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Python Cheatsheet | PariksaHub

Python Cheatsheet

Comprehensive Python programming reference guide covering syntax, data structures, functions, OOP, and more. Essential Python commands and examples for quick reference.

Programming

Contents21 sections

Variables and Data Types

Python variable assignment and basic data types

basics

Python is dynamically typed. You don't need to declare variable types.

python

# Variable assignment
name = "Python"
age = 30
price = 99.99
is_active = True

# Multiple assignment
x, y, z = 1, 2, 3

# Type checking
type(name)  # <class 'str'>
type(age)   # <class 'int'>

Strings

String operations and methods

basics

Python strings are immutable and support various operations including slicing, formatting, and methods.

python

# String basics
s = "Hello World"
len(s)              # 11
s[0]                # 'H'
s[-1]               # 'd'

Lists and List Comprehensions

List operations, methods, and comprehensions

data-structures

Lists are mutable ordered collections. List comprehensions provide a concise way to create lists.

python

# List basics
my_list = [1, 2, 3, 4, 5]
my_list.append(6)

Dictionaries

Dictionary operations and methods

data-structures

Dictionaries are key-value pairs. Keys must be immutable types (strings, numbers, tuples).

python

# Dictionary basics
person = {
    "name": "John",
    "age": 30,
    "city": "NYC"

Tuples and Sets

Tuple and set operations

data-structures

python

# Tuples (immutable)
tuple1 = (1, 2, 3)
tuple2 = 1, 2, 3  # Parentheses optional
x, y,

Control Flow

Conditionals, loops, and control statements

basics

Python uses indentation for code blocks. Control flow includes if/elif/else, for/while loops, and break/continue statements.

python

# If/elif/else
age = 20
if age < 18:
    print("Minor")
elif age < 65:

Functions

Function definition, arguments, and advanced features

functions

Functions are first-class objects in Python. They support default arguments, *args, **kwargs, lambda expressions, and decorators.

python

# Basic function
def greet(name):
    return f"Hello, {name}"

# Default arguments
def

List/Dict/Set Comprehensions

Comprehensive guide to comprehensions

data-structures

List Comprehensions

Create lists concisely: [expression for item in iterable]

Dictionary Comprehensions

Create dictionaries: {key: value for item in iterable}

Set Comprehensions

Create sets: {expression for item in iterable}

Comprehensions are more Pythonic and often faster than loops for simple transformations.

File Operations

Reading and writing files

python

# Reading files
with open("file.txt", "r") as f:
    content = f.read()
    lines = f.readlines()

Object-Oriented Programming

Classes, inheritance, and OOP concepts

oop

Python supports OOP with classes, inheritance, encapsulation, and polymorphism. Special methods (dunder methods) allow operator overloading.

python

# Basic class
class Person:
    # Class attribute
    species = "Homo sapiens"
    
    # Constructor

Error Handling

Try/except blocks and exception handling

basics

python

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

# Multiple exceptions

Modules and Packages

Importing modules and creating packages

basics

Importing Modules

Use import module, from module import function, or import module as alias

Common Standard Library Modules

os - Operating system interface
sys - System-specific parameters
datetime - Date and time handling
json - JSON encoder/decoder
re - Regular expressions
collections - Specialized data structures
itertools - Iterator functions

Regular Expressions

Pattern matching with re module

text-processing

python

import re

# Basic patterns
pattern = r"\d+"  # One or more digits
text = "Price is 100 dollars"
match = re.search(pattern, text)

Generators and Iterators

Generator functions and iterator protocol

advanced

Generators are memory-efficient iterators created with yield. They produce values lazily, one at a time.

python

# Generator function
def countdown(n):
    while n > 0:
        yield n
        n -= 1

for

Decorators

Function decorators and their uses

advanced

python

# Simple decorator
def my_decorator(func):
    def wrapper():
        print("Before function call")
        func(

Context Managers

Using with statements and creating custom context managers

advanced

Using Context Managers

The with statement ensures proper resource cleanup. Files, database connections, and locks use context managers.

Creating Custom Context Managers

Use __enter__ and __exit__ methods, or the @contextmanager decorator from contextlib.

List Operations and Methods

Comprehensive list methods reference

data-structures

python

my_list = [1, 2, 3]

# Adding elements
my_list.append(4)           # [1, 2, 3, 4]
my_list.

Dictionary Operations

Comprehensive dictionary methods

data-structures

python

d = {"a": 1, "b": 2, "c": 3}

# Accessing
value

String Formatting

Different string formatting methods

basics

python

name = "John"
age = 30

# f-strings (Python 3.6+, recommended)
f"Hello, {name}"                    # 'Hello, John'
f"Age: {age +

Working with Collections

Advanced collection types

data-structures

collections Module

Counter - Count hashable objects
defaultdict - Dict with default factory
OrderedDict - Dict that remembers insertion order
deque - Double-ended queue
namedtuple - Tuple with named fields
ChainMap - Multiple dicts as single mapping

These specialized collections provide efficient solutions for common problems.

Working with Dates and Time

datetime module usage

python

from datetime import datetime, date, time, timedelta

# Current date/time
now = datetime.now()
today = date.today()

# Creating dates

Back

Python Cheatsheet

Comprehensive Python programming reference guide covering syntax, data structures, functions, OOP, and more. Essential Python commands and examples for quick reference.

Programming

Contents21 sections

Variables and Data Types

Python variable assignment and basic data types

basics

Python is dynamically typed. You don't need to declare variable types.

python

# Variable assignment
name = "Python"
age = 30
price = 99.99
is_active = True

# Multiple assignment
x, y, z = 1, 2, 3

# Type checking
type(name)  # <class 'str'>
type(age)   # <class 'int'>

Strings

String operations and methods

basics

Python strings are immutable and support various operations including slicing, formatting, and methods.

python

# String basics
s = "Hello World"
len(s)              # 11
s[0]                # 'H'
s[-1]               # 'd'

Lists and List Comprehensions

List operations, methods, and comprehensions

data-structures

Lists are mutable ordered collections. List comprehensions provide a concise way to create lists.

python

# List basics
my_list = [1, 2, 3, 4, 5]
my_list.append(6)

Dictionaries

Dictionary operations and methods

data-structures

Dictionaries are key-value pairs. Keys must be immutable types (strings, numbers, tuples).

python

# Dictionary basics
person = {
    "name": "John",
    "age": 30,
    "city": "NYC"

Tuples and Sets

Tuple and set operations

data-structures

python

# Tuples (immutable)
tuple1 = (1, 2, 3)
tuple2 = 1, 2, 3  # Parentheses optional
x, y,

Control Flow

Conditionals, loops, and control statements

basics

Python uses indentation for code blocks. Control flow includes if/elif/else, for/while loops, and break/continue statements.

python

# If/elif/else
age = 20
if age < 18:
    print("Minor")
elif age < 65:

Functions

Function definition, arguments, and advanced features

functions

Functions are first-class objects in Python. They support default arguments, *args, **kwargs, lambda expressions, and decorators.

python

# Basic function
def greet(name):
    return f"Hello, {name}"

# Default arguments
def

List/Dict/Set Comprehensions

Comprehensive guide to comprehensions

data-structures

List Comprehensions

Create lists concisely: [expression for item in iterable]

Dictionary Comprehensions

Create dictionaries: {key: value for item in iterable}

Set Comprehensions

Create sets: {expression for item in iterable}

Comprehensions are more Pythonic and often faster than loops for simple transformations.

File Operations

Reading and writing files

python

# Reading files
with open("file.txt", "r") as f:
    content = f.read()
    lines = f.readlines()

Object-Oriented Programming

Classes, inheritance, and OOP concepts

oop

Python supports OOP with classes, inheritance, encapsulation, and polymorphism. Special methods (dunder methods) allow operator overloading.

python

# Basic class
class Person:
    # Class attribute
    species = "Homo sapiens"
    
    # Constructor

Error Handling

Try/except blocks and exception handling

basics

python

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

# Multiple exceptions

Modules and Packages

Importing modules and creating packages

basics

Importing Modules

Use import module, from module import function, or import module as alias

Common Standard Library Modules

os - Operating system interface
sys - System-specific parameters
datetime - Date and time handling
json - JSON encoder/decoder
re - Regular expressions
collections - Specialized data structures
itertools - Iterator functions

Regular Expressions

Pattern matching with re module

text-processing

python

import re

# Basic patterns
pattern = r"\d+"  # One or more digits
text = "Price is 100 dollars"
match = re.search(pattern, text)

Generators and Iterators

Generator functions and iterator protocol

advanced

Generators are memory-efficient iterators created with yield. They produce values lazily, one at a time.

python

# Generator function
def countdown(n):
    while n > 0:
        yield n
        n -= 1

for

Decorators

Function decorators and their uses

advanced

python

# Simple decorator
def my_decorator(func):
    def wrapper():
        print("Before function call")
        func(

Context Managers

Using with statements and creating custom context managers

advanced

Using Context Managers

The with statement ensures proper resource cleanup. Files, database connections, and locks use context managers.

Creating Custom Context Managers

Use __enter__ and __exit__ methods, or the @contextmanager decorator from contextlib.

List Operations and Methods

Comprehensive list methods reference

data-structures

python

my_list = [1, 2, 3]

# Adding elements
my_list.append(4)           # [1, 2, 3, 4]
my_list.

Dictionary Operations

Comprehensive dictionary methods

data-structures

python

d = {"a": 1, "b": 2, "c": 3}

# Accessing
value

String Formatting

Different string formatting methods

basics

python

name = "John"
age = 30

# f-strings (Python 3.6+, recommended)
f"Hello, {name}"                    # 'Hello, John'
f"Age: {age +

Working with Collections

Advanced collection types

data-structures

collections Module

Counter - Count hashable objects
defaultdict - Dict with default factory
OrderedDict - Dict that remembers insertion order
deque - Double-ended queue
namedtuple - Tuple with named fields
ChainMap - Multiple dicts as single mapping

These specialized collections provide efficient solutions for common problems.

Working with Dates and Time

datetime module usage

python

from datetime import datetime, date, time, timedelta

# Current date/time
now = datetime.now()
today = date.today()

# Creating dates