Python

This document explores Flask's capabilities for large-scale web development covering extensibility, scaling considerations, modular development patterns real-world enterprise applications, and HTTP status code handling for production deployments.

This document introduces Flask, a Python micro web framework, covering its main features, installation process, built-in dependencies, popular community extensions, and key differences from Django.

This document covers Python implementations (CPython, PyPy, Jython) development environments, and IDE comparisons. It provides guidance on choosing and setting up the right tools for Python development and data science work.

This document covers additional debugging techniques including IDE breakpoints, Visual Studio Code debugger features, conditional breakpoints variable inspection, and comparing IDE debugging with command-line approaches. IDE-based debugging strategies.

This document demonstrates debugging Python exceptions using PDB debugger covering traceback analysis, KeyError investigation, and fixing UTF-8 BOM encoding issues in CSV files. Practical case study of database import script debugging.

This document provides a concise overview of Python APIs, data manipulation with Pandas, web scraping, HTTP methods, and file formats, summarizing their practical applications and key concepts for effective Python data science development.

This document explores common file formats used in data science, including their structure, advantages, and typical use cases for data storage and exchange.

This document explains how to install Flask, create and deploy a Python web application, and use Flask's features for CRUD operations and template rendering.

This document explains how to use dynamic routes in Flask, including passing parameters in URLs, calling external APIs, and validating parameter types for RESTful endpoints.

This document explains HTTP status codes, error handling in Flask, and how to return appropriate error responses from API endpoints, including application-level error handlers.

This document details the main features of the Flask web framework, its dependencies, installation, and key differences from Django. It covers Flask’s extensibility, built-in tools, and popular community extensions for web development.

This document explains the differences between Python libraries and frameworks, and introduces Flask as a web development framework. It covers core concepts, setup, and practical usage for building web applications.

This document explains Python modules, packages, and libraries, and provides step-by-step guidance on creating, verifying, and using Python packages for code organization and reuse.

This document explains the Flask request and response objects, their attributes, and how to handle HTTP methods, headers, query parameters, and custom responses in Flask web applications.

This document explains how to create and configure routes in Flask, return responses, manage application configuration, and structure Flask projects for maintainability. It covers decorators, JSON responses, environment variables and best practices for organizing code.

This document outlines Python style guidelines and coding conventions including PEP-8, naming standards, and static code analysis. It explains how to write readable, maintainable code and ensure compliance using automated tools.

This document introduces unit testing in Python, covering the process, naming conventions, test structure, and result interpretation. It explains how to build, execute, and review unit tests for reliable code quality.

This document explains how to use the Python Requests library for HTTP communication, covering GET and POST requests, query strings, request/response objects, and practical examples for web APIs.

This document explains the HTTP protocol, URL structure, request and response cycles, status codes, and HTTP methods, focusing on REST APIs and their role in web communication and data transfer.

This document introduces APIs, API libraries, and REST APIs in Python covering concepts, request/response cycles, and practical examples using PyCoinGecko and pandas for time series and candlestick charting.