Data Visualization with Python for Beginners

AI Publishing

Description

Data Visualization using Python for BeginnersAre you looking for a hands-on approach to learn Python for Data Visualization Fast?Do you need to start learning Python for Data Visualization from Scratch?This book is for you. This book works as a guide to present fundamental Python Libraries and a basis related to Data Visualization using Python.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.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.We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for Data Visualization such as Pandas, Numpy, Matplotlib, Seaborn, etc .What this book offers... You will learn all about python in three modules, one for Plotting with Matplotlib, one for Plotting with Seaborn, and a final one Pandas for Data Visualization. All three modules will contain hands-on projects using real-world datasets and a lot of exercises.Clear and Easy to Understand SolutionsAll solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill.What this book aims to do... This book is written with one goal in mind - to help beginners overcome their initial obstacles to learning Data Visualization using Python.A lot of times, newbies tend to feel intimidated by coding and data.The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project.Beginners in Python coding and Data Science does not have to be scary or frustrating when you take one step at a time.Ready to start practicing and visualizing your data using Python? Click the BUY button now to download this bookTopics Covered:Basic Plotting with MatplotlibAdvanced Plotting with MatplotlibIntroduction to the Python Seaborn LibraryAdvanced Plotting with SeabornIntroduction to Pandas Library for Data AnalysisPandas for Data Visualization3D Plotting with MatplotlibInteractive Data Visualization with BokehInteractive Data Visualization with Plotly Hands-on Project ExercisesClick the BUY button and download the book now to start learning and coding Python for Data Visualization.

Sujets

• Informatique
• Aprendizaje automático
• Machine learning
• Information visualization
• Apprentissage automatique

Informations

• Publié par: AI Sciences
• Date de parution: 14 février 2020
• Nombre de lectures: 2
• EAN13: 9781956591002
• Langue: English
• Poids de l'ouvrage: 4 Mo

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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Warning
In Python, indentation is very important. Python indentation is a way of telling a Python interpreter that the group of statements belongs to a particular code block. After each loop or if-condition, be sure to pay close attention to the intent.
Example

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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