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IT4nextgen > Best-Of > Best Data Visualization Libraries for Python and R Programming

Best Data Visualization Libraries for Python and R Programming

Last Updated October 11, 2019 By Saket Leave a Comment

Data Visualization is one of the most important aspects of Data Analytics, Business Intelligence, and Data Science technologies present today. By representing the large amounts of data into a visual representation, it becomes easier to communicate the insights to anyone with ease. Data visualization helps to make the sense of the data available that directly reduces the effort of the ones reading it. Efficient data visualization will lead to better decision making for its application in any industry, so it is crucial to choose the data visualization libraries wisely. Many data visualization tools and libraries have come up to create visualization diagrams and plots using programming languages like Python, JavaScript and R. Here are some of the most novel and interactive data visualization libraries available for data scientists-

1 GGPlot

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.

Check GGPlot Documentation

2 Pandas

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.

Pandas User Guide

3 Matplotlib

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.

Check More Details

4 Seaborn

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.

Know More about Seaborn Library

5 Bokeh

Bokeh has been handcrafted for the data scientists with a creative mind. It provides methods to visualize the data in a more interactive manner. Bokeh provides high-performance capabilities to create interactive plots, graphs, and data statistical graphs. The most important instrument of Bokeh is that it is ideally built for targeting web browser representations and visuals. Also, Bokeh has multiple language support like Julia, Python, JavaScript, and R. The Bokeh plots are high performing and very interactive. The latest visualization scripts like D3.js has even enabled the visualization of streaming data. Bokeh provides flexibility in visual styling, flask connectivity as well can build complex plots with only a few lines of code or commands.

Welcome to Bokeh

6 Plotly

Plotly is an open-source interactive graphics library used by huge enterprises for their analytical solutions. Plotly has recently extended its functionality with the introduction of plotly.js graphics library.  Plotly is seamless in the manner of maintaining consistency in its offerings. It is open source and can be used among many programming languages like R, Python, and JavaScript. Plotly figures are beautiful yet simple to develop using the straightforward modules and packages present in the library. Plotly can be easily used in any IDE, Anaconda being one of them.

Plotly Graphing Libraries

7 Altair

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.

View More Details

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.

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