This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins 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, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard.
FEATURES
Includes a chapter on how to generate a variety of Python 3 code samples via Gemini
Covers basic concepts of Python 3 such as loops, conditional logic, reserved words, user input, manage exceptions, work with command-line arguments, and more
Includes companion files for downloading with source code and figures(available from the publisher with Amazon proof of purchase by writing to
[email protected])
Author(s): Oswald Campesato
Publisher: Mercury Learning and Information
Year: 2024This book
Language: English
Pages: 235
Cover
Title Page
Copyright Page
Dedication
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
