The Python Package Index contains libraries for almost every data visualization requirement, from Pastalog for real-time visualizations of neural network training to Gaze Parser for eye movement research. Some of these libraries are used in a variety of fields. Despite this, many of them are intensely focused on completing a specific task.
Here’s a rundown of 11 interdisciplinary Python data visualization libraries that you’ll learn about in this post, from most popular to least popular. Continue reading to learn how to use Data Visualization Tool and Python data visualization libraries to create data visualization charts.
Table of Contents
Matplotlib Python Library is used to generate simple yet powerful visualizations. It is more than a decade old and the most widely used library for plotting in the Python community. Matplotlib can plot a wide range of graphs – from histograms to heat plots.
Matplotlob is the first Python data visualization library. Therefore, many other libraries are built on top of Matplotlib and designed to work with the analysis. Libraries like pandas and matplotlib are “wrappers” over Matplotlib, allowing access to several Matplotlib methods with less code.
Matplotlib’s versatility allows for visualization types such as:
- Scatter plots
- Bar charts and Histograms
- Line plots
- Pie charts
- Stem plots
- Contour plots
- Quiver plots
You can create grids, labels, legends, etc., with ease since everything is easily customizable.
Seaborn is a popular data visualization library built on top of Matplotlib. Seaborn’s default styles and color palettes are much more sophisticated than Matplotlib. Seaborn puts visualization at the core of understanding any data. Seaborn is a higher-level library- it’s easier to generate specific plots, including heat maps, time series, and violin plots.
ggplot is a Python visualization library based on R’s ggplot2 and the Grammar of Graphics. You can construct plots using high-level grammar without worrying about the implementation details. ggplot operates differently compared to Matplotlib: it lets users layer components to create a complete plot. For example, the user can start with axes and add points, a line, a trend line, etc. The Grammar of Graphics has been hailed as an “intuitive” method for plotting. However, seasoned Matplotlib users might need time to adjust to this new mindset.
Bokeh, native to Python, is also based on The Grammar of Graphics like ggplot. It also supports streaming and real-time data. The unique selling proposition is its ability to create interactive, web-ready plots, accessible output as JSON objects, HTML documents, or interactive web applications.
Bokeh has three interfaces with varying degrees of control to accommodate different types of users. The topmost level is for creating charts quickly. It includes methods for building common charts such as bar plots, box plots, and histograms. The middle level allows the user to control the basic building blocks of each chart (for example, the dots in a scatter plot) and has the same specificity as Matplotlib. The bottom level is geared toward developers and software engineers. It has no pre-set defaults and requires the user to define every element of the chart.
While Plotly is widely known as an online platform for data visualization, very few people know it is accessible from a Python notebook. Like Bokeh, Plotly’s strength lies in making interactive plots, and it offers contour plots, which are not found in most libraries.
Pygal, like Plotly and Bokeh, offers interactive plots that you can embed in a web browser. The ability to output charts as SVGs is its prime differentiator. For work involving smaller datasets, SVGs will do just fine. However, charts with hundreds of thousands of data points become sluggish and have trouble rendering.
It’s easy to create a nice-looking chart with just a few lines of code since each chart type is packaged into a method and the built-in styles are great.
Altair is a declarative statistical visualization Python library based on Vega-Lite. You only need to mention the links between data columns to the encoding channels, such as x-axis, y-axis, color, etc. The rest of the plotting details are handled automatically. This fact makes Altair simple, friendly, and consistent. It is easy to design compelling and beautiful visualizations with a minimal amount of code using Altair.
Geoplotlib is a toolbox used for plotting geographical data and map creation. It can create a variety of map types, like choropleths, heatmaps, and dot-density maps. Pyglet (an object-oriented programming interface) is required to use Geoplotlib.
Geoplotlib reduces the complexity of designing visualizations by providing a set of in-built tools for the most common tasks such as density visualization, spatial graphs, and shapefiles.
Since most Python data visualization libraries don’t offer maps, it’s good to have a library dedicated to them.
Dealing with missing data is cumbersome. Missingno can quickly gauge the completeness of a dataset rather than painstakingly searching through a table. The user can filter and sort data based on completion or spot correlations with a heat map or a dendrogram.
Leather is designed to work with all data types and produces charts such as SVGs. It is scalable without losing image quality. Leather’s creator, Christopher Groskopf, puts it best: “Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.”
Since this library is relatively new, some of the documentation is still in progress. The charts are pretty basic—but that’s the intention.
There is a wide range of visualization tools, with huge diversity, depending on the focus of the task at hand available for Python. The sheer number of libraries available reflects this fact. It is imperative for the users to bear in mind the differences between the approaches and their implications before zeroing in on a particular approach.
Would you add any other Python data visualization libraries to this list? Please share your favorites in a comment below.
Guest Author – Quincy is part of the team at Springboard and is passionate about online learning and strong coffee.