Python 3 Data Visualization Using Google Gemini E-Book

Mercury Learning and Information

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

Seitenzahl: 223

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 VISUALIZATIONUSINGGOOGLE GEMINI

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

PYTHON 3DATA VISUALIZATIONUSINGGOOGLE GEMINI

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 Google Gemini.

ISBN: 978-1-50152-280-2

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: 2024932122

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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 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

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: 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 4: 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 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 Data Visualization

Working With Charts and Graphs

Bar Charts

Pie Charts

Line Graphs

Heatmap

Histogram

Box Plot

Pareto Chart

Radar Chart

Treemap

Waterfall Chart

Line Plots With Matplotlib

A Pie Chart Using Matplotlib

Box and Whisker Plots Using Matplotlib

Stacked Bar Charts With Matplotlib

Donut Chart Using Matplotlib

3D Surface Plots With Matplotlib

Matplotlib’s Contour Plots

Streamplot for Vector Fields

Polar Plots

Bar Charts

Scatter Plot With Regression Line

Heatmap for Correlation Matrix With Seaborn

Histograms With Seaborn

Violin Plots With Seaborn

Summary

Index

PREFACE

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK?

This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhance their visualization skills, this book explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by insightful Bard-generated code samples.

CONTENT HIGHLIGHTS

The first chapter contains a quick tour of basic Python 3, followed by a chapter that introduces you to NumPy. The third and fourth chapters introduce you to data visualization with Matplotlib and how to create graphics effects with Seaborn. The fifth chapter introduces you to Google Gemini, which also includes a discussion of GPT-4. The sixth and concluding chapter contains Gemini-generated Python code samples for performing various programming tasks.

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 4 where you will learn about some of the Sklearn built-in datasets. If you decide to research machine learning, you will have already been introduced to some aspects of Sklearn.

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 assorted topics that are covered.

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

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 more prone to bugs). If you decide to use any of the code in this book on 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].

O. Campesato

February 2024