There are dozens of great python libraries and tools available to Data Scientists that want to create engaging plot and graph visualizations.
From the old guard like Matplotlib to the beautifully crafted newer entrants like PlotAPI, each has unique capabilities, syntax, flexibility, and levels of interactivity.
We think there are seven python libraries in particular that every Data Scientist should know about: Matplotlib, Seaborn, Folium, Bokeh, Plotly, Altair, and PlotAPI.
Choosing the right one depends mainly on what features and capabilities you need and your experience level.
Read on to dig deeper into each library, visualize it, and get the tl;dr on its pros and cons. You'll get all the info you need to select the best library for your next python visualization project.
Interactive data visualization is more engaging for stakeholders, without a doubt. But interactive plots can take more time to create.
Decide how interactive you want your plots to be because it will affect the framework you choose.
For instance, Matplotlib and Seaborn are suitable for creating static visualizations that effectively explain concepts in a presentation, paper, or slide deck.
Great to move quickly and get a report or app published and shared, albeit less likely to wow your audience.
On the other hand, Altair, Bokeh, PlotAPI, and Plotly allow for the creation of highly interactive graphs that enable users to explore the data for themselves.
Great to get something beautiful into the hands of your stakeholders, but it may take slightly longer.
You also need to know the syntax of each library and how it aligns with your data and use case.
Lower-level libraries like Matplotlib offer a lot of flexibility, but the API may be more verbose.
On the other hand, libraries like Altair and Seaborn offer more declarative syntax, making it easier to map your data to the visualization.
If you have a specialized use case, like a geographical plot or large data, or a specific plot type, you may want a library tailored to those needs.
For example, Folium is specifically designed for creating geographical plots.
Libraries such as Bokeh and Plotly offer a wide range of plot types and are good for unique visualization needs.
Libraries like PlotAPI supercharge your visuals even more, adding an extra layer of quality to your report or app.
When you've chosen a library and built your visualizations, remember to think carefully about how you deliver those insights to colleagues or stakeholders.
Downloading a report as a PDF or screenshotting your notebook is not only underwhelming, but you also lose interactivity and visual quality.
Exporting to HTML, even if you strip your notebook of the code, is better - but you'll need to update it every time you make a change.
Tools like Streamlit and Dash can be great for building and sharing interactive python analysis as a web app but might require learning new skills.
Whilst Datapane lets you convert any notebook into a beautiful, shareable, interactive app from inside Jupyter.
Remember this last step. The format your visualization takes is often as important as the library!
Altair is a declarative statistical visualization library for Python.
Altair's design means that users can create visualizations by simply "declaring" the links between data columns and visualizations without needing to write complex plotting commands.
Its API is simple, friendly, consistent, and built on top of the powerful Vega-Lite visualization grammar, so it's great for producing beautiful and compelling visualizations with minimal code.
You can install Altair using:
We built a simple visualization using Altair and Datapane. See its documentation to get started.
Bokeh is a browser-optimized data visualization library for Python that enables users to create interactive, web-based plots and charts.
It has an intuitive API, plots can be interactive, and users can apply themes to easily control the appearance of simple plots or complex, multi-layered visualizations.
Because it's built for web browsers and easily customizable, it's a good tool for creating interactive visualizations that can be easily shared with others.
You can install Bokeh using:
$ pip install bokeh
$ conda install bokeh
We built a simple visualization using Bokeh and Datapane. See its documentation to get started.
PlotAPI is software that lets you turn your data into engaging, beautiful, and interactive visualizations.
The power of PlotAPI can be accessed directly through the API if you’re a coder or using the friendly APP if you prefer not to code.
PlotAPI is designed to be “Beautiful by Default” - a head-turning visualization is only a single click or line of code away.
We built a simple visualization using PlotAPI and Datapane. To start using PlotAPI yourself, see its documentation.
Folium is a mapping data visualization library that utilizes Leaflet.js in its design and is particularly useful for visualizing and analyzing data on a geographic scale.
Its easy-to-add markers, plugins, and customization options make it a great option for creating maps or visualizing geographic data.
You can install Folium using:
$ pip install folium
$ conda install folium -c conda-forge
We built a simple visualization using Folium and Datapane. To start using it yourself, see its documentation.
Plotly is a powerful library that allows users to create various dynamic and interactive plots, charts, and graphics that are publication-quality.
As a result, it is widely used for handling financial, statistical, and scientific data and is particularly popular for its appealing visuals.
Plotly’s API functions are effective in local and browser modes, making it easy to use in various environments.
You can install Plotly using:
$ pip install plotly
$ conda install -c plotly plotly=5.11.0
We built a simple visualization using Plotly and Datapane. To start using it yourself, see its documentation.
Matplotlib is one of Python's most widely-used libraries for creating static and animated plots.
It's simple, flexible, and a great starting point for data visualization in Python, but it's limited for more advanced visualizations.
You can't write a list of the "top" visualization libraries without including this workhorse.
You can install Matplotlib using:
$ pip install matplotlib
$ conda install matplotlib
We built a simple visualization using Matplotlib and Datapane. To start using it yourself, see its documentation.
Seaborn is a visualization library built on Matplotlib. It provides more attractive visualizations than its parent package.
Seaborn is better for more advanced statistical plots, whilst Matplotlib has a lower learning curve.
Neither do interactive visualizations particularly well.
You can install Seaborn using:
$ pip install seaborn
$ conda install seaborn
We built a simple visualization using Seaborn and Datapane. To start using it yourself, see its documentation.
When choosing a Python visualization library for your next project, which we assume is why you've got this far, remember that a lot of it depends on you!
Your use case, Python proficiency, and the capabilities you need in the report or app you create.
There are dozens of excellent python visualization libraries; all have different capabilities, syntax, flexibility, and interactivity.
We've named a few of the libraries we think are the best for 2023, but if you think there's one missing, get in touch!