Python Cheat Sheet – Basics to Regex, Syntax, and Data Types [UPDATED]

Python is a programming language that is interpreted object-oriented and high-level. It has semantics making it ideal for Rapid Application Development and, as a scripting or glue language to connect components. Python’s built-in data structures and dynamic typing make it highly appealing. Its syntax is simple and easy to learn emphasizing readability and reducing program maintenance costs.

Table of Contents

• • Python Operators
  • Python Datatypes
  • Functions
  • Regex or Regular Expressions
  • Object Oriented Programming
  • Decorators
  • Debugging
  • Conclusion

Python Cheat Sheet

Download a Printable Python Cheat Sheet PDF 

Python Operators

Operators in Python perform operations on values and variables using standard symbols for logic and math.

1. Arithmetic Operators in Python

Python arithmetic operators are used to perform mathematical operations on numeric values.

2. Comparison Operators in Python

Python comparison operators are used to compare the values of two operands and return True or False based on whether the condition is met.

3. Assignment Operators in Python

The assignment operator in Python is used to assign values to variables.

4. Logical Operators in Python

Python logical operators help us to perform logic operations on nodes, groups, or numbers.

5. Identity Operators in Python

Python identity operators are used to compare the memory location of two objects.

x = [“apple”, “banana”]

y = [“apple”, “banana”]

6. Membership Operators in Python

Python membership operators are used to test whether a value or variable exists in a sequence (string, list, tuples, sets, dictionary) or not. 

x = [“apple”, “banana”]

7. Bitwise Operators in Python

Bitwise operators in Python are used to perform bitwise operations on integers.

x = 10   # Binary: 1010

y = 4    # Binary: 0100

Data Types in Python

Data types denote the classification of data items, indicating the operations feasible on individual data.

1. Integer Data Type

An integer in Python is a whole number, positive or negative, without decimals, of unlimited length.

x = 10    

y = 20   

print(type(x))  # Output: int

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2. Float Data Type

The float data type in Python is used to represent real numbers with both an integer and fractional component.

x = 4.2

y = 10.5

print(type(x))   # Output: float

3. Boolean Data Type

The Boolean data type in Python is one of the built-in data types. It represents one of two values: True or False.

x = True                

y = False

print(type(x))         # Output: bool

4. Complex Data Type

The complex data type in Python consists of two values, the first one is the real part and the second one is the imaginary part of the complex number.

x = 2 + 3i           

print(type(x))     # Output: complex

5. String data type

A string is a sequence of characters enclosed in single quotes or double quotes.

5.1. Define a string

my_string = "Hello, World!"

my_string = ‘Hello, World!’

5.2. Indexing in a string

The process of accessing the element in a sequence using their position in the sequence is called Indexing

print(my_string[0])   # Output: H

print(my_string[7])   # Output: W

print(my_string[-1])  # Output: !

5.3. Length of a string

Len function returns the length of an object

print(len(my_string)) # Output: 13

5.4. String Concatenation

Joining more than one string together

string1 = "Hello"

string2 = "Intellipaat"

result = string1 + ", " + string2 + "!"

print(result)  # Output: Hello, Intellipaat!

5.5. String Methods

5.6. String Slicing

Slicing is the extraction of a part of a string, list, or tuple

s = "Python"

print(s[2:5])   # Output: tho

print(s[:4])    # Output: Pyth

print(s[3:])

5.7. Escape Characters

An escape character is a backslash followed by the character you want to insert in the string.

print("This is a "quote" inside a string.")  # Output: This is a "quote" inside a string.

print("This is a nnew line.")   # Output: This is a #  new line.

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5.8. String Formatting

5.8.1. Old Style Formatting

name = "Alice"

age = 30

# Using placeholders %s (string) and %d (integer) within the string, 

# followed by the % operator and a tuple containing the values to substitute.

print("Name: %s, Age: %d" % (name, age))

5.8.2. str.format() Method

name = "Rohit"

age = 25

# Using `{}` as a placeholder within the string, followed by the `.format()` method

# and passing the values to substitute as arguments to the method.

print("Name: {}, Age: {}".format(name, age))

5.8.3. Formatted String Literals (f-strings) (Python 3.6+)

name = "Ashish"

age = 35

# Using f-string notation with `{}` placeholders directly within the string, 

# and using the variables directly within the curly braces.

print(f"Name: {name}, Age: {age}")

5.8.4. Template Strings

from string import Template

name = "David"

age = 40

# Creating a Template object with placeholders as $variable, 

# and using the `substitute()` method to replace placeholders with values.

template = Template("Name: $name, Age: $age")

print(template.substitute(name=name, age=age))

6. Lists in Python

A list is a data structure in Python that is a mutable, or changeable, ordered sequence of elements.

6.1. Define a List

my_list = [1, 2, 3, 4, 5]

6.2. Accessing Elements in a List

print(my_list[0])   # Output: 1

print(my_list[2])   # Output: 3

print(my_list[-1])  # Output: 5

6.3. Length of a List

Len function returns the length of an object

print(len(my_list)) # Output: 5

6.4. List Slicing

Slicing is the extraction of a part of a string, list, or tuple

my_list = [1, 2, 3, 4, 5]

print(my_list[1:3])   # Output: [2, 3]

print(my_list[:3])    # Output: [1, 2, 3]

print(my_list[3:])    # Output: [4, 5]

6.5. List Methods

my_list = [1,2,3,4,5]

6.6. Checking the Membership operator in the list 

Allows you to check the presence or absence of a substring within a given string

print(4 in my_list)   # Output: True

print(6 in my_list)   # Output: False

6.7. List Comprehensions

Writing the operation performed on the data within a line or few

squares = [x**2 for x in range(5)]

print(squares)        # Output: [0, 1, 4, 9, 16]

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6.8. Nested Lists

List inside a list is called Nested List

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

print(matrix[1][2])   # Output: 6

6.9. Copying Lists

Creating a copy of the original list so that the changes do not occur in the original

original_list = [1, 2, 3]

copied_list = original_list.copy()

7. Dictionary

A dictionary in Python is a collection of key-value pairs. Each key is associated with a value, and you can access the value by using the key.

7.1. Creating a dictionary

my_dict = {"key1": "value1", "key2": "value2", "key3": "value3"}

7.2. Accessing values in a dictionary

print(my_dict["key1"])   # Output: value1

print(my_dict["key2"])   # Output: value2

7.3. Length of a dictionary

print(len(my_dict))      # Output: 3

7.4. Dictionary Methods

7.4.1. Adding or updating key-value pairs

dict1["key4"] = "value4"     # Add new key-value pair

print(dict1)                 # Output: {'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4'}

dict1["key1"] = "new_value"  # Update existing value

print(dict1)                 # Output: {'key1': 'new_value', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4'}

7.4.2. Removing key-value pairs

del dict1["key3"]            # Delete a key-value pair

print(dict1)                 # Output: {'key1': 'new_value', 'key2': 'value2', 'key4': 'value4'}

dict1.pop("key2")            # Remove and return the value associated with the key

print(dict1)                 # Output: {'key1': 'new_value', 'key4': 'value4'}

7.4.3. Clearing a dictionary

dict1.clear()                # Remove all items in the dictionary

print(dict1)                 # Output: {}

7.5. Dictionary Iteration

7.5.1. Iterating over keys

for key in my_dict:

    print(key)   # Output: key1

                      #key4

7.5.2. Iterating over values

for value in my_dict.values():

    print(value) # Output: new_value

                       #         value4

7.5.3. Iterating over key-value pairs

for key, value in my_dict.items():

    print(key, value)  # Output: key1 new_value

                               #         key4 value4

7.6. Dictionary Comprehensions

squares = {x: x**2 for x in range(5)}

print(squares)   # Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

7.7. Nested Dictionaries

nested_dict = {"key1": {"nested_key1": "nested_value1"}, "key2": {"nested_key2": "nested_value2"}}

print(nested_dict["key1"]["nested_key1"])   # Output: nested_value1

8. Tuple in Python

In Python, a tuple is an unchangeable ordered collection of elements, defined with parentheses and separated by commas.

8.1. Creating a Tuple:

my_tuple = (1, 2, 3, 4, 5)

8.2. Accessing Elements in tuple:

first_element = my_tuple[0]   # Accessing the first element

last_element = my_tuple[-1]   # Accessing the last element

8.3. Length of a Tuple:

length = len(my_tuple)   # Returns the number of elements in the tuple

8.4. Iterating Over a Tuple:

for item in my_tuple:

     print(item)

8.5. Tuple Concatenation:

tuple1 = (1, 2, 3)

tuple2 = (4, 5, 6)

concatenated_tuple = tuple1 + tuple2   # Results in (1, 2, 3, 4, 5, 6)

8.6. Tuple Repetition:

repeated_tuple = (1, 2) * 3   # Results in (1, 2, 1, 2, 1, 2)

8.7. Checking if an Element Exists in a tuple:

if 3 in my_tuple:

    print("3 is present in the tuple")

8.8. Tuple Methods:

count = my_tuple.count(3)   # Returns the number of occurrences of the element 3 in the tuple

8.9. Finding the Index of an Element in tuple:

index = my_tuple.index(3)   # Returns the index of the first occurrence of the element 3 in the tuple

8.10. Immutability:

# Attempting to modify a tuple will result in an error

# my_tuple[0] = 10  # This will raise an error

8.11. Unpacking Tuples:

x, y, z = my_tuple   # Unpacking into variables

first, *middle, last = my_tuple   # Unpacking with asterisk operator

9. Sets in Python

In Python, a set is a mutable unordered collection of unique elements, defined using curly braces {} and separated by commas.

9.1. Creating a Set

my_set = {1, 2, 3, 4, 5}        # Set with normal elements

my_set = {[1,2,3,4,5]}          # Set with a list as an element

9.2. Adding an element in Set

my_set.add(4)       # This will add 4 to the set at a random place 

# Output: {1,2,3,4}

9.3. Checking the length of the Set

len(my_set)         # len will return the total length of the set  

# Output: 5

9.4. Set Methods

9.5. Set Comprehensions

x = {0,1,2,3,4}

squares = {x**2 for x in range(5)}   # Performs the operation on the set elements

# Output: {0, 1, 4, 9, 16}

9.6. Frozen Set

Immutable sets are called Frozen Sets. Once created elements cannot be added or removed. It can be used as a key in the dictionary.

9.6.1. Creating the Frozenset

frozen_set = frozenset([1, 2, 3, 4, 5])

9.6.2. Operations on frozen sets

print(1 in frozen_set)

# Output: True

print(len(frozen_set))

# Output: 5

Flow Control Statements in Python

Flow control statements in Python are used to control the flow of execution of a program. They allow you to make decisions about which code to execute and when to execute it.

1. Conditional Statements in Python

In Python, a conditional statement allows for decision-making based on certain conditions.

1.1. If Statement

x = 10 

if x > 5:

# Checks for the condition to be True or False

print("x is greater than 5")

# If True then this line of code gets executed

# Output: x is greater than 5

1.2. If-else statement

x = 4

if x > 5:    # Checks for the condition

print("x is greater than 5")   # Runs if the condition is True

else:

print("x is less than or equal to 5") # Runs if the condition is False

 # Output: x is less than or equal to 5

1.3. If-elif-else statement

x = 3
if x > 5:   # Checks for the first condition

    print("x is greater than 5")   # Runs if the first condition is True

elif x == 5:  # Checks for the second condition when the first condition becomes False

    print("x is equal to 5")  # Runs if the condition is True

else:          

    print("x is less than 5")   # Runs if both the condition becomes False

# Output: x is less than 5

1.4. Nested if Statements

If inside an if statement is called nested if

x = 10
if x > 5:   # Check for the condition that goes inside the block
if x ; 15:   # Again checks for the condition, if True moves inside the if block
print("x is between 5 and 15")  # Runs if the condition is True

else:

print("x is greater than or equal to 15")  # Runs if the condition is False
else:
print("x is less than or equal to 5")  # Runs if the condition is False

# Output: x is between 5 and 15

2. Ternary Operator

The ternary operator in Python is a concise way to write conditional expressions in a single line.

x = 10

result = “x is greater than 5” if x > 5 else “x is less than or equal to 5” # CombinedIf-else

print(result)

3. Logical Operators with Conditional Statements

3.1. AND Operator

x = 10
y = 6
if x > 5 and y > 5:   # Checks for both the condition
print("Both x and y are greater than 5")
# Executes only if both the condition is true 
# Output: Both x and y are greater than 5

3.2. OR Operator

x =2
y = 6
if x > 5 or y > 5:   # Checks for both the condition
print("Either x or y is greater than 5")  
# Executes even if one of the conditions is True
# Output: Either x or y is greater than 5

3.3. NOT Operator

x = 5
y = 6
if not x == y:  # Checks for the condition
print("x is not equal to y")  
# Runs if the condition is True
# Output: x is not equal to y

3.4. break Statement

The break statement immediately exits the loop and continues with the code following the loop as soon as it meets the break

i = 0
while i < 5:
if i == 3:
break   # The loop will exit 
i++         
# Output: m 0,1,2

3.5. continue Statement

The continue statement immediately skips to the next iteration as soon as the condition is satisfied

i = 0
while i < 5:
if i == 3:
continue   # The loop will exit 
i++         
# Output:m 0,1,2,4

4. For Loop in Python

A for loop in Python is a programming construct that allows us to execute a block of code repeatedly until a certain condition is met.

4.1. Syntax:

for <variable> in <sequence>:
  <code block>

4.2. Iterating Over a Range using for loop

for i in range(5):   # Executes the below line of code 5 times
  print(i)             
 # Output: 0,1,2,3,4

4.3. Iterating Over a Sequence of lists using for loop

my_list = [1, 2, 3, 4, 5]
for item in my_list:
print(item)

4.4. Nested for Loop

for i in range(3):
for j in range(2):
print(i, j)

5. While Loop in Python

A while loop in Python is a programming construct that allows us to execute a block of code repeatedly, as long as a certain condition is met.

i = 0
while i < 5:   # Loop executes as long as i is less than 5
print(i)   # Output: 0, 1, 2, 3, 4
i += 1     # Increment i to eventually terminate the loop

Python Random Module

In Python, the random module is a built-in module that allows us to generate random elements.

1. Syntax

import random

2. seed() method

seed() is used to initialize the random number generator.

import random
# Generate a sequence of random numbers with the same seed
random.seed(42)
print(random.random())  # Output: 0.6394267984578837
print(random.random())  # Output: 0.025010755222666936

# Reinitialize the random number generator with the same seed
random.seed(42)
print(random.random())  # Output: 0.6394267984578837 (same as previous)
print(random.random())  # Output: 0.025010755222666936 (same as previous)

# Generate a different sequence with a different seed
random.seed(123)
print(random.random())  # Output: 0.052363598850944326 (different from previous)
print(random.random())  # Output: 0.08718667752263232 (different from previous)

3. randint() method

The randint() method returns an integer number from the selected specified range.

Syntax:

random.randint(start: int, stop: int) 
import random
# Generate a random integer between -100 and 100
random_number = random.randint(-100, 100)
print("Random number between -100 and 100:", random_number)

# Generate a random integer between 1 and 6 (inclusive), simulating a six-sided die roll
die_roll = random.randint(1, 6)
print("Die roll result:", die_roll)

4. choice() method

The choice method returns a randomly selected element from an iterable, like a list, set, or str:

import random
# List of different names
names = ['Sophia', 'Liam', 'Olivia', 'Noah']

# Randomly choose a name from the list
random_name = random.choice(names)
print("Randomly selected name:", random_name)

5. shuffle() method

The shuffle method takes in an iterable and shuffles it:

import random
# List of numbers
numbers = [1, 2, 3, 4, 5]

# Shuffle the list in place
random.shuffle(numbers)

# Print the shuffled list
print("Shuffled list:", numbers)

6. sample() method

sample returns a list with a random selection from an iterable. The number of elements returned is equal to the k parameter:

Syntax: 

random.sample(iterable, k: int)
import random
# Population of letters
letters = ['a', 'b', 'c', 'd', 'e']

# Select a sample of 3 unique letters from the population
sampled_letters = random.sample(letters, 3)
# Print the sampled letters
print("Sampled letters:", sampled_letters)

7. random() method

The random method returns a random floating point number between 0.0 and 1.0:

import random
# Generate a random floating-point number between 0 and 1
random_number = random.random()

# Print the random number
print("Random number:", random_number)

8. uniform() method

The uniform method is similar to randint, but returns a floating point number:

import random
# Generate a random floating-point number between 3.0 and 7.0
random_number = random.uniform(3.0, 7.0)

# Print the random number
print("Random number:", random_number)

Functions in Python 

A function is a block of code which only runs when it is called. You can pass data, known as parameters, into a function.

In Python, a function is defined using the def keyword:

Syntax:

def function_name(parameters):
"""function_docstring"""
# function body
return result

Example:

def greet(name):
print("Hello, " + name)

Built-in Functions

Parameters and Arguments:

Parameters: Parameters refer to the variables defined during the creation of a function, used to receive input values when the function is called

Arguments are the values passed into a function when it is called, corresponding to the parameters defined in the function’s definition.

def greet(name, message):   # Creation of function with def keyword
print(message + ", " + name)
greet(message="Good morning", name="Intellipaat") 
# Calling the function and passing the argumentambiance

Returning Values from Functions:

We can return values from a function using the return statement.

def minus(x, y):
return x - y
result = minus(3, 5)

Local and Global Scope:

Local Scope: 

• Variables defined within a function are considered to be in the local scope of that function.
• These variables can only be accessed within the function where they are defined.

def my_function():
x = 10  # Local variable
print("Inside function:", x)
my_function()
# Trying to access the local variable outside the function will raise an error
# print("Outside function:", x)  # This will raise NameError

Global Scope:

• Variables defined outside of any function or in the global scope are considered global variables.
• Global variables can be accessed and modified from anywhere in the code, including inside functions.

y = 20  # Global variable
def my_function():
print("Inside function accessing global variable:", y)
my_function()

 Function modifying global variables using the global keyword:

z = 30  # Global variable
def modify_global_variable():
global z
 z += 5  # Modifying the global variable
print("Before modification:", z)
modify_global_variable()
print("After modification:", z)

Default Parameters:

Default values can be assigned to parameters in a function, ensuring that if no argument is provided for a parameter during a function call, the default value will be used.

def greet(name="Intellipaat"):
print("Hello, " + Intellipaat)
greet()       # Output: Hello, World
greet("Ankit")# Output: Hello, Ankit

Arbitrary Number of Arguments:

We can define functions that accept an arbitrary number of arguments using *args or **kwargs.

*args: 

def sum_all(*args):
total = 0
for num in args:
total += num
return total
print(sum_all(1, 2, 3, 4))   # Output: 10

Kwargs:

def display_info(**kwargs):
for key, value in kwargs.items():
print(f"{key}: {value}")
# Calling the function with keyword arguments
display_info(name="Alice", age=30, city="New York")

Lambda Functions (Anonymous Functions):

Small, anonymous functions defined using the lambda keyword are called the Lambda Function.

square = lambda x: x * 2
print(square(5))   # Output: 10

Recursive Functions:

A function calling a function is called the Recursive Function. They are useful for solving problems that can be broken down into smaller and similar subproblems.

def factorial(a):

if a == 0:
return 1
else:
return a * factorial(a-1)   # The function is called again
print(factorial(5))   # Output: 120

Regex in Python:

Regex, or Regular Expression is a pattern-matching language used to search, manipulate, and validate text data efficiently and flexibly.

Normal Characters:

import re
pattern = r'apple'
text = 'I like apples'
match = re.search(pattern, text)
if match:
print('Found:', match.group())  # Output: Found: apple

Character Classes:

[ ]: Matches any single character within the brackets.

import re
pattern = r'[aeiou]'
text = 'I like apples'
match = re.search(pattern, text)
if match:
print('Found:', match.group())  
# Output: Found: i

Quantifiers:

*: Matches zero or more occurrences of the preceding character or group.

import re
pattern = r'ap*le'
text = 'I like apple'
match = re.search(pattern, text)
if match:
print('Found:', match.group())  # Output: Found: apple

Anchors:

^: Matches the start of the string

import re
pattern = r'^I'
text = 'I like apples'
match = re.search(pattern, text)
if match:
print('Found:', match.group())  # Output: Found: I

Groups and Capturing:

( ): Groups multiple tokens together.

import re
pattern = r'(app)les'
text = 'I like apples'
match = re.search(pattern, text)
if match:
print('Found:', match.group(1))  # Output: Found: app

Predefined Character Classes:

d: Matches any digit character.

Modifiers:

i: Case-insensitive matching.

import re
pattern = r'apple'
text = 'I like Apples'
match = re.search(pattern, text, re.IGNORECASE)
if match:
print('Found:', match.group())  # Output: Found: Apples

Object-Oriented Programming (OOPs) in Python

Object-oriented programming (OOP) is a programming paradigm based on the concept of objects, which encapsulate data and methods. It promotes code organization, reusability, and abstraction.

Class:

A class in Python is a blueprint for creating objects. It defines the attributes and methods common to all objects of that type.

Syntax: 

class MyClass:
pass

Object Creation:

An object in Python is an instance of a class, encapsulating both data (attributes) and behavior (methods).

obj1 = MyClass()
obj2 = MyClass()

Instance Variables and Initialization.

Instance variables in Python are variables that are defined within the methods of a class and belong to a specific instance of that class. They are unique to each object created from the class and hold data that is specific to that instance.

class Person:

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

Accessing Instance Variables

person1 = Person("Rohit", 30)
person2 = Person("Ram", 25)
print(person1.name)  # Output: Rohit
print(person2.age)   # Output: 25

Methods:

Actions that can be performed by the object in the class are called Methods.

class Dog:
def bark(self):
print(“Woof!”)
dog = Dog()
dog.bark()  # Output: Woof!

Inheritance:

Inheritance in Python is a mechanism where a new class inherits properties and behaviors from an existing class, allowing for code reuse and creating a hierarchical relationship between classes.

Animal:
def speak(self):
pass
class Dog(Animal):
def speak(self):
print("Woof!")
dog = Dog()
dog.speak()  
# Output: Woof!

Encapsulation:

Encapsulation in Python refers to the bundling of data and methods within a class, hiding the internal state of an object from outside access and modification.

class Car:
def __init__(self):
self.__max_speed = 200
def drive(self):
print(f"Driving at max speed: {self.__max_speed}")
car = Car()
car.drive()  # Output: Driving at max speed: 200

Polymorphism:

Polymorphism in Python refers to the ability of different objects to respond to the same method call in different ways. 

class Animal:
def speak(self):
pass
class Dog(Animal):
def speak(self):
print("Woof!")
class Cat(Animal):
def speak(self):
print("Meow!")
animals = [Dog(), Cat()]
for animal in animals:
animal.speak()  
# Outputs: Woof! Meow!

Class and Static Methods:

A class method in Python is a method that belongs to the class rather than an instance. It is decorated with the @classmethod decorator and takes the class itself (cls) as the first argument instead of the instance.

A static method in Python is a method bound to the class rather than the object instance. It does not require access to object or class state and is defined using the @staticmethod decorator.


class Math:
@classmethod
def add(cls, x, y):
return x + y
class Utility:
@staticmethod
def greet(name):
return f"Hello, {name}!"
sum_result = Math.add(3, 5)
greeting = Utility.greet("Virat")

Class Attributes:

Class attributes in Python are variables defined within a class that are shared by all instances of that class. They are accessed using the class name rather than instance variables.

class Circle:
pi = 3.14
def __init__(self, radius):
self.radius = radius
def area(self):
return Circle.pi * self.radius ** 2
print(Circle.pi)  # Output: 3.14
circle = Circle(5)
print(circle.area())  # Output: 78.5

Dataclasses in Python:

Dataclasses in Python provide a simple way to create classes primarily used for storing data. They automatically generate special methods like __init__(), __repr__(), __eq__(), and more, based on class variables, reducing boilerplate code.

• Defining a Dataclass:

To create a dataclass, use the “dataclass” decorator from the dataclasses module. Define class attributes within the class. 

Dataclasses generate methods are `__init__`, `__repr__`, `__eq__`, and `__hash__`.

from dataclasses import dataclass
@dataclass
class Point:
    x: int
    y: int
p = Point(3, 4)
print(p)  # Output: Point(x=3, y=4)

• Default Values and Type Hints:

An assignment in the attribute declaration gives default values.

from dataclasses import dataclass
@dataclass
class Rectangle:
   width: int = 0
    height: int = 0
r = Rectangle(width=5)
print(r)  # Output: Rectangle(width=5, height=0)

• Frozen Dataclasses:

A process for creating immutable sets that shields them from errors and unintended alterations.

from dataclasses import dataclass
@dataclass(frozen=True)
class Point:
    x: int
    y: int
p = Point(3, 4)
p.x = 5  # This will raise a TypeError since p is immutable

• Inheritance and Methods:

Dataclasses support inheritance, and you can add additional methods in a data class.

from dataclasses import dataclass

@dataclass
class Person:
name: str
age: int
def greet(self):
return f"Hello, my name is {self.name} and I am {self.age} years old."
p = Person("Rohit", 30)
print(p.greet())  # Output: Hello, my name is Rohit and I am 30 years old.

JSON and YAML 

JSON

JSON is based on JavaScript syntax and is widely used for transmitting data between a server and a web application.

• Importing the JSON 

import json

• Encode Python Object to JSON

json_data = json.dumps(python_object)

• Decode JSON to Python Object

python_object = json.loads(json_data)

• Read JSON from File

with open('file.json', 'r') as file:
python_object = json.load(file)

• Write JSON to File

with open('file.json', 'w') as file:
json.dump(python_object, file)

YAML

YAML, on the other hand, aims to be human-readable and is often used for configuration files and data serialization.

• Importing YAML 

import pyyaml

• Encode a Python Object to YAML

yaml_data = yaml.dump(python_object)

• Decode YAML to a Python Object

python_object = yaml.load(yaml_data, Loader=yaml.Loader)

• Read YAML From the File

with open('file.yaml', 'r') as file:
python_object = yaml.load(file, Loader=yaml.Loader)

• Write YAML to the File

with open('file.yaml', 'w') as file:
yaml.dump(python_object, file)

• Customized YAML Dumping

yaml.dump(python_object, file, default_flow_style=False)

Files and Directory Path in Python

Files in Python represent a collection of data stored on a storage device, while directory paths specify the location of files or directories within a file system. Can be used to read and write a file

Working with Files

• Opening a File

file = open('filename.txt', 'r')  # Open for reading
file = open('filename.txt', 'w')  # Open for writing (truncate existing)
file = open('filename.txt', 'a')  # Open for writing (append to end)

• Closing File

file.close()   # Closes the file

• Reading from Files

content = file.read()               # Reads entire file
content = file.read(size)           # Reads specified number of bytes
content = file.readline()           # Reads one line
content = file.readlines()          # Reads all lines into a list

• Writing to Files

file.write('text')                  # Writes text to file
file.writelines(['line1', 'line2']) # Writes a list of lines to file

• Iterating Over Lines

for line in file:
print(line)

• File Modes

'r': Read (default mode)
'w': Write (truncate existing)
'a': Append (write to end)
'b': Binary mode (e.g., 'rb' for reading binary)
'+': Read/write mode (e.g., 'r+' for reading and writing)

Working with Directories

To work with directories, you will need to import the module called os.

• Importing the OS module

import the

• Listing Contents

import os
contents = os.listdir('path')          # Lists directory contents

• Creating Directories

os.makedirs('path/to/directory')       # Creates directories recursively

• Renaming and Moving

os.rename('old_name', 'new_name')      # Renames a file or directory
os.replace('old_name', 'new_name')     # Same as rename, but overwrites if new_name already exists
os.rename('path/to/old_location', 'path/to/new_location')  # Moves a file or directory

• Removing

os.remove('filename.txt')              
# Removes a file
os.rmdir('path/to/empty_directory')    
# Removes an empty directory
os.removedirs('path/to/empty_directories')  
# Removes empty directories recursively

• Checking Existence

os.path.exists('path')                 # Checks if path exists
os.path.isfile('filename.txt')         # Checks if path is a file
os.path.isdir('path/to/directory')     # Checks if path is a directory

Exception Handling in Python

Exception handling in Python refers to the process of managing and responding to errors or exceptional situations that occur during the execution of a program. It involves using try, except, and finally blocks to handle exceptions gracefully and prevent program crashes.

• Basics:

try: Encloses the code where exceptions may occur.
except: Catches and handles exceptions raised in the try block.
else: Executes if no exceptions occur in the try block.
finally: Executes regardless of whether exceptions occur.

• Handling Specific Exceptions:

try:
# Code that may raise an exception
except ValueError:
# Handle ValueError
except ZeroDivisionError:
# Handle ZeroDivisionError
except Exception as e:
# Handle any other exception
print("An error occurred:", e)

• Catching Multiple Exceptions:

try:
# Code that may raise an exception
except (ValueError, ZeroDivisionError) as e:
# Handle ValueError or ZeroDivisionError
print("An error occurred:", e)

• Catching All Exceptions:

try:
# Code that may raise an exception
except:
# Handle any exception (not recommended for clean code)

• Raising Exceptions:

raise ValueError("Invalid value")

• Custom Exceptions:

class CustomError(Exception):
pass
raise CustomError("Custom error message")

• Else Clause:

try:
# Code that may raise an exception
except:
# Handle exception
else:
# Execute if no exception occurs
print("No exceptions occurred")

• Finally Clause:

try:
# Code that may raise an exception
except:
# Handle exception
finally:
# Execute regardless of exceptions
print("Finally block executed")

• Exception Attributes:

try:
# Code that may raise an exception
except Exception as e:
print("Type:", type(e))
print("Message:", str(e))

• Handling Exceptions in List Comprehensions:

result = [value / 0 for value in range(5) if value != 0 except ZeroDivisionError]

• Logging Exceptions:

import logging
try:
# Code that may raise an exception
except Exception as e:
logging.exception("An error occurred:")

Decorators in Python

Decoratrs in Python are functions that modify the behavior of other functions or methods. They allow you to add functionality to existing code without modifying its structure.

Syntax:

def decorator(func):
def wrapper(*args, **kwargs):
# Add extra functionality here
return func(*args, **kwargs)
return wrapper
@decorator
def function():
pass

• Example: Logging Decorator

def log(func):
def wrapper(*args, **kwargs):
print(f"Calling function {func.__name__} with arguments {args}, {kwargs}")
return func(*args, **kwargs)
return wrapper
@log
def add(a, b):
return a + b
result = add(2, 3)
# Output: Calling function add with arguments (2, 3), {}

• Decorator with Arguments

def repeat(n):
def decorator(func):
def wrapper(*args, **kwargs):
for _ in range(n):
result = func(*args, **kwargs)
return result
return wrapper
return decorator
@repeat(3)
def greet(name):
print(f"Hello, {name}!")
greet("Alice")
# Output: Hello, Alice!
#         Hello, Alice!
#         Hello, Alice!

• Built-in Decorators:

@staticmethod: Declares a static method in a class.
@classmethod: Declares a class method in a class.
@property: Defines a property in a class.

• Chaining Decorators:

@decorator1
@decorator2
def function():
pass

• Decorator Classes:

class Decorator:
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
# Add extra functionality here
return self.func(*args, **kwargs)
@Decorator
def function():
pass

Use Cases:

• Logging: Log function calls and arguments.
• Authorization: Check user permissions before executing a function.
• Caching: Store the results of function calls for future use.
• Validation: Validate function arguments before execution.

Debugging in Python

Debugging in Python refers to the process of identifying and resolving errors or defects in a program, ensuring it behaves as intended.

• pdb (Python Debugger)

To debug the code import pdb and use pdb.set_trace() to set breakpoints in your code.

After importing use these commands

n (next), c (continue), s (step), q (quit), l (list), p (print), h (help), etc.
import pdb   # importing the library
def some_function():
x = 5
pdb.set_trace()  # using the set_trace() function
print("Value of x:", x)
some_function()

• traceback Module

Import the taceback module to print detailed information at the time of execution

import traceback  #importing traceback library
try:
x = 1 / 0
except ZeroDivisionError:
traceback.print_exc()  # using traceback

Context Manager in Python

A context manager in Python is a tool for managing resources, providing a convenient way to handle setup and cleanup actions within a specific context using the with statement.

Syntax:

with context_manager() as alias:
# Code block

• Using with Statement:

Automatically calls __enter__() method before block execution and __exit__() method after.

Handles resource cleanup and exception handling automatically.

Implementing Context Managers:

• Function-based:

from contextlib import contextmanager
@contextmanager
def my_context_manager():
# Code executed before entering the block
yield resource
# Code executed after exiting the block

• Class-based:

class MyContextManager:
def __enter__(self):
# Code executed before entering the block
return resource
def __exit__(self, exc_type, exc_value, traceback):
# Code executed after exiting the block

• Nested Context Managers:

with outer_context() as oc:
with inner_context() as ic:
# Code block

Built-in Context Managers:

• File Handling:

with open('file.txt', 'r') as f:
# Code to read from file

• Locks:

import threading
lock = threading.Lock()
with lock:
# Code block protected by the lock

• Database Connections:

import sqlite3
with sqlite3.connect('database.db') as conn:
# Code block accessing database

Setup.py in Python

A setup.py file in Python contains metadata for a package, including its name, version, author, and dependencies. It’s used for package installation and distribution.

Required Imports:

from setuptools import setup, find_packages

Metadata:

• name: Name of the package.
• version: The version number of the package.
• author: Author’s name.
• author_email: Author’s email address.
• description: Short description of the package.
• long_description: Detailed description of the package (usually from a README file).
• url: URL for the project homepage or repository.
• license: License for the package.

Example setup.py:

setup(
name='my_package',
version='0.1',
author='intellipaat',
author_email='[email protected]',
description='A Python package',
long_description=open('README.md').read(),
long_description_content_type='text/markdown',
url='https://github.com/example/my_package',
license='MIT',
packages=find_packages(),
install_requires=[
'dependency1',
'dependency2',
],
)

Additional Options:

• packages: List of packages to include in the distribution.
• install_requires: List of dependencies required for the package.
• entry_points: Specify executable scripts or console scripts.
• classifiers: List of trove classifiers for categorizing the package.
• keywords: List of keywords describing the package.
• package_data: Additional data files to include in the package.
• include_package_data: Flag indicating whether to include package data files.
• extras_require: Define additional dependencies for extra features.

Building and Distributing:

• Build: Run python setup.py build to build the package.
• Install: Run python setup.py install to install the package.
• Distribution: Run python setup.py sdist to create a source distribution.
• Upload: Use twine or setuptools to upload the distribution to PyPI.

Example MANIFEST.in:

include README.md
include LICENSE
recursive-include my_package/data *.json

Main: Top-Level Script in Python

The main function in Python is not inherently special like in some other programming languages, but it’s a convention often used to denote the entry point of a script or program.

def main():
# Your main code logic here
print("Hello, world!")
if __name__ == "__main__":
main()

def main(): This defines a function named main. This is where you’ll put the main logic of your script.

if __name__ == “__main__”: This condition checks if the script is being run directly by the Python interpreter. If it is, it executes the code block beneath it.

main(): This line calls the main function when the script is executed directly.

DateTime in Python

DateTime in Python refers to a module that provides classes for manipulating dates and times.

• Importing the datetime library

import datetime  # Used to import the library

• Creating a datetime object

my_date = datetime.datetime(year, month, day, hour, minute, second) 

• Current date and time

current_datetime = datetime.datetime.now()  # Gives current date and time

• Formatting a datetime object

formatted_date = my_date.strftime(format_string)

• Parsing a string to datetime

parsed_date = datetime.datetime.strptime(date_string, format_string)

• Date arithmetic

new_date = my_date + datetime.timedelta(days=5)

• Accessing components of a datetime object

year = my_date.year
month = my_date.month
day = my_date.day
hour = my_date.hour
minute = my_date.minute
second = my_date.second

• Comparing datetime objects

if date1 > date2:
# logic goes here

• Converting a timestamp to a datetime object

timestamp = datetime.datetime.fromtimestamp(timestamp)

Zipfile

ZipFile in Python is a module that allows manipulation of ZIP archives.

• Importing the module

import zipfile  # Allows you to use the zipfile module

• Extracting a ZIP file

with zipfile.ZipFile('file.zip', 'r') as zip_ref:
zip_ref.extractall('destination_directory')   # extractall will extract the contents of zip file

• Creating a ZIP file

with zipfile.ZipFile('file.zip', 'w') as zip_ref:
zip_ref.write('file.txt', 'file_in_zip.txt')  # write() will allow you to write in the file in
# inside zip file

• Adding files to an existing ZIP file

with zipfile.ZipFile('file.zip', 'a') as zip_ref:
zip_ref.write('new_file.txt', 'new_file_in_zip.txt')  # Adding a new file in the zip

• Extracting a single file from a ZIP archive

with zipfile.ZipFile('file.zip', 'r') as zip_ref:
zip_ref.extract('file_inside_zip.txt', 'destination_folder') # Extract the file at specified location

• Listing contents of a ZIP file

with zipfile.ZipFile('file.zip', 'r') as zip_ref:
file_list = zip_ref.namelist()   # Assigns all the files to the file_list variable
print(file_list)

• Checking if a file exists in a ZIP archive

with zipfile.ZipFile('file.zip', 'r') as zip_ref:
if 'file_inside_zip.txt' in zip_ref.namelist():
print('File exists in ZIP archive.')
else:
print('File does not exist in ZIP archive.')

Itertools

Itertools is a Python module providing functions to create iterators for efficient looping, combining, and manipulating data.

• Importing the module

import itertools  # imports the module for use

• Infinite Interators

count_iterator = itertools.count(start=0, step=1)  # From where to start and step count
cycle_iterator = itertools.cycle([1, 2, 3])   # Range
repeat_iterator = itertools.repeat('Hello', times=3) # How many times to repeat

• Combining multiple iterables

iter1 = [1, 2, 3]
iter2 = ['a', 'b', 'c']
combined_iter = itertools.chain(iter1, iter2)  # Combines two iterator into one using chain()

• Permutations and Combinations

permutations = itertools.permutations(iterable, r)
combinations = itertools.combinations(iterable, r)

• Iterating over multiple iterables simultaneously

zipped_iter = itertools.zip_longest(iter1, iter2, fillvalue=None)

• Flattening a list of iterables

flattened_list = itertools.chain.from_iterable(iterables)

Shelves

A “shelf” works like a permanent dictionary. Unlike “dbm” databases, shelves can store all sorts of Python things as values, not just keys, while still acting like a dictionary.

• Importing the module

import shelve   # Helps to import and use the shelve module

• Opening a shelf file

with shelve.open('filename') as shelf:
 # Access shelf contents inside this block

• Storing data on a shelf

with shelve.open('my_data') as shelf:
shelf['key'] = value

• Retrieving data from a shelf

with shelve.open('my_data') as shelf:
value = shelf['key']

• Deleting data from a shelf

with shelve.open('my_data') as shelf:
del shelf['key']

• Checking if a key exists on a shelf

with shelve.open('my_data') as shelf:
if 'key' in shelf:
# code to perform any operation

• Iterating over keys on a shelf

with shelve.open('my_data') as shelf:
for key in shelf.keys():
# perform operation with each key

• Closing a shelf

shelf.close()

Conclusion

Speed up your Python learning with our other Python cheat sheets – your go-to guide for everything Python.

1. Pandas Cheat Sheet
2. NumPy Cheat Sheet
3. Scikit-Learn Cheat Sheet
4. Data Structures with Python Cheat Sheet