Remember to log in to Datapane with your token beforehand if you want to use the Blob. import datapane as dp dp.Blob.get(name='github_data', owner='khuyentran1401').download_df() To follow the article, you either download the csv file from here or get the direct data from Datapane Blob. If you want to see more real-world examples, check out the user-contributed reports in Datapane gallery. I’m going to be embedding the visualizations from each library using Datapane, which is a Python framework and API for publishing and sharing Python reports. To make it easier to compare, I will use the real data I scrape from Github in this article: Some libraries such as Altair are very declarative, which makes it easier to map to your data.Īre you working with a specialized use-case, such as a geographical plot, with large data, or using a plot-type which is only supported by a certain library? Data How does the syntax for each library differ? Lower-level libraries such as Matplotlib let you do anything you could conceivably want - but at the cost of a more complex API. On the other hand, libraries like Altair, Bokeh, and Plotly allow you to create interactive graphs that your users can dive into and explore themselves Some libraries, such as Matplotlib, render visualizations as images thus, they are good for explaining concepts (in a paper, slide deck, or presentation). We will do this by using a sample dataset and exploring it using each library, focusing on a few specific attributes:ĭo you want your visualization to be interactive? By the end of this article, you should be able to distinguish the different features of each library and have an easier time choosing the optimal one. This article will show the pros and cons of each.
If you have a DataFrame waiting to be visualized, which one should you pick? Some libraries might be better in specific cases than the others. If you’re starting off with Python visualization, you may be overwhelmed with the number of libraries and examples: You can find the latest version of the blog here.
This guide has been updated with some more in-depth examples.
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