It is the predominant data visualization library for R-programming. With the help of its advanced structure, it is easy to understand its functionalities and is quite efficient in its working environment. GGPlot is one of the most popular R packages used today. GGPlot2 is the latest version with better features and provides a higher level of abstraction. It follows a general schema in which the data is transformed into a couple of layers and modules before the final scaling occurs for the plot.
Pandas is one of the most used data processing library which can also commonly used for the visualization aspect of data. It is capable of all the basic visualization functionalities like a bar graph, histograms, pie charts, etc. Pandas is an excessively used data manipulation and formatting Python library that is used by millions of developers in python programming projects. Pandas also contain multiple data visualization and statistical methods of creating interactive plots and graphs for the data in processing. It can be highly used in light projects where there is a comparatively lower requirement of visualization.
Matplotlib is a highly powerful and the foremost python library for creating interactive graphs as well as statistical graphics. It provides many numerical and mathematical aspects for enhancing the capability of its visualization graphs. One of the wonderful benefits of using Matplotlib library is that it allows the programmer to access huge amounts of data from various data frames and sources to create visual graphs and plots. Some examples of plots included in this library are Histograms, scatter plot, line graph, bar graph, etc. The Matplotlib library has been developed on NumPy arrays.
Seaborn is another highly used attractiveness enhancing visualization library for python. Seaborn is developed on top of matplotlib library and is strongly integrated for pandas supportability. Seaborn provides a higher degree of statistical data visualization ability for its users. With seaborn the developers can also create informative statistical graphics which makes the plots look more attractive. It also allows the developers/analysts to enhance the matplotlib interpreted figures in a more stylistic manner due to its advanced aesthetics augmenting functions.
Altair Limited has come up with its own library for high-level visualization. It is quite fast and requires only a few lines of code to perform the visualization activities. It is based on a declarative approach that means that the developer/analyst has to only provide the basic links between the x and y axes of the plot, and the rest is automatically handled by the library functionalities and methods. Altair is definitely worth trying if speed is to be achieved to carry out the visualization process.
Try these Best Data Visualization Libraries
The main purpose of data visualization is to funnel down a large amount of data into understandable graphics that reduce the complexity and enhances readability of the insights or trends in the data. Data Scientists are using the above-mentioned libraries for creating high standard visualization plots across various domains through code is written in languages like python and R to uplift a multitude of businesses across the globe.