Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn (Conclusion)
At this point, you’ve seen more than a handful of functions and methods to choose from for plotting a Python histogram. How do they compare? In short, there is no one-size-fits-all answer. Here’s a recap of the functions and methods you’ve covered so far, all of which relate to breaking down and representing distributions in Python:
|You Have/Want To||Consider Using||Note(s)|
|Clean-cut integer data housed in a data structure such as a list, tuple, or set, and you want to create a Python histogram without importing any third party libraries.||
||This is a frequency table, so it doesn’t use the concept of binning as a “true” histogram does.|
|Large array of data, and you want to compute the “mathematical” histogram that represents bins and the corresponding frequencies.||NumPy’s
||For more, check out
|Tabular data in Pandas’
||Pandas methods such as
||Check out the Pandas visualization docs for inspiration.|
|Create a highly customizable, fine-tuned plot from any data structure.||
||Matplotlib, and especially its object-oriented framework, is great for fine-tuning the details of a histogram. This interface can take a bit of time to master, but ultimately allows you to be very precise in how any visualization is laid out.|
|Pre-canned design and integration.||Seaborn’s
||Essentially a “wrapper around a wrapper” that leverages a Matplotlib histogram internally, which in turn utilizes NumPy.|
With that, best of luck creating histograms in the wild. Whatever you do, just don’t use a pie chart!
Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.
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