Python 3 Data Visualization Using ChatGPT / GPT-4 E-Book

Mercury Learning and Information

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 328

Veröffentlichungsjahr: 2024

Beschreibung

This book teaches Python 3 programming and data visualization, exploring cutting-edge techniques with ChatGPT/GPT-4 for generating compelling visuals. It starts with Python essentials, covering basic data types, loops, functions, and advanced constructs like dictionaries and matrices. The journey progresses to NumPy's array operations and data visualization using libraries such as Matplotlib and Seaborn. The book also covers tools like SVG graphics and D3 for dynamic visualizations.
The course begins with foundational Python concepts, moves into NumPy and data visualization with Pandas, Matplotlib, and Seaborn. Advanced chapters explore ChatGPT and GPT-4, demonstrating their use in creating data visualizations from datasets like the Titanic. Each chapter builds on the previous one, ensuring a comprehensive understanding of Python and visualization techniques.
These concepts are crucial for Python practitioners, data scientists, and anyone in data analytics. This book transitions readers from basic Python programming to advanced data visualization, blending theoretical knowledge with practical skills. Companion files with code, datasets, and figures enhance learning, making this an essential resource for mastering Python and data visualization.

Leseprobe

PYTHON 3DATA VISUALIZATIONUSINGCHATGPT / GPT-4

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Companion files for this title are available by writing to the publisher at [e-mail].

PYTHON 3DATA VISUALIZATIONUSINGCHATGPT / GPT-4

Oswald Campesato

MERCURY LEARNING AND INFORMATION

Boston, Massachusetts

Copyright ©2024 by MERCURY LEARNING AND INFORMATION. An Imprint of DeGruyter Inc. All rights reserved.

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O. Campesato. Python 3 Data Visualization Using ChatGPT / GPT -4.

ISBN: 978-1-50152-232-1

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I’d like to dedicate this book to my parents– may this bring joy and happiness into their lives.

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

PREFACE

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK?

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. 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 the world of 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). From foundational Python concepts to the intricacies of data visualization, this book serves as a comprehensive resource for both beginners and seasoned professionals.

THE TARGET AUDIENCE

This book is intended primarily for people who have worked with Python and are interested in learning about graphics effects with Python libraries. This book is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. Consequently, this book uses standard English rather than colloquial expressions that might be confusing to those readers. This book provides a comfortable and meaningful learning experience for the intended readers.

WHY ARE THE CODE SAMPLES PRIMARILY IN PYTHON?

Most of the code samples are short (usually less than one page and sometimes less than half a page), and if necessary, you can easily and quickly copy/paste the code into a new Jupyter notebook. For the Python code samples that reference a CSV file, you do not need any additional code in the corresponding Jupyter notebook to access the CSV file. Moreover, the code samples execute quickly, so you won’t need to avail yourself of the free GPU that is provided in Google Colaboratory.

If you do decide to use Google Colaboratory, you can easily copy/paste the Python code into a notebook, and also use the upload feature to upload existing Jupyter notebooks. Keep in mind the following point: if the Python code references a CSV file, make sure that you include the appropriate code snippet (details are available online) to access the CSV file in the corresponding Jupyter notebook in Google Colaboratory.

WHY DOES THIS BOOK INCLUDE SKLEARN MATERIAL?

First, keep in mind that the Sklearn material in this book is minimalistic because this book is not about machine learning. Second, the Sklearn material is located in Chapter 6 where you will learn about some of the Sklearn built-in datasets. If you decide to delve into machine learning, you will have already been introduced to some aspects of Sklearn.

GETTING THE MOST FROM THIS BOOK

Some programmers learn well from prose, others learn well from sample code (and lots of it), which means that there’s no single style that can be used for everyone.

Moreover, some programmers want to run the code first, see what it does, and then return to the code to delve into the details (and others use the opposite approach).

Consequently, there are various types of code samples in this book: some are short, some are long, and other code samples “build” from earlier code samples.

WHAT DO I NEED TO KNOW FOR THIS BOOK?

Current knowledge of Python 3.x is the most helpful skill. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and constructs. The less technical knowledge that you have, the more diligence will be required in order to understand the various topics that are covered.

As for the non-technical skills, it’s important to have a strong desire to learn about data visualization, along with the motivation and discipline to read and understand the code samples.

DO THE COMPANION FILES OBVIATE THE NEED FOR THIS BOOK?

The companion files contain all the code samples to save you time and effort from the error-prone process of manually typing code into a text file. In addition, there are situations in which you might not have easy access to the companion disc. Furthermore, the code samples in the book provide explanations that are not available on the companion files.

DOES THIS BOOK CONTAIN PRODUCTION-LEVEL CODE SAMPLES?

The primary purpose of the code samples in this book is to show you Python-based libraries for data visualization. Clarity has higher priority than writing more compact code that is more difficult to understand (and possibly more prone to bugs). If you decide to use any of the code in this book in a production website, you ought to subject that code to the same rigorous analysis as the other parts of your code base.

HOW DO I SET UP A COMMAND SHELL?

If you are a Mac user, there are three ways to do so. The first method is to use Finder to navigate to Applications > Utilities and then double click on the Utilities application. Next, if you already have a command shell available, you can launch a new command shell by typing the following command:

open /Applications/Utilities/Terminal.app

A second method for Mac users is to open a new command shell on a MacBook from a command shell that is already visible simply by clicking command+n in that command shell, and your Mac will launch another command shell.

If you are a PC user, you can install Cygwin (open source https://cygwin.com/) that simulates bash commands, or use another toolkit such as MKS (a commercial product). Please read the online documentation that describes the download and installation process. Note that custom aliases are not automatically set if they are defined in a file other than the main start-up file (such as .bash_login).

COMPANION FILES

All the code samples and figures in this book may be obtained by writing to the publisher at [e-mail].

WHAT ARE THE “NEXT STEPS” AFTER FINISHING THIS BOOK?

The answer to this question varies widely, mainly because the answer depends heavily on your objectives. If you are interested primarily in NLP, then you can learn more advanced concepts, such as attention, transformers, and the BERT-related models.

If you are primarily interested in machine learning, there are some subfields of machine learning, such as deep learning and reinforcement learning (and deep reinforcement learning) that might appeal to you. Fortunately, there are many resources available, and you can perform an Internet search for those resources. One other point: the aspects of machine learning for you to learn depend on who you are: the needs of a machine learning engineer, data scientist, manager, student or software developer are all different.

O. Campesato

December 2023