The Data Visualization Workshop: A self-paced, practical approach to transforming your complex data into compelling, captivating graphics

Description

Explore a modern approach to visualizing data with Python and transform large real-world datasets into expressive visual graphics using this beginner-friendly workshop Key FeaturesDiscover the essential tools and methods of data visualizationLearn to use standard Python plotting libraries such as Matplotlib and SeabornGain insights into the visualization techniques of big companiesBook Description Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data? The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities. Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you'll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You'll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you've ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you'll use the Bokeh library to create dynamic visualizations that can be integrated into any web page. By the end of this workshop, you'll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data. What you will learnUnderstand the importance of data visualization in data scienceImplement NumPy and pandas operations on real-life datasetsCreate captivating data visualizations using plotting librariesUse advanced techniques to plot geospatial data on a mapIntegrate interactive visualizations to a webpageVisualize stock prices with Bokeh and analyze Airbnb data with MatplotlibWho this book is for The Data Visualization Workshop is for beginners who want to learn data visualization, as well as developers and data scientists who are looking to enrich their practical data science skills. Prior knowledge of data analytics, data science, and visualization is not mandatory. Knowledge of Python basics and high-school-level math will help you grasp the concepts covered in this data visualization book more quickly and effectively. Table of ContentsThe Importance of Data Visualization and Data ExplorationAll You Need to Know about PlotsA Deep Dive into MatplotlibSimplifying Visualizations Using SeabornPlotting Geospatial DataMaking Things Interactive with BokehCombining What We Have Learned Cover FM Copyright Table of Contents Preface Chapter 1: The Importance of Data Visualization and Data Exploration Introduction Introduction to Data Visualization The Importance of Data Visualization Data Wrangling Tools and Libraries for Visualization Overview of Statistics Measures of Central Tendency Measures of Dispersion Correlation Types of Data Summary Statistics NumPy Exercise 1.01: Loading a Sample Dataset and Calculating the Mean Using NumPy Activity 1.01: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation of a Dataset Basic NumPy Operations Indexing Slicing Splitting Iterating Exercise 1.02: Indexing, Slicing, Splitting, and Iterating Advanced NumPy Operations Filtering Sorting Combining Reshaping Exercise 1.03: Filtering, Sorting, Combining, and Reshaping pandas Advantages of pandas over NumPy Disadvantages of pandas Exercise 1.04 Loading a Sample Dataset and Calculating the Mean using Pandas Exercise 1.05: Using pandas to Compute the Mean, Median, and Variance of a Dataset Basic Operations of pandas Indexing Slicing Iterating Series Exercise 1.06: Indexing, Slicing, and Iterating Using pandas Advanced pandas Operations Filtering Sorting Reshaping Exercise 1.07: Filtering, Sorting, and Reshaping Activity 1.02: Forest Fire Size and Temperature Analysis Summary Chapter 2: All You Need to Know about Plots Introduction Comparison Plots Line Chart Uses Example Design Practices Bar Chart Use Don’ts of Bar Charts Examples Design Practices Radar Chart Uses Examples Design Practices Activity 2.01: Employee Skill Comparison Relation Plots Scatter Plot Uses Examples Design Practices Variants: Scatter Plots with Marginal Histograms Examples Bubble Plot Use Example Design Practices Correlogram Examples Design Practices Heatmap Use Examples Design Practice Activity 2.02: Road Accidents Occurring over Two Decades Composition Plots Pie Chart Use Examples Design Practices Variants: Donut Chart Design Practice Stacked Bar Chart Use Examples Design Practices Stacked Area Chart Use Examples Design Practice Activity 2.03: Smartphone Sales Units Venn Diagram Use Example Design Practice Distribution Plots Histogram Use Example Design Practice Density Plot Use Example Design Practice Box Plot Use Examples Violin Plot Use Examples Design Practice Activity 2.04: Frequency of Trains during Different Time Intervals Geoplots Dot Map Use Example Design Practices Choropleth Map Use Example Design Practices Connection Map Use Examples Design Practices What Makes a Good Visualization? Common Design Practices Activity 2.05: Analyzing Visualizations Activity 2.06: Choosing a Suitable Visualization Summary Chapter 3: A Deep Dive into Matplotlib Introduction Overview of Plots in Matplotlib Pyplot Basics Creating Figures Closing Figures Format Strings Plotting Plotting Using pandas DataFrames Ticks Displaying Figures Saving Figures Exercise 3.01: Creating a Simple Visualization Basic Text and Legend Functions Labels Titles Text Annotations Legends Activity 3.01: Visualizing Stock Trends by Using a Line Plot Basic Plots Bar Chart Activity 3.02: Creating a Bar Plot for Movie Comparison Pie Chart Exercise 3.02: Creating a Pie Chart for Water Usage Stacked Bar Chart Activity 3.03: Creating a Stacked Bar Plot to Visualize Restaurant Performance Stacked Area Chart Activity 3.04: Comparing Smartphone Sales Units Using a Stacked Area Chart Histogram Box Plot Activity 3.05: Using a Histogram and a Box Plot to Visualize Intelligence Quotient Scatter Plot Exercise 3.03: Using a Scatter Plot to Visualize Correlation between Various Animals Bubble Plot Layouts Subplots Tight Layout Radar Charts Exercise 3.04: Working on Radar Charts GridSpec Activity 3.06: Creating a Scatter Plot with Marginal Histograms Images Basic Image Operations Activity 3.07: Plotting Multiple Images in a Grid Writing Mathematical Expressions Summary Chapter 4: Simplifying Visualizations Using Seaborn Introduction Advantages of Seaborn Controlling Figure Aesthetics Seaborn Figure Styles Removing Axes Spines Controlling the Scale of Plot Elements Exercise 4.01: Comparing IQ Scores for Different Test Groups by Using a Box Plot Color Palettes Categorical Color Palettes Sequential Color Palettes Diverging Color Palettes Exercise 4.02: Surface Temperature Analysis Activity 4.01: Using Heatmaps to Find Patterns in Flight Passengers' Data Advanced Plots in Seaborn Bar Plots Activity 4.02: Movie Comparison Revisited Kernel Density Estimation Plotting Bivariate Distributions Visualizing Pairwise Relationships Violin Plots Activity 4.03: Comparing IQ Scores for Different Test Groups by Using a Violin Plot Multi-Plots in Seaborn FacetGrid Activity 4.04: Visualizing the Top 30 Music YouTube Channels Using Seaborn's FacetGrid Regression Plots Activity 4.05: Linear Regression for Animal Attribute Relations Squarify Exercise 4.03: Water Usage Revisited Activity 4.06: Visualizing the Impact of Education on Annual Salary and Weekly Working Hours Summary Chapter 5: Plotting Geospatial Data Introduction The Design Principles of geoplotlib Geospatial Visualizations Voronoi Tessellation Delaunay Triangulation Choropleth Plot Exercise 5.01: Plotting Poaching Density Using Dot Density and Histograms Activity 5.01: Plotting Geospatial Data on a Map The GeoJSON Format Exercise 5.02: Creating a Choropleth Plot with GeoJSON Data Tile Providers Exercise 5.03: Visually Comparing Different Tile Providers Custom Layers Exercise 5.04: Plotting the Movement of an Aircraft with a Custom Layer Activity 5.02: Visualizing City Density by the First Letter Using an Interactive Custom Layer Summary Chapter 6: Making Things Interactive with Bokeh Introduction Concepts of Bokeh Interfaces in Bokeh Output Bokeh Server Presentation Integrating Basic Plotting Exercise 6.01: Plotting with Bokeh Exercise 6.02: Comparing the Plotting and Models Interfaces Activity 6.01: Plotting Mean Car Prices of Manufacturers Adding Widgets Exercise 6.03: Building a Simple Plot Using Basic Interactivity Widgets Exercise 6.04: Plotting Stock Price Data in Tabs Activity 6.02: Extending Plots with Widgets Summary Chapter 7: Combining What We Have Learned Introduction Activity 7.01: Implementing Matplotlib and Seaborn on the New York City Database Bokeh Activity 7.02: Visualizing Stock Prices with Bokeh Geoplotlib Activity 7.03: Analyzing Airbnb Data with Geoplotlib Summary Appendix Index