Python Data Visualization Essentials Guide: Become a Data Visualization expert by building strong proficiency in Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh
- Length: 366 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2021-07-30
- ISBN-10: 9391030076
- ISBN-13: 9789391030070
- Sales Rank: #2429902 (See Top 100 Books)
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.
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.
About the Authors
Kallur Rahman is an IT industry leader with over 2 decades of experience in software development, testing, program/ project management, and management consultancy. He has been a developer, designer, technical architect, test program manager, delivery unit head, IT Services, and COE/Factory Services leader of various complexity spanning telecommunications, Life Sciences, Retail, and Healthcare Industries. He has a master’s degree in Business Administration preceded by an Engineering degree in Computer Science. He has counseled CxO level executives in market-leading corporations for testing, business and technology transformation programs. As a thought-leader, he is a frequent invitee at several industry events spanning technical and domain-focused themes.
LinkedIn Bio: https://www.linkedin.com/in/kalilurrahman/
Cover Page Title Page Copyright Page Dedication Page About the Author Acknowledgement Preface Errata Table of Contents 1. Introduction to Data Visualization Structure Objective What is data visualization? Brilliant use of data visualization in history Key elements of data visualization Elements of data visualization Strategy Story Style Structure Data User Importance of data visualization Conclusion Questions 2. Why Data Visualization? Structure Objective The power of visual storytelling Good examples of data visualization Benefits of visualization Recommendations and resources Conclusion Questions 3. Various Data Visualization Elements and Tools Structure Objectives Different types of charts and graphs used in data visualization Other types of charts and diagrams used for visualization Different methods for selection of the right data visualization elements Grouping and categorization of data visualization tools Software tools and libraries available for data visualization Conclusion Questions 4. Using Matplotlib with Python Structure Objective Introduction to Matplotlib The definition of figure, plots, subplots, axes, ticks, and legends Matplotlib plotting functions Plotting functions Subplot functions Coloring functions Config functions Matplotlib modules Matplotlib toolkits Examples of various types of charts Line plot Exercise 4-3 Bar plots Scatter plots Histogram plot Mid-chapter exercise Box plots An exploratory question: Pie charts Donut/Doughnut charts Area charts Matshow Violin plot Treemap charts Saving a file in Matplotlib Annotating a plot Exercises and Matplotlib resources Example: Use of subplots and different charts in a single visualization Example: Simple use of Error Bars – Using a horizontal bar chart and a standard error bar Example 4-27: Code to use error bars as a standalone feature Example 4-28: Use of log charts in Matplotlib Exercise 4-29: Contour plots on various mathematical equations Exercise 4-30: An example of a comparison of pseudocolor, contour, and filled contour chart for a common dataset Exercise 4-31: An example of a quiver plot using arrows Exercise 4-32: An example of a lollipop plot Exercise 4-33: An example of 3D surface plots End of the chapter exercise Exercise 4-34: A stock market example Matplotlib resources Conclusion Questions 5. Using Pandas for Plotting Structure Objective Introduction to Pandas plotting Pandas features and benefits Pandas plotting functions Dataframe.plot Pandas.plotting functions Pandas modules and extensions Pandas extensions Examples for various types of charts using Pandas Bar charts Horizontal bar chart Pie chart Scatter plot Exercises 5-10 to 5-40 – An exploration of the stock market using Pandas charts Exercise 5-13: Building a scatter matrix from the dataset Exercise 5-14: Generating a stock returns graph Exercise 5-15 Exercise 5-16: Histogram of all stocks Exercise 5-17: Individual line plot of all stocks Exercise 5-18 - Generating a simple HexBin chart comparing two stocks Exercise 5-19: Subplots of HexBin charts comparing stocks Exercise 5-20: Generating a density chart for stock values Exercise 5-21: Bootstrap plot for stocks Exercise 5-22: Autocorrelation plot for a stock Exercise 5-23: Question – What will be the outcome? Exercise 5-24: Box plots for stocks Exercise 5-25: Box plots for stock returns Exercise 5-26: Lag plot for stocks Exercise 5-27: Lag plot for stock returns Exercise 5-28: Lag plot for stock returns Exercise 5-29: Calculating the total returns of the stocks Exercise 5-30: Visualizing some area charts Exercise 5-31: Visualizing some bar charts Exercise 5-32: Using a table as a legend Exercise 5-33: Using horizontal bar charts Exercise 5-34: Using parallel coordinates charts Exercise 5-35: Using RadViz charts Exercise 5-36: Question – scatter matrix Exercise 5-37: Using secondary axis in a line chart Exercise 5-38: Using secondary axis in a bar chart Exercise 5-39: Andrews curves using subplots Exercise 5-40 to 5-48: Chapter exercises Exercise 5-40: Question – Random HexBin chart exercise Exercise 5-41 to 5-48: Question on multiple charts Case study 3: Analyzing the Titanic passenger dataset Exercise 5-49: Data exploration and a histogram Exercise 5-50: Histogram of two parameters Exercise 5-51: Histogram of two parameters Exercise 5-52 to 5-68: Further analysis and visualization of the Titanic dataset Exercise 5-69 to 5-71 Generating map outline using scatter plot using latitude/longitude and population data Exercise 5-69: Creation of an approximate outline of India map using a scatter plot Exercise 5-70 and 5-71: Creating an approximate outline of the world and US maps using a scatter plot Pandas resources Conclusion Questions 6. Using Seaborn for Visualization Structure Objective Introduction to Seaborn visualization Seaborn features and benefits Examples and case studies for various types of visualizations using Seaborn Exercise 6-1: Plotting stock market analysis data Exercise 6-2: Trying Seaborn set_theme(), color_codes, despline(), and barplot() Exercise 6-3: Categorical data plotting – boxplot(), violinplot(), and boxenplot() Exercise 6-4: Categorical data plotting – catplot() with the point, bar, strip, and swarm plot Exercise 6-5: Distribution of data plotting – distplot() kernel density estimate Exercise 6-6: Distribution of data plotting – barplot() with NumPy filter functions Exercise 6-7: Simple jointplot() with HexBin option Exercise 6-8: Simple pairplot() Exercise 6-9: A pairplot with the Titanic dataset Exercise 6-10: A pair grid with the Titanic dataset Exercise 6-11: A pair plot with the Titanic sub dataset Exercise 6-12: Application of various plots for real-time stock visualization Exercise 6-13 to 6-20: Application 2 - Data visualization of soccer team rankings using Seaborn Exercise 6-13: A simple pair plot with the soccer team rankings dataset Exercise 6-14: Different types of jointplots() on the soccer rankings dataset Exercise 6-15: Different types of relplots() with the soccer team rankings dataset Exercise 6-16: A clustermap() with the soccer team rankings dataset Exercise 6-17: Write code to create a PairGrid using Soccer team rankings dataset to create Scatter/KDE and histogram across the grid Exercise 6-18: Write code for a simple pairplot() for one of the leagues with a hue on the team name Exercise 6-19: Write a program to create a PairGrid for English Premier League (Barclays Premier League) Exercise 6-20: Simple relationship plot between two variables on the soccer dataset Exercise 6-21: Simple relationship plot between two variables on the Titanic dataset Exercise 6-22: Use of distribution plots Exercise 6-23: Use of histogram and KDE plots Exercise 6-24: Use of Matrix plots – Heatmap() Exercise 6-25: Write a program to generate a heatmap on the stock market dataset Exercise 6-26: Write a program to create jointplot() on the Titanic and soccer datasets Exercise 6-27: Boxenplot() categorical plotting on Titanic dataset Exercise 6-28: Regression plots Exercise 6-29: Write a program to create a jointplot() on the soccer dataset and kdeplot() on the Titanic dataset Exercise 6-30: A PairGrid on the soccer dataset Exercise 6-32: Write a program to create a PairGrid() on the soccer dataset Seaborn resources Conclusion Questions 7. Using Bokeh with Python Structure Objective Introduction to Bokeh visualization Introduction to Bokeh visualization Bokeh API modules and functions Examples and case studies for various types of visualizations using Bokeh A simple scatter chart Line plot with a square pin marker Bar chart A simple mathematical formula plot Use of patches Use of grid plots and tool selects Simple mathematical plot Use of data for a financial plotting Multiple bar charts with the use of dodge Dynamic line plot Scatter plot Use of colormap, colorbar, and linear colormap for plotting a scatter plot Histogram plot using Bokeh Pie and donut charts in Bokeh Pie chart code Donut chart code Area charts Scatter plot to build a map outline Hex tile plot Dynamic plotting using widgets in Bokeh Bokeh case study Bar chart and difference in data Bokeh – additional resources Conclusion Questions 8. Using Plotly, Folium, and Other Tools for Visualization Structure Objective Introduction to other popular libraries – Plotly, Folium, MPLFinance Examples for various types of visualizations using Plotly Plotly chart types Simple Plotly scatter plot Simple mathematical plot Line plot using the gapminder() dataset Scatter plot using markers and size Pie chart and donut charts using the gapminder dataset Use of bar charts in Plotly 3D Scatter chart and 3D line chart Case study - Use of Plotly for the COVID-19 dataset analysis Plotly animation Scatter matrix plotting capability of Plotly Treemap using Plotly Examples for various types of geographic visualizations using Folium folium features Simple world map Folium heatmap with time animation Simple Heatmap Examples for various types of stock and finance data visualizations using MPLFinance Other examples Resources Conclusion 9. Hands-on Visualization Exercises, Case Studies, and Further Resources Structure Objective Case studies and examples using Seaborn Case studies and examples using Bokeh Case studies and examples using Plotly Case studies and examples using Folium Folium - more choropleth examples Air travel data case study - Use Bokeh, Matplotlib, Plotly, or any choice Open case study – Using Altair Recommended exercises to try out Solution file Resources Conclusion Index
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