Python 3 cheat sheet

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Basics

The print() function displays the given message on the screen.

print("Hello World")
# Hello World
print(20+5)
# 25
print(f'10 + 5 is {10+5}')
# 10 + 5 is 15
print("python" * 2)
# python python
a, b, c = 1, 2, 3

Variables

In programming, a variable is a value that can be changed. All variables will have a storage location and a name. The variable name is usually used to refer to the value that is stored in a variable.

In Python, a variable is declared when you set a value to the variable. Unlike some other programming languages, Python does not have special keywords for declaring a variable.

Read more about Python variables.

a = 10  # int
b = 10.5  # float
c = "Python"  # string
d = 'p'  # string
e = 10j  # complex
f = True  # bool

Type conversion

We perform type conversions to convert a variable from one data type to another.

Generally, there are two types of type conversions.

• Implicit conversions – This conversion is type-safe and requires no special code. It is performed by the compiler and no data loss occurs.
• Explicit conversions – These conversions require a cast operator. Explicit conversion cannot be made without the risk of losing information. A cast is a way of informing the compiler that you wish to make a conversion and you are aware of the possible data loss.

i = int("5")  # 5
i = float(1)  # 1.0
i = bool(10)  # True
i = bool(0)  # False
i = str(10)  # 10

Conditional statements

These statements are used to make decisions based on one or more conditions. If the condition specified in a conditional statement is found true, then a set of instructions will be executed and otherwise, something else will be executed.

if a == 0:
    print('a = 0')
elif a == 1:
    print('a = 1')
else:
    print('a')

a, b, c = 1, 2, 3
# if a>b>c:
if a>b and a>c:
    print('A is largest')
elif b>c:
    print('B is largest')
else:
    print('C is largest')

Ternary operator

It is a special conditional operator that compares two values and determines the third value based on the comparison.

a = 20
x = True if a >= 1 else False
# x = False

print('Yes') if 11 > 10 else print("No")  # Yes
a, b = 10, 11
r = 'Yes' if a >= b else 'No'
print(r)  # No

Loops

With the help of a loop or looping statements, you can execute a statement or a set of statements until an expression is evaluated to false.

# For loop
# for i in range(5)
for i in range(0, 5):
    print(i)

for i in range(4, -1, -1):
    print(i)

# While loop
i=0
while i <= 2:
    print(i)
    i+=1

Functions

A function contains a set of statements that creates output based on the given input or parameters.

# Empty function
def myfun():
    pass

def myfun1():
    print("hi")

myfun()  # No output
myfun1()  # hi

def add(a, b):
    return a + b

add(1, 2)  # 3

def calc(a, b):
    return a+b, a-b

res = calc(5, 2)
print("After addition", res[0])  # After addition 7
print("After subtraction", res[1])  # After subtraction 3

def msg(name="John Doe"):
    print("Hi", name)

msg()  # Hi John Doe
msg("Jane")  # Hi Jane

def fnvar(*data):
    print(data[0])

fnvar(1, 2, 3)  # 1

def fnkwargs(**data):
    print(data['name'])

fnkwargs(name='John Doe', age=20)  # John Doe

List

In Python, a list is an ordered collection of items of different data types. The items of a list are enclosed in [ ] brackets. Each list item is separated by a comma (,).

Create a list

# Creating a list
lst = [0, 1, 2]
print(lst[0])  # 0
print(lst[0:2])  # [0, 1]
lst = ['1'] * 3
print(lst)  # ['1', '1', '1']

Add elements

lst = [1, 2, 3]
lst.append('last')  # [1, 2, 3, 'last']
lst.insert(0, 'first')  # ['first', 1, 2, 3, 'last']
lst.insert(20, 'New element')  # ['first', 1, 2, 3, 'last', 'New element']

Remove elements

lst = [1, 2, 3, 4, 5, 2, 8, 9]
print(lst.pop())  # Removes and returns the last element
print(lst.pop(0))  # Removes and returns element at index 0
lst.remove(2)  # Removes the first occurrence of 2 (Remove by value)

del lst[0]  # Removes element at index 0
del lst  # Deletes the list

Modify elements

lst = [1, 2, 3]
lst[0] = 5  # [5, 2, 3]

Sorting

# Sort
lst = [0, 5, 3, 2, 4, 1]
lst.sort()  # [0, 1, 2, 3, 4, 5]
lst.sort(reverse=True)  # [5, 4, 3, 2, 1, 0]

# Custom sorting
ages = [
    ["John", 20],
    ["Jane", 22],
    ["Janet", 21]
]
# Sorts based on age
ages.sort(key=lambda age: age[1])
print(ages)  # [['John', 20], ['Janet', 21], ['Jane', 22]]

Tuple

A list is an ordered collection of items of various data types. The items of a tuple are enclosed in () brackets. Each list item is separated by a comma (,). Unlike a list, the elements of a tuple cannot be modified.

tpl = (1, 2, 3)
print(tpl[0])  # 1
print(len(tpl))  # 3
print(tpl[0:2])  # (1, 2)

Dictionary

A dictionary is a collection of data as key-value pairs. Each item of a dictionary is contained in {} brackets.

Create a dictionary

d = {"name": 'python', "version": 3.7, 0: 1}
print(d['name'])  # python
print(d[0])  # 1
print(len(d))  # 3

Add elements

d = {"name": 'python'}
d['version'] = '3.7'  # Add element
d.update({"type": "dynamic", "oop": "yes"})  # Add multiple elements

Remove elements

d = {"name": "python", "type": "dynamic", "year": 1991}
print(d.pop("year"))  # Removes and returns the item
del d["type"]  # Removes the item
print(d)  # {'name': 'python'}
d.clear()  # Removes all the elements

Sets

Similar to a dictionary, the elements of a set are contained in {} brackets. The main difference between sets and other datatypes like list, tuple, and dictionary is that sets do not have an index.

Create a set

s = {5, 2, 1, 4}
print(s)  # {1, 2, 4, 5}

Add / remove items

s = {5, 2, 1, 4}
s.add(55)
print(s)  # {1, 2, 4, 5, 55}
print(s.pop())  # 1

Comprehension

Comprehensions in Python provide us with a short and succinct way to create new lists, set, or dictionary using iterable objects that have been already defined. Python supports the following comprehensions:

• List Comprehensions
• Dictionary Comprehensions
• Set Comprehensions

# List comprehension
lst = [x for x in range(1, 6)]  
# [1, 2, 3, 4, 5]

# Set comprehension
st = {x for x in range(1, 6)}  
# {1, 2, 3, 4, 5}

# Dictionary comprehension
dict = {x: x*2 for x in range(1, 6)}  
# {1: 2, 2: 4, 3: 6, 4: 8, 5: 10}

Generator expression

Similar to comprehensions, generator expressions are also a convenient way to create iterable objects.

res = (x for x in range(6))
print(res)  # <generator object <genexpr> at 0x000001E65CD5BE08>
print(res[0])  # Error
print(len(res))  # Error
lst = list(res)  # Converts to list

Unpacking

Unpacking is a method by which we extract the values of an iterable object to separate variables.

a = [1, 2, 3]
b = [4, 5, 6]
c = [*a, *b, "unpacked"]
print(c)  # [1, 2, 3, 4, 5, 6, 'unpacked']

x = {"x": 1}
y = {"y": 2}
z = {**x, **y}
print(z)  # {'x': 1, 'y': 2}

a = [1, 2, 3]
def myfun(a, b, c):
    print(a+b+c)

# myfun(a)  # Error
myfun(*a)

Lambda

A lambda expression is a special syntax to create functions without names.

x = lambda a, b, c: a + b + c
print(x(5, 6, 2))  # 13

def myfunc(b):
    return lambda a: a * b

dbl = myfunc(2)
print(dbl(11))  # => 22

Datetime module

Python DateTime module comes with everything you need to access date and time.

import datetime
a=datetime.date.today()
print(a.strftime("%B/%A/%Y/%D"))  # August/Friday/2019/08/23/19

Exception handling

Exception handling is the mechanism by which errors in the application are captured and handled. It is one of the most important features of a programming language.

In Python, Exception handling is done with the help of the following keywords:

• try
• except
• finally
• raise

try:
    a = 1/0
except:
    print('Division by zero is not possible')

try:
    print(a)
except NameError:
    print('Variable is not declared')

try:
    10/0
except ArithmeticError:
    print('An arithmetic error has occurred')
finally:
    print('I will be executed even if there is no error')

try:
    raise Exception("An error occurred")
finally:
    print("Error occured")

try:
    raise ArithmeticError("An error has occurred")
except Exception as e:
    print(e)

try:
    10/0
except Exception as e:
    print(type(e).__name__)

try:
    a = int(input("Enter a positive number"))
    if a < 0:
        raise ValueError("Number is not valid")
    if a == 0:
        raise ArithmeticError("Division by zero error")
except (ValueError , ArithmeticError):
    print("A value error or arithmetic error has occurred")

Class

A class is a blueprint of an object. An object in the real world will have properties like shape, color, weight, etc. Likewise, in object-oriented programming, a class defines certain properties, events, and functionalities that an object can have.

# Class
class MyClass:
    name = "Hi from the class"

    # Constructor
    def __init__(self):
        self.age = 22
        gender = 'Male'

    # Method
    def show_data(self):
        print(self.name)
        print(self.age)
        # print(gender)  # Error

# Creating object
obj = MyClass()
obj.show_data()

Single inheritance

Single inheritance enables a derived class to inherit properties and behavior from a single parent class.

class A:

    def fun1(self):
        print('Function 1')

class B (A):

    def fun2(self):
        print('Function 2')

obj = B()
obj.fun1()
obj.fun2()

Multiple inheritance

Single inheritance enables a derived class to inherit properties and behavior from multiple classes.

class A:

    __m = 'Private'
    def __init__(self):
        self.msg = 'Class 1'

    def fun1(self):
        print('Function 1')

    def same(self):
        print('Same A')

class B:

    def fun2(self):
        print('Function 2')

    def same(self):
        print('Same B')

class C (A, B):

    def fun3(self):
        self.fun1()
        self.fun2()
        self.same()
        # print(self.__m)  # Cannot access private members

    # def same(self):  # If uncommented, this method will be executed
    #    print("override")

obj = C()
obj.fun3()

Multilevel inheritance

Multiple inheritance means that a class is inheriting the properties of a derived class.

class A:

    __m = 'Private'
    def __init__(self):
        self.msg = 'Class 1'

    def fun1(self):
        print('Function 1')

class B (A):

    def fun2(self):
        print('Function 2')

class C (B):

    def fun3(self):
        self.fun1()
        self.fun2()
        # print(self.__m)

obj = C()
obj.fun3()

Abstract classes

Generally, an abstract class is a class that contains abstract methods and cannot be instantiated. It serves as a blueprint for other classes expecting its child classes to implement its abstract methods.

Python by default does not provide abstract classes. But it has a module named ABC which provides the base for defining Abstract Base classes(ABC).

from abc import ABC, abstractmethod

class Class1 (ABC):

    @abstractmethod
    def showmessage(self):
        print('From base class')

    @abstractmethod
    def logic(self):
        print('Logic in the derived class')

    @abstractmethod
    def errorchecking(self):
        print('You should implement logic in the child class')

class Class2(Class1):

    def showmessage(self):
        super().showmessage()

    def logic(self):
        print('Custom logic')

    def errorchecking(self):
        print('Try commenting this method')

obj = Class2()
obj.showmessage()
obj.logic()
obj.errorchecking()

Attribute methods

These methods allow you to get or check the properties of an object.

class MyClass:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def showdata(self):
        print(f'Name: {self.name}, Age: {self.age}')

cls = MyClass('John Doe', 25)
print(getattr(cls, 'name'))
setattr(cls, 'age', 22)
print(getattr(cls, 'age'))
print(hasattr(cls, 'name'))
delattr(cls, 'age')
print(hasattr(cls, 'age'))

Class methods

Class methods are not specific to any instances. So, they can be accessed without creating an object. It can access and modify the state of a class.

class MyClass:
    @classmethod
    def fun1(self):
        print('Hello World')

MyClass.fun1()  # THE METHOD CAN BE CALLED WITHOUT CREATING AN OBJECT

Class methods can access and modify the state of an instance.

class MyClass:
    a = 20
    @classmethod
    def fun1(self):
        self.a = 21

    def show_data(self):
        print(self.a)

obj = MyClass()
obj.show_data()  # 20
MyClass.fun1()
obj.show_data()  # 21

Static methods

Like class methods, static methods can also be called without creating an object. But it cannot access or modify the state of a class. Also, it does not accept the class parameter and is created only once.

class MyClass:

    a = 'Static method will print this'
    @staticmethod
    def my_static_method():
        print('Hi from static method')
        print(MyClass.a)

    def my_method(self):
        self.a = 'Instance method'
        print(f'Hi from {self.a}')

cls = MyClass()
cls.my_method()
MyClass.my_static_method()
# Hi from Instance method
# Hi from static method
# Static method will print this

Private methods

Private methods are methods that cannot be accessed outside the class and by child classes using inheritance.

class SeeMee:

    def youcanseeme(self):
        return 'you can see me'

    def __youcannotseeme(self):
        return 'you cannot see me'


Check = SeeMee()
print(Check.youcanseeme())
# you can see me
# print(Check.__youcannotseeme())
# AttributeError: 'SeeMee' object has no attribute '__youcannotseeme'


##########BUT YOU CAN STILL ACCESS IT################
print(Check._SeeMee__youcannotseeme())