IBM-FSSD

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.

This document explains Python loops, including for and while loops, with practical examples using lists, tuples, and the range function. It covers loop syntax, iteration methods, and common use cases for data manipulation.

This document explains Python conditions and branching, including comparison operators, Boolean logic, if/else/elif statements, and practical examples for decision-making in code.

This document explains Python dictionaries, including keys, values, creation access, modification, deletion, and methods for managing key-value pairs.

This document explains Python expressions and variables, including arithmetic operations, assignment, variable naming, and practical usage for storing and manipulating values.

This document explains Python lists and tuples, including indexing, slicing mutability, concatenation, nesting, methods, and aliasing, with practical examples for data manipulation.

This document provides a comprehensive introduction to Jupyter, a freely available web application for interactive computing. It covers Jupyter's key features, advantages for data science, and practical guidance on operating notebooks, including cell management, working with multiple notebooks presenting results, and managing sessions.

This document explains Python strings, including indexing, slicing concatenation, replication, immutability, escape sequences, and string methods for manipulating character data.