce precedence with parentheses
(1 + 3) * 2 # => 8
# Boolean values are primitives (Note: the capitalization)
True
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
# Note using Bool operators with ints
# False is 0 and True is 1
# Don't mix up with bool(ints) and bitwise and/or (&,|)
0 and 2 # => 0
-5 or 0 # => -5
0 == False # => True
2 == True # => False
1 == True # => True
-5 != False != True #=> True
# 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
# Comparisons can be chained!
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! But try not to do this.
"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
"This is a string"[0] # => 'T'
# You can find the length of a string
len("This is a string") # => 16
# .format can be used to format strings, like this:
"{} can be {}".format("Strings", "interpolated") # => "Strings can be interpolated"
# You can repeat the formatting arguments to save some typing.
"{0} be nimble, {0} be quick, {0} jump over the {1}".format("Jack", "candle stick")
# => "Jack be nimble, Jack be quick, Jack jump over the candle stick"
# You can use keywords if you don't want to count.
"{name} wants to eat {food}".format(name="Bob", food="lasagna") # => "Bob wants to eat lasagna"
# If your Python 3 code also needs to run on Python 2.5 and below, you can also
# still use the old style of formatting:
"%s can be %s the %s way" % ("Strings", "interpolated", "old") # => "Strings can be interpolated the old way"
# You can also format using f-strings or formatted string literals
name = "Reiko"
f"She said her name is {name}." # => "She said her name is Reiko"
# 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
# Note: In earlier versions of Python, input() method was named as raw_input()
# 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
"yahoo!" if 3 > 2 else 2 # => "yahoo!"
# 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] # => [2, 4]
# Omit the beginning and return the list
li[2:] # => [4, 3]
# Omit the end and return the list
li[:3] # => [1, 2, 4]
# Select every second entry
li[::2] # =>[1, 4]
# Return a reversed copy of the list
li[::-1] # => [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
# 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"} # => 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. Note - Dictionary key
# ordering is not guaranteed. Your results might not match this exactly.
list(filled_dict.keys()) # => ["three", "two", "one"]
# 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]
# 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
####################################################
## 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 to 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)
"""
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 # 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)
# 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
# You can grab all the elements of an iterator by calling list() on it.
list(filled_dict.keys()) # => Returns ["one", "two", "three"]
####################################################
## 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.
# (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 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.
# 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 an 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, 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*"
# Cannot call static method with instance of object
# because i.grunt() will automatically put "self" (the object i) as an argument
print(i.grunt()) # => TypeError: grunt() takes 0 positional arguments but 1 was given
# 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
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 :(
