Python-Data-Visualization
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D-Lab's 3 hour introduction to data visualization with Python. Learn how to create histograms, bar plots, box plots, scatter plots, compound figures, and more, using matplotlib and seaborn.
This update makes some small changes to improve PyVis, in preparation for a full update in the coming curriculum cycle. - Remove quotes - Reduce the amount of times pandas...
Another important package for modern data visualization with Python is plot.ly. Consider including at least an example or reference in a 'Next Steps' section, or a larger section introducing the...
Instead of looking at area/bars in an isolated case in the comparisons in the theory section, consider using a real-world example comparing area/length by looking at a pie chart vs...
For each function, add the link to the package documentation
Choose the most important key quotes and principles of theory in the first section, and lighten the theory to focus on examples and application.
Separate out notebooks into sections: matplotlib, seaborn, customization.
In particular, move up the 'DataHub' and 'Binder' buttons to the top of the README
Update challenge question format and scaffolding to the D-Lab standard.
optional question in challenge 2 in barplot asks students to make a pie chart; pie charts are outdated, add a different question and remove pie charts altogether
installing pyroot through ! for the terminal only works for mac - add the windows version