Basics: comment, indentation, print, input
# === Comments ===
- single line comment beging with: #
- """ Triple double quote is used for Multiline strings
and are often used as documentation.
"""
# === Indentation ===
- Indentation acts like { in c#
- use 4 spaces to indent, not tabs.
# === semicolone; ===
- Is used to separate numerous statements on a single line
but are not neccessary in single line statements
# === print function ===
print("I'm Python. Nice to meet you!")
print("Hello, World", end="!") # Hello, World! # ( a newline is default )
# === Input function ===
input_string_var = input("Enter some data: ") # gets input and Returns the data as a string
variables
some_var = 5 # New varaiable declartion is not needed
# variable naming Convention is: lower_case_with_underscores
some_var # 5
# accessing a previously unassigned variable is an exception
some_unknown_var # Raises a NameError
if
"yay!" if 0 > 1 else "nay!" # "nay!" if can be used as an expression
if some_var > 10:
print("some_var is totally bigger than 10.")
elif some_var < 10:
print("some_var is smaller than 10.") # This elif clause is optional.
else:
print("some_var is indeed 10.") # This is optional too.
range(number)
# returns an iterable of numbers from 0 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
|
for loop
for animal in ["dog", "cat", "mouse"]:
print("{} is a mammal".format(animal)) # You can use format() to interpolate formatted strings
|
dog is a mammal
cat is a mammal
mouse is a mammal
|
# to loop 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 loop
x = 0
while x < 4:
print(x)
x += 1 # Shorthand for x = x + 1
|
0
1
2
3
|
Exception handling
try:
raise IndexError("This is an index error") # Use "raise" to raise an 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
Files
# === Reading a file ===
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}
Numbers
# Math operators are what you expect :
1 + 1 # 2
8 - 1 # 7
10 * 2 # 20
35 / 5 # 7.0
# Integer division rounds down for both positive and negative numbers :
5 // 3 # 1
-5 // 3 # -2
5.0 // 3.0 # 1.0 # works on floats too
-5.0 // 3.0 # -2.0
# The result of division is always a float :
10.0 / 3 # 3.3333333333333335
# Modulo operation :
7 % 3 # 1
# i % j have the same sign as j, unlike C
-7 % 3 # 2
# Exponentiation (x**y, x to the yth power) :
2**3 # 8
# Enforce precedence with parentheses :
1 + 3 * 2 # 7
(1 + 3) * 2 # 8
Booleans
# 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
1 == True # True
2 == True # False
-5 != False # True
# Using boolean logical operators on ints casts them to booleans for evaluation,
#but their non-cast value is returned
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 :
1 < 10 # True
1 > 10 # False
2 <= 2 # True
2 >= 2 # True
# Chaining can be used for checking wheter a value is in a range :
# 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:
var1 = [1, 2, 3, 4] # Points var1 at a new list, [1, 2, 3, 4]
var2 = var1 # Points var2 at what var1 is pointing to
var2 is var1 # True, var1 and var2 refer to the same object
var2 == var1 # True, var1's and var2's objects are equal
var2 = [1, 2, 3, 4] # Points var2 at a new list, [1, 2, 3, 4]
var2 is var1 # False, var1 and var2 do not refer to the same object
var2 == var1 # True, var1's and var2's objects are equal
Strings
# 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 can be concatenated without using '+'(but not variables)
"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
# === f strings ===
# 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
# None is an object
# so don't use the "==" 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
Lists
# are used to store sequences
li = []
# You can start with a prefilled list
other_li = [4, 5, 6]
# append(): 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]
# pop(): 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.
# Accessing a list, is just like an array :
li[0] # 1
# Accesing the last element :
li[-1] # 3
# Looking out of bounds is an IndexError:
li[4] # Raises an IndexError
# Slice syntax :
# You can access at ranges within a list using slice syntax.
li[1:3] # Return list from index 1 to 3 => [2, 4]
li[2:] # Return list starting from index 2 => [4, 3]
li[:3] # Return list from beginning until index 3 => [1, 2, 4]
li[::2] # Return list selecting every second entry => [1, 4]
li[::-1] # Return list in reverse order => [3, 4, 2, 1]
# Use any combination of these to make advanced slices :
# li[start:end:step]
# Make a one layer deep copy using slices:
li2 = li[:] # li2 = [1, 2, 4, 3] but (li2 is li) will result in false.
# Remove arbitrary elements from a list with "del"
del li[2] # li is now [1, 2, 3]
# Remove first occurrence of a value
li.remove(2) # li is now [1, 3]
li.remove(2) # Raises a ValueError as 2 is not in the list
# Insert an element at a specific index
li.insert(1, 2) # li is now [1, 2, 3] again
# Get the index of the first item found matching the argument
li.index(2) # 1
li.index(4) # Raises a ValueError as 4 is not in the list
# You can add lists
# Note: values for li and for other_li are not modified.
li + other_li # [1, 2, 3, 4, 5, 6]
# Concatenate lists with "extend()"
li.extend(other_li) # Now li is [1, 2, 3, 4, 5, 6]
# Check for existence in a list with "in"
1 in li # True
# Examine the length with "len()"
len(li) # 6
#Lists : to store sequences []"
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
Dictionary
# 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 - 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
#sets, unlike lists or tuples, cannot have multiple occurrences of the same element and store unordered values.
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
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
# 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
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}
import
# modules can be imported
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.
Modules and 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 are called when this class is instantiated.
# - The double underscores denote objects or attributes
# that are used by Python but that live in user-controlled namespaces.
# Methods(or objects or attributes) like: __init__, __str__, __repr__ etc. are called special methods
# (or sometimes called dunder methods)
# You should not invent such names on your own.
def __init__(self, name):
# Assign the argument to the instance's name attribute
self.name = name
# Initialize property
self._age = 0
# An instance method. All methods take "self" as the first argument
def say(self, msg):
print("{name}: {message}".format(name=self.name, message=msg))
# Another instance method
def sing(self):
return 'yo... yo... microphone check... one two... one two...'
# A class method is shared among all instances
# They are called with the calling class as the first argument
@classmethod
def get_species(cls):
return cls.species
# A static method is called without a class or instance reference
@staticmethod
def grunt():
return "*grunt*"
# A property is just like a getter.
# It turns the method age() into a read-only attribute of the same name.
# There's no need to write trivial getters and setters in Python, though.
@property
def age(self):
return self._age
# This allows the property to be set
@age.setter
def age(self, age):
self._age = age
# This allows the property to be deleted
@age.deleter
def age(self):
del self._age
# When a Python interpreter reads a source file it executes all its code.
# This __name__ check makes sure this code block is only executed when this
# module is the main program.
if __name__ == '__main__':
# Instantiate a class
i = Human(name="Ian")
i.say("hi") # "Ian: hi"
j = Human("Joel")
j.say("hello") # "Joel: hello"
# i and j are instances of type Human, 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
# i.age # this would raise an AttributeError
`;
Classes
# 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))
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__) # (,
# ,
# ,
# , )
# 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
`;
title1="Inheritance";
options[i++]= { title:title1, code:code1, parent:objother };
title1="Advanced";
code:`
# 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!