Python Cheat Sheet: Full Guide

A Python cheat sheet offers a quick reference for key syntax, concepts, and commands to help developers write efficient code without memorizing everything.

Python is one of the most beginner-friendly and powerful programming languages used in web development, data science, automation, artificial intelligence, and more.In this Python Cheat Sheet you will understand and get into flow of Python. You will meet every concepts of python with structured way.

Step 1: Python Basics

1. Variables and Data Types

Variable:Variable in Python is a name used to store data. Python is dynamically typed, so you don’t need to declare the data type explicitly.

Example:

name = "Alice"        # string
age = 25              # integer
height = 5.4          # float
is_student = True     # boolean 

Common Data Types:

Checking the Data Type:

x = 25
print(type(x))  # <class 'int'> 

2. Comments

Comments in Python are used to explain the code and are ignored by the Python interpreter.

Single-line Comment:

# This is a single-line comment
print("Hello")  # Comment at the end 

Multi-line Comment:

"""
This is a multi-line comment
Used for documentation or explanation
""" 

3. Type Casting

Type Casting means converting the data type of a value from one type to another using built-in functions.

Common Conversion Functions:

Examples

x = int("10")
y = float("5.5")
z = str(25)

print(bool(0))   # False
print(bool(5))   # True 

Common Error:

int("hello")  # ValueError: invalid literal for int() 

Step 2: Operators in Python

Operators in Python are symbols used to perform operations on variables and values. Python has a variety of operators for different types of tasks.

Types of Python Operators

1. Arithmetic Operators

Used to perform mathematical operations.

2. Comparison Operators

Used to compare values and return Boolean results.

3. Assignment Operators

Used to assign values and perform shorthand operations.

4. Logical Operators

Used to combine conditional statements.

5. Identity Operators

Used to compare memory locations (not just values).

Example

a = [1, 2]
b = [1, 2]
print(a == b)   # True (same values)
print(a is b)   # False (different memory objects) 

6. Membership Operators

Used to check if a value is part of a sequence.

7. Bitwise Operators

Used for binary-level operations.

Step 3: Data Structures

Python provides built-in data structures that are essential for organizing and storing data efficiently. These include both simple and complex structures like Lists, Tuples, Sets, and Dictionaries.

1. List

A list is an ordered, mutable collection that allows duplicate items.

fruits = ["apple", "banana", "cherry"]
print(fruits[0])  # Output: apple

• Mutable (can change after creation)
• Supports indexing and slicing
• Methods: append(), remove(), pop(), sort()

2. Tuple

A tuple is an ordered, immutable collection.

coordinates = (10, 20)
print(coordinates[1])  # Output: 20

• Immutable (cannot change after creation)
• Supports indexing
• Used for fixed data

3. Set

A set is an unordered collection of unique elements.

numbers = {1, 2, 3, 2}
print(numbers)  # Output: {1, 2, 3} 

• No duplicates allowed
• Unordered — no indexing
• Methods: add(), remove(), union(), intersection()

4. Dictionary

A dictionary is an unordered collection of key-value pairs.

student = {"name": "Alice", "age": 22}
print(student["name"])  # Output: Alice

• Keys must be unique and immutable
• Values can be of any data type
• Methods: keys(), values(), get(), update()

Comparison Table

Step 4: Control Flow

Control flow statements in Python determine the order in which instructions are executed. Python supports the following control flow constructs:

1. Conditional Statements

Used to perform different actions based on different conditions.

if Statement

x = 10
if x > 5:
    print("x is greater than 5") 

if-else Statement

x = 3
if x > 5:
    print("x is greater than 5")
else:
    print("x is 5 or less") 

if-elif-else Statement

x = 5
if x > 5:
    print("Greater")
elif x == 5:
    print("Equal")
else:
    print("Less") 

2. Loops

Loops allow us to execute a block of code multiple times.

for Loop

Used for iterating over a sequence (like list, tuple, string).

for i in range(3):
    print(i) 

while Loop

Repeats while a condition is true.

count = 0
while count < 3:
    print(count)
    count += 1 

3. Loop Control Statements

These control how loops are executed.

• break: Exits the loop prematurely.
• continue: Skips the current iteration and continues with the next one.
• pass: Does nothing; used as a placeholder.

break Example

for i in range(5):
    if i == 3:
        break
    print(i) 

continue Example

for i in range(5):
    if i == 2:
        continue
    print(i) 

pass Example

for i in range(5):
    if i == 2:
        pass
    print(i) 

4. Nested Control Flow

You can nest if-else statements or loops within each other.

for i in range(3):
    if i % 2 == 0:
        print(f"{i} is even")
    else:
        print(f"{i} is odd") 

Step 5: Functions

Python Functions are blocks of reusable code that perform a specific task. They help in modular programming and reduce code redundancy.

1. Defining a Function

Functions are defined using the def keyword followed by the function name and parentheses.

def greet():
    print("Hello, world!") 

2. Calling a Function

Once defined, a function can be called using its name followed by parentheses.

greet() 

3. Function with Parameters

Functions can accept input values, called parameters or arguments.

def greet(name):
    print(f"Hello, {name}!")
greet("Alice") 

4. Function with Return Value

Functions can return values using the return statement.

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

result = add(5, 3)
print(result)  # Output: 8 

5. Default Parameters

Parameters can have default values which are used if no argument is passed.

def greet(name="Guest"):
    print(f"Hello, {name}!")

greet()
greet("Bob") 

6. Keyword Arguments

Arguments can be passed in the form of key=value pairs.

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

student_info(age=21, name="John") 

7. Variable-Length Arguments

Python allows you to handle functions with an arbitrary number of arguments.

*args (Non-keyword Arguments)

def total(*numbers):
    return sum(numbers)

print(total(1, 2, 3, 4)) 

**kwargs (Keyword Arguments)

def print_info(**info):
    for key, value in info.items():
        print(f"{key}: {value}")

print_info(name="Alice", age=25) 

8. Lambda Functions

Lambda function in Python are anonymous functions written in a single line using the lambda keyword.

square = lambda x: x * x
print(square(5))  # Output: 25 

9. Nested Functions

Functions defined inside another function.

def outer():
    def inner():
        print("Inside inner function")
    inner()

outer() 

10. Recursion

A function calling itself is known as recursion.

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n - 1)

print(factorial(5))  # Output: 120 

Step 6: Exception Handling

Exception handling in Python is used to gracefully handle errors that occur during program execution. It prevents the program from crashing and allows custom error responses.

1. What is an Exception?

An exception is an error that occurs during the execution of a program, disrupting the normal flow of instructions.

2. Basic try-except Block

The try block lets you test a block of code for errors. The except block lets you handle the error.

try:
    x = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!") 

3. Catching Multiple Exceptions

try:
    x = int("abc")
except (ValueError, TypeError):
    print("Invalid input!") 

4. Using else with try-except

The else block executes if no exceptions occur.

try:
    x = int("10")
except ValueError:
    print("Conversion failed.")
else:
    print("Conversion successful:", x) 

5. finally Block

The finally block runs no matter what, even if an exception is raised or not.

try:
    f = open("test.txt", "r")
except FileNotFoundError:
    print("File not found.")
finally:
    print("Execution completed.") 

6. Raising Exceptions

You can raise exceptions using the raise keyword.

def divide(a, b):
    if b == 0:
        raise ValueError("Denominator cannot be zero")
    return a / b

print(divide(10, 2)) 

7. Custom Exceptions

You can define your own exception classes by inheriting from the Exception class.

class AgeTooSmallError(Exception):
    pass

age = 12
if age < 18:
    raise AgeTooSmallError("You must be at least 18 years old.") 

8. Common Python Exceptions

• ZeroDivisionError: Division by zero
• ValueError: Invalid value
• TypeError: Invalid type
• IndexError: Index out of range
• KeyError: Key not found in dictionary
• FileNotFoundError: File not found

9. Best Practices

Step 7: File Handling

1. Reading a File

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

2. Writing to a File

with open("file.txt", "w") as file:
    file.write("Hello, World")
  

Step 8: Object-Oriented Programming

Class and Object

class Person:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print("Hello", self.name)

p = Person("Alice")
p.greet()

Step 9: Modules and Packages

1. Importing Modules

import math
print(math.sqrt(16))
  

2. Custom Module

# mymodule.py
def add(x, y):
    return x + y
  

# usage
import mymodule
print(mymodule.add(5, 3))
  

Step 10: List Comprehension

List comprehension is a concise way to create lists in Python. It offers a shorter syntax compared to traditional for loops for building new lists by applying an expression to each item in an iterable.

1. Basic Syntax

[expression for item in iterable] 

This creates a new list by evaluating the expression for each item in the iterable.

Example:

squares = [x**2 for x in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16] 

2. With Condition (Filtering)

[expression for item in iterable if condition] 

Example:

even_numbers = [x for x in range(10) if x % 2 == 0]
print(even_numbers)  # Output: [0, 2, 4, 6, 8] 

3. With if-else in Expression

[expression_if_true if condition else expression_if_false for item in iterable] 

Example:

labels = ['even' if x % 2 == 0 else 'odd' for x in range(5)]
print(labels)  # Output: ['even', 'odd', 'even', 'odd', 'even'] 

4. Nested List Comprehension

You can use nested list comprehensions to flatten lists or generate combinations.

matrix = [[1, 2], [3, 4], [5, 6]]
flattened = [num for row in matrix for num in row]
print(flattened)  # Output: [1, 2, 3, 4, 5, 6] 

5. Without List Comprehension (Using Loop)

# Traditional way
squares = []
for x in range(5):
    squares.append(x**2)
print(squares) 

List comprehension gives the same result more compactly.

6. List Comprehension vs Generator Expression

• List comprehension returns a list in memory.
• Generator expressions use lazy evaluation and return a generator object.

# Generator expression
squares_gen = (x**2 for x in range(5))
print(next(squares_gen))  # Output: 0 

7. Use Cases of List Comprehension

• Creating a list of transformed data
• Filtering elements
• Flattening nested lists
• Extracting specific elements from collections

8. Best Practices

Step 11: Useful Built-in Functions

Python provides a rich set of built-in functions that are always available for use without importing any modules. These functions help perform common tasks efficiently.

1. print()

Displays output to the console.

print("Hello, World!") 

2. len()

Returns the number of items in an object like list, string, or dictionary.

len("Python")  # Output: 6 

3. type()

Returns the data type of an object.

type(42)  # Output: <class 'int'> 

4. input()

Accepts user input as a string.

name = input("Enter your name: ")
print("Hello", name) 

5. int(), float(), str()

Used for type casting (converting between types).

int("10")     # Output: 10
float("3.14") # Output: 3.14
str(25)       # Output: "25" 

6. sum()

Returns the sum of all items in an iterable.

sum([1, 2, 3])  # Output: 6 

7. max() and min()

Return the largest and smallest items in an iterable.

max([1, 9, 4])  # Output: 9
min([1, 9, 4])  # Output: 1 

8. sorted()

Returns a sorted list from an iterable.

sorted([3, 1, 2])  # Output: [1, 2, 3] 

9. range()

Generates a sequence of numbers (used in loops).

list(range(5))  # Output: [0, 1, 2, 3, 4] 

10. enumerate()

Adds a counter to an iterable and returns a tuple (index, value).

for index, value in enumerate(['a', 'b', 'c']):
    print(index, value) 

11. zip()

Combines multiple iterables element-wise into tuples.

list(zip([1, 2], ['a', 'b']))  # Output: [(1, 'a'), (2, 'b')] 

12. map()

Applies a function to each item in an iterable.

list(map(str.upper, ['python', 'java']))  # Output: ['PYTHON', 'JAVA'] 

13. filter()

Filters elements based on a condition (function).

list(filter(lambda x: x % 2 == 0, [1, 2, 3, 4]))  # Output: [2, 4] 

14. all() and any()

• all() returns True if all elements are True.
• any() returns True if any element is True.

all([True, True, False])  # Output: False
any([True, False, False])  # Output: True 

15. abs()

Returns the absolute value of a number.

abs(-7)  # Output: 7 

16. round()

Rounds a number to the nearest integer or to the specified decimal places.

round(3.14159, 2)  # Output: 3.14 

17. isinstance()

Checks if an object is an instance of a particular class or type.

isinstance(5, int)  # Output: True 

18. eval()

Parses and evaluates a Python expression from a string.

eval("2 + 3")  # Output: 5 

Step 12: Python Virtual Environments

python -m venv env
source env/bin/activate   # Mac/Linux
env\Scripts\activate      # Windows
  

Step 13: Pip Package Installer

pip install numpy
pip uninstall pandas
pip list
  

Step 14: Popular Python Libraries

Step 15: Debugging Tips

• Use print statements to debug
• Use the pdb module: import pdb; pdb.set_trace()
• Use IDEs like VS Code or PyCharm for built-in debuggers

Conclusion

This detailed Python cheat sheet covers everything from syntax and data types to functions, loops, OOP, and more. Whether you are a beginner or brushing up your skills, keeping this cheat sheet handy will make you a more confident and efficient Python developer.

Python is a versatile and beginner-friendly programming language with a wide range of features and capabilities. From basic syntax, variables, and control structures to powerful concepts like list comprehensions, exception handling, and popular libraries, Python provides all the tools necessary for rapid development and problem-solving. Whether you're scripting small tasks, analyzing data, or building full-scale web applications, Python has you covered.  Also if you want to learn free try our Free Python Programming Course its totally worth it. Keep experimenting, keep coding, and enjoy your Python journey!

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