Spotify releases Chartify, a new data visualization library in python for easier chart creation

Spotify announced, last week, that it has come out with Chartify, a new open source Python data visualization library, making it easy for data scientists to create charts. It comes with features such as concise and user-friendly syntax and consistent data formatting among others.

Let’s have a look at these features in this new library.

Concise and user-friendly syntax

Despite the abundance of tools such as Seaborn, Matplotlib, Plotly, Bokeh, etc, used by data scientists at Spotify, chart creation has always been a major issue in the data science workflow.

Chartify solves that problem as the syntax in it is considerably more concise and user-friendly, as compared to the other tools. There are suggestions added in the docstrings, allowing users to recall the most common formatting options. This, in turn, saves time, allowing data scientists to spend less time on configuring chart aesthetics, and more on actually creating charts.

Consistent data formatting

Another common problem faced by data scientists is that different plotting methods need different input data formats, requiring users to completely reformat their input data. This leads to data scientists spending a lot of time manipulating data frames into the right state for their charts.

Chartify’s consistent input data formatting allows you to quickly create and iterate on charts since less time is spent on data munging.



Chartify

Other features

Since a majority of the problems could be solved by just a few chart types, Chartify focuses mainly on these use cases and comes with a complete example notebook that presents the full list of chart types that Chartify is capable of generating.

Moreover, adding color into charts greatly help simplify the charting process, which is why Chartify has different palette types aligned to the different use cases for color. Additionally, Chartify offers support for Bokeh, an interactive python library for data visualization, providing users the option to fall back on manipulating Chartify charts with Bokeh if they need more control.

For more information, check out the official Chartify blog post.

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