Data Visualization Libraries in Python

1. Matplotlib

Matplotlib is the most popular data visualization library in Python. It has various applications across multiple platforms in an interactive environment. Matplotlib can also be used in Python scripts, the Python and IPython shells, the Jupyter Notebook, web application servers, etc. You can create all sorts of data visualization charts, such as charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, etc. And that’s not all! You can also use Matplotlib for embedding your applications using various GUI toolkits like Tkinter, GTK+, wxPython, Qt, etc. There is also a plot module available in Matplotlib that provides a MATLAB-like interface that is just as versatile and useful as MATLAB while being free and open source.

2. ggplot

Ggplot is a Python data visualization library that is based on the implementation of ggplot2, which was created for the programming language R. Check out ggplot2 as well in the R section! ggplot in Python can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. You can also add different types of data visualization components, which are called layers, to a single visualization. These layers include the type of plot, and various aesthetics in the plot, such as its color, size, etc., then the filters in the plot, and so on. Once ggplot has been told all the layers, it can easily create the plot so that the user can focus on interpreting the visualizations and take less time creating them. But this also means that it is not possible to create highly customized graphics in ggplot.

3. Seaborn

Seaborn is a Python data visualization library that is based on Matplotlib and closely integrated with the Numpy and Pandas data structures. Seaborn has various dataset-oriented plotting functions that operate on data frames and arrays that have whole datasets within them. Then it internally performs the necessary statistical aggregation and mapping functions to create informative plots that the user desires. It is a high-level interface for creating beautiful and informative statistical graphics that are integral to exploring and understanding data. The Seaborn data graphics can include bar charts, pie charts, histograms, scatterplots, error charts, etc. Seaborn also has various tools for choosing color palettes that can reveal patterns in the data.

4. Geoplotlib

While Matplotlib is perfect for charts and other data visualizations, it does not provide that many options for creating geographical maps. That is why geoplotlib is such an important Python library whenever you want to use geographical data. It supports and specializes in geographical maps with various options, such as dot-density maps, choropleths, symbol maps, etc. One thing to keep in mind is that it requires numpy and pyglet as prerequisites before installation, but that is not a big disadvantage. Especially since you want to create geographical maps, geoplotlib is the only excellent option for maps out there!

Data is becoming the backbone of our current society. Companies can use data to predict their customer reactions, the success of their products and services, and the areas they need to work on. Data can also be used to understand many social and natural phenomena in the world such as social media trends, mass migration, global warming, etc. However, while data scientists can understand all these using various analytical procedures and statistical modeling of the data, it’s a very different thing to convey these findings to other people. That’s where data visualization is extremely important!

Data visualization allows data scientists to understand the layout and patterns in the data in visual form. It also allows them to demonstrate the findings from their data in simple terms to other people who are not experienced in data science. Therefore, data visualization is the language that is used to convey the data to regular people. Many data visualization libraries across programming languages can be used for this task. This article deals with the most popular data visualization libraries in Python, R, and Javascript. You can use these libraries to create basic charts like bar charts, pie charts, histograms, scatterplots, sparklines, dendrograms, contour plots, choropleth maps, etc. If you want to be more creative, you can even add animations to your charts and create data stories. So what are you waiting for? Check out these libraries and begin your data visualization journey by creating your first chart!