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.
Basics
Comments
# This is a single-line comment
"""
This is a
multi-line comment
"""
Variables
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}"
Lists
my_list = [1, 2, 3, "four", 5.0]
Tuples
my_tuple = (1, 2, 3)
Dictionaries
my_dict = {"key": "value", "num": 42}
Conditions
if condition:
# code
elif another_condition:
# code
else:
# code
Loops
for item in my_list:
# code
while condition:
# code
Functions
def my_function(param1, param2):
# code
return result
Advanced Data Structures
Sets
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 = file.read()
Writing to a File
with open("filename.txt", "w") as file:
file.write("Hello, File!")
Exception Handling
try:
# 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)
obj.my_method()
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.
FAQ
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.