Mastering Data Visualization and Animation with Python

Create a first data animation by loading Excel data, building a bar race visualization in a Jupyter notebook, applying custom colors and category icons for clear storytelling.

Modify an existing Python visualization to share on social media by converting to a square layout, adjusting position, title, date, and colors, and adding static images for engaging visuals.

Learn to save an animation as a shareable video with OBS Studio, set up display capture, and adjust base and output resolutions for a clean 1440 by 1440 frame.

Create your first animated pie chart using the svg visualizer, swapping the bar race for a pie plot, and color and icon data from course files to finalize the visualization.

Combine multiple charts in a single animation by reusing a template, adding an animated pie chart with SJ Visualizer via add subplot, and swapping data files for layout and position.

Learn how stacked bar charts in Python visualize changes in quantities over time, comparing bar and pie charts to choose the most effective data visualization method.

Enable bar charts and the visualizer by building a stacked bar chart in Python within a Jupyter notebook, using version 0.0.7 or later and adjusting font color with RGB values.

Explore how optional arguments customize stacked bar charts, including controlling the number of horizontal bars displayed. Add a title to the graph using a static text object.

Learn to mix chart types by adding a bar race beside a stacked bar graph using the SGI visualizer. Adjust width, bars, background, and position to finalize the animation.

Learn to create an animated line chart that reveals trends over time and the complete picture using USA national debt data, and explore storytelling options to highlight significant events.

Ensure you are using SJ Visualizer version 0.0.8 or newer; check installed version with pip list, uninstall older versions, and reinstall via pip to update to the latest version.

Create an animated line chart of the United States debt as a percentage of GDP from 1900 by loading Excel data in a Jupyter notebook and rendering with SVG Visualizer.

Load the events json into a dictionary, map events to their start and end dates, and feed them into line chart to show World War One and the Great Depression.

Add a subtitle showing the data as a percentage of GDP and set the unit accordingly. Cite IMF in the footnote and use a west anchor for left aligned text.

Master area charts in a Jupyter notebook to visualize trends and proportions of multiple data series over time, using world population by continent from 1850 to 2100 as an example.

Build a basic animated world map of banana production using the visualizer library in Python, guided by an Excel dataset and a Jupyter notebook workflow.

Build an advanced animated map in Python by loading Excel data with a data handler, rendering a world map with a linked bar chart on a canvas.

Learn design basics for pleasing data visuals by enhancing a prior visualization and creating a Canva-based background, using a simple, free web tool to improve aesthetics.

Create a simple Canva background for a data visualization by building a square Instagram post, adding a title and 'percentage of GDP', and exporting as PNG.

Organize project files, add the new background to assets, and update the notebook to render the background as a canvas subplot, then adjust the graph to fit and refine labels.