Described as the moment in which data becomes information, data visualization is one of the final and most important steps of the data science lifecycle. Incorporating communication, storytelling, and aesthetics, visualizing a dataset through charts and graphs is one of the easiest ways for data scientists to present their findings to an audience. Data visualizations are used not only to interpret data but also to convey information in a way that is accessible to a general audience and aesthetically pleasing, which is why programming languages have incorporated data science libraries exclusively focused on data visualization.
In a departure from previous decades' simple and static graphs, data science libraries include myriad tools and resources for displaying data in more engaging ways for viewers. Python offers several data visualization libraries that specialize in developing unique and interactive visualizations that greatly influence the way we share information and data.
What is Data Visualization?
Data visualization is when the data is concretized through the creation of a visual element, such as a chart or graph, and is typically viewed as the final step in the data science lifecycle. Whether as a single graphic or multiple images, data visualization is used to communicate the primary argument or findings of a data science project or dataset. The purpose of a project can be lost in translation if the wrong visualization format is chosen. The data visualization stage of a project is considered one of the most important steps in the life cycle because it is the do-or-die moment when the project is presented to an audience.
While many data scientists and analysts are familiar with the language of data, data literacy is not as common outside of the STEM fields. Yet, the importance of graphics or visualizations accurately communicating key findings while ensuring that those findings are comprehensible to diverse audiences cannot be overstated. By using rhetorical techniques, such as data storytelling and data science tools that specialize in visualization, data scientists and analysts can increase the chances that their projects and presentations can be most effectively utilized.
Top 5 Python Tools for Data Visualization
While Python is most commonly associated with automation and machine learning, you can also use it to create stunning data visualizations. Python has several data science libraries, packages, and tools specializing in data visualization. It is an excellent resource for anyone looking to broaden their data science portfolio, whether you need tools that include APIs (application programming interfaces) to help you choose the best charts and graphs or packages for coding more complex visualizations.
1. Matplotlib
Widely viewed as the go-to data visualization library for Python data scientists and developers, Matplotlib can be used to create traditional graphs and charts as well as more complex interactive visualizations. Matplotlib offers multiple plot types and formats and is compatible with other Python libraries, such as NumPy. Python’s vast community of contributors ensures that Python users have access to dozens of documents providing line-by-line instructions on creating a variety of data visualizations with the library. After using Matplotlib to create visualizations, data scientists working in JupyterLab can embed their graphics in a notebook interface to share their findings or collaborate with a larger team.
2. Seaborn
Based on Matplotlib, Seaborn is known for statistical data visualization and can be used in conjunction with Python libraries like Pandas to plot functions and draw a variety of graphs. Seaborn also includes an API that makes it easy to switch between multiple visualizations for one dataset. Using the replot function, this library ensures that the best graph is used to accurately and clearly display your data. There are also functions geared towards specific types of data and graphs, such as categorical data functions, distributed variables, and comparative graphics for multivariate analysis. Similar to Matplotlib, data visualizations that are created with the Seaborn library can also be imported into Jupyter Notebooks.
3. Altair
Focused on declarative visualization, Altair is a low-code data visualization library accessible to beginner and more advanced Python programmers and data scientists. Based on Vega, a visualization grammar that operates under the JSON format, declarative visualization is a type of language used to create links, or relationships, between aspects of your dataset using fewer lines of code than other types of data science packages. As a declarative visualization tool, Altair uses an API to intuit the interactions of a graph. Additionally, this library can be used to create interactive charts by binding interactions to each other. These charts can then be displayed in a notebook environment, like Jupyter Notebooks, or a web-based environment like Apache Zeppelin.
4. Plotly
Plotly is another free and open-source Python library developed for the creation of static and interactive graphs. As a graphing library, Plotly offers dozens of visualizations and charts, many of which offer high-quality color and imagery. Utilizing an API with a range of objects and expressions, Plotly incorporates custom buttons, animations, and controls to transform static graphs into interactive diagrams. So, whether you are interested in creating maps from geolocation data or diagrams from machine learning models, the Plotly library has a graph for any project and programming language.
5. plotnine
Commonly paired with ggplot2, plotnine is used with Python libraries and code to create graphics and data visualizations. Widely known by programmers working with the R language, ggplot2 is a graphics library that takes a layered approach to creating images and data visualizations. When paired with plotnine, this graphics library can create complex and layered graphs for data scientists working with several datasets and libraries at the same time. Similar to Altair, this grammar makes it easier to write code that can then be used to plot graphs with Python.
Want to learn more about using Python for Data Visualization?
Although data visualization is considered one of the most important steps in the data science life cycle, many data scientists devote more time to learning about programming and data analysis instead. While knowledge of computer programming can be useful for both analyzing and visualizing data, data scientists learning how to program with Python can improve their capabilities by also receiving training in Python for data visualization. Noble Desktop’s Data Science classes include training in data science libraries for analytics, machine learning, and visualization.
Python classes also include training in popular data visualization libraries and packages. For example, the Python for Data Science Bootcamp offers fledgling data scientists hands-on instruction in libraries like Matplotlib. In addition, this bootcamp focuses on creating visualizations to communicate findings, as well as building your data science portfolio. Beginners and experts alike can benefit from Noble Desktop’s Data Science and Python classes!