Primitive Datatypes and Operators
Python supports basic data types like integers, floats, booleans, and strings. Operations such as addition, subtraction, multiplication, and division behave as expected. Python also supports integer division (floor division), modulus for remainders, and exponentiation. Boolean values (True and False) are treated as 1 and 0 in numerical contexts. Comparisons such as ==, !=, <, >, etc., evaluate relationships between values. Python evaluates certain objects like None, empty collections, or zero as False, while all other objects are True.
Variables and Collections
Variables in Python are dynamically typed, meaning you don’t need to declare their type explicitly. Lists, tuples, dictionaries, and sets are the core data structures for storing collections of data. Lists are ordered and mutable, while tuples are ordered but immutable. Dictionaries store key-value pairs and sets store unique elements. Python uses zero-based indexing for lists and tuples, and provides a variety of methods for adding, removing, or modifying these structures.
Control Flow and Iterables
Python uses if, elif, and else for control flow, with indentation playing a key role in defining code blocks. Loops (for and while) iterate over sequences like lists or strings. Python also provides tools for list comprehensions, allowing for concise iteration and transformation of lists.
This tutorial overview covers the basics of Python syntax and logic, focusing on core concepts and data manipulation techniques.
In continuation further covers file handling, iterable and iterator usage, as well as deep dives into Python functions and object-oriented programming, including class creation, inheritance, and multiple inheritance.
File Handling:
- Use of with open() context manager for safe file handling, with examples for reading and writing text and JSON data.
- Proper handling of files using string and JSON formats.
Iterable and Iterator:
- Explanation of Python's iterable objects and how to traverse them using an iterator.
- Illustrations on looping through dictionaries and other iterable objects.
Functions:
- Function creation using def, handling positional and keyword arguments, variadic functions (*args, **kwargs), and more advanced features like closures and first-class functions.
- An explanation of global and local scopes, and how to modify global variables inside functions.
Object-Oriented Programming:
- Comprehensive coverage of class creation using class, including instance methods, class methods, static methods, and properties.
- Inheritance, method overriding, and the use of super() to call parent class methods.
- Multiple inheritance is demonstrated using examples like Batman, which inherits from both Superhero and Bat classes, showcasing how to resolve issues with multiple inheritance using method resolution order (MRO) and calling constructors from multiple parent classes. This material would be useful for those looking to deepen their understanding of Python, particularly when working with object-oriented design patterns, file handling, and advanced function mechanics.
# 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)) # => <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") 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__) # => (<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()
# The Method Resolution Order
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 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
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