8 Best Python Visualization Libraries for Python Programmers

Data Visualization is a very important aspect of any type of Data Analyzation. Data Visualization provides a comprehensive and easy to understand summary of data in the form of charts and pictographs. There are many tools and libraries available which can be used to create data visualizations depending on your needs. In this guide, we will cover some of the Popular Python Visualization Libraries in python which can be used to create interactive web charts, simple graphs, detailed plots, weather maps or geographical maps.

All the Popular Python Visualization Libraries libraries in this article are interdisciplinary which means that they are not native to a specific data set or task, so you can use them for various purposes as per your needs.

8 Popular Python Visualization Libraries List for You to Learn



Matplotlib is one of the most popular python libraries for

plotting and it has been used for about a decade now. Matplotib was designed to

work similar to the MATLAB programming language which is also used for


According to Developers, Matplotib is very powerful for

creating any kind of data visualization but is very complex to use at the same


Matplotib has been the base for many modern libraries in

python. Libraries like Seaborn and panda are majorly based on Matplotib; they

give you access to all the functionalities of Matplotib without the complexity

that is present in the Matplotlib library.



Seaborn is completely based on Matplotib. It has all the features of Matplotib but is not as complicated to work with as Matplotib. The major advantage of Seaborn is its color palettes and default styles. You can create visualizations which are easier to understand because of the modern design palettes and color schemes that Seaborn library provides. However, to become an advanced Seaborn user, you will need to know how to use Matplotib first as Seaborn is based on it.

Read Also: – 9 Best Web Technologies That Every Developer Should Know



ggplot is an R plotting system which is based on the concepts

of The Grammar of Graphics. ggplot works differently than Matplotib, it allows

you to create different layers of components to create a complete 2D plot. For

better understanding, you can start plotting with the axes, add points and then

a line, a trendline or any other components you may want to use.

Even though The Grammar of Graphics is considered very

user-friendly, developers who are accustomed to Matplotib might face some

difficulties in getting comfortable with using ggplot.

Note: if you want to create highly customized graphics than ggplot is not for you. The designer of ggplot recommends it to people who want to create simple plotting graphics without any complexity.



Bokeh is also based on The Grammar

of Graphics like ggplot. The key difference between ggplot and Bokeh is that

the latter is native to Python only and is not ported from R. It can be used to

create interactive, web-ready plots. The plots can be easily exported as HTML

documents, JSON objects or interactive web applications. With Bokeh, you can

also plot real-time data and stream it to a specified channel.

With Bokeh, you have the option to use three different

interfaces with varying levels of control depending upon your requirements. The

top-most level can be used to create charts quickly. Bar plots, Boxplots, and

histograms can be created by using the top level.

The middle level works similar to that of Matplotib. You can

control the different building blocks of individual charts; for example, the

dots in a scatter plot.

The lowest level of Bokeh interface is oriented for software

engineers and developers. It features no presets so you can create your own




pygai is similar to Bokeh in some terms. It also offers

interactive data visualization. pygai is a dynamic SVG charting library

developed in Python. You can see an example of pygai chart plotting in the

above image. The key difference between pygai and Bokeh is that the former can

export data visualization charts as SVGs. SVGs are considered to be the most

feasible option when small data sets are being worked on. However, the

renderings can get a bit sluggish when large data sets are incorporated.

Different types of chart styles are packaged into a method

in pygai so creating some decent looking visualizations is a matter of just a

few lines of code.



You may have already heard of Plotly as the online platform

for creating data visualizations but there is also a library you can use to

incorporate it in your own python code. It can be used to create interactive

data visualizations. The unique feature of Plotly is the vast variety of charts

it has to offer. You can find some aesthetically pleasing charts like

dendrograms, 3D charts and contour plots which are not found in most of the

other libraries.



As the name suggests, geoplotlib can be used to create maps

and geographical data plottings. geoplotlib’s functionalities can be used to

create an immense variety of maps; like heatmaps, dot density maps, and

choropleths. However, if you want to use geoplotlib, you need to install Pyglet

first, which is an object-oriented programming interface. There are not many

libraries which offer inbuilt mapping charts so geoplotlib is a must have for

those who need to work with maps every now and then.



Gleam is a python library based on R’s Shiny package. It allows you to take analytical data and turn it into interactive web pages as you can see in the above image. The major advantage of using Gleam is that you only need to know python as it does not require any other language. Gleam is compatible with any available Python data visualization library. Once a plot is created, Gleam can be used to create field layers on top of it so that the users can filter and sort data according to their needs.

These were some of the Popular Python Visualization Libraries you can use in your data analytics to create easy to understand charts or to give a summarized view of large data sets.

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