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Visualization for Data Science in Python

Getting started & overview.

Keno Leon
Keno Leon
Jan 19, 2018 · 4 min read

Choosing a library :

There seems to be a number of libraries out there, our first objective will be to try out a few of them to get a feel on how easy or hard they are, our first dataset will simply be a labeled time series:

1. Matplotlib:

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Super straightforward no frills out of the box solution, you can add options and formatting galore.

2. Seaborn:

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Rather than a different type of library, seaborn rides on top of matplotlib and adds both style and a number of data science & statistics features in API form.

3. Bokeh:

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Bokeh is different in a good way, it creates an html friendly interactive display you can embed, zoom and pan, but you can also export static images, it comes with varied and well documented API for all kinds of displays, and works nicely with dataframes.

Styling for instance, can be done by adding a few lines:

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Conclusion:

While there are a few other libraries and solutions for plotting your data in python, these 3 I think are a good place to start, in general I would recommend bokeh ( for ease of use) , seaborn (if you need a quick statistics focused solution ) and if nothing works matplotlib.

EDIT :

Other Libraries :

4. Plotly:

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Plotly is a whole ecosystem devoted to high quality graphical visualizations and they provide a free Python API ( it is part of a commercial operation though ), there’s a heavy cloud component ( but you can also work offline )and out of the box it is easy to work with and the results are good, like bokeh you get a nice interactive html page, highly recommended as long as you can deal with it not being 100% open source.

Keno.

About the Author :

Born Eugenio Noyola Leon (Keno) I am a Designer, Developer/programmer, Artist and Inventor, currently living in Mexico City, you can find me at: www.k3no.com

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