Which Python library should you pick for data visualization? Read on to see our assessment!
Data visualization is an increasingly valuable skill, one that’s sought after in many organizations. It helps you to find insight into data and to communicate your findings to less technical audiences. You can benefit from it in your career and use it to pivot toward a data-focused role.
There are many paths to learning and practicing data visualization. Many require setting up, maintaining, and using elaborate BI tools with limited capabilities.
Python, the number one language in data science, offers a better way. It is versatile, needs little maintenance, and can access almost any available data source.
If you want to use Python to get insights from data, check out our Introduction to Python for Data Science course. It offers over 140 exercises to improve your Python skills and practice loading, transforming, and visualizing data.
You might wonder which Python data visualization library you should learn or use for a given project. Python has a vast ecosystem of visualization tools; it can be hard to pick the right one.
Python’s visualization landscape in 2018 (source).
This article helps you with that. It lays out why data visualization is important and why Python is one of the best visualization tools. It goes on to showcase the top five Python data visualization libraries, their main features, and when it is a good idea to use them.
Why Data Visualization Is Important
Data visualization is a powerful way to gain and communicate insights from data.
The main challenge of data analysis is understanding relationships within a dataset and their relevance to the use case. A good visualization often reveals insights faster than hours of data munging (aka data wrangling) and is more intuitive for non-technical audiences. For these reasons, data visualization is a central activity in any organization that wants to make complex data-based decisions.
An example of a sales data visualization (source)
There are many situations where you can benefit from visualizing data – like doing a sales presentation, conducting market research, or setting up a KPI dashboard. You can also use different tools for that. However, some of the tools require too much overhead to set up or are limited in their capabilities.
What if there was a tool that would be versatile enough to use with a wide range of problems, data sources, and use cases? And had little infrastructural requirements?
Fortunately, there is such a tool: Python!
Why Python is a Great Language for Data Visualization
Python is currently one of the most popular programming languages and the primary one when it comes to data science, making it a safe learning choice.
Python is excellent to learn for your career and is a great language to introduce to your organization. It is easy to learn, helps with automation, and provides access to data and analytics. Many big companies use Python to run critical operations within their business.
Python has a thriving data science ecosystem, including data visualization libraries that surpass Excel’s capabilities. This makes Python especially useful in domains where you need to complement your work with analytics, like marketing or sales.
However, Python’s popularity and rich ecosystem might be intimidating for newcomers, as it is hard to understand which visualization library to use for which use case. To help you with that, the rest of our article will give you an overview of the top five Python visualization libraries.
Data Visualization Libraries
We can characterize data visualization libraries using the following factors:
- Interactivity: Whether the library offers interactive elements.
- Syntax: What level of control the library offers, and whether it follows a specific paradigm.
- Main Strength and Use Case: In what situation is the library the best choice?
The following table summarizes the top Python visualization libraries according to these factors:
| Library | Interactive Features | Syntax | Main Strength and Use Case |
|---|---|---|---|
| Matplotlib | Limited | Low-level | Highly customized plots |
| seaborn | Limited (via Matplotlib) | High-level | Fast, presentable reports |
| Bokeh | Yes | High- and low-level, influenced by grammar of graphics | Interactive visualization of big data sets |
| Altair | Yes | High level, declarative, follows grammar of graphics | Data exploration, and interactive reports |
| Plotly | Yes | High- and low-level | Commercial applications and dashboards |
Let’s discuss each library individually.
Matplotlib
Matplotlib is the most widely used visualization library. It was born in 2003 as an open-source replacement of MATLAB, a scientific graphing package.
Because of its early start and popularity, there is a huge community around Matplotlib. You can easily find tutorials and forums discussing it, and many toolkits extend its use (e.g. into geographic data or 3D use cases). Also, many Python libraries (e.g. pandas) rely on it in their visualization features.
Matplotlib provides granular control of plots, making it a versatile package with a wide range of graph types and configuration options. However, its many configuration possibilities complicate its use and can lead to boilerplate code. The default Matplotlib theme does not follow visualization best practices. You also need to rely on other packages (e.g. time data handling) for some fundamental features.
Matplotlib is a good choice in the following cases:
- You need detailed control over your plots (e.g. in a research setting with unique visualization problems).
- You want something reliable, with a huge community.
- You don’t mind the learning curve.
Matplotlib plots (source)
seaborn
seaborn is a visualization library that makes Matplotlib plots practical. It abstracts away Matplotlib’s complexity and offers an intuitive syntax and presentable results right out of the box.
The seaborn library supports the creation of statistical graphs. It interfaces well with pandas dataframes, provides data mapping onto visualizations, and can transform the data as part of plot creation.
It also has a meaningful default theme, and it offers different color palettes defined around best practices.
Because seaborn is a wrapper around Matplotlib, you can configure your plots by accessing the underlying Matplotlib objects.
seaborn is a good choice if:
- You value speed.
- You do not need interactivity.
- You don’t need low-level configuration.
Heatmap created with seaborn (source)
Bokeh
Bokeh is a visualization library influenced by the grammar of graphics paradigm developed for web-based visualizations of big datasets.
It provides a structured way to create plots and support server-side rendering of interactive visualizations in web applications. It has both a high-level and a low-level interface that you can use depending on your actual need, time, and skill.
Use Bokeh when:
- You need to create interactive visualizations in a web application (e.g. a dashboard).
- You like the grammar of graphics approach but do not find Altair intuitive.
- You have high-level and low-level use cases (e.g. data science and production).
An interactive Bokeh plot (source)
Altair
Altair is a visualization library that provides a unique declarative syntax for interactive plot creation. It relies on the Vega-Lite grammar specification, allowing you to compose charts from graphical units and combine them in a modular way.
Altair’s declarative approach allows focusing on the intended visualization outcome and leaving the data transformations to the library. This feature is especially useful for data exploration, when you try to combine different ways to examine and visualize a problem.
Altair is especially useful in the following cases:
- You are doing lots of data exploration and experimentation and want to share the results in an interactive format.
- You don’t need low-level customization.
- You like the grammar of graphics approach and prefer Altair’s syntax.
An Altair visualization with interactive linked brush filtering (source)
Plotly
Plotly is an open-source data visualization library and part of the ecosystem developed by Plotly, Inc. The company also develops Dash, a Python dashboarding library, and offers data visualization application services for enterprise clients. For this reason, Plotly is a great tool for building business-focused interactive visualizations and dashboards.
Plotly offers a high-level interface for fast development and a low-level one for more control. It also renders plots from simple dictionaries and has a wide range of predefined graph types.
Plotly is beneficial when:
- You are building commercial products and dashboards with complex relationships and data pipelines.
- You need a wide range of interactive graphs used in business and research.
- You have high-level and low-level use cases (e.g. data science and production).
An interactive Plotly report (source)
Learn to Visualize Your Data with Python!
This article showed you the usefulness of Python in data visualization. It gave you an overview of the top five Python data visualization libraries. We hope this has helped you pick the right library for your project.
Regardless of your choice, you need to know your way around Python to be able to use these libraries. One of the best ways to do this is to learn and practice Python in a course that’s built around practical projects. We created our Python learning tracks, Python Basics and Python for Data Science, especially with these aspects in mind. Feel free to check them out!
