Python Data Visualization Essentials Guide

Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Data Visualization using Python for Beginners Are 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 guide to present fundamental Python Libraries and 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 Solutions All 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 book Topics Covered: Basic Plotting with Matplotlib Advanced Plotting with Matplotlib Introduction to the Python Seaborn Library Advanced Plotting with Seaborn Introduction to Pandas Library for Data Analysis Pandas for Data Visualization 3D Plotting with Matplotlib Interactive Data Visualization with Bokeh Interactive Data Visualization with Plotly Hands-on Project Exercises Click the BUY button and download the book now to start learning and coding Python for Data Visualization. ** MONEY BACK GUARANTEE BY AMAZON ** If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us by sending an email at [email protected]. **GET YOUR COPY NOW, the price will be 19.99$ soon**

This book works as a guide to present fundamental Python libraries and basics related to data visualization using PythonKey Features* Detailed introductions to several data visualization libraries such as Matplotlib and Seaborn* Guided instructions to more advanced data visualization skills such as 3D plotting or interactive visualization* Hands-on projects for interactive practice designed to cement your new skills in practical memoryBook DescriptionData science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data to find patterns that can be used for decision making at different levels. Data visualization can be considered a subdomain of data science. You visualize data with graphs and tables to find out which data is most significant and help identify meaningful patterns.This book is dedicated to data visualization and explains how to perform data visualization on different 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 some of the most famous libraries for data visualization, such as Pandas, NumPy, Matplotlib, and Seaborn.You will learn all about Python in three modules--plotting with Matplotlib, 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. By the end of this course, you will have the knowledge and skills required to visualize data with Python all on your own.The code bundle for this course is available at https://www.aispublishing.net/book-data-visualizationWhat you will learn* Begin visualizing data with Matplotlib* Explore the Python Seaborn library for advanced plotting* Analyze data with the Pandas library* Expand your visualization skills with Pandas* Plot in three dimensions with Matplotlib* Practice interactive data visualization with Bokeh and Plotly* Complete several hands-on projectsWho this book is forThis book is written with one goal in mind--to help beginners overcome their initial obstacles in learning data visualization using Python. This book aims to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project. As such, no prior experience is required.

Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert.

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story

Data Visualization is the presentation of data in graphical format.In this tutorial for beginners, you will learn how to present data graphically with Python, Matplotlib, and Seaborn. If you need a short book to master data vizualisation from scratch, this guide is for you. Get your copy now!!!Book ObjectivesThis book is an exploration of data visualization in Python programming language. Here are the objectives of the book: To help you understand the need for data visualization and appreciate its power in data analysis. To help you learn the various types of plots that you can create to visualize your data. To help you know the various tools that you can use for data visualization, including basic, specialized and advanced tools. To help you make the right decision in choosing the tool and the kind of plot to use to visualize your data. To help you learn the power of Python in data visualization. To equip you with data visualization skills in Python programming language. To help you learn the various Python libraries that you can use for data visualization. Who this Book is for? The author targets the following groups of people: Anyone who needs to know the need for data visualization in an organization. Any individual who needs to know the various tools they can use for data visualization. Any individual who needs to know the various types of graphics they can use to represent their data and how to interpret the graphics. Anybody who needs to learn data visualization in Python using various libraries such as Pandas, Matplotlib, Seaborn and Folium. Anyone who needs to learn how to visualize different types of data including textual, numerical and geospatial data. RequirementsThe author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. What is inside the book? BASICS OF DATA VISUALIZATION BASIC AND SPECIALIZED DATA VISUALIZATION TOOLS ADVANCED VISUALIZATIONS TOOLSEXPLORING THE LIBRARIES DATA VISUALIZATION WITH MATPLOTLIBDATA VISUALIZATION WITH PANDAS DATA VISUALIZATION WITH SEABORN CREATING MAPS AND VISUALIZING GEOSPATIAL DATA The author has discussed everything related to data visualization. You are first familiarized with the fundamentals of data visualization to help you know what it is and why it is of importance to any organization. The author has then discussed the various types of tools that can be used for data visualization. These tools include the basic, specialized and advanced ones. Practically, the author focuses on how to visualize data in the Python programming language. The process of plotting different types of data using different types of plots has been discussed. You will learn how to plot textual, numerical and geospatial data in Python using different libraries such as Pandas, Matplotlib, Seaborn and Folium. Python codes have been provided alongside images of the expected outputs and the corresponding code descriptions.