Python Cheat Sheet

A comprehensive Python reference guide with syntax, examples, and usage instructions. Find the Python concepts, functions, and commands you need using the search bar or browse by category.

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`Variables`

Basic Syntax

Assign values to variables in Python

Syntax:

variable_name = value

Examples:

name = 'John' String variable

age = 25 Integer variable

price = 19.99 Float variable

is_active = True Boolean variable

Notes:

Python is dynamically typed - no need to declare variable types

`Comments`

Basic Syntax

Add comments to Python code

Syntax:

# Single line comment
'''Multi-line comment'''

Examples:

# This is a single line comment Single line comment

'''
This is a
multi-line comment
'''

Multi-line comment using triple quotes

"""Another way to write
multi-line comments"""

Multi-line comment using double quotes

Notes:

Use comments to explain your code and make it more readable

`print()`

Basic Syntax

Output text to console

Syntax:

print(value1, value2, ...)

Examples:

print('Hello, World!') Print a string

print(42) Print a number

print('Name:', name, 'Age:', age) Print multiple values

print(f'Hello {name}, you are {age} years old') Print with f-string formatting

Notes:

print() automatically adds a newline. Use end='' to prevent this

`input()`

Basic Syntax

Get user input from console

Syntax:

input(prompt)

Examples:

name = input('Enter your name: ') Get string input

age = int(input('Enter your age: ')) Get integer input

price = float(input('Enter price: ')) Get float input

Notes:

input() always returns a string, convert to other types as needed

`int`

Data Types

Integer numbers in Python

Syntax:

int_var = number

Examples:

x = 42 Positive integer

y = -17 Negative integer

z = int('123') Convert string to integer

binary = 0b1010 # 10 in decimal Binary literal

Notes:

Python integers have unlimited precision

`float`

Data Types

Floating point numbers in Python

Syntax:

float_var = number.decimal

Examples:

pi = 3.14159 Float literal

scientific = 1.5e6 # 1,500,000 Scientific notation

result = float('3.14') Convert string to float

infinity = float('inf') Represent infinity

Notes:

Floats are double precision by default

`str`

Data Types

Text strings in Python

Syntax:

'string' or "string" or '''multi-line'''

Examples:

name = 'Alice' Single quotes

message = "Hello World" Double quotes

multiline = '''Line 1
Line 2
Line 3'''

Multi-line string

formatted = f'Hello {name}!' f-string formatting

Notes:

Strings are immutable in Python

`bool`

Data Types

Boolean values True and False

Syntax:

bool_var = True/False

Examples:

is_valid = True Boolean True

is_empty = False Boolean False

result = bool(1) # True Convert to boolean

check = 5 > 3 # True Boolean from comparison

Notes:

Only True and False (capitalized) are boolean values

`list`

Data Structures

Ordered, mutable collection of items

Syntax:

my_list = [item1, item2, item3]

Examples:

numbers = [1, 2, 3, 4, 5] List of integers

fruits = ['apple', 'banana', 'cherry'] List of strings

mixed = [1, 'hello', True, 3.14] Mixed data types

empty = [] Empty list

Notes:

Lists are ordered and allow duplicates. Use square brackets []

`List Methods`

Data Structures

Common methods for list manipulation

Syntax:

list.method()

Examples:

fruits.append('orange') Add item to end

fruits.insert(1, 'grape') Insert at specific position

fruits.remove('banana') Remove first occurrence

item = fruits.pop() Remove and return last item

Notes:

Lists are mutable - methods modify the original list

`dict`

Data Structures

Key-value pairs collection

Syntax:

my_dict = {'key1': 'value1', 'key2': 'value2'}

Examples:

person = {'name': 'John', 'age': 30} Dictionary with string and int values

colors = dict(red='#FF0000', blue='#0000FF') Dictionary using dict() constructor

empty = {} Empty dictionary

nested = {'person': {'name': 'Alice', 'age': 25}} Nested dictionary

Notes:

Dictionaries are unordered and keys must be immutable

`tuple`

Data Structures

Ordered, immutable collection of items

Syntax:

my_tuple = (item1, item2, item3)

Examples:

coordinates = (10, 20) Tuple of coordinates

colors = ('red', 'green', 'blue') Tuple of strings

single = (42,) Single item tuple (note the comma)

empty = () Empty tuple

Notes:

Tuples are immutable - cannot be changed after creation

`set`

Data Structures

Unordered collection of unique items

Syntax:

my_set = {item1, item2, item3}

Examples:

unique_numbers = {1, 2, 3, 4, 5} Set of unique numbers

letters = set('hello') # {'h', 'e', 'l', 'o'} Set from string

empty = set() Empty set (cannot use {})

fruits = {'apple', 'banana', 'apple'} # duplicates removed Automatic duplicate removal

Notes:

Sets automatically remove duplicates and are unordered

`if/elif/else`

Control Flow

Conditional statements for decision making

Syntax:

if condition:
    # code
elif condition:
    # code
else:
    # code

Examples:

if age >= 18:
    print('Adult')

Simple if statement

if score >= 90:
    grade = 'A'
elif score >= 80:
    grade = 'B'
else:
    grade = 'C'

if-elif-else chain

if x > 0 and x < 100:
    print('Valid range')

Multiple conditions with 'and'

result = 'positive' if x > 0 else 'non-positive' Ternary operator

Notes:

Python uses indentation to define code blocks - no braces needed

`for loop`

Control Flow

Iterate over sequences or ranges

Syntax:

for item in sequence:
    # code

Examples:

for i in range(5):
    print(i)

Loop through range 0-4

for fruit in fruits:
    print(fruit)

Loop through list items

for i, item in enumerate(fruits):
    print(f'{i}: {item}')

Loop with index using enumerate

for key, value in person.items():
    print(f'{key}: {value}')

Loop through dictionary items

Notes:

for loops work with any iterable object

`while loop`

Control Flow

Repeat code while condition is true

Syntax:

while condition:
    # code

Examples:

count = 0
while count < 5:
    print(count)
    count += 1

Basic while loop

while True:
    user_input = input('Enter quit to exit: ')
    if user_input == 'quit':
        break

Infinite loop with break

x = 10
while x > 0:
    print(x)
    x -= 1

Countdown loop

Notes:

Be careful to avoid infinite loops - ensure the condition eventually becomes false

`break/continue`

Control Flow

Control loop execution flow

Syntax:

break  # exit loop
continue  # skip to next iteration

Examples:

for i in range(10):
    if i == 5:
        break
    print(i)

Break out of loop at i=5

for i in range(10):
    if i % 2 == 0:
        continue
    print(i)

Skip even numbers

for i in range(3):
    for j in range(3):
        if j == 1:
            break
        print(i, j)

Break only affects inner loop

Notes:

break exits the loop completely, continue skips to next iteration

`def`

Functions

Define a function in Python

Syntax:

def function_name(parameters):
    # code
    return value

Examples:

def greet(name):
    return f'Hello, {name}!'

Simple function with parameter

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

Function with multiple parameters

def say_hello():
    print('Hello!')

Function with no parameters or return

def greet(name='World'):
    return f'Hello, {name}!'

Function with default parameter

Notes:

Functions must be defined before they are called

`lambda`

Functions

Create anonymous functions

Syntax:

lambda parameters: expression

Examples:

square = lambda x: x**2 Lambda function to square a number

add = lambda a, b: a + b Lambda function with multiple parameters

numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, numbers))

Using lambda with map()

even_numbers = list(filter(lambda x: x % 2 == 0, numbers)) Using lambda with filter()

Notes:

Lambda functions are limited to single expressions

`*args **kwargs`

Functions

Variable number of arguments

Syntax:

def func(*args, **kwargs):

Examples:

def sum_all(*args):
    return sum(args)

Accept any number of positional arguments

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

Accept any number of keyword arguments

def flexible_func(*args, **kwargs):
    print('Args:', args)
    print('Kwargs:', kwargs)

Accept both *args and **kwargs

flexible_func(1, 2, 3, name='Alice', age=30) Call with mixed arguments

Notes:

*args collects positional arguments, **kwargs collects keyword arguments

`class`

Classes

Define a class in Python

Syntax:

class ClassName:
    def __init__(self, parameters):
        # constructor

Examples:

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

Basic class definition

class Dog:
    species = 'Canis lupus'  # class variable
    
    def __init__(self, name):
        self.name = name  # instance variable

Class with class and instance variables

person = Person('Alice', 30) Create an instance of the class

Notes:

Use PascalCase for class names. __init__ is the constructor method

`Class Methods`

Classes

Methods inside a class

Syntax:

def method_name(self, parameters):
    # code

Examples:

class Calculator:
    def add(self, a, b):
        return a + b

Instance method

class Person:
    def __init__(self, name):
        self.name = name
    
    def introduce(self):
        return f'Hi, I am {self.name}'

Method using instance variable

class Circle:
    def __init__(self, radius):
        self.radius = radius
    
    def area(self):
        return 3.14159 * self.radius ** 2

Method performing calculation

Notes:

All instance methods must have 'self' as the first parameter

`Inheritance`

Classes

Create a class that inherits from another

Syntax:

class ChildClass(ParentClass):

Examples:

class Animal:
    def __init__(self, name):
        self.name = name
    
    def speak(self):
        pass

Parent class

class Dog(Animal):
    def speak(self):
        return f'{self.name} says Woof!'

Child class inheriting from Animal

class Cat(Animal):
    def speak(self):
        return f'{self.name} says Meow!'

Another child class

dog = Dog('Buddy')
print(dog.speak())  # Buddy says Woof!

Using inherited class

Notes:

Child classes inherit all methods and attributes from parent class

`len()`

Built-in Functions

Get the length of a sequence

Syntax:

len(sequence)

Examples:

len('hello') Length of string (5)

len([1, 2, 3, 4]) Length of list (4)

len({'a': 1, 'b': 2}) Length of dictionary (2)

len((1, 2, 3)) Length of tuple (3)

Notes:

Works with any sequence or collection type

`type()`

Built-in Functions

Get the type of an object

Syntax:

type(object)

Examples:

type(42) Returns <class 'int'>

type('hello') Returns <class 'str'>

type([1, 2, 3]) Returns <class 'list'>

type(3.14) Returns <class 'float'>

Notes:

Useful for debugging and type checking

`range()`

Built-in Functions

Generate a sequence of numbers

Syntax:

range(start, stop, step)

Examples:

list(range(5)) [0, 1, 2, 3, 4] - stop only

list(range(2, 8)) [2, 3, 4, 5, 6, 7] - start and stop

list(range(0, 10, 2)) [0, 2, 4, 6, 8] - with step

list(range(10, 0, -1)) [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] - reverse

Notes:

range() is commonly used in for loops

`List Comprehension`

Comprehensions

Create lists using compact syntax

Syntax:

[expression for item in iterable if condition]

Examples:

squares = [x**2 for x in range(5)] [0, 1, 4, 9, 16] - squares of 0-4

evens = [x for x in range(10) if x % 2 == 0] [0, 2, 4, 6, 8] - even numbers

words = ['hello', 'world', 'python']
lengths = [len(word) for word in words]

[5, 5, 6] - lengths of words

matrix = [[i*j for j in range(3)] for i in range(3)] Nested comprehension for 2D matrix

Notes:

More concise and often faster than traditional loops

`Dictionary Comprehension`

Comprehensions

Create dictionaries using compact syntax

Syntax:

{key_expr: value_expr for item in iterable if condition}

Examples:

squares = {x: x**2 for x in range(5)} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

word_lengths = {word: len(word) for word in ['cat', 'dog', 'bird']} {'cat': 3, 'dog': 3, 'bird': 4}

filtered = {k: v for k, v in original_dict.items() if v > 10} Filter dictionary by values

Notes:

Useful for transforming existing dictionaries

`try/except`

Exception Handling

Handle errors gracefully

Syntax:

try:
    # risky code
except ExceptionType:
    # handle error

Examples:

try:
    result = 10 / 0
except ZeroDivisionError:
    print('Cannot divide by zero')

Handle specific exception

try:
    age = int(input('Enter age: '))
except ValueError:
    print('Please enter a valid number')

Handle invalid input

try:
    file = open('data.txt')
except FileNotFoundError:
    print('File not found')
finally:
    print('This always runs')

Using finally block

Notes:

Use specific exception types when possible, avoid bare except:

`open()`

File Operations

Read and write files

Syntax:

open(filename, mode)

Examples:

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

Read entire file

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

Write to file

with open('data.txt', 'r') as f:
    lines = f.readlines()

Read all lines into list

with open('log.txt', 'a') as f:
    f.write('New log entry\n')

Append to file

Notes:

Use 'with' statement for automatic file closing

`os module`

Libraries

Operating system interface

Syntax:

import os

Examples:

import os
print(os.getcwd())

Get current working directory

os.listdir('.') List files in directory

os.path.exists('file.txt') Check if file exists

os.makedirs('new_folder', exist_ok=True) Create directory

Notes:

Essential for file system operations

`sys module`

Libraries

System-specific parameters and functions

Syntax:

import sys

Examples:

import sys
print(sys.version)

Python version information

sys.argv Command line arguments

sys.exit() Exit the program

sys.path.append('/custom/path') Add to Python path

Notes:

Useful for system information and program control

`json module`

Libraries

JSON data handling

Syntax:

import json

Examples:

import json
data = json.loads('{"name": "Alice", "age": 30}')

Parse JSON string

json_string = json.dumps({'name': 'Bob', 'age': 25}) Convert to JSON string

with open('data.json', 'r') as f:
    data = json.load(f)

Load JSON from file

with open('output.json', 'w') as f:
    json.dump(data, f, indent=2)

Save JSON to file

Notes:

Essential for API communication and data storage

`datetime module`

Libraries

Date and time handling

Syntax:

import datetime

Examples:

from datetime import datetime
now = datetime.now()

Current date and time

birthday = datetime(1990, 5, 15) Specific date

formatted = now.strftime('%Y-%m-%d %H:%M:%S') Format datetime as string

from datetime import timedelta
future = now + timedelta(days=30)

Add time duration

Notes:

Use for timestamps, scheduling, and time calculations

`String Manipulation`

String Methods

Common string operations

Syntax:

string.method()

Examples:

'hello world'.upper() 'HELLO WORLD' - convert to uppercase

'PYTHON'.lower() 'python' - convert to lowercase

' spaces '.strip() 'spaces' - remove whitespace

'apple,banana,cherry'.split(',') ['apple', 'banana', 'cherry'] - split string

Notes:

Strings are immutable - methods return new strings

`String Formatting`

String Methods

Format strings with variables

Syntax:

f'text {variable}' or 'text {}'.format(variable)

Examples:

name = 'Alice'
f'Hello {name}!'

f-string formatting (preferred)

'Hello {}!'.format(name) .format() method

f'{3.14159:.2f}' '3.14' - format float to 2 decimals

f'{42:>5}' ' 42' - right-align in 5 characters

Notes:

f-strings (Python 3.6+) are the most readable and efficient

Python Programming Tips

Best Practices

• Follow PEP 8 style guidelines for consistent, readable code
• Use meaningful variable and function names that describe their purpose
• Keep functions small and focused on a single task
• Use list comprehensions for simple transformations and filtering
• Handle exceptions appropriately with try/except blocks

Performance Tips

• Use built-in functions and libraries - they're optimized in C
• Choose appropriate data structures (list vs set vs dict)
• Use generators for memory-efficient iteration over large datasets
• Avoid global variables and prefer local scope when possible
• Use f-strings for string formatting - they're fastest and most readable

Common Python Patterns

Pythonic Patterns

• EAFP: Easier to Ask for Forgiveness than Permission
• Duck Typing: "If it walks like a duck, it's a duck"
• Context Managers: Use 'with' for resource management
• Enumerate: Use enumerate() instead of range(len())

Data Processing

• List Comprehensions: Concise data transformations
• Generator Expressions: Memory-efficient processing
• Zip Function: Combine multiple iterables
• Map/Filter/Reduce: Functional programming approach

Learning Python

Getting Started

• Install Python from python.org
• Set up IDE (PyCharm, VS Code, or IDLE)
• Learn basic syntax and data types
• Practice with simple scripts

Intermediate Topics

• Object-Oriented Programming
• File handling and I/O operations
• Error handling and debugging
• Working with APIs and JSON

Advanced Topics

• Decorators and metaclasses
• Async programming with asyncio
• Testing with unittest/pytest
• Package management with pip

Quick Reference Guide

Data Types Reference

Basic Data Types

Type	Example	Mutable	Description
int	42	No	Integer numbers
float	3.14	No	Floating point
str	'hello'	No	Text strings
bool	True	No	Boolean values
list	[1, 2, 3]	Yes	Ordered collection
dict	{'a': 1}	Yes	Key-value pairs
tuple	(1, 2, 3)	No	Immutable sequence
set	{1, 2, 3}	Yes	Unique items

Common Operations

Type Checking:

type(obj) - Get object type

isinstance(obj, type) - Check if object is of type

Type Conversion:

int(), float(), str(), bool() - Convert types

list(), tuple(), set(), dict() - Convert collections

Length and Size:

len(obj) - Get length of sequence/collection

sys.getsizeof(obj) - Get memory size

Common Built-in Functions

Data Processing

• len() - Get length
• sum() - Sum numbers
• min() - Find minimum
• max() - Find maximum
• sorted() - Sort sequence

Iteration

• range() - Generate numbers
• enumerate() - Add indices
• zip() - Combine sequences
• map() - Apply function
• filter() - Filter items

I/O & Utilities

• print() - Output text
• input() - Get user input
• open() - Open files
• type() - Get object type
• help() - Get documentation

Operators Reference

Arithmetic

+ (addition)

- (subtraction)

* (multiplication)

/ (division)

// (floor division)

% (modulo)

** (exponentiation)

Comparison

== (equal)

!= (not equal)

< (less than)

> (greater than)

<= (less or equal)

>= (greater or equal)

is (identity)

Logical

and (logical AND)

or (logical OR)

not (logical NOT)

in (membership)

not in (not member)