Type summary

• Bar plots: < 10 categories. 3 types: regular, grouped, stacked.
• Box plots: show distributions, more info than histogram.
  • IQR = 25th to 75th range.
  • Whisker on each side: from edge of the box on that side extend by 1.5*IQR
  • Observations outside the whisker are outliers
• Scatter plots: show 2 variables, but you can also add color, circle size for more info (3d, 4d). Also check out the 4d bubble plots ref below.
• Flow charts: Lucidspark, Lucidchart.

General

• Comprehensive Data Explorations with Matplotlib.
• Guide to Data Visualization with Python: Part 1
• 5 Steps to Amazing Visualizations with Matplotlib.
  • If known, matplotlib is good enough. If want fancy, use Seaborn.
  • For interactive dashboards: Flask and Bokeh.

Advanced

• 4-Dimensional Data Visualization: Time in Bubble Charts.
  • Bubble charts typically display 3 dimensions using x, y, and bubble size, but adding time as a 4th dimension can provide deeper insights.
• Bar Chart Race of World Population by 2020 in Python.
• Create the fancy bar-race chart in R.
• Creating diagnostic plots in Python.
• Spice Up Your Python Visualizations with Matplotlib Animations.
• Publish Interactive Data Visualizations for Free with Python and Marimo. Python library marimo: it’s called the future of Python notebooks.
  • Options: Flask, Dash, Streamlit, bokeh, HoloViews, altair, plotly, shinyapps, Pyodide.

Tools

• Qlik Sense.
• Qlik View.
• Tableau.
• SAP HANA.
• Power BI.

Books & Resources

• Fundamentals of Data Visualization. (Oreilly, Claus O. Wilke)
• Data Visualization with R - 100 examples.
• R Graphics Cookbook.
• Geographic Data Science with Python.