Python Cheatsheet - Essential syntax, structures, and patterns for modern Python development

Oct 13, 2026

Python Cheatsheet is a concise quick-reference covering essential Python concepts from core syntax to advanced patterns. It explains operator precedence, fundamental data types (int, float, str, bool), and common data structures like lists, tuples, dictionaries, and sets, including typical operations. It also covers function definitions and lambda functions, control flow with conditionals and loops, comprehensions, and modern string formatting with f-strings and number formatting, plus practical examples of usage.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages1

AccessFree to download

Core Syntax

Basics: Operators & Data Types

Functions: Definition & Lambda

Lists & Tuples

Dictionaries & Sets

Control Flow

Conditionals

For Loops

While Loops

Advanced Patterns

Comprehensions

String Formatting

String Manipulation

[labex.io](labex.io) Python Cheatsheet Essential syntax, structures, and patterns for modern Python development This cheatsheet provides a quick reference to fundamental Python concepts, syntax, and advanced features, ideal for both beginners and experienced developers. Core Syntax Operators, data types, variables Functions & Control Definitions, parameters, flow control Data Structures Lists, dicts, sets, tuples Advanced Patterns OOP, decorators, comprehensions File & Package Management I/O operations, virtual environments Basics: Operators & Data Types Operator Precedence Operators determine how values are combined. Precedence dictates the order of operations. ** > % // / * > - + \# 20 (multiplication before addition) 2 + 3 * 6 \# 30 (parentheses override precedence)(2 + 3) * 6 \# 256 (exponentiation) 2 ** 8 Core Data Types Python's fundamental data types define the kind of values variables can hold. \# int (integer, whole number) age = 25 \# float (floating-point number, decimal) price = 19.99 \# str (string, sequence of characters) name = "Alice" \# bool (boolean, True or False) is_student = True \# list (ordered, mutable collection) scores = [85, 92, 78] \# dict (dictionary, key-value pairs) person = {'name': 'Bob'} Functions: Definition & Lambda Functions allow you to encapsulate reusable blocks of code. \# Define a function to print a greeting def say_hello(name): print(f'Hello {name}') \# Call the function with an argument say_hello('Carlos') \# Functions can return values def sum_two_numbers(a, b): \# Returns the sum return a + b \# Lambda functions are small, anonymous functions for simple expressions. \# Defines a lambda function that takes x, y and returns their sum add = lambda x, y: x + y add(5, 3) \# 8 Lists & Tuples Lists (Mutable) Lists are ordered collections of items that can be changed after creation. furniture = ['table', 'chair'] \# 'table' (access item by index) furniture[0] \# slice (get a sub-list) furniture[1:3] \# Add item to end furniture.append('bed') \# Remove specific item furniture.remove('chair') \# Sort items in place furniture.sort() Tuples (Immutable) Tuples are ordered collections similar to lists, but they cannot be changed after creation. coords = (10, 20) \# 10 (access item by index) coords[0] \# Cannot modify once created \# coords[0] = 15 \# Error! (attempting to change an immutable tuple) Dictionaries: Key-Value Pairs Dictionaries store data in unordered key-value pairs, allowing efficient lookup by key. my_cat = {'size': 'fat','color': 'gray', 'disposition': 'loud'} \# Add a new key-value pair or modify an existing one my_cat['age_years'] = 2 \# Iterate over key-value pairs in the dictionary for key, value in my_cat.items(): print(f'{key}: {value}') \# Safe retrieval: Use .get() to avoid KeyError if the key doesn't exist, providing a default value instead. my_cat.get('breed', 'unknown') Sets: Unique Collections Sets are unordered collections of unique items, useful for membership testing and eliminating duplicates. Create Sets Sets can be created from a list or by directly listing elements. \# Directly create a sets = {1, 2, 3} \# Convert a list to a sets = set([1, 2, 3]) Set Operations Perform mathematical set operations like union, intersection, and difference. s1 = {1, 2, 3} s2 = {3, 4, 5} \# {1,2,3,4,5} (all unique elements from both sets) s1.union(s2) \# {3} (elements common to both sets) s1.intersection(s2) \# {1,2} (elements in s1 but not in s2) s1.difference(s2) Control Flow Control flow statements determine the order in which code instructions are executed. 1 2 3 Conditionals Use `if`, `elif` (else if), and `else` for decision-making logic. if name == 'Debora': \# Executes if name is 'Debora'print('Hi!') elif name == 'George': \# Executes if name is 'George'print('Hello!') else: \# Executes if neither of the

FAQs

What does operator precedence mean in Python?

Operator precedence determines the order in which expressions are evaluated when multiple operators appear in a statement.

How are lists and tuples different in Python?

Lists are mutable and can be changed after creation, while tuples are immutable and cannot be modified once created.

What are f-strings used for in Python?

F-strings provide a readable way to embed variables and expressions directly inside string literals, including flexible number formatting.

Oct 13, 2026

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