{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[12,2,12,3],[13,2,13,3],[15,2,15,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910915667303321"]
[null,null,"Autore",null,"Campesato Oswald"]
[null,null,"Titolo",null,"Python 3 Using ChatGPT / GPT-4"]
[null,null,"Pubbl/distr/stampa",null,"Bloomfield : , : Mercury Learning & Information, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9781501518737"]
[{"$nulls":4},"1501518739"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (202 pages)"]
[null,null,"Collana",null,"MLI Generative AI Series"]
[null,null,"Soggetti",null,"Python (Computer program language)"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python 3 -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multilines -- Quotation and Comments in Python -- Saving Your Code in a Module -- Some Standard Modules in Python -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types in Python -- Working With Numbers -- Working With Other Bases -- The chr() Function -- The round() Function in Python -- Formatting Numbers in Python -- Working With Fractions -- Unicode and UTF-8 -- Working With Unicode -- Working With Strings -- Comparing Strings -- Formatting Strings in Python -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working With Dates -- Converting Strings to Dates -- Exception Handling in Python -- Handling User Input -- Command-Line Arguments -- Summary -- Chapter 2: Conditional Logic, Loops, and Functions -- Precedence of Operators in Python -- Python Reserved Words -- Working with Loops in Python -- Python for Loops -- A for Loop with try/except in Python -- Numeric Exponents in Python -- Nested Loops -- The split() Function With for Loops -- Using the split() Function to Compare Words "]
[{"$nulls":4},"-- Using the split() Function to Print Justified Text -- Using the split() Function to Print Fixed-Width Text -- Using the split() Function to Compare Text Strings -- Using the split() Function to Display Characters in a String -- The join() Function."]
[{"$nulls":4},"Python while Loops -- Conditional Logic in Python -- The break/continue/pass Statements -- Comparison and Boolean Operators -- The in/not in/is/is not Comparison Operators -- The and, or, and not Boolean Operators -- Local and Global Variables -- Uninitialized Variables and the Value None -- Scope of Variables -- Pass by Reference Versus Value -- Arguments and Parameters -- Using a while loop to Find the Divisors of a Number -- Using a while loop to Find Prime Numbers -- User-Defined Functions in Python -- Specifying Default Values in a Function -- Returning Multiple Values From a Function -- Functions With a Variable Number of Arguments -- Lambda Expressions -- Recursion -- Calculating Factorial Values -- Calculating Fibonacci Numbers -- Calculating the GCD of Two Numbers -- Calculating the LCM of Two Numbers -- Summary -- Chapter 3: Python Data Structures -- Working With Lists -- Lists and Basic Operations -- Reversing and Sorting a List -- Lists and Arithmetic Operations -- Lists and Filter-Related Operations -- Sorting Lists of Numbers and Strings -- Expressions in Lists -- Concatenating a List of Words -- The BubbleSort in Python -- The Python range() Function -- Counting Digits, Uppercase, and Lowercase Letters -- Arrays and the append() Function -- Working with Lists and the split() Function -- Counting Words in a List -- Iterating Through Pairs of Lists -- Other List-Related Functions -- Using a List as a Stack and a Queue -- Working With Vectors -- Working With Matrices -- The NumPy Library for Matrices -- Queues -- Tuples (Immutable Lists) -- Sets -- Dictionaries -- Creating a Dictionary -- Displaying the Contents of a Dictionary -- Checking for Keys in a Dictionary -- Deleting Keys From a Dictionary -- Iterating Through a Dictionary -- Interpolating Data From a Dictionary -- Dictionary Functions and Methods -- Dictionary Formatting."]
[{"$nulls":4},"Ordered Dictionaries -- Sorting Dictionaries -- Python Multidictionaries -- Other Sequence Types in Python -- Mutable and Immutable Types in Python -- The type() Function -- Summary -- Chapter 4: Introduction to NumPy and Pandas -- What Is NumPy? -- Useful NumPy Features -- What Are NumPy arrays? -- Working With Loops -- Appending Elements to Arrays (1) -- Appending Elements to Arrays (2) -- Multiply Lists and Arrays -- Doubling the Elements in a List -- Lists and Exponents -- Arrays and Exponents -- Math Operations and Arrays -- Working With \"-1\" Subranges With Vectors -- Working With \"-1\" Subranges With Arrays -- Other Useful NumPy Methods -- Arrays and Vector Operations -- NumPy and Dot Products (1) -- NumPy and Dot Products (2) -- NumPy and the \"Norm\" of Vectors -- NumPy and Other Operations -- NumPy and the reshape() Method -- Calculating the Mean and Standard Deviation -- Calculating Mean and Standard Deviation -- What Is Pandas? -- Pandas DataFrames -- Dataframes and Data Cleaning Tasks -- A Labeled Pandas DataFrame -- Pandas Numeric DataFrames -- Pandas Boolean DataFrames -- Transposing a Pandas DataFrame -- Pandas DataFrames and Random Numbers -- Combining Pandas DataFrames (1) -- Combining Pandas DataFrames (2) -- Data Manipulation With Pandas DataFrames (1) -- Data Manipulation With Pandas DataFrames (2) -- Data Manipulation With Pandas DataFrames (3) -- Pandas DataFrames and CSV Files -- Pandas DataFrames and Excel Spreadsheets -- Select, Add, and Delete Columns in DataFrames -- Pandas DataFrames and Scatterplots -- Pandas DataFrames and Simple Statistics -- Useful One-Line "]
[{"$nulls":4},"Commands in Pandas -- Summary -- Chapter 5: ChatGPT and GPT-4 -- What Is Generative AI? -- Key Features of Generative AI -- Popular Techniques in Generative AI -- What Makes Generative AI Different -- Conversational AI Versus Generative AI -- Primary Objective."]
[{"$nulls":4},"Applications -- Technologies Used -- Training and Interaction -- Evaluation -- Data Requirements -- Is DALL-E Part of Generative AI? -- Are ChatGPT-3 and GPT-4 Part of Generative AI? -- DeepMind -- DeepMind and Games -- Player of Games (PoG) -- OpenAI -- Cohere -- Hugging Face -- Hugging Face Libraries -- Hugging Face Model Hub -- AI21 -- InflectionAI -- Anthropic -- What Is Prompt Engineering? -- Prompts and Completions -- Types of Prompts -- Instruction Prompts -- Reverse Prompts -- System Prompts Versus Agent Prompts -- Prompt Templates -- Prompts for Different LLMs -- Poorly Worded Prompts -- What Is ChatGPT? -- ChatGPT: GPT-3 \"on Steroids\"? -- ChatGPT: Google \"Code Red\" -- ChatGPT Versus Google Search -- ChatGPT Custom Instructions -- ChatGPT on Mobile Devices and Browsers -- ChatGPT and Prompts -- GPTBot -- ChatGPT Playground -- Plugins, Advanced Data Analysis, and Code Whisperer -- Plugins -- Advanced Data Analysis -- Advanced Data Analysis Versus Claude-2 -- Advanced Data Analysis and Charts and Graphs -- Code Whisperer -- Detecting Generated Text -- Concerns About ChatGPT -- Code Generation and Dangerous Topics -- ChatGPT Strengths and Weaknesses -- Sample Queries and Responses From ChatGPT -- Alternatives to ChatGPT -- Google Bard -- YouChat -- Pi From Inflection -- What Is InstructGPT? -- VizGPT and Data Visualization -- What Is GPT-4? -- GPT-4 and Test-Taking Scores -- GPT-4 Parameters -- GPT-4 Fine Tuning -- ChatGPT and GPT-4 Competitors -- Bard -- CoPilot (OpenAI/Microsoft) -- Codex (OpenAI) -- Apple GPT -- PaLM-2 -- Med-PaLM M -- Claude 2 -- LlaMa-2 -- How to Download LlaMa-2 -- LlaMa-2 Architecture Features -- Fine Tuning LlaMa-2 -- When Is GPT-5 Available? -- Summary -- Chapter 6: ChatGPT and Python Code -- Simple Calculator -- Simple File Handling -- Simple Web Scraping -- Basic Chat Bot -- Basic Data Visualization -- Basic Pandas."]
[{"$nulls":4},"Generate Random Data -- Recursion: Fibonacci Numbers -- Object-Oriented Programming -- Asynchronous Programming With asyncio -- Working With Requests in Python -- Image Processing With PIL -- Exception Handling -- Generators in Python -- Roll 7 or 11 With Two Dice -- Roll 7 or 11 With Three Dice -- Roll 7 or 11 With Four Dice -- Mean and Standard Deviation -- Summary -- Index."]
[null,null,null,"Sommario/riassunto","This book provides a comprehensive guide to using Python 3 in conjunction with ChatGPT and GPT-4, focusing on practical applications and programming techniques. It covers Python installation, basic syntax, data structures, and advanced topics such as NumPy and Pandas. The book aims to equip readers with the skills to effectively utilize Python for various computational tasks, including data analysis and algorithm development. It is intended for programmers, data scientists, and students looking to enhance their Python programming abilities, with a particular emphasis on integrating machine learning models into their workflows."]