Chapter 15 Further Reading: Advanced Charts and Dashboards

Official Documentation

seaborn - seaborn official documentation: https://seaborn.pydata.org/ The tutorial section is unusually good — it covers each plot type with worked examples and explains the statistical concepts behind them. Start with the "Statistical data visualization" and "Plotting with categorical data" sections. - seaborn API reference: https://seaborn.pydata.org/api.html Complete function signatures for every function introduced in this chapter. Bookmark the heatmap and pairplot pages specifically.

plotly - plotly Python documentation: https://plotly.com/python/ The examples gallery is organized by chart type. Every page includes a live interactive example and the complete code. The "Subplots" and "Hover Text and Formatting" pages are essential. - plotly express API reference: https://plotly.com/python-api-reference/plotly.express.html All px.* functions with full parameter documentation. - Dash by plotly: https://dash.plotly.com/ When you're ready to move beyond single-file HTML dashboards to full web applications, Dash is the natural next step. It uses plotly charts as components inside a Python web framework. No JavaScript required.

Books

"Storytelling with Data" by Cole Nussbaumer Knaflic This is not a Python book. It is a book about visualization principles and design decisions — when to use which chart type, how to reduce clutter, how to focus the viewer's attention. The principles apply directly to seaborn and plotly chart construction. Widely used in data analyst roles at major companies. Recommended for anyone who communicates with charts regularly.

"The Functional Art" and "The Truthful Art" by Alberto Cairo Cairo's two books are more academic than Knaflic's but go deeper into perception, cognition, and the ethics of visualization. "The Truthful Art" has a particularly good chapter on how chart design choices can mislead even without intentional deception — relevant when someone asks you to "make the growth look more impressive."

"Python for Data Analysis" (3rd ed.) by Wes McKinney McKinney created pandas. His book covers seaborn and matplotlib in a data analysis workflow context. The visualization chapters are light compared to a dedicated visualization book, but the workflow integration is excellent.

Online Tutorials

seaborn Tutorial (official) https://seaborn.pydata.org/tutorial.html This structured tutorial walks through every major seaborn concept with executable examples. The "Controlling figure aesthetics" section answers most questions about customization.

plotly Python Tutorial Gallery https://plotly.com/python/plotly-express/ The express gallery shows px.* one-liners alongside their outputs. Excellent for discovering which function you need.

The Python Graph Gallery https://www.python-graph-gallery.com/ Charts organized by type and library. Click any chart type to see seaborn, matplotlib, and plotly versions side by side. Useful for comparing approaches quickly.

From Data to Viz — Decision Tree https://www.data-to-viz.com/ An interactive flowchart: input your data type (one continuous variable, two categorical variables, etc.) and it recommends appropriate chart types with code examples. Genuinely useful when you're uncertain which chart type fits your data structure.

Color Resources

ColorBrewer 2.0 https://colorbrewer2.org/ The definitive tool for choosing color palettes for data visualization. All palettes are available in seaborn. The tool lets you specify sequential/diverging/categorical and filter for colorblind-safe options.

Coolors https://coolors.co/ Useful for generating custom categorical palettes for branded charts. Export hex codes and use them directly in plotly's color_discrete_sequence or seaborn's palette parameter.

Viz Palette https://projects.susielu.com/viz-palette Specifically designed for data visualization palettes. Shows how a palette looks in bar charts, scatter plots, and line charts, and how it appears under various types of color blindness.

Plotly-Specific Deep Dives

Plotly Hovertemplate Guide https://plotly.com/python/hover-text-and-formatting/ Everything about formatting hover tooltips. The customdata pattern (for passing additional columns to the template) is explained with complete examples.

Plotly Subplots and Multiple Axes https://plotly.com/python/subplots/ Covers make_subplots with all its configuration options: shared axes, variable column widths, mixed chart types, and insets.

Plotly Animation https://plotly.com/python/animations/ When you're ready to build animated charts (Chapter 15, Exercise 15-16), this is the reference. Animations are particularly effective for showing data over time in presentations.

Color Accessibility

Approximately 8% of men and 0.5% of women have some form of color vision deficiency. Designing visualizations that work for color-blind viewers is both ethical practice and professional good sense. Two practical resources:

  • Coblis Color Blindness Simulator: https://www.color-blindness.com/coblis-color-blindness-simulator/ Upload a screenshot of your chart to see how it appears under different types of color blindness.

  • seaborn "colorblind" palette: Available directly in seaborn as sns.color_palette("colorblind"). Based on Wong (2011), designed to be distinguishable for the most common forms of color vision deficiency. Use it for any chart where color is the primary distinguishing variable.

Next Chapter

Chapter 16 covers the other direction of the Python ↔ data exchange: writing data from Python back into Excel. You'll use openpyxl to create formatted workbooks with multiple sheets, styled headers, number formatting, and embedded charts. The chapter culminates in Maya's complete invoicing system — a script that reads project data from CSV and generates one professionally formatted Excel invoice per client in under a minute.