Python 3 and Data Visualization Using ChatGPT /GPT-4
1st Edition
Published on 15. December 2023
296 pages
978-1-5015-1880-5 (ISBN)
Description
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 Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn''s rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It''s also perfect for educators seeking material for teaching advanced data visualization techniques.
FEATURES
- Explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories
- Contains detailed tutorials that guide you through the creation of complex visuals
- Tackles actual data scenarios and builds your expertise as you apply learned concepts to real datasets
- Features data manipulation and cleaning with Pandas to prepare flawless datasets ready for visualization
- Includes companion files with source code, data sets, and figures
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More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
US School Grade: College Graduate Student
File size
20,94 MB
ISBN-13
978-1-5015-1880-5 (9781501518805)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Python 3 and Data Visualization Using ChatGPT /GPT-4
Book
12/2023
1st Edition
De Gruyter
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Python 3 and Data Visualization Using ChatGPT /GPT-4
E-Book
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Content
- 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
- 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
- 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
- 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
- Stacked Bar Charts with Matplotlib
- Donut Charts Using Matplotlib
- 3D Surface Plots with Matplotlib
- Radial or Spider Charts with Matplotlib
- Matplotlib's Contour Plots
- Stream Plots for Vector Fields
- Quiver Plots for Vector Fields
- Polar Plots
- Bar Charts with Seaborn
- Scatterplots with a Regression Line Using Seaborn
- Heat Maps for Correlation Matrices with Seaborn
- Histograms with Seaborn
- Violin Plots with Seaborn
- Pair Plots Using Seaborn
- Facet Grids with Seaborn
- Hierarchical Clustering
- Swarm Plots
- Joint Plot for Bivariate Data
- Point Plots for Factorized Views
- Seaborn's KDE Plots for Density Estimations
- Seaborn's Ridge Plots
- Summary
- Index
