A REAL Python Cheat Sheet for Beginners

Get a Python Cheat Sheet (PDF) and learn the basics of Python, like working with data types, dictionaries, lists, and Python functions: Python Cheat Sheet.

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.

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

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
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

"Hello " + "world!"  # => "Hello world!"

String literals (but not variables) can be concatenated without using '+'

"Hello " "world!"    # => "Hello world!"

A string can be treated like a list of characters

"Hello world!"[0]  # => 'H'

You can find the length of a string

len("This is a string")  # => 16

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

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

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

f"{name} is {len(name)} characters long." # => "Reiko is 5 characters long."

None is an object

None  # => None

Don't use the equality "==" symbol to compare objects to None

Use "is" instead. This checks for equality of object identity.

"etc" is None  # => False
None is None   # => True

None, 0, and empty strings/lists/dicts/tuples all evaluate to False.

All other values are True

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

2. Variables and Collections

Python has a print function

print("I'm Python. Nice to meet you!")  # => I'm Python. Nice to meet you!

By default the print function also prints out a newline at the end.

Use the optional argument end to change the end string.

print("Hello, World", end="!")  # => Hello, World!

Simple way to get input data from console

input_string_var = input("Enter some data: ") # Returns the data as a string

There are no declarations, only assignments.

Convention is to use lower_case_with_underscores

some_var = 5
some_var  # => 5

Accessing a previously unassigned variable is an exception.

See Control Flow to learn more about exception handling.

some_unknown_var  # Raises a NameError

if can be used as an expression

Equivalent of C's '?:' ternary operator

"yay!" if 0 > 1 else "nay!"  # => "nay!"

Lists store sequences

li = []

You can start with a prefilled list

other_li = [4, 5, 6]

Add stuff to the end of a list with append

li.append(1)    # li is now [1]
li.append(2)    # li is now [1, 2]
li.append(4)    # li is now [1, 2, 4]
li.append(3)    # li is now [1, 2, 4, 3]

Remove from the end with pop

li.pop()        # => 3 and li is now [1, 2, 4]

Let's put it back

li.append(3)    # li is now [1, 2, 4, 3] again.

Access a list like you would any array

li[0]   # => 1

Look at the last element

li[-1]  # => 3

Looking out of bounds is an IndexError

li[4]  # Raises an IndexError

You can look at ranges with slice syntax.

The start index is included, the end index is not

(It's a closed/open range for you mathy types.)

li[1:3]   # Return list from index 1 to 3 => [2, 4]
li[2:]    # Return list starting from index 2 => [4, 3]
li[:3]    # Return list from beginning until index 3  => [1, 2, 4]
li[::2]   # Return list selecting every second entry => [1, 4]
li[::-1]  # Return list in reverse order => [3, 4, 2, 1]

Use any combination of these to make advanced slices

li[start:end:step]

Make a one layer deep copy using slices

li2 = li[:]  # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false.

Remove arbitrary elements from a list with "del"

del li[2]  # li is now [1, 2, 3]

Remove first occurrence of a value

li.remove(2)  # li is now [1, 3]
li.remove(2)  # Raises a ValueError as 2 is not in the list

Insert an element at a specific index

li.insert(1, 2)  # li is now [1, 2, 3] again

Get the index of the first item found matching the argument

li.index(2)  # => 1
li.index(4)  # Raises a ValueError as 4 is not in the list

You can add lists

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

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 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)
To loop over a list, and retrieve both the index and the value of each item in the list
prints:
    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                 # Pass is just a no-op. Usually you would do recovery here.
except (TypeError, NameError):
    pass                 # Multiple exceptions can be handled together, if required.
else:                    # Optional clause to the try/except block. Must follow all except blocks
    print("All good!")   # Runs only if the code in try raises no exceptions
finally:                 # Execute under all circumstances
    print("We can clean up resources here")

Instead of try/finally to cleanup resources you can use a with statement

with open("myfile.txt") as f:
    for line in f:
        print(line)

Writing to a file

contents = {"aa": 12, "bb": 21}
with open("myfile1.txt", "w+") as file:
    file.write(str(contents))        # writes a string to a file

with open("myfile2.txt", "w+") as file:
    file.write(json.dumps(contents)) # writes an object to a file

Reading from a file

with open('myfile1.txt', "r+") as file:
    contents = file.read()           # reads a string from a file
print(contents)

print: {"aa": 12, "bb": 21}

with open('myfile2.txt', "r+") as file:
    contents = json.load(file)       # reads a json object from a file
print(contents)

print: {"aa": 12, "bb": 21}

Python offers a fundamental abstraction called the Iterable. An iterable is an object that can be treated as a sequence. The object returned by the range function, is an iterable.

filled_dict = {"one": 1, "two": 2, "three": 3}
our_iterable = filled_dict.keys()
print(our_iterable)  # => dict_keys(['one', 'two', 'three']). This is an object that implements our Iterable interface.

We can loop over it.

for i in our_iterable:
    print(i)  # Prints one, two, three

However we cannot address elements by index.

our_iterable[1]  # Raises a TypeError

An iterable is an object that knows how to create an iterator.

our_iterator = iter(our_iterable)

Our iterator is an object that can remember the state as we traverse through it. We get the next object with "next()".

next(our_iterator)  # => "one"

It maintains state as we iterate.

next(our_iterator)  # => "two"
next(our_iterator)  # => "three"

After the iterator has returned all of its data, it raises a StopIteration exception

next(our_iterator)  # Raises StopIteration

We can also loop over it, in fact, "for" does this implicitly!

our_iterator = iter(our_iterable)
for i in our_iterator:
    print(i)  # Prints one, two, three

You can grab all the elements of an iterable or iterator by calling list() on it.

list(our_iterable)  # => Returns ["one", "two", "three"]
list(our_iterator)  # => Returns [] because state is saved

4. Functions

Use "def" to create new functions

def add(x, y):
    print("x is {} and y is {}".format(x, y))
    return x + y  # Return values with a return statement

Calling functions with parameters

add(5, 6)  # => prints out "x is 5 and y is 6" and returns 11

Another way to call functions is with keyword arguments

add(y=6, x=5)  # Keyword arguments can arrive in any order.

You can define functions that take a variable number of positional arguments

def varargs(*args):
    return args
varargs(1, 2, 3)  # => (1, 2, 3)

You can define functions that take a variable number of keyword arguments, as well

def keyword_args(**kwargs):
    return kwargs

Let's call it to see what happens

keyword_args(big="foot", loch="ness")  # => {"big": "foot", "loch": "ness"}

You can do both at once, if you like

def all_the_args(*args, **kwargs):
    print(args)
    print(kwargs)
all_the_args(1, 2, a=3, b=4) prints:
    (1, 2)
    {"a": 3, "b": 4}

When calling functions, you can do the opposite of args/kwargs! Use * to expand tuples and use ** to expand kwargs.

args = (1, 2, 3, 4)
kwargs = {"a": 3, "b": 4}
all_the_args(*args)            # equivalent to all_the_args(1, 2, 3, 4)
all_the_args(**kwargs)         # equivalent to all_the_args(a=3, b=4)
all_the_args(*args, **kwargs)  # equivalent to all_the_args(1, 2, 3, 4, a=3, b=4)

Returning multiple values (with tuple assignments)

def swap(x, y):
    return y, x  # Return multiple values as a tuple without the parenthesis.
                 # (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

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 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

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 :(

Get the code: learnpython3.py

#python #programming 

What is GEEK

Buddha Community

A REAL Python Cheat Sheet for Beginners

Tumbu John

1591726262

this is very helpful even for pros especially when you deal with many computer languages.

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Sival Alethea

Sival Alethea

1624291780

Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python

Ray  Patel

Ray Patel

1619636760

42 Exciting Python Project Ideas & Topics for Beginners [2021]

Python Project Ideas

Python is one of the most popular programming languages currently. It looks like this trend is about to continue in 2021 and beyond. So, if you are a Python beginner, the best thing you can do is work on some real-time Python project ideas.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Python project ideas which beginners can work on to put their Python knowledge to test. In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python

Moreover, project-based learning helps improve student knowledge. That’s why all of the upGrad courses cover case studies and assignments based on real-life problems. This technique is ideally for, but not limited to, beginners in programming skills.

But first, let’s address the more pertinent question that must be lurking in your mind:

#data science #python project #python project ideas #python project ideas for beginners #python project topics #python projects #python projects for beginners

August  Larson

August Larson

1624286340

Python for Beginners #2 — Importing files to python with pandas

Use pandas to upload CSV, TXT and Excel files

Story time before we begin

Learning Python isn’t the easiest thing to do. But consistency is really the key to arriving at a level that boosts your career.

We hear a lot about millennials wanting things to easy. In reality, there are a lot of young professionals who believe that they can do more for their companies but are being held back by the work cultures they are faced with at the onset of their careers.

Having been lucky enough to have found a job after my studies, I remember immediately feeling a wave of disappointment a very short while after starting my new job. I felt like a cog in a massive machine. I wasn’t really anything other than a ‘resource’. An extra 8–15 hours of daily man power depending on my boss’ whim.

The result, was the eventual disenchantment and lack of motivation simply because, for the most part, I was expected to be quiet and do my job in the hope of one day being senior enough to effect significant changes. And while the older generation would generally tell me to suck it up, I couldn’t see myself sucking it up for 5 years or more. I knew I’d get stale and afraid of change, much like those telling me to stay in my place.

For anyone in a similar situation,**_ do your best to improve on your skills _**and find an environment that works for you. That’s the whole purpose of these articles. To get you on your way to freedom.

Introduction

For this demonstration, I’ll use data from this Kaggle competition. It’s a simple CSV file containing data on individuals in the Titanic and the different profiles i.e. (age, marital status etc.)

I want to import this file to python. I’ll show you how to do this alongside all the possible troubleshoots you may encounter.

Table of Contents

  1. Where should you put your files?
  2. Reading CSV and TXT files
  3. Reading excel (XLSX) files

#python #programming #pandas #python for beginners #importing files to python with pandas #python for beginners #2 — importing files to python with pandas