fbpx

Over time, Python has become the most popular tool for data visualization. This is because Python and its libraries offer various benefits in addition to its ease of learning. One of the reasons why Python is preferred among data scientists is because of a plethora of benefits that its libraries offer. The following are some of the best Python libraries for data visualization.

 

Matplotlib

Matplotlib is one of the oldest Python libraries for plotting 2D figures. It is highly efficient when it comes to creating various plotting schemes such as pie charts, line plots, scatter plots, spectrograms among others. The additional benefit is that in order to achieve the required results, very few lines of coding is required.

 

Seaborn

Seaborn is a much more advanced form of a data visualization library and is stacked with PyData and consists of support for numpy and pandas data structure. Seaborn is a preferred library for more complex forms of data visualization such as heat maps, violin plots, plot kernel density estimates among others.

 

Pygal

This library is best used with interactive plots which have to be embedded in a web browser. Although the library produces good results with a small data set, the results with large data sets aren’t very pleasing. The library can create SVG presentations making it work better with interactive files.

 

Ggplot

Ggplot is a visualization library which is tightly integrated with pandas. It assists in creating plots without delving too much into implementation details. Essentially what Ggplot does is that it sacrifices complexity for a very simple method of data visualization and plotting.

 

There are a variety of other data visualization libraries that Python has to offer, such as Plotly, Geoplotlib, Dash, Altair among others. Though Python gives a huge variety of libraries to choose from, choosing a library comes down to the task that one requires to achieve through the usage of the library. It could be basic plotting or complicated graphs. The functionalities of the library also play an integral role in choosing such a library for a designated task.

Check out our articles on the best python libraries for building financial instruments or for machine learning.