Docs

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 the fundamentals of web applications and APIs, their differences, architectures, and how they enable communication between software components. It covers web app structure, API roles, and practical examples for modern development.

This document explains web scraping using Python, covering HTTP requests, HTML parsing, data extraction, and best practices with requests, BeautifulSoup, and pandas.

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.

This document provides a concise overview of Python file handling, Pandas for data analysis, and Numpy for numerical operations, summarizing their practical applications and key concepts for effective Python data science development.

This document explains how to create, index, slice, and perform operations on 2D Numpy arrays, including matrix addition, scalar multiplication, Hadamard product, and matrix multiplication.

This document introduces Numpy for scientific computing, covering array creation, indexing, slicing, vector operations, universal functions, and plotting. Key concepts include speed, memory efficiency, and practical data science applications.

This document explains how to analyze, filter, and save data using Pandas focusing on finding unique values, filtering rows by conditions, and exporting results to CSV and other formats.

This document introduces the Pandas library for data analysis, covering its import, usage for reading files, creating DataFrames, and accessing data efficiently. Key concepts include working with CSV and Excel files, DataFrame operations, and indexing methods.

This document explains how to write to files in Python using the open function, file objects, writing methods, appending, and best practices for file creation and data output.

This document explains how to read files in Python using the open function file objects, reading methods, and best practices for file handling and data extraction.

This document provides a concise overview of Python conditions, branching loops, functions, exception handling, and object-oriented programming summarizing their practical applications and key concepts for effective Python development.

This document explains Python objects and classes, including data types attributes, methods, class construction, and practical examples for object-oriented programming.

This document explains Python exception handling, including try, except, else and finally statements, with practical examples for robust error management and program control.

This document explains Python functions, including built-in and user-defined functions, their syntax, scope, parameters, and practical use cases for code reuse and data processing.