Comprehensive Python core repository including data structures, problem-solving, and real-world coding examples.
This folder contains fundamental Python concepts with definitions and examples.
variables.py: Variable declaration and scoping.input_output.py: User input and console output.comments.py: Single-line, multi-line, and docstrings.type_casting.py: Converting data types.operators.py: Arithmetic, comparison, logical, assignment, identity, and membership operators.hello_world.py: Your first Python program.external_lib.py: Using external libraries (e.g.,pyjokes)strings.py: String manipulation.lists.py: Mutable sequences.tuples.py: Immutable sequences.dictionaries.py: Key-value pairs.conditionals.py: if, elif, else statementsloops.py: for and while loops.
This folder covers intermediate Python topics including functions, OOP, and file handling.
functions.py: Defining and calling functions, default arguments.lambdas.py: Anonymous functions and their usage withfilter().file_handling.py: Reading from and writing to files.exception_handling.py: Using try-except-finally for error management.classes_objects.py: Basics of Object-Oriented Programming (OOP).modules_import.py: Importing and using built-in Python modules.
This folder explores advanced Python features like decorators, generators, and context managers.
decorators.py: Enhancing functions without modification.generators.py: Memory-efficient iteration usingyield.list_comprehensions.py: Concise syntax for list creation.context_managers.py: Managing resources withwithstatements.args_kwargs.py: Handling variable numbers of arguments.
This folder explores essential modules from Python's standard library.
collections_module.py: Specialized container datatypes likeCounterandnamedtuple.itertools_module.py: Tools for efficient iteration and looping.json_module.py: Parsing and creating JSON data.random_module.py: Generating random numbers and making random selections.threading_module.py: Basics of running concurrent operations using threads.
This folder covers fundamental data structures and common algorithms implemented in Python.
linked_list.py: Implementation of a Singly Linked List.stack_queue.py: Stack (LIFO) and Queue (FIFO) basics.binary_tree.py: Binary tree structure and inorder traversal.bubble_sort.py: Simple comparison-based sorting algorithm.binary_search.py: Efficient searching in sorted arrays.
This folder contains simple Python projects that demonstrate practical application of the concepts learned.
calculator.py: A basic arithmetic calculator.number_guessing.py: A simple number guessing game with random numbers.todo_cli.py: A command-line interface for managing a to-do list.password_generator.py: A tool to generate secure, random passwords.unit_converter.py: Converts between units like temperature and distance.rock_paper_scissors.py: A classic game against the computer.simple_atm.py: Simulates basic ATM transactions and balance management.
This folder contains more complex Python projects that integrate multiple concepts like dictionaries, regex, and simulated APIs.
url_shortener.py: Logic for shortening URLs using hashing.expense_tracker.py: Tracks and summarizes daily expenses.weather_app_sim.py: Simulates fetching weather data for cities.quiz_app.py: A multiple-choice quiz application.inventory_management.py: Track products, stock, and pricing.contact_book.py: Simple contact storage and search system.markdown_to_html.py: A basic regex-based Markdown to HTML converter.budget_planner.py: Helps plan monthly income and expenses.
This folder contains common programming problems and their solutions in Python.
palindrome_check.py: Checks if a string or number is a palindrome.factorial.py: Calculates factorial using recursive and iterative methods.fibonacci_series.py: Generates Fibonacci sequence up to n terms.prime_number.py: Checks if a number is prime.armstrong_number.py: Checks if a number is an Armstrong number.anagram_check.py: Checks if two strings are anagrams.reverse_string.py: Different ways to reverse a string in Python.
This folder contains common competitive programming problems and their solutions in Python.
two_sum.py: Finds indices of two numbers that add up to a target.valid_parentheses.py: Checks if a string of brackets is valid.merge_sorted_lists.py: Merges two sorted linked lists.max_subarray.py: Finds the contiguous subarray with the largest sum.climbing_stairs.py: Calculates ways to climb stairs.best_time_to_buy_sell_stock.py: Maximize stock profit.contains_duplicate.py: Checks if an array contains any duplicates.
This folder covers common software design patterns implemented in Python.
singleton.py: Ensures a class has only one instance.factory_method.py: Interface for creating objects in a superclass.observer_pattern.py: Subscription mechanism to notify multiple objects.strategy_pattern.py: Family of interchangeable algorithms.adapter_pattern.py: Allows incompatible interfaces to collaborate.command_pattern.py: Encapsulates a request as a standalone object.template_method.py: Skeleton of an algorithm in a base class.facade_pattern.py: Simplified interface to a complex set of classes.
This folder demonstrates how to interact with the web and automate system tasks.
basic_requests.py: Simulating HTTP GET requests.bs4_demo.py: Simulation of HTML parsing using BeautifulSoup.browser_automation_sim.py: Concepts of browser-level automation.os_operations.py: Navigating the file system and managing processes.shutil_demo.py: High-level file operations (copy, move).env_vars.py: Managing application environment variables.subprocess_basics.py: Spawning and communicating with system processes.
This folder covers essential techniques for managing, parsing, and verifying data.
csv_basics.py: Reading and writing tabular CSV data.json_advanced.py: Working with complex and nested JSON structures.sqlite_demo.py: Using Python's built-in SQL database engine.logging_basics.py: Implementing a robust event logging system.unit_testing_sim.py: Basics of testing code with theunittestframework.regex_patterns.py: Using regular expressions for advanced text matching.datetime_advanced.py: Advanced date and time manipulation.
This folder delves deep into the core concepts of Object-Oriented Programming (OOP) in Python.
classes_and_objects.py: Defining blueprints and creating instances.inheritance.py: Reusing code from parent classes.polymorphism.py: Using a single interface for entities of different types.encapsulation.py: Restricting access to methods and variables.abstraction.py: Hiding complex implementation details using abstract base classes.class_and_static_methods.py: Understanding@classmethodand@staticmethod.magic_dunder_methods.py: Customizing built-in behaviors with methods like__str__and__len__.multiple_inheritance.py: Inheriting from multiple parent classes.
This folder explores software architecture patterns and system design principles.
mvc_pattern.py: Model-View-Controller architecture.layer_architecture.py: Horizontal layering of application components.microservices_sim.py: Loose coupling and independent services.pub_sub_system.py: Messaging pattern for asynchronous communication.caching_strategy.py: Improving performance with temporary data storage.rate_limiting.py: Controlling request flow to prevent abuse.load_balancer_sim.py: Distributing traffic across multiple servers.database_sharding_sim.py: Partitioning data across multiple database instances.
This folder focuses on writing secure Python applications and preventing common vulnerabilities.
password_hashing.py: Securely storing user credentials with salts.jwt_token_sim.py: Token-based authentication and claim representation.sql_injection_prevention.py: Using parameterized queries to secure databases.input_validation.py: Ensuring data integrity and correctness.encryption_decryption.py: Basic data encoding and decoding techniques.secure_api_keys.py: Managing secrets using environment variables.cors_explanation.py: Understanding cross-origin resource sharing.rbac_model.py: Implementing role-based access control.
This folder introduces concepts related to modern software deployment and automation.
docker_basics_sim.py: Containerization basics and image building.github_actions_sim.py: Automating CI/CD workflows.
This folder covers the fundamentals of writing and running tests in Python using the Pytest framework.
test_basic.py: Simple unit tests and assertions.test_fixtures.py: Managing test setup and teardown with fixtures.test_parameterized.py: Running tests with multiple data sets.test_markers.py: Categorizing and skipping tests.test_mocking.py: Isolating code with mock objects.test_assertions.py: Using complex assertions for verification.test_exceptions.py: Testing error handling and exceptions.test_conftest_sim.py: Sharing configuration withconftest.py.test_plugins_sim.py: Extending Pytest with plugins.test_coverage_sim.py: Measuring code execution during tests.
This folder introduces concepts for deploying and managing Python applications in the cloud.
aws_lambda_sim.py: Serverless function execution.s3_bucket_sim.py: Object storage management.api_gateway_sim.py: Routing and managing API requests.docker_compose_sim.py: Managing multi-container applications.kubernetes_basics_sim.py: Orchestrating container deployments.serverless_framework_sim.py: Automating serverless infrastructure.cloud_watch_logging_sim.py: Monitoring and logging in the cloud.terraform_hcl_sim.py: Infrastructure as Code (IaC) basics.ec2_instance_management_sim.py: Managing virtual servers in the cloud.cloud_security_groups_sim.py: Implementing network security firewalls.
This folder introduces core concepts and libraries used in data science and machine learning.
numpy_basics_sim.py: Fundamental multi-dimensional array operations.pandas_dataframe_sim.py: Data analysis using DataFrame structures.matplotlib_plot_sim.py: Creating static and interactive visualizations.scikit_learn_sim.py: Basics of predictive data analysis and machine learning models.data_cleaning_concepts.py: Techniques for fixing and removing corrupted or duplicate data.
This folder covers the fundamentals of network communication and socket programming in Python.
tcp_server_sim.py: Establishing reliable network conversations via TCP.udp_socket_sim.py: Low-latency, connectionless communication via UDP.http_header_analysis.py: Parsing and analyzing HTTP request/response headers.port_scanner_sim.py: Probing hosts for open ports to verify security policies.ip_address_utils.py: Utilities for managing and resolving IP addresses and hostnames.
This folder covers the fundamentals and advanced concepts of building web backends with Python.
flask_basics.py: Introduction to the Flask micro-framework.django_concepts.py: Understanding the Django "batteries-included" framework.fast_api_intro.py: Building high-performance APIs with FastAPI.rest_api_principles.py: Core principles of RESTful API design.graphql_basics.py: Querying data with GraphQL.web_sockets_sim.py: Real-time full-duplex communication simulation.authentication_jwt.py: Stateless authentication using JSON Web Tokens.database_orm_sim.py: Interacting with databases using Object-Relational Mapping.middleware_concepts.py: Processing requests through a middleware chain.deployment_gunicorn.py: Running production-grade WSGI servers with Gunicorn.
This folder explores the powerful world of concurrency and non-blocking I/O in Python.
asyncio_basics.py: Foundations of theasynciolibrary and event loops.await_keyword.py: Pausing execution for asynchronous tasks.concurrent_tasks.py: Managing multiple coroutines simultaneously.async_iterators.py: Iterating over data fetched asynchronously.async_context_managers.py: Usingasync withfor resource management.event_loop_sim.py: Understanding the core mechanism of async execution.threading_vs_asyncio.py: Comparing different multitasking models in Python.multiprocessing_intro.py: Leveraging multiple CPUs with themultiprocessingmodule.async_http_requests.py: High-concurrency network requests with libraries likeaiohttp.race_conditions_sim.py: Understanding and preventing synchronization issues.