If you take a closer look at Python data libraries, you'll find libraries for almost any data visualization task, from tools for studying eye movement to visualizing the learning process of a neural network.
It’s worth keeping in mind that most of these visualization libraries are task-oriented. However, some tools can be used for a broader range of tasks.
“Visualization gives you answers to questions you didn't even know you had.”
— Ben Schneiderman, American computer scientist
Data and Visualization in Real World
Data is a crucial component in every phase of the research process. The larger the amount of data, the more difficult it is to analyze and process correctly. Thanks to visualization tools, it is easy to present information in graphs and tables. You can create an information map from an array of data you have already processed. This makes it even easier to work with large amounts of data.
In today's world, visualization is a powerful tool for presenting data and an effective means of communication between researchers and scientists. Visualization is a universal medium for presenting information; it is much easier for your colleagues to understand your ideas using visualizations rather than massive amounts of data.
Thanks to rapid and constant development of the Python programming language, the number of tools for visualizing information in data science is also growing. Python is currently the primary programming language in data science. With its help, you can visualize information.
But before choosing one of the visualization libraries for your project, you should familiarize yourself with their pros and cons so you can identify which one is right for your task. Only with the help of the right library can you create the ideal charts and tables using data science.
Data visualization is a vital process, no matter what your role in the project is. Are you doing data analysis? Use visualizations to present the results of your research quickly. Are you creating prototypes for machine learning models? Data visualization skills won't be out of place for you.
However, choosing the right tool is not easy since the Python programming language visualization environment is quite tricky for inexperienced users.
How to visualize data in Python? Use Python data visualization libraries!
At the PyCon conference in 2017, Jake VanderPlas described the entire Python visualization landscape. In this way, he showed the audience exactly how the different visualization libraries function and how they can interact with each other.
Data Visualization Tools List
This article will take an in-depth look at six of the most popular data visualization libraries. Pay attention to each library's pros and cons to conclude which one is ideal for your project. Only by analyzing each library will you choose the right one for your work and tasks.
“Data visualization is a critical step in any big data project. For the result to meet your expectations, you need to choose the right library for data visualization.”
— Vlad Medvedovsky at Proxet, custom software development solutions company
Matplotlib is a popular Python plot library. This tool was developed to replace MATLAB, an ancient programming language.
It is the first library for Python data visualization. Most of the other libraries took Matplotlib as their base. Some libraries exist only to extend the functionality of Matplotlib and work together with less code.
“It is extremely powerful, but with that power comes complexity.”
— Chris Moffitt, in his Overview of Python Visualization Tools
- This tool is versatile. It will give you the ability to visualize any data set, any research results. Thanks to a large amount of documentation and manuals, you can find a way to create plot visualizations of any array of data.
- Matplotlib makes it easy to see the property of the information. Use this library to quickly create a visualization of a distribution of data.
- You can build anything with Matplotlib, but this tool is significantly inferior to other libraries in data presentation aesthetics. You should consider this when choosing a library for your project.
- Matplotlib's interface is too low-level and user-unfriendly. It will be difficult for newbies to understand this library.
Let's look at some Matplotlib alternatives.
Seaborn is a library for visualizing data arrays based on a Matplotlib python plot package. This tool makes it possible to use previous library advantages, reducing the amount of code used. With Seaborn, one can simplify the creation of individual graphs and heat maps greatly. Its best feature is that it beautifully presents processed data.
- The tool helps you create more beautiful data visualizations while using fewer lines of code. You will use the same data sets, but they will be much easier to work with.
- Seaborn is ideal for creating popular charts and graphs because the visualization results will look much prettier. Colors and shapes will look better than a similar dataset in the Matplotlib library.
- However, there are some disadvantages. Seaborn is limited compared to its progenitor Matplotlib, as it does not have such an extensive collection.
Seaborn vs Matplotlib – how to choose? If you’ve already dealt with visualization libraries, it is better to choose Matplotlib.
Plotly is a data visualization library that can significantly simplify the process of creating graphs for data sets.
- If you prefer to work in R and don't want to lose the benefits of working with a programming language when you switch to Python, this tool will give you the same quality graphs.
- Easy-to-create interactive plots.
- Plotly also makes it easy to create interactive plots. Interactive plots allow you to explore each data point in detail.
With this tool, you can easily create plots that are usually difficult to develop. Plotly is the perfect tool for creating interactive plots with just a few lines of code.
Bokeh is an interactive information visualization library designed to present information in browsers.
- It is as similar as possible to Matplotlib and provides similar functionality with less code.
- Bokeh can create reasonably complex graphics with fewer lines of code and higher resolution.
- Bokeh requires more code to create graphs similar to Seaborn, Altair, or Plotly.
Altair is a simplified Python library for creating statistical quality visualizations. This library is based on Vega-Lite. It is ideal for creating graphs with lots of statistical transformations.
- Simple visualization grammar. Altair looks abstract, but it makes a big difference when working with information and making visualizations as user-friendly and straightforward as possible.
- Altair does not work with datasets of more than 5,000 samples, and recommends aggregating data before visualizations.
This library is an ideal complex graphing module to display statistics. Altair lets you create meaningful, elegant, and efficient visualizations in very little time with just a few lines of code.
Folium allows you to easily visualize data on an interactive map. There are built-in sets of tiles from OpenStreetMap and Mapbox.
- Folium uses an open street map so that you can get a closer look at Google Map with minimal use of code.
- Folium allows users to add markers to show their position to other users.
With Folium, you can easily create interactive maps using minimal code, making your work with maps as close as possible to Google Maps.
Graphing And Plotting – Same or Not
Graphing is more visually primitive compared to plotting or simplified histograms. The main difference is the amount of data processed and presented. With graphing, you can visualize the results of the analyzed data. If you want to show how you analyzed the data, it is best to use plotting to visualize it.
Right Way For Your Project
With the Python programming language, you have many options to visualize the same information in different ways. You can quickly create python charts with sufficient background knowledge in any of the libraries, but many of them require local configuration.
While choosing the right library to visualize your project's data is important, making the chart as useful as possible is even more important. Regardless of the tools you use, you should first decide on the main task that the library should solve, and then determine which library is most suitable for your job.
The best solution is not always the most complex and popular tool. Sometimes, you can use a simpler tool that uses less code and takes much less time.
Once you are familiar with each library's basic functionality and features, you can easily select the most suitable for solving problems in your projects. Not satisfied with the visualization of your data? Then try another library, and eventually everything will work out for sure!
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