Top 10 Libraries for Data Visualization in 2020
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 on 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 Libraries in Python
Matplotlib is the most popular data visualization library in Python. It has various applications across multiple platforms with 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 pyplot module available in matplotlib that provides a MATLAB-like interface that is just as versatile and useful as MATLAB while being totally free and open source.
Ggplot is a Python data visualization library that is based on the implementation of ggplot2 which is 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 in a single visualization. These layers include the type of plot, 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 in creating them. But this also means that it is not possible to create highly customized graphics in ggplot.
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.
While Matplotlib is perfect for charts and other data visualizations, it does not provide that many options for greeting 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 requires numpy and pyglet as prerequisites before installation but that is not a big disadvantage. Especially since you want to create geographical maps and geoplotlib is the only excellent option for maps out there!
Data Visualization Libraries in R
ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations for data exploration such as histograms, scatterplots, error charts, etc. and for data explanation such as bar charts, pie charts, scatterplots, etc. It also allows you to add different types of data visualization components or layers in a single visualization. One advantage of ggplot2 is that you only need to specify the variables and all the layers for the plot and it easily creates what you want. But this also means that there is not much room for detailed customization in ggplot2. But there are a lot of resources in the RStudio community and Stack Overflow which can provide help in ggplot2 when needed. Just like dplyr, if you want to install ggplot2, you can install the tidyverse or you can just install ggplot2 using install.packages(“ggplot2”)‘
Esquisse can be used in R with the help of ggplot2 to create detailed data visualizations. These include any and all charts you can imagine such as scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. Esquisse also allows its users to export these graphs or access the code for creating these graphs. Esquisse is such a famous and easily used data visualization tool because of its drag and drops ability that makes it popular even among beginners. You can install Esquisse from CRAN using install.packages(“esquisse”) or install the development version from GitHub using remotes::install_github(“dreamRs/esquisse”).
After you have checked out all the libraries for data visualization mentioned above, you can then focus on the specific ones you wish to go deeper in. If you are experienced in Python, you may want to try matplotlib first or you may prefer ggplot2 if you are acquainted with R. D3 is also an excellent option for creating interactive visualizations and adding the animations you need. So go on and dive deeper into the world of data visualization so that you can better explain your data to your audience!
Please Login to comment...