Python 3 and Data Visualization E-Book

Mercury Learning and Information

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

Seitenzahl: 291

Veröffentlichungsjahr: 2024

Beschreibung

Python 3 and Data Visualization provides an in-depth exploration of Python 3 programming and data visualization techniques. The course begins with an introduction to Python, covering essential topics from basic data types and loops to advanced constructs such as dictionaries and matrices. This foundation prepares readers for the next section, which focuses on NumPy and its powerful array operations, seamlessly leading into data visualization using prominent libraries like Matplotlib.
Chapter 6 delves into Seaborn's rich visualization tools, providing insights into datasets like Iris and Titanic. The appendix covers additional visualization tools and techniques, including SVG graphics and D3 for dynamic visualizations. The companion files include numerous Python code samples and figures, enhancing the learning experience.
From foundational Python concepts to advanced data visualization techniques, this course serves as a comprehensive resource for both beginners and seasoned professionals, equipping them with the necessary skills to effectively visualize data.

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PYTHON 3ANDDATA VISUALIZATION

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PYTHON 3ANDDATA VISUALIZATION

Oswald Campesato

MERCURY LEARNING AND INFORMATION

Dulles, Virginia

Boston, Massachusetts

New Delhi

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

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O. Campesato. Python 3 and Data Visualization.

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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 3

Some Standard Modules in Python

Simple Data Types in Python

Working With Numbers

Working With Other Bases

The chr() Function

The round() Function in Python

Unicode and UTF-8

Working With Unicode

Working With Strings

Comparing Strings

Uninitialized Variables and the Value None in Python

Slicing and Splicing Strings

Testing for Digits and Alphabetic Characters

Search and Replace a String in Other Strings

Precedence of Operators in Python

Python Reserved Words

Working With Loops in Python

Python for Loops

Numeric Exponents in Python

Nested Loops

The split() Function With for Loops

Using the split() Function to Compare Words

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

Scope of Variables

Pass by Reference versus Value

Arguments and Parameters

User-Defined Functions in Python

Specifying Default Values in a Function

Returning Multiple Values From a Function

Lambda Expressions

Working With Lists

Lists and Basic Operations

Lists and Arithmetic Operations

Lists and Filter-Related Operations

The join(), range(), and split() Functions

Arrays and the append() Function

Other List-Related Functions

Working With List Comprehensions

Working With Vectors

Working With 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

Other Sequence Types in Python

Mutable and Immutable Types in Python

Summary

Chapter 2: NumPy and Data Visualization

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 Nonlinear Datasets?

The MSE (Mean Squared Error) Formula

Other Error Types

Nonlinear Least Squares

Calculating the MSE Manually

Find the Best-Fitting Line in NumPy

Calculating MSE by Successive Approximation (1)

Calculating 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 applymap() 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 With JSON-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 BeautifulSoup

Parsing an HTML Web Page

BeautifulSoup and Pandas

BeautifulSoup and Live HTML Web Pages

Summary

Chapter 5: Matplotlib for Data 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 Matplotlib

Plot a Pie Chart 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

Appendix: SVG and D3

Basic Two-Dimensional Shapes in SVG

SVG Gradients and the <path> Element

SVG <polygon> Element

Bézier Curves and Transforms

SVG Filters and Shadow Effects

Rendering Text Along an SVG <path> Element

SVG Transforms

SVG and HTML

CSS3 and SVG

Similarities and Differences Between SVG and CSS3

Introduction to D3

What Is D3?

D3 Boilerplate

Method Chaining in D3

The D3 Methods select() and selectAll()

Specifying UTF-8 in HTML5 Web Pages With D3

Creating New HTML Elements

The Most Common Idiom in D3

Binding Data to Document-Object-Model Elements

Generating Text Strings

Creating Simple Two-Dimensional Shapes

Bézier Curves and Text

A Digression: Scaling Arrays of Numbers to Different Ranges

Tweening in D3

Formatting Numbers

Working With Gradients

Linear Gradients

Radial Gradients

Adding HTML <div> Elements With Gradient Effects

Other D3 Graphics Samples

D3 Application Programming Interface Reference

Other Features of D3

Summary

Index

PREFACE

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK?

This book contains a fast-paced introduction to relevant information about Python-based data visualization. You will learn how to generate graphics using Pandas, Matplotlib, and Seaborn. In addition, an appendix contains SVG-based and D3-based graphics effects, along with links for many additional code samples.

THE TARGET AUDIENCE

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

WHAT WILL I LEARN?

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 Pandas as well as Pandas with JSON data. MySQL and SQL.

The fifth chapter delves into data visualization with Matplotlib and also working with SweetViz and Skimpy. The final chapter of this book shows you how to create graphics effects with Seaborn, and an example of a rendering graphics effects in Bokeh. In addition, an appendix is included with graphics effects based on SVG and D3.

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 need be, 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 it 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 study 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?

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 very important to have a strong desire to learn about data visualization, along with the motivation and discipline to read and understand the code samples.

DON’T 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 these files. Furthermore, the code samples in the book provide explanations that are not available in 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 [email protected].

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.

Oswald Campesato

September 2023