Introduction to Data Visualization with Plotly in Python
Create interactive data visualizations in Python using Plotly.
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Course Description
Producing high-quality, interactive visualizations historically required complex code, extensive time, and effort. Not anymore. In this course, you’ll learn how to create publication-quality graphs harnessing the power of JavaScript, without leaving the comfort of the Python programming language we all love. You’ll create, style, and customize a variety of stunning, interactive graphs—using datasets ranging from stock prices to basketball team stats, and even penguin beak sizes! Using the Plotly charting library, you’ll also learn to customize interactivity such as hover information, range sliders, custom buttons, and even drop-downs that reactively change the visualization. Are you ready to level-up your data visualization skills?
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Introduction to Plotly
FreeEnter the world of Plotly! In this first chapter, you’ll learn different ways to create plots and receive an introduction to univariate plots. You’ll then build several popular plot types, including box plots and histograms, and discover how to style them using the Plotly color options.
Plotly and the Plotly Figure50 xpPlaying with a Plotly Figure50 xpFixing a Plotly figure100 xpUnivariate visualizations50 xpStudent scores bar graph100 xpBox plot of company revenues100 xpHistogram of company revenues100 xpCustomizing color50 xpWhat color did we use?50 xpColoring student scores bar graph100 xpSide-by-side revenue box plots with color100 xpRevenue histogram with stacked bars100 xp - 2
Customizing Plots
Add to your Plotly toolkit as you explore and implement bivariate plots. Next, you’ll explore how to customize your plots to make them look amazing with annotations, hover information, legends, and custom axis titles and scales.
Bivariate visualizations50 xpBuilding a scatterplot with specific colors100 xpBird feature correlations100 xpCustomizing hover information and legends50 xpGDP vs. life expectancy legend100 xpEnhancing our GDP plot100 xpAdding annotations50 xpAnnotating your savings100 xpA happier histogram plot100 xpEditing plot axes50 xpThe log-scale parameter50 xpAnalyzing basketball stats100 xpStyling scientific research100 xp - 3
Advanced Customization
Take your Plotly graphs to the next level with more advanced customizations. Through hands-on exercises, you’ll learn how to layer multiple interactive chart types in the same plot (such as a bar chart with line chart over the top). You’ll then create time-series selectors, such as year to date (YTD), to help you zoom in and out of your line charts.
Subplots50 xpRevenue box subplots100 xpRevenue histogram subplots100 xpLayering multiple plots50 xpSpecies on different islands100 xpMonthly temperatures layered100 xpTime buttons50 xpA time button dictionary50 xpTime buttons on our rainfall graph100 xpFinance line chart with custom time buttons100 xp - 4
Advanced Interactivity
In this final chapter, you’ll harness Plotly’s advanced user interactivity as you learn how to create buttons, dropdowns, and sliders that change everything from graph types to annotations and much more. Take your Plotly skills to the next level and build truly interactive user experiences.
Datasets
Penguins dataRevenue dataAAPL dataWorld Bank population dataSydney temperature dataRainfall dataMonthly salesRevenue Data ExtendedCollaborators
Prerequisites
Intermediate PythonAlex Scriven
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Senior Data Scientist @ Atlassian
Alex is a Senior Data Scientist working for Atlassian in Sydney, Australia and has previous experience in government, agency and startup. He also holds lecturing and research positions at the University of Technology Sydney and the University of New South Wales. He has built and delivered several Masters-level courses in machine learning & deep learning whilst researching on applications of machine learning & data science in industry. From a heavily commercial background, Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.
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