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Introduction to Data Visualization with Plotly in Python

Create interactive data visualizations in Python using Plotly.

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4 Hours14 Videos45 Exercises
10,944 Learners

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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?
  1. 1

    Introduction to Plotly

    Free

    Enter 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.

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    Plotly and the Plotly Figure
    50 xp
    Playing with a Plotly Figure
    50 xp
    Fixing a Plotly figure
    100 xp
    Univariate visualizations
    50 xp
    Student scores bar graph
    100 xp
    Box plot of company revenues
    100 xp
    Histogram of company revenues
    100 xp
    Customizing color
    50 xp
    What color did we use?
    50 xp
    Coloring student scores bar graph
    100 xp
    Side-by-side revenue box plots with color
    100 xp
    Revenue histogram with stacked bars
    100 xp
  2. 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.

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    Bivariate visualizations
    50 xp
    Building a scatterplot with specific colors
    100 xp
    Bird feature correlations
    100 xp
    Customizing hover information and legends
    50 xp
    GDP vs. life expectancy legend
    100 xp
    Enhancing our GDP plot
    100 xp
    Adding annotations
    50 xp
    Annotating your savings
    100 xp
    A happier histogram plot
    100 xp
    Editing plot axes
    50 xp
    The log-scale parameter
    50 xp
    Analyzing basketball stats
    100 xp
    Styling scientific research
    100 xp
  3. 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.

    Play Chapter Now
    Subplots
    50 xp
    Revenue box subplots
    100 xp
    Revenue histogram subplots
    100 xp
    Layering multiple plots
    50 xp
    Species on different islands
    100 xp
    Monthly temperatures layered
    100 xp
    Time buttons
    50 xp
    A time button dictionary
    50 xp
    Time buttons on our rainfall graph
    100 xp
    Finance line chart with custom time buttons
    100 xp
  4. 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.

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    Custom buttons
    50 xp
    Rainfall plot type buttons
    100 xp
    Changing annotations with buttons
    100 xp
    Dropdowns
    50 xp
    Growth locations dropdown
    100 xp
    Housing prices dropdown
    100 xp
    Sliders
    50 xp
    Hiding a slider trace
    50 xp
    Rainfall by season slider
    100 xp
    What you learned
    50 xp

Datasets

Penguins dataRevenue dataAAPL dataWorld Bank population dataSydney temperature dataRainfall dataMonthly salesRevenue Data Extended

Collaborators

Collaborator's avatar
Amy Peterson

Prerequisites

Intermediate Python
Alex Scriven HeadshotAlex Scriven

Data Scientist @ New South Wales Government

Alex is a Data Scientist working for the New South Wales Government in Sydney, Australia. He is also a Lecturer at the University of Technology Sydney where he teaches into several courses in their Master of Data Science & Innovation program in machine learning & deep learning. He is also a partner of a boutique data analytics firm, Madlytics and is passionate about building communities around technology & entrepreneurship.
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