Today, I’ll share one of my favorite bonus resources in the Data Portfolio Accelerator: Matplotlib and Beyond: 7 Visualization libraries for beginners and intermediate users.
This bonus resource is full of helpful information about several visualization libraries (besides Matplotlib) to help you choose the best libraries for your specific data visualization projects and get stunning visuals.
Let’s dive in.
Matplotlib is an amazing visualization library that has been around for more than 20 years 🤯.
I really like it’s flexibility and also that it works really well with Numpy and Pandas, which is something I use quite often for data analysis. The syntax could be a little bit wordy, but in return provide a lot of control to how I want my figures to look.
Also, if you get lost, the Matplotlib documentation will be a great resource to get you unstuck.
Even though Matplotlib is very reliable and versatile, I like to also explore other libraries with different capacities and functions. Some of these libraries are even built on top of Matplotlib.
There are so many libraries, some focus in specific types of visualizations and others provide more user-friendly interfaces.
But with many possibilities, how to choose the best visualization library for your projects?
To help you, I gathered a selection of amazing libraries and added a short descriptions of it’s functionalities and key features.
Let’s explore these libraries in detail:
Seaborn
Seaborn is a Python data visualization library based on Matplotlib. You can think of it like matplotlib’s stylish cousin. It makes statistical visualizations look really nice using less code. It’s great for quick, pretty plots, especially for things like distribution plots and regression visualizations. It provides a high-level interface for drawing attractive and informative statistical graphics. +Info
Plotly
Plotly is all about interactive, web-based visualizations. If, for example, you’re frequently doing dashboards or need interactivity being added to your plots, then Plotly is a really good option. +Info
*Plotly is not built on top of Matplotlib.
Cartopy
For Geospatial data processing. Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses. +Info
YT
yt is a community-developed analysis and visualization toolkit for volumetric data. yt has been applied mostly to astrophysical simulation data, but it can be applied to many different types of data including seismology, radio telescope data, weather simulations, and nuclear engineering simulations. yt is developed in Python under the open-source model. +Info
mpld3
Bringing Matplotlib to the browse API. The mpld3 project brings together Matplotlib and D3js, the popular JavaScript library for creating interactive data visualizations for the web. The result is a simple API for exporting your Matplotlib graphics to HTML code which can be used within the browser, within standard web pages, blogs, or tools such as the IPython notebook. It’s great if you want to power up your Jupyter notebooks or create shareable visualizations. +Info.
Datashader
It’s the best when you’re drowning in data 😵. Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly. Datashader breaks the creation of images into a series of explicit steps that allow computations to be done on intermediate representations. This approach allows accurate and effective visualizations to be produced automatically without trial-and-error parameter tuning, and also makes it simple for data scientists to focus on particular data and relationships of interest. +Info
Plotnine
It’s basically ggplot for Python. It will be your best friend if you’re coming from R and miss that R’s declarative plotting style. Plotnine is an implementation of a grammar of graphics in Python based on ggplot2. The grammar allows you to compose plots by explicitly mapping variables in a dataframe to the visual objects that make up the plot. +Info
What do you think about these resources? Have you used any of these libraries?
Add your thoughts to the comments.
Lina Marieth xx
Quote of the week
The doors that closed turned you toward the ones that were opening. The lessons were always leading you. Every time you got it wrong, you were one step closer to having it right – Brianna Wiest