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At the moment of writing, there are over 8 million people in the world using Python to write code. This powerful programming language has a long list of use cases in apps and websites. It’s most popular use case is data visualization and data analysis.
With Python, you can analyze complex data sets and then visualize them in the form of charts, graphs, heatmaps, histograms, tree maps, and more. To do this, a developer would have to use different Python libraries. Today, we take a look at the top choices and ask the difficult question: is there a better way to visualize data?
Having data points in the form of a giant table with rows and columns belongs in the 20th century. Visualizing data with bar charts, graphs, line charts, scatter plots and more helps the end-user understand the numbers they are seeing.
Here are some of the best Python libraries for creating interactive visualizations.
Altair is a free and open source Python library that is commonly used for interactive and statistical visualizations. For businesses looking to do exploratory data analysis and statistical data visualization, this is one of the best free choices out there.
It’s based on Vega and Vega-lite, which ensures that the 3D charts, box plots, bar plots and other visualization types are fully interactive. When using Altair, you can install dependencies such as Numpy and Pandas for data wrangling. It relies on JSON to present the charts and add any specifications or customizations to the chart’s look and feel.
Plotly, the Python chart library is built on top of Plot.ly, the Javascript library. For dynamic businesses that need fast, interactive dashboards, this chart library is an excellent choice.
It has a rich selection of visualizations to choose from, including line plots, scatter plots, 3D plots and many others. The basic version of Plotly is free to use and if you want access to the more advanced features, you’ll have to opt for a paid plan.
Based on the Grammar of Graphics, this is one of the most popular Python chart libraries for a wide variety of use cases. For interactive libraries and working with data sets large and small, Bokeh is fast and adaptable. It can be used in HTML pages, JSON objects or apps.
Bokeh is free and open source, which means it won’t cost you much as long as you have a really good Python developer. Because of its interactivity and flexibility, it’s a superb choice for visualizing data in apps.
For most Python developers, Matplotlib is the default choice for visualizing data. It’s been around since 2003 and it can be used for interactive visualization across different platforms. You can use it in Python scripts, the Jupyter notebook, and with Python and iPython shells. The interface for the library resembles MATLAB.
It supports a rich variety of visualizations, such as line plots, scatter plots, bar charts, histograms and many others. It’s also open source and you can use it completely free.
If your developers are familiar with visualizing data in R, they’ll love GGplot, as this is the Python port of GGplot2. For plotting and visualizing data with the grammar of graphics approach, this high-level-API library is an excellent choice. It allows developers to combine complex plots from data in one dataframe.
It’s open source and free to use.
Plotnine is the popular visualization library used in the programming language R. In this library, you layer components to create a complete plot. It’s integrated with Pandas and it’s one of the best choices if you need to visualize complex data sets.
GGPlot2 is technically used in R, but libraries such as Plotnine are heavily influenced by it. It’s an excellent choice for R users making a transition to Python.
It’s open source and completely free.
Built on top of Matplotlib, Seaborn gives a high-level interface for building statistical visualizations. This is a solid choice if you want to visualize distributions and relationships in data sets.
It’s free to use.
For developers looking to create map-type visualizations, Geoplotlib should be the first choice. On top of its narrow use case, it’s also very easy to use and great for beginners. Density maps, heatmaps, dot density maps and other geographic visualizations are a breeze with this chart library type.
Geoplotlib is open source and free.
This Python chart library is built with interactivity in mind and it’s great for use in web apps and pages. The difference between Pygal and the rest is that the output files are SVG. This means that there is no loss in quality since these are scalable vector charts.
It’s highly customizable, free and open source.
If you’re constantly struggling with datasets that are incomplete, this is the right Python library for you. Before visualizing anything, Missingno gives you a high-level interface, showing you the data you’re missing for the dataset to be complete. If you’re new to data analytics and you’re worried about data structure and integrity, you may want to check this one out.
It’s free and open source.
This is the Python chart library that helps you turn visualizations into web apps. Gleam lets you easily build a web application interface, where you can choose from a number of input fields your users can control, like filters or selectors. Then, it uses other, existing Python libraries such as Matplotlib and Plotly for creating the data visualizations. If you’re keen on building an app from scratch with Python, this is a good alternative to purchasing off-the-shelf visualization software.
Like most other tools on this list, it’s free to use.
If getting the job done is all that matters, Leather is as good as it gets. This chart library was intentionally created for those developers who need quick and easy visualizations without the added bells and whistles. You can get end results as SVG charts, but the dashboards you can create are fairly basic, as is the documentation.
It’s also open source and free.
If you need to visualize geographical data and make it interactive, consider this chart library instead of the more popular variants mentioned above. Folium allows for a certain level of interactivity, letting your end users easily zoom in and out of maps for more intricate details. It uses Leaflet.js as a foundation.
The strong suites of this library are geographical maps, heatmaps, choropleth maps and similar. It’s open source and free to use.
The low (or non-existent) cost, the options for customization (color palettes, chart types) and interactivity, the ease of use: Python chart libraries have a lot going for them. However, similarly to Javascript chart libraries, there are some downsides to be aware of as well.
Performance issues: some chart libraries have difficulties handling large datasets. If you have complex data structures and want to visualize huge volumes of data at once, the dashboards you create may take a long time to load. This can be a deal breaker for time-intensive use cases such as embedded analytics where real-time data access is critical.
Customization limitations: while you can customize some of the visualizations (e.g. interactive plots, pie charts) and the way they look, there is another issue. The visualizations should fit in with the rest of your website or app and look great on a phone or in a web browser.
Integration into existing websites or business applications: creating different chart types in Python is one thing but making sure they integrate into a web-based application is another. You’re going to need a really good developer who knows their way around APIs and integrations for web applications.
Complexity and learning curve: there are different types of Python libraries and developers. And while the majority of libraries are open source and anyone can learn them by taking a deep dive in Github, really mastering different types of charts is going to require more than a simple tutorial.
Maintenance and support: if you choose an open source Python library, beware that they will lack the support and maintenance that commercial libraries have.
Licensing and compliance: your business model may have compliance and licensing requirements that will make it difficult to use Python libraries and you’ll have to use visualization tools instead.
Need to visualize data in your software? Instead of relying on open source libraries and hoping your developers can wrangle contour plots and choropleths, get a dedicated tool for the job. At Luzmo, we help businesses visualize data in their app, for themselves and their end users. And you also get access to AI and machine learning features out of the box.
Our embedded analytics platform has a capable API that allows any developer to integrate a dashboard in your product or website within hours, not weeks or months.
Grab a free demo with our team to see how Luzmo can work for you!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
