book

The Data Visualization Workshop

Name: The Data Visualization Workshop
ISBN: 9781800568846

by Mario Döbler, Tim Großmann, Rohan Chikorde, Joshua Görner, Anshu Kumar, Piotr Malak, Ankit Verma

July 2020

Beginner to intermediate

536 pages

8h 47m

English

Packt Publishing

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The Data Visualization Workshop
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstalling PythonInstalling Python on WindowsInstalling Python on LinuxInstalling Python on macOSInstalling LibrariesWorking with JupyterLab and Jupyter NotebookImporting Python LibrariesAccessing the Code Files
1. The Importance of Data Visualization and Data Exploration
IntroductionIntroduction to Data VisualizationThe Importance of Data VisualizationData WranglingTools and Libraries for VisualizationOverview of StatisticsMeasures of Central TendencyMeasures of DispersionCorrelationTypes of DataSummary StatisticsNumPyExercise 1.01: Loading a Sample Dataset and Calculating the Mean Using NumPyActivity 1.01: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation of a DatasetBasic NumPy OperationsIndexingSlicingSplittingIteratingExercise 1.02: Indexing, Slicing, Splitting, and IteratingAdvanced NumPy OperationsFilteringSortingCombiningReshapingExercise 1.03: Filtering, Sorting, Combining, and ReshapingpandasAdvantages of pandas over NumPyDisadvantages of pandasExercise 1.04 Loading a Sample Dataset and Calculating the Mean using PandasExercise 1.05: Using pandas to Compute the Mean, Median, and Variance of a DatasetBasic Operations of pandasIndexingSlicingIteratingSeriesExercise 1.06: Indexing, Slicing, and Iterating Using pandasAdvanced pandas OperationsFilteringSortingReshapingExercise 1.07: Filtering, Sorting, and ReshapingActivity 1.02: Forest Fire Size and Temperature AnalysisSummary
2. All You Need to Know about Plots
IntroductionComparison PlotsLine ChartUsesExampleDesign PracticesBar ChartUseDon’ts of Bar ChartsExamplesDesign PracticesRadar ChartUsesExamplesDesign PracticesActivity 2.01: Employee Skill ComparisonRelation PlotsScatter PlotUsesExamplesDesign PracticesVariants: Scatter Plots with Marginal HistogramsExamplesBubble PlotUseExampleDesign PracticesCorrelogramExamplesDesign PracticesHeatmapUseExamplesDesign PracticeActivity 2.02: Road Accidents Occurring over Two DecadesComposition PlotsPie ChartUseExamplesDesign PracticesVariants: Donut ChartDesign PracticeStacked Bar ChartUseExamplesDesign PracticesStacked Area ChartUseExamplesDesign PracticeActivity 2.03: Smartphone Sales UnitsVenn DiagramUseExampleDesign PracticeDistribution PlotsHistogramUseExampleDesign PracticeDensity PlotUseExampleDesign PracticeBox PlotUseExamplesViolin PlotUseExamplesDesign PracticeActivity 2.04: Frequency of Trains during Different Time IntervalsGeoplotsDot MapUseExampleDesign PracticesChoropleth MapUseExampleDesign PracticesConnection MapUseExamplesDesign PracticesWhat Makes a Good Visualization?Common Design PracticesActivity 2.05: Analyzing VisualizationsActivity 2.06: Choosing a Suitable VisualizationSummary
3. A Deep Dive into Matplotlib
IntroductionOverview of Plots in MatplotlibPyplot BasicsCreating FiguresClosing FiguresFormat StringsPlottingPlotting Using pandas DataFramesTicksDisplaying FiguresSaving FiguresExercise 3.01: Creating a Simple VisualizationBasic Text and Legend FunctionsLabelsTitlesTextAnnotationsLegendsActivity 3.01: Visualizing Stock Trends by Using a Line PlotBasic PlotsBar ChartActivity 3.02: Creating a Bar Plot for Movie ComparisonPie ChartExercise 3.02: Creating a Pie Chart for Water UsageStacked Bar ChartActivity 3.03: Creating a Stacked Bar Plot to Visualize Restaurant PerformanceStacked Area ChartActivity 3.04: Comparing Smartphone Sales Units Using a Stacked Area ChartHistogramBox PlotActivity 3.05: Using a Histogram and a Box Plot to Visualize Intelligence QuotientScatter PlotExercise 3.03: Using a Scatter Plot to Visualize Correlation between Various AnimalsBubble PlotLayoutsSubplotsTight LayoutRadar ChartsExercise 3.04: Working on Radar ChartsGridSpecActivity 3.06: Creating a Scatter Plot with Marginal HistogramsImagesBasic Image OperationsActivity 3.07: Plotting Multiple Images in a GridWriting Mathematical ExpressionsSummary
4. Simplifying Visualizations Using Seaborn
IntroductionAdvantages of SeabornControlling Figure AestheticsSeaborn Figure StylesRemoving Axes SpinesControlling the Scale of Plot ElementsExercise 4.01: Comparing IQ Scores for Different Test Groups by Using a Box PlotColor PalettesCategorical Color PalettesSequential Color PalettesDiverging Color PalettesExercise 4.02: Surface Temperature AnalysisActivity 4.01: Using Heatmaps to Find Patterns in Flight Passengers' DataAdvanced Plots in SeabornBar PlotsActivity 4.02: Movie Comparison RevisitedKernel Density EstimationPlotting Bivariate DistributionsVisualizing Pairwise RelationshipsViolin PlotsActivity 4.03: Comparing IQ Scores for Different Test Groups by Using a Violin PlotMulti-Plots in SeabornFacetGridActivity 4.04: Visualizing the Top 30 Music YouTube Channels Using Seaborn's FacetGridRegression PlotsActivity 4.05: Linear Regression for Animal Attribute RelationsSquarifyExercise 4.03: Water Usage RevisitedActivity 4.06: Visualizing the Impact of Education on Annual Salary and Weekly Working HoursSummary
5. Plotting Geospatial Data
IntroductionThe Design Principles of geoplotlibGeospatial VisualizationsVoronoi TessellationDelaunay TriangulationChoropleth PlotExercise 5.01: Plotting Poaching Density Using Dot Density and HistogramsActivity 5.01: Plotting Geospatial Data on a MapThe GeoJSON FormatExercise 5.02: Creating a Choropleth Plot with GeoJSON DataTile ProvidersExercise 5.03: Visually Comparing Different Tile ProvidersCustom LayersExercise 5.04: Plotting the Movement of an Aircraft with a Custom LayerActivity 5.02: Visualizing City Density by the First Letter Using an Interactive Custom LayerSummary
6. Making Things Interactive with Bokeh
IntroductionConcepts of BokehInterfaces in BokehOutputBokeh ServerPresentationIntegratingBasic PlottingExercise 6.01: Plotting with BokehExercise 6.02: Comparing the Plotting and Models InterfacesActivity 6.01: Plotting Mean Car Prices of ManufacturersAdding WidgetsExercise 6.03: Building a Simple Plot Using Basic Interactivity WidgetsExercise 6.04: Plotting Stock Price Data in TabsActivity 6.02: Extending Plots with WidgetsSummary
7. Combining What We Have Learned
IntroductionActivity 7.01: Implementing Matplotlib and Seaborn on the New York City DatabaseBokehActivity 7.02: Visualizing Stock Prices with BokehGeoplotlibActivity 7.03: Analyzing Airbnb Data with GeoplotlibSummary
Appendix
1. The Importance of Data Visualization and Data ExplorationActivity 1.01: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation of a DatasetActivity 1.02: Forest Fire Size and Temperature Analysis2. All You Need to Know about PlotsActivity 2.01: Employee Skill ComparisonActivity 2.02: Road Accidents Occurring over Two DecadesActivity 2.03: Smartphone Sales UnitsActivity 2.04: Frequency of Trains during Different Time IntervalsActivity 2.05: Analyzing VisualizationsFirst VisualizationSecond VisualizationActivity 2.06: Choosing a Suitable Visualization3. A Deep Dive into MatplotlibActivity 3.01: Visualizing Stock Trends by Using a Line PlotActivity 3.02: Creating a Bar Plot for Movie ComparisonActivity 3.03: Creating a Stacked Bar Plot to Visualize Restaurant PerformanceActivity 3.04: Comparing Smartphone Sales Units Using a Stacked Area ChartActivity 3.05: Using a Histogram and a Box Plot to Visualize Intelligence QuotientActivity 3.06: Creating a Scatter Plot with Marginal HistogramsActivity 3.07: Plotting Multiple Images in a Grid4. Simplifying Visualizations Using SeabornActivity 4.01: Using Heatmaps to Find Patterns in Flight Passengers' DataActivity 4.02: Movie Comparison RevisitedActivity 4.03: Comparing IQ Scores for Different Test Groups by Using a Violin PlotActivity 4.04: Visualizing the Top 30 Music YouTube Channels Using Seaborn's FacetGridActivity 4.05: Linear Regression for Animal Attribute RelationsActivity 4.06: Visualizing the Impact of Education on Annual Salary and Weekly Working Hours5. Plotting Geospatial DataActivity 5.01: Plotting Geospatial Data on a MapActivity 5.02: Visualizing City Density by the First Letter Using an Interactive Custom Layer6. Making Things Interactive with BokehActivity 6.01: Plotting Mean Car Prices of ManufacturersActivity 6.02: Extending Plots with Widgets7. Combining What We Have LearnedActivity 7.01: Implementing Matplotlib and Seaborn on the New York City DatabaseActivity 7.02: Visualizing Stock Prices with BokehActivity 7.03: Analyzing Airbnb Data with geoplotlib

Content preview from The Data Visualization Workshop

5. Plotting Geospatial Data

Overview

By the end of this chapter, you will be able to utilize geoplotlib to create stunning geographical visualizations and identify the different types of geospatial charts. You will be able to demonstrate datasets containing geospatial data for plotting and create complex visualizations using tile providers and custom layers.

Introduction

geoplotlib is an open-source Python library for geospatial data visualizations. It has a wide range of geographical visualizations and supports hardware acceleration. It also provides performance rendering for large datasets with millions of data points. As discussed in earlier chapters, Matplotlib provides various ways to visualize geographical data.

However, Matplotlib ...

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ISBN: 9781800568846

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Soft Skills

The Data Visualization Workshop

by Mario Döbler, Tim Großmann, Rohan Chikorde, Joshua Görner, Anshu Kumar, Piotr Malak, Ankit Verma

5. Plotting Geospatial Data

Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.