Google Gemini for Python E-Book

Oswald Campesato

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

Seitenzahl: 232

Veröffentlichungsjahr: 2024

Beschreibung

This book bridges Python 3 programming and Generative AI, equipping readers with the skills to navigate both domains confidently. It starts with Python basics, covering data types, number formatting, text manipulation, loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words. You'll also learn about handling user input, managing exceptions, and working with command-line arguments.
The journey continues into Generative AI, distinguishing it from Conversational AI. It introduces popular platforms and models, including Bard (now called Gemini) and its competitors, providing insights into their capabilities, strengths, weaknesses, and applications. The final chapters show how to generate various Python 3 code samples using Gemini.
Understanding these concepts is crucial for modern programming and AI applications. This book transitions readers from basic Python programming to advanced AI techniques, blending theoretical knowledge with practical skills. Companion files with source code and figures enhance the learning experience, making this an essential resource for mastering Python 3 and Generative AI.

Leseprobe

GOOGLE GEMINIFORPYTHON

LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY

By purchasing or using this book and companion files (the “Work”), you agree that this license grants permission to use the contents contained herein, including the disc, but does not give you the right of ownership to any of the textual content in the book / disc or ownership to any of the information or products contained in it. This license does not permit uploading of the Work onto the Internet or on a network (of any kind) without the written consent of the Publisher. Duplication or dissemination of any text, code, simulations, images, etc. contained herein is limited to and subject to licensing terms for the respective products, and permission must be obtained from the Publisher or the owner of the content, etc., in order to reproduce or network any portion of the textual material (in any media) that is contained in the Work.

MERCURY LEARNING AND INFORMATION (“MLI” or “the Publisher”) and anyone involved in the creation, writing, or production of the companion disc, accompanying algorithms, code, or computer programs (“the software”), and any accompanying Web site or software of the Work, cannot and do not warrant the performance or results that might be obtained by using the contents of the Work. The author, developers, and the Publisher have used their best efforts to ensure the accuracy and functionality of the textual material and/or programs contained in this package; we, however, make no warranty of any kind, express or implied, regarding the performance of these contents or programs. The Work is sold “as is” without warranty (except for defective materials used in manufacturing the book or due to faulty workmanship).

The author, developers, and the publisher of any accompanying content, and anyone involved in the composition, production, and manufacturing of this work will not be liable for damages of any kind arising out of the use of (or the inability to use) the algorithms, source code, computer programs, or textual material contained in this publication. This includes, but is not limited to, loss of revenue or profit, or other incidental, physical, or consequential damages arising out of the use of this Work.

The sole remedy in the event of a claim of any kind is expressly limited to replacement of the book and/or disc, and only at the discretion of the Publisher. The use of “implied warranty” and certain “exclusions” varies from state to state and might not apply to the purchaser of this product.

Companion files for this title are available by writing to the publisher at [email protected].

GOOGLE GEMINIFORPYTHON

Coding with Bard

Oswald Campesato

MERCURY LEARNING AND INFORMATION

Boston, Massachusetts

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

This publication, portions of it, or any accompanying software may not be reproduced in any way, stored in a retrieval system of any type, or transmitted by any means, media, electronic display or mechanical display, including, but not limited to, photocopy, recording, Internet postings, or scanning, without prior permission in writing from the publisher.

Publisher: David Pallai

MERCURY LEARNING AND INFORMATION

121 High Street, 3rd Floor

Boston, MA 02110

[email protected]

www.merclearning.com

800-232-0223

O. Campesato. Google® Gemini for Python: Coding with Bard.

ISBN: 978-1-50152-274-1

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.

Library of Congress Control Number: 2024930869

242526321    This book is printed on acid-free paper in the United States of America.

Our titles are available for adoption, license, or bulk purchase by institutions, corporations, etc. For additional information, please contact the Customer Service Dept. at 800-232-0223 (toll free).

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 [email protected]. The sole obligation of MERCURY LEARNING AND INFORMATION to the purchaser is to replace the files, 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 Bubble Sort in Python

The Python range() Function

Counting Digits and 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 Multi Dictionaries

Other Sequence Types in Python

Mutable and Immutable Types in Python

The type() Function

Working with Bard

Counting Digits and Uppercase and Lowercase Letters

Bard Python Code for a Queue

Bard Python Code for a Stack

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 Quartiles With Numpy

What is Pandas?

Pandas Data Frames

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

Working with Bard

A Pandas DataFrame with Random Values

Pandas DataFrame and a Bar Chart

Pandas DataFrames and Statistics

Summary

Chapter 5: Generative AI, Bard, and Gemini

What is Generative AI?

Key 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 Gemini 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

Poorly-Worded Prompts

What is Gemini?

Gemini Ultra Versus GPT-4

Gemini Strengths

Gemini’s Weaknesses

Gemini Nano on Mobile Devices

What is Bard?

Sample Queries and Responses from Bard

Alternatives to Bard

YouChat

Pi from Inflection

CoPilot (OpenAI/Microsoft)

Codex (OpenAI)

Apple GPT

Claude 2

Summary

Chapter 6: Bard and Python Code

CSV Files for Bard

Simple Web Scraping

Basic Chatbot

Basic Data Visualization

Basic Pandas

Generating Random Data

Recursion: Fibonacci Numbers

Generating a Python Class

Asynchronous Programming

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

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 Bard and its competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of Bard, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via Bard.

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 Bard with Python. This book is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. In addition, 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.

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 Bard 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.

WHAT DO I NEED TO KNOW FOR THIS BOOK?

Although this book is introductory in nature, some knowledge of Python 3.x with 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.

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 Bard. 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.

COMPANION FILES

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

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 will depend on your career: the needs of a machine learning engineer, data scientist, manager, student, or software developer are all different.

Oswald Campesato

January 2024