Plotly Charts are Making Python Smarter

As data science has become a larger and larger part of my life, so has the need to visualize, share, and present the findings of my work. I don’t need to convince you that graphing in Python is crucial, but if I had to, I’d refer you to any beginner Python course, and you’d see how early they bring graphing into the mix.

Python has many robust packages to make charts. Matplotlib and Seaborn are two prominent ones.

These packages are great for visual checks within your analysis, but many people struggle with the syntax and rigidity of the charts. Once a chart is made, Python users often want to go back and make tweaks. This requires more tedious editing of the code — and the syntax can be very complex and hard to remember.

Plotly alleviates some of these pain points and pushes graphing within Python to a new level.

Plotly offers the standard charts that every Python users would want to use. Here is a screenshot from their website:

But they also give access to advanced graphics, centered around ML and AI.

But the real innovations is Plotly’s focus on interactive charts. Instead of having to worry about adjusting the code anytime you want to change the view, Plotly gives you a GUI to do so.

This is the first example from the Plotly Graphing Package documenation:

In even this simple example, you can change the color of the graph using the UI dropdown on the right.

In terms of a combination of ease of set-up and flexibility of graph, Plotly is the best graphing package I have come across.

Integrating Plotly into Mito

My team and I have been building a data science tool called Mito. Mito is a spreadsheet interface for Python, allowing you to generate Python by editing a spreadsheet inside of Jupyter.

As we supported more use cases, we quickly heard requests for graphing features. We were delighted to find that Plotly is embeddable in other apps (and it is super easy to do!). Leave a comment if you’d like to hear more about this process — there are a lot of interesting details.

We were able to make Plotly an out of the box feature of Mito. In Mito, you don’t need to write any code to make your charts — just click the graph button and start configuring your Plotly chart.

Mito graphs come in many shapes and sizes (literally!). Mito supports bar charts, box plots, histograms, and scatter plots. Plotly graphs also support adding multiple series to each axis. Creating a stacked graph is a helpful tool for identifying relationships between series along a common attribute.

In the future, we hope to integrate more of Plotly’s advanced graphs into Mito and we can’t wait to see where that takes the product.

If you have any thoughts on what I’ve written, leave a comment or reach out: jake@sagacollab.com