Python 3 Using ChatGPT / GPT-4 E-Book

Mercury Learning and Information

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Veröffentlichungsjahr: 2024

Beschreibung

This book is for people who want to learn Python 3 and how to use ChatGPT with Python. It starts with an introduction to Python programming, covering data types, number formatting, Unicode handling, and text manipulation. The book then covers loops, conditional logic, reserved words, user input, exception management, and command-line arguments.
The journey continues into Generative AI, discussing its distinction from Conversational AI. Popular platforms like ChatGPT and GPT-4 are explored, along with their strengths, weaknesses, and potential applications. The book shows how to generate Python 3 code samples via ChatGPT using the “Code Interpreter” plugin.
Understanding these concepts is crucial for navigating Python and AI. This book transitions readers from basic Python programming to advanced AI applications, blending theory with practical skills. Companion files with code samples and figures enhance learning, making this an essential resource for mastering Python and ChatGPT.

Leseprobe

PYTHON 3USINGCHATGPT / GPT-4

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

PYTHON 3USINGCHATGPT / GPT-4

Oswald Campesato

MERCURY LEARNING AND INFORMATION

Boston, Massachusetts

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

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

ISBN: 978-1-50152-228-4

The publisher recognizes and respects all marks used by companies, manufacturers, and developers as a means to distinguish their products. All brand names and product names mentioned in this book are trademarks or service marks of their respective companies. Any omission or misuse (of any kind) of service marks or trademarks, etc. is not an attempt to infringe on the property of others.

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All of our titles are available in digital format at academiccourseware.com and other digital vendors. Companion files (figures and code listings) for this title are available by contacting [e-mail]. The sole obligation of MERCURY LEARNING AND INFORMATION to the purchaser is to replace the disc, based on defective materials or faulty workmanship, but not based on the operation or functionality of the product.

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 3

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 Multilines

Quotation 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 in Python

Working With Numbers

Working With Other Bases

The chr() Function

The round() Function in Python

Formatting Numbers in Python

Working With Fractions

Unicode and UTF-8

Working With Unicode

Working With Strings

Comparing Strings

Formatting Strings in Python

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: Conditional Logic, Loops, and Functions

Precedence of Operators in Python

Python Reserved Words

Working with Loops in Python

Python for Loops

A for Loop with try/except in Python

Numeric Exponents in Python

Nested Loops

The split() Function With for Loops

Using the split() Function to Compare Words

Using the split() Function to Print Justified Text

Using the split() Function to Print Fixed-Width Text

Using the split() Function to Compare Text Strings

Using the split() Function to Display Characters in a String

The join() Function

Python while Loops

Conditional Logic in Python

The break/continue/pass Statements

Comparison and Boolean Operators

The in/not in/is/is not Comparison Operators

The and, or, and not Boolean Operators

Local and Global Variables

Uninitialized Variables and the Value None

Scope of Variables

Pass by Reference Versus Value

Arguments and Parameters

Using a while loop to Find the Divisors of a Number

Using a while loop to Find Prime Numbers

User-Defined Functions in Python

Specifying Default Values in a Function

Returning Multiple Values From a Function

Functions With a Variable Number of Arguments

Lambda Expressions

Recursion

Calculating Factorial Values

Calculating Fibonacci Numbers

Calculating the GCD of Two Numbers

Calculating the LCM of Two Numbers

Summary

Chapter 3: Python Data Structures

Working With Lists

Lists and Basic Operations

Reversing and Sorting a List

Lists and Arithmetic Operations

Lists and Filter-Related Operations

Sorting Lists of Numbers and Strings

Expressions in Lists

Concatenating a List of Words

The BubbleSort in Python

The Python range() Function

Counting Digits, Uppercase, and Lowercase Letters

Arrays and the append() Function

Working with Lists and the split() Function

Counting Words in a List

Iterating Through Pairs of Lists

Other List-Related Functions

Using a List as a Stack and a Queue

Working With Vectors

Working With Matrices

The NumPy Library for Matrices

Queues

Tuples (Immutable Lists)

Sets

Dictionaries

Creating a Dictionary

Displaying the Contents of a Dictionary

Checking for Keys in a Dictionary

Deleting Keys From a Dictionary

Iterating Through a Dictionary

Interpolating Data From a Dictionary

Dictionary Functions and Methods

Dictionary Formatting

Ordered Dictionaries

Sorting Dictionaries

Python Multidictionaries

Other Sequence Types in Python

Mutable and Immutable Types in Python

The type() Function

Summary

Chapter 4: Introduction to NumPy and Pandas

What Is NumPy?

Useful NumPy Features

What Are NumPy arrays?

Working With Loops

Appending Elements to Arrays (1)

Appending Elements to Arrays (2)

Multiply 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 “Norm” of Vectors

NumPy and Other Operations

NumPy and the reshape() Method

Calculating the Mean and Standard Deviation

Calculating Mean and Standard Deviation

What Is Pandas?

Pandas DataFrames

Dataframes and Data Cleaning Tasks

A Labeled Pandas DataFrame

Pandas Numeric DataFrames

Pandas Boolean DataFrames

Transposing a Pandas DataFrame

Pandas DataFrames and Random Numbers

Combining Pandas DataFrames (1)

Combining Pandas DataFrames (2)

Data Manipulation With Pandas DataFrames (1)

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

Pandas DataFrames and Scatterplots

Pandas DataFrames and Simple Statistics

Useful One-Line Commands in Pandas

Summary

Chapter 5: ChatGPT and GPT-4

What Is Generative AI?

Key Features of Generative AI

Popular Techniques in Generative AI

What Makes Generative AI Different

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, Advanced Data Analysis, and Code Whisperer

Plugins

Advanced Data Analysis

Advanced Data Analysis Versus Claude-2

Advanced Data Analysis and Charts and Graphs

Code Whisperer

Detecting Generated Text

Concerns About ChatGPT

Code Generation and Dangerous Topics

ChatGPT Strengths and Weaknesses

Sample Queries and Responses From ChatGPT

Alternatives to ChatGPT

Google Bard

YouChat

Pi From Inflection

What Is InstructGPT?

VizGPT and Data Visualization

What Is GPT-4?

GPT-4 and Test-Taking 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 Is GPT-5 Available?

Summary

Chapter 6: ChatGPT and Python Code

Simple Calculator

Simple File Handling

Simple Web Scraping

Basic Chat Bot

Basic Data Visualization

Basic Pandas

Generate Random Data

Recursion: Fibonacci Numbers

Object-Oriented Programming

Asynchronous Programming With asyncio

Working With Requests in Python

Image Processing With PIL

Exception Handling

Generators in Python

Roll 7 or 11 With Two Dice

Roll 7 or 11 With Three Dice

Roll 7 or 11 With Four Dice

Mean and Standard Deviation

Summary

Index

PREFACE

WHAT IS THE VALUE PROPOSITION FOR THIS BOOK?

This book starts with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition you will learn about loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments.

Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the “Advanced Data Analysis” plugin (formerly known as the “Code Interpreter” plugin).

In essence, this book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently.

THE TARGET AUDIENCE

This book is intended primarily for people who want to learn both Python and how to use ChatGPT with Python. This book is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. In addition, it 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.

DO I NEED TO LEARN THE THEORY PORTIONS OF THIS BOOK?

The answer depends on the extent to which you plan to become involved in working with ChatGPT and Python, perhaps involving LLMs and generative AI. In general, it’s probably worthwhile to learn the more theoretical aspects of LLMs that are discussed in this book.

GETTING THE MOST FROM THIS BOOK

Some people learn well from prose, others learn well from sample code (and large amounts 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, and some are long.

WHAT DO I NEED TO KNOW FOR THIS BOOK?

Although this book is introductory in nature, some knowledge of Python 3.x will certainly be helpful for the code samples. 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.

If you want to be sure that you can grasp the material in this book, glance through some of the code samples to get an idea of how much is familiar to you and how much is new for you.

DOES THIS BOOK CONTAIN PRODUCTION-LEVEL CODE SAMPLES?

This book contains basic code samples that are written in Python, and their primary purpose is to familiarize you with basic Python to help you understand the Python code generated via ChatGPT. Moreover, 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, you ought to subject that code to the same rigorous analysis as the other parts of your code base.

WHAT ARE THE NON-TECHNICAL PREREQUISITES FOR THIS BOOK?

Although the answer to this question is more difficult to quantify, it’s especially important to have a strong desire to learn about NLP, along with the motivation and discipline to read and understand the code samples. As a reminder, even simple APIs can be a challenge to understand the first time you encounter them, so be prepared to read the code samples several times.

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 about other LLMs (large language 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

October 2023