{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":3,"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":47,"n_cols":5,"rows_kept":47,"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],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[11,4,11,5],[12,2,12,3],[12,4,12,5],[13,2,13,3],[14,2,14,3],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[22,2,22,3],[22,4,22,5],[23,2,23,3],[23,4,23,5],[24,2,24,3],[24,4,24,5],[25,2,25,3],[27,2,27,3],[29,2,29,3],[30,2,30,3],[31,2,31,3],[32,2,32,3],[34,2,34,3],[35,2,35,3],[37,2,37,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNICAMPANIASUN0111534"]
[null,null,"Autore",null,"Croce, Benedetto <1866-1952>"]
[null,null,"Titolo",null,"1: Vita, pensiero, letteratura / Benedetto Croce"]
[null,null,"Pubbl/distr/stampa",null,"VIII, 384 p. : 1 ritratto ; 22 cm"]
[null,null,"Edizione",null,"[Napoli : Riccardo Ricciardi, 1948]"]
[null,null,"Descrizione fisica",null,"Data in copertina: 1949"]
[null,null,"Disciplina",null,"809"]
[null,null,"Soggetti",null,"Letteratura italiana - Saggi"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA990001319320203316"]
[null,null,"Titolo",null,"vol. 1. : Collezione di tutti i poemi in lingua napoletana / introduzione ala ristampa di Luca Torre"]
[null,null,"Pubbl/distr/stampa",null,"Napoli : Centro editoriale del Mezzogiorno, 1976"]
[null,null,"Descrizione fisica",null,"XII, 263 p. ; 26 cm"]
[null,null,"Disciplina",null,"850.8"]
[null,null,"Soggetti",null,"Letteratura italiana - Autori napoletani - Collezioni"]
[null,null,"Collocazione",null,"XV.1.C. 347/1(V B 765/1)"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Ripr. facs. dell'ed. : Napoli presso Giuseppe Maria Porcelli"]
[null,"3.","Record Nr.",null,"UNINA9910915670003321"]
[null,null,"Autore",null,"Campesato Oswald"]
[null,null,"Titolo",null,"Python 3 Data Visualization Using ChatGPT / GPT-4"]
[null,null,"Pubbl/distr/stampa",null,"Bloomfield : , : Mercury Learning & Information, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9781501518805"]
[{"$nulls":4},"1501518801"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (314 pages)"]
[null,null,"Collana",null,"MLI Generative AI Series"]
[null,null,"Soggetti",null,"Python (Computer program language)"]
[{"$nulls":4},"Information visualization"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-Line Comments -- Quotations and Comments in Python -- Saving Your Code in a Module -- Some Standard Modules in Python -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling in Python -- Handling User Input -- Command-Line Arguments -- Summary -- Chapter 2: Introduction to NumPy -- What is NumPy? -- Useful NumPy Features -- What are NumPy Arrays? -- Working with Loops -- Appending Elements to Arrays (1) -- Appending Elements to Arrays (2) -- Multiplying Lists and Arrays -- Doubling the Elements in a List -- Lists and Exponents -- Arrays and Exponents -- Math Operations and Arrays -- Working "]
[{"$nulls":4},"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 Length of Vectors -- NumPy and Other Operations."]
[{"$nulls":4},"NumPy and the reshape() Method -- Calculating the Mean and Standard Deviation -- Code Sample with Mean and Standard Deviation -- Trimmed Mean and Weighted Mean -- Working with Lines in the Plane (Optional) -- Plotting Randomized Points with NumPy and Matplotlib -- Plotting a Quadratic with NumPy and Matplotlib -- What is Linear Regression? -- What is Multivariate Analysis? -- What about Non-Linear Datasets? -- The MSE (Mean Squared Error) Formula -- Other Error Types -- Non-Linear Least Squares -- Calculating the MSE Manually -- Find the Best-Fitting Line in NumPy -- Calculating the MSE by Successive Approximation (1) -- Calculating the MSE by Successive Approximation (2) -- Google Colaboratory -- Uploading CSV Files in Google Colaboratory -- Summary -- Chapter 3: Pandas and Data Visualization -- What Is Pandas? -- Pandas DataFrames -- Dataframes and Data Cleaning Tasks -- A Pandas DataFrame Example -- Describing a Pandas DataFrame -- Pandas Boolean DataFrames -- Transposing a Pandas DataFrame -- Pandas DataFrames and Random Numbers -- Converting Categorical Data to Numeric Data -- Matching and Splitting Strings in Pandas -- Merging and Splitting Columns in Pandas -- Combining Pandas DataFrames -- Data Manipulation With Pandas DataFrames -- Data Manipulation With Pandas DataFrames (2) -- Data Manipulation With Pandas DataFrames (3) -- Pandas DataFrames and CSV Files -- Pandas DataFrames and Excel Spreadsheets -- Select, Add, and Delete Columns in DataFrames -- Handling Outliers in Pandas -- Pandas DataFrames and Scatterplots -- Pandas DataFrames and Simple Statistics -- Finding Duplicate Rows in Pandas -- Finding Missing Values in Pandas -- Sorting DataFrames in Pandas -- Working With groupby() in Pandas -- Aggregate Operations With the titanic.csv Dataset -- Working with apply() and mapapply() in Pandas -- Useful One-Line Commands in Pandas."]
[{"$nulls":4},"What is Texthero? -- Data Visualization in Pandas -- Summary -- Chapter 4: Pandas and SQL -- Pandas and Data Visualization -- Pandas and Bar Charts -- Pandas and Horizontally Stacked Bar Charts -- Pandas and Vertically Stacked Bar Charts -- Pandas and Nonstacked Area Charts -- Pandas and Stacked Area Charts -- What Is Fugue? -- MySQL, SQLAlchemy, and Pandas -- What Is SQLAlchemy? -- Read MySQL Data via SQLAlchemy -- Export SQL Data From Pandas to Excel -- MySQL and Connector/Python -- Establishing a Database Connection -- Reading Data From a Database Table -- Creating a Database Table -- Writing Pandas Data to a MySQL Table -- Read XML Data in Pandas -- Read JSON Data in Pandas -- Working WithJSON-Based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Pandas and Regular Expressions (Optional) -- What Is SQLite? -- SQLite Features -- SQLite Installation -- Create a Database and a Table -- Insert, Select, and Delete Table Data -- Launch SQL Files -- Drop Tables and Databases -- Load CSV Data Into a sqlite Table -- Python and SQLite -- Connect to a sqlite3 Database -- Create a Table in a sqlite3 Database -- Insert Data in a sqlite3 Table -- Select Data From a sqlite3 Table -- Populate a Pandas Dataframe From a sqlite3 Table -- Histogram With Data From a sqlite3 Table (1) -- Histogram With Data From a sqlite3 Table (2) -- Working With sqlite3 Tools -- SQLiteStudio Installation -- DB Browser for SQLite Installation -- SQLiteDict (Optional) -- Working With Beautiful Soup -- Parsing an HTML Web Page -- Beautiful Soup and Pandas -- Beautiful Soup and Live HTML "]
[{"$nulls":4},"Web Pages -- Summary -- Chapter 5: Matplotlib and Visualization -- What is Data Visualization? -- Types of Data Visualization -- What is Matplotlib? -- Matplotlib Styles -- Display Attribute Values -- Color Values in Matplotlib -- Cubed Numbers in Matplotlib."]
[{"$nulls":4},"Horizontal Lines in Matplotlib -- Slanted Lines in Matplotlib -- Parallel Slanted Lines in Matplotlib -- A Grid of Points in Matplotlib -- A Dotted Grid in Matplotlib -- Two Lines and a Legend in Matplotlib -- Loading Images in Matplotlib -- A Checkerboard in Matplotlib -- Randomized Data Points in Matplotlib -- A Set of Line Segments in Matplotlib -- Plotting Multiple Lines in Matplotlib -- Trigonometric Functions in Matplotlib -- A Histogram in Matplotlib -- Histogram with Data from a sqlite3 Table -- Plot Bar Charts in Matplotlib -- Plot a Pie Chart in Matplotlib -- Heat Maps in Matplotlib -- Save Plot as a PNG File -- Working with SweetViz -- Working with Skimpy -- 3D Charts in Matplotlib -- Plotting Financial Data with MPLFINANCE -- Charts and Graphs with Data from Sqlite3 -- Summary -- Chapter 6: Seaborn for Data Visualization -- Working With Seaborn -- Features of Seaborn -- Seaborn Dataset Names -- Seaborn Built-In Datasets -- The Iris Dataset in Seaborn -- The Titanic Dataset in Seaborn -- Extracting Data From Titanic Dataset in Seaborn (1) -- Extracting Data From Titanic Dataset in Seaborn (2) -- Visualizing a Pandas Dataset in Seaborn -- Seaborn Heat Maps -- Seaborn Pair Plots -- What Is Bokeh? -- Introduction to Scikit-Learn -- The Digits Dataset in Scikit-learn -- The Iris Dataset in Scikit-Learn -- Scikit-Learn, Pandas, and the Iris Dataset -- Advanced Topics in Seaborn -- Summary -- Chapter 7: ChatGPT and GPT-4 -- What is Generative AI? -- Important Features of Generative AI -- Popular Techniques in Generative AI -- What Makes Generative AI Unique -- Conversational AI Versus Generative AI -- Primary Objective -- 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."]
[{"$nulls":4},"Player of Games (PoG) -- OpenAI -- Cohere -- Hugging Face -- Hugging Face Libraries -- Hugging Face Model Hub -- AI21 -- InflectionAI -- Anthropic -- What is Prompt Engineering? -- Prompts and Completions -- Types of Prompts -- Instruction Prompts -- Reverse Prompts -- System Prompts Versus Agent Prompts -- Prompt Templates -- Prompts for Different LLMs -- Poorly Worded Prompts -- What is ChatGPT? -- ChatGPT: GPT-3 \"on Steroids\"? -- ChatGPT: Google \"Code Red\" -- ChatGPT Versus Google Search -- ChatGPT Custom Instructions -- ChatGPT on Mobile Devices and Browsers -- ChatGPT and Prompts -- GPTBot -- ChatGPT Playground -- Plugins, Code Interpreter, and Code Whisperer -- Plugins -- Advanced Data Analysis -- Advanced Data Analysis Versus Claude-2 -- Code Whisperer -- Detecting Generated Text -- Concerns About ChatGPT -- Code Generation and Dangerous Topics -- ChatGPT Strengths and Weaknesses -- Sample Queries and Responses from ChatGPT -- Chatgpt and Medical Diagnosis -- Alternatives to ChatGPT -- Google Bard -- YouChat -- Pi From Inflection -- Machine Learning and Chatgpt -- What is InstructGPT? -- VizGPT and Data Visualization -- What is GPT-4? -- GPT-4 and Test Scores -- GPT-4 Parameters -- GPT-4 Fine-Tuning -- ChatGPT and GPT-4 Competitors -- Bard -- CoPilot (OpenAI/Microsoft) -- Codex (OpenAI) -- Apple GPT -- PaLM-2 -- Med-PaLM M -- Claude-2 -- Llama-2 -- How to Download Llama-2 -- Llama-2 Architecture Features -- Fine-Tuning Llama-2 -- When Will GPT-5 Be Available? -- Summary -- Chapter 8: ChatGPT and Data Visualization -- Working with Charts and Graphs -- Bar Charts -- Pie Charts -- Line Graphs -- Heat Maps -- Histograms -- Box Plots -- "]
[{"$nulls":4},"Pareto Charts -- Radar Charts -- Treemaps -- Waterfall Charts -- Line Plots with Matplotlib -- A Pie Chart Using Matplotlib -- Box and Whisker Plots Using Matplotlib -- Time Series Visualization with Matplotlib."]
[{"$nulls":4},"Stacked Bar Charts with Matplotlib."]
[null,null,null,"Sommario/riassunto","This book provides a comprehensive guide to Python 3 data visualization using ChatGPT and GPT-4. It covers essential tools and techniques for Python programming, including installation, virtual environments, and standard modules. The book delves into the use of NumPy for mathematical operations and array manipulations, and Pandas for data manipulation and visualization tasks. Readers will learn to create and manage dataframes, handle missing values, and perform data cleaning. The book also explores integration with SQL databases and features libraries like Matplotlib for plotting. It aims to equip readers with practical skills for implementing data visualization solutions in Python, targeting programmers and data analysts seeking to enhance their data visualization capabilities."]