Python 3 Data Visualization Using ChatGPT / GPT-4

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