Python 3 and Data Visualization Using ChatGPT /GPT-4
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Pyth...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Herndon, VA
Mercury Learning & Information
2023
Mercury Learning and Information |
| Vydání: | 1 |
| Edice: | MLI Generative AI Series |
| Témata: | |
| ISBN: | 9781501522321, 1501522329 |
| On-line přístup: | Získat plný text |
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- 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 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
- 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
- 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 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
- 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 -- 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
- 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
- Stacked Bar Charts with Matplotlib
- Contents --
- Chapter 7: ChatGPT and GPT-4 --
- Chapter 8: ChatGPT and Data Visualization --
- Preface --
- Chapter 2: Introduction to NumPy --
- Frontmatter --
- Chapter 6: Seaborn for Data Visualization --
- Index
- Chapter 4: Pandas and SQL --
- Chapter 3: Pandas and Data Visualization --
- Chapter 5: Matplotlib and Visualization --
- Chapter 1: Introduction to Python --