Python Programming Language Cheatsheet

Explore this comprehensive Python cheatsheet covering essential syntax, data structures, file handling, and object-oriented programming. Learn the fundamentals of Python programming with examples and quick references for both beginners and experienced developers.



# This is a single-line comment

This is a
multi-line comment


variable_name = 10
string_variable = "Hello, World!"

Data Types

int_num = 42
float_num = 3.14
string = "text"
boolean = True

Basic Input/Output

user_input = input("Enter something: ")
print("Output:", user_input)

String Manipulation

s1 = "Hello"
s2 = "World"
concatenated_string = s1 + " " + s2
formatted_string = f"{s1} {s2}"


my_list = [1, 2, 3, "four", 5.0]


my_tuple = (1, 2, 3)


my_dict = {"key": "value", "num": 42}


if condition:
    # code
elif another_condition:
    # code
    # code


for item in my_list:
    # code

while condition:
    # code


def my_function(param1, param2):
    # code
    return result

Advanced Data Structures


my_set = {1, 2, 3, 4}

List Comprehension

squared_numbers = [x**2 for x in range(1, 6)]

Dictionary Comprehension

my_dict = {key: value for key, value in zip(keys, values)}

Lambda Functions

square = lambda x: x**2

Map and Filter

squared_numbers = list(map(lambda x: x**2, my_list))
filtered_numbers = list(filter(lambda x: x > 0, my_list))

File Handling

Reading from a File

with open("filename.txt", "r") as file:
    content =

Writing to a File

with open("filename.txt", "w") as file:
    file.write("Hello, File!")

Exception Handling

    # code
except ExceptionType as e:
    # handle exception

Modules and Packages

import module_name
from module_name import function_name

Object-Oriented Programming (OOP)

Class Definition

class MyClass:
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2

    def my_method(self):
        # code

Object Instantiation

obj = MyClass(arg1, arg2)

External Libraries (Example – NumPy)

Installing External Libraries

# Installing External Libraries
# pip install numpy

Using NumPy

# Using NumPy
import numpy as np

# NumPy Arrays
my_array = np.array([1, 2, 3, 4])
array_sum = np.sum(my_array)

Refer to the official documentation for more in-depth information and examples.


What is Python and why is it popular?

Python is a high-level, interpreted programming language known for its simplicity and readability. It is versatile and widely used in various domains, including web development, data science, machine learning, artificial intelligence, automation, and more. Python’s popularity is attributed to its ease of learning, clean syntax, extensive libraries, and a strong community.

What is the difference between Python 2 and Python 3?

Python 2 and Python 3 are two major versions of the Python programming language. Python 3 introduced several improvements and changes to address issues in Python 2. Key differences include print syntax, Unicode support, division behavior, and the input() function. Python 2 reached its end of life on January 1, 2020, and developers are encouraged to use Python 3 for ongoing projects.

Explain the Global Interpreter Lock (GIL) in Python.

The Global Interpreter Lock is a mechanism in CPython (the default and most widely used Python interpreter) that ensures only one thread executes Python bytecode at a time in a single process. This can limit the parallelism of multi-threaded Python programs. While it simplifies memory management, it can be a challenge for CPU-bound and multi-core-bound tasks. Other implementations of Python, such as Jython and IronPython, don’t have a GIL.

What are decorators in Python?

Decorators are a powerful and flexible feature in Python that allows the modification or extension of the behavior of functions or methods. They are denoted by the @decorator syntax and are commonly used for tasks such as logging, timing, access control, and more. Decorators can be applied to functions or methods to alter their behavior without modifying their source code directly.

What is the difference between a list and a tuple in Python?

Both lists and tuples are used to store ordered collections of items in Python. The main difference is that lists are mutable (can be modified after creation), while tuples are immutable (cannot be modified after creation). Lists are defined using square brackets [], and tuples use parentheses (). Tuples are generally used for fixed collections, while lists are preferred for dynamic collections.