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 form of charts and pictographs. There are many tools and libraries available which can be used to create data visualizations depending upon your requirements. In this Python Data Visualization Libraries guide, we will cover some of the best data visualization libraries in python which can be used to create interactive web charts, simple graphs, detailed plots, weather maps or geographical maps.
All the Data Visualization 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.
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 MATLAB programming language which is also used for simulation.
Developers say that Matplotib is very powerful forcreating any kind of data visualization but is very complex to use at the same time.
Matplotib is 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 stays away from the same level of complexity that Matplotib has. 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.
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 mappings.
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 best data 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.