A REAL Python cheat sheet for beginners

A REAL Python cheat sheet for beginners

Python was created by Guido van Rossum in the early 90s. It is now one of the most popular languages in existence. I fell in love with Python for its syntactic clarity. It’s basically executable pseudocode.

Python 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.

Get the code: learnpython3.py

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

# 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

# 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

# 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
# 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
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! 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 (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 statement inside the braces and it will be output in the string.
f"{name} is {len(name)} 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
# 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  # 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"}  # => 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 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
        # 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

# 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 :(

Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Description
Learn Hands-On Python Programming By Creating Projects, GUIs and Graphics

Python is a dynamic modern object -oriented programming language
It is easy to learn and can be used to do a lot of things both big and small
Python is what is referred to as a high level language
Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps!
SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily
If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing Project and GUIs.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

When and how do I start a career as a Python programmer?

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

Python Job Profiles
Software Engineer
Research Analyst
Data Analyst
Data Scientist
Software Developer
Python Salary

The median total pay for Python jobs in California, United States is $74,410, for a professional with one year of experience
Below are graphs depicting average Python salary by city
The first chart depicts average salary for a Python professional with one year of experience and the second chart depicts the average salaries by years of experience
Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis
Take this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future! Best of Luck
Who is the target audience?

Anyone who wants to learn to code
For Complete Programming Beginners
For People New to Python
This course was designed for students with little to no programming experience
People interested in building Projects
Anyone looking to start with Python GUI development
Basic knowledge
Access to a computer
Download Python (FREE)
Should have an interest in programming
Interest in learning Python programming
Install Python 3.6 on your computer
What will you learn
Build Python Graphical User Interfaces(GUI) with Tkinter
Be able to use the in-built Python modules for their own projects
Use programming fundamentals to build a calculator
Use advanced Python concepts to code
Build Your GUI in Python programming
Use programming fundamentals to build a Project
Signup Login & Registration Programs
Quizzes
Assignments
Job Interview Preparation Questions
& Much More

Guide to Python Programming Language

Guide to Python Programming Language

Guide to Python Programming Language

Description
The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.

The course is begin continuously developing by adding lectures regularly.

Please see the Promo and free sample video to get to know more.

Hope you will enjoy it.

Basic knowledge
An Enthusiast Mind
A Computer
Basic Knowledge To Use Computer
Internet Connection
What will you learn
Will Be Expert On Python Programming Language
Build Application On Python Programming Language

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Description
Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn't means that i will not talk about the advanced stuff as well.

As you may all know by now that my approach of teaching is very simple and straightforward.In this series i will be talking about the all the things you need to know to jump start you python programming skills. This series is designed for noobs who are totally new to programming, so if you don't know any thing about

programming than this is the way to go guys Here is the links to all the videos that i will upload in this whole series.

In this video i will talk about all the basic introduction you need to know about python, which python version to choose, how to install python, how to get around with the interface, how to code your first program. Than we will talk about operators, expressions, numbers, strings, boo leans, lists, dictionaries, tuples and than inputs in python. With

Lots of exercises and more fun stuff, let's get started.

Download free Exercise files.

Dropbox: https://bit.ly/2AW7FYF

Who is the target audience?

First time Python programmers
Students and Teachers
IT pros who want to learn to code
Aspiring data scientists who want to add Python to their tool arsenal
Basic knowledge
Students should be comfortable working in the PC or Mac operating system
What will you learn
know basic programming concept and skill
build 6 text-based application using python
be able to learn other programming languages
be able to build sophisticated system using python in the future

To know more: