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Description
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Publié par | AI Sciences |
Date de parution | 14 février 2020 |
Nombre de lectures | 2 |
EAN13 | 9781956591002 |
Langue | English |
Poids de l'ouvrage | 4 Mo |
Informations légales : prix de location à la page 0,1200€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.
Extrait
DATA VISUALIZATION
WITH PYTHON
FOR BEGINNERS
Visualize Your Data Using Pandas, Matplotlib and Seaborn
AI PUBLISHING
© Copyright 2020 by AI Publishing
All rights reserved.
First Printing, 2020
Edited by AI Publishing
Ebook Converted and Cover by Gazler Studio Published by AI Publishing LLC
ISBN-13: 978-1-7330426-8-0
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Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.
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Warning
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Table of Contents
How to contact us
About the Publisher
AI Publishing Is Searching for Authors Like You
Preface
Chapter 1: Introduction
1.1. What is Data Visualization
1.2. Environment Setup
1.3. Python Crash Course
1.4. Data Visualization Libraries
Exercise 1.1
Exercise 1.2
Chapter 2: Basic Plotting with Matplotlib
2.1. Introduction
2.2. Line Plots
2.3. Titles, Labels, and Legends
2.4. Plotting Using CSV Data Source
2.5. Plotting Using TSV Data Source
2.6. Scatter Plot
2.7. Bar Plots
2.8. Histograms
2.9. Pie Charts
2.10. Stack Plot
Exercise 2.1
Exercise 2.2
Chapter 3: Advanced Plotting with Matplotlib
3.1. Introduction
3.2. Plotting Multiple Plots
3.3. Plotting in Object-Oriented Way
3.4. Using Subplots Function to Create Multiple Plots
3.5. Saving a Matplotlib Plot
Exercise 3.1
Exercise 3.2
Chapter 4: Introduction to the Python Seaborn Library
4.1. Introduction
4.2. The Dist Plots
4.3. Joint Plot
4.4. Pair Plot
4.5. Rug Plot
4.6. Bar Plot
4.7. Count Plot
4.8. Box Plot
4.9. Violin Plot
4.10. Strip Plot
4.11. Swarm Plot
Exercise 4.1
Exercise 4.2
Chapter 5: Advanced Plotting with Seaborn
5.1. Scatter Plot
5.2. Styling Seaborn Plots
5.3. Heat Maps
5.4. Cluster Maps
5.5. Pair Grids
5.6. Facet Grids
5.7. Regression Plots
Exercise 5.1
Exercise 5.2
Chapter 6: Introduction to Pandas Library for Data Analysis
6.1. Introduction
6.2. Reading Data into the Pandas Dataframe
6.3. Filtering Rows
6.4. Filtering Columns
6.5. Concatenating Dataframes
6.6. Sorting Dataframes
6.7. Apply Function
6.8. Pivot & Crosstab
6.9. Arithmetic Operations with Where
Exercise 6.1
Exercise 6.2
Chapter 7: Pandas for Data Visualization
7.1. Introduction
7.2. Loading Datasets with Pandas
7.3. Plotting Histograms with Pandas
7.4. Pandas Line Plots
7.5. Pandas Scatter Plots
7.6. Pandas Bar Plots
7.7. Pandas Box Plots
7.8. Pandas Hexagonal Plots
7.9. Pandas Kernel Density Plots
7.10. Pandas for Time Series Data Visualization
Exercise 7.1
Exercise 7.2
Chapter 8: 3D Plotting with Matplotlib
8.1. 3D Line Plot
8.2. 3D Scatter Plot
8.3. 3D Bar Plot
Exercise 8.1
Chapter 9: Interactive Data Visualization with Bokeh
9.1. Installation
9.2. Line Plots
9.3. Bar Plots
9.4. Scatter Plots
Exercise 9.1
Exercise 9.2
Chapter 10: Interactive Data Visualization with Plotly
10.1. Installation
10.2. Line Plot
10.3. Bar Plot
10.4. Scatter Plot
10.5. Box Plot
10.6. Histogram
Exercise 10.1
Exercise 10.2
Hands-on Project
From the Same Publisher
Exercise Solutions
Exercise 1.1
Exercise 1.2
Exercise 2.1
Exercise 2.2
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
Exercise 7.1
Exercise 7.2
Exercise 8.1
Exercise 9.1
Exercise 9.2
Exercise 10.1
Exercise 10.2
Preface
§ Book Approach
The book follows a very simple approach. It is divided into 10 chapters. Chapter 1 contains an introduction while the 2 nd and 3 rd chapters cover the Matplotlib library. Python’s Seaborn library is covered in 4 th and 5 th chapters while the 6 th and 7 th chapters explore the Pandas library. The 8 th chapter covers 3-D plotting, while the 9 th chapter explains how to draw maps via the Basemap library. Finally, the 10 th chapter covers interactive data visualization via the Plotly library.
In each chapter, different types of plots have been explained theoretically, followed by practical examples. Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter. The Python notebook for each chapter is provided in the resources. It is advised that instead of copying the code, you write the code yourself, and in case of error, you match your code with the corresponding Python notebook, find, and then correct the error.
§ Data Science and Data Visualization
Data science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data in order to find patterns that can be used for decision making at different levels. Data visualization can be considered as a subdomain of data science where you visualize data with the help of graphs and tables in order to find out which data is most significant and can help in the identification of important patterns. Data visualization can also be considered as a standalone discipline where you just want to visually analyze data and base your decision on the visual representation of data.
This book is dedicated to data visualization and explains how to perform data visualization on a variety of datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science.
§ Who Is This Book For?
This book explains the process of data visualization using various libraries from scratch. Hence, the book is aimed ideally at absolute beginners to data visualization. Though a background in the Python programming language and data visualization can h