10 Python Libraries For Data Visualization
Tags: python, datascience, datavisualization, analytics
Published: 2022-01-27 06:21:48

Since its first appearance in 1991, Python has become one of the most popular languages to be used for data analytics. Moreover, the Python Package Index has rich built-in libraries for practically every data visualization that is needed, be it a simple plotting of demand-sale ratio or a real-time visualization of neural network-trained data. 

Now, let us understand a few of these Python libraries specifically meant to be used for data visualization.

**Matplotlib**
The Matplotlib is the most common standard Python library used for plotting 2D data visualizations. This library was created by John D. Hunter and is currently maintained by a team of Python developers. The Matplotlib library is mainly used for creating plots that can be zoomed in on a section of the plot and pan around the plot using the toolbar in the plot window. It is the first data visualization library to be developed in Python, and later many other libraries were built on top of it for various other ways of visualizations. This library is used to create a variety of visualization graphs such as line plots, pie charts, scatter plots, bar charts, histograms, stem plots, and spectrograms. The Matplotlib allows easy use of labels, axes titles, grids, legends, and other graphic requirements with customizable values and text.
Download here > [Matplotlib](https://matplotlib.org)

**seaborn**
The seaborn library couples the power of the Matplotlib library to create artistic charts with very few lines of code. The seaborn library follows creative styles and rich color palettes, which allows you to create visualization plots to be more attractive and modern. Today’s visualization graph is mainly plotted in seaborn rather than Matplotlib, primarily because of the seaborn library’s rich color palettes and graphic styles that are much more stylish and sophisticated than Matplotlib. As seaborn is considered to be a higher-level library, there are certain special visualization tools such as violin plots, heat maps, and time series plots that can be created using this library.
Download here > [seaborn](https://seaborn.pydata.org)

**ggplot**
The ggplot library is based on the ggplot2 library which is an R plotting system and concepts are based on the Grammar of Graphics. The ggplot library creates a layer of components for creating plots which makes it different from Matplotlib based on the operations of plotting graphs. It is integrated with pandas and is mainly used for creating very simple graphics. This library sacrifices the complexity of plotting complex graphs as its primary focus is on building basic graphs that are often required for analyzing simple data distribution.
Download here > [ggplot](https://ggplot2.tidyverse.org/reference/ggplot.html)

**Bokeh**
The Bokeh library is native to Python and is mainly used to create interactive, web-ready plots, which can be easily output as HTML documents, JSON objects, or interactive web applications. Like ggplot, its concepts are also based on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots, and box plots. It can also handle very minute points of a graph such as handling a dot of a scatter plot. Using Bokeh, it is easy for a user to control and define every element of the chart without using any default values and designs. There are three varying interfaces supported by Bokeh for being used by different types of users. The topmost level interface allows building simple, common charts and graphs very easily. The middle-level interface allows designing visualization graphs with a provision to control the basic building blocks of each graph. Lastly, the bottom-level interface is used mainly by software engineers and developers as this interface provides full support for controlling and customizing each and every component of a graph to deal with complex graphics.
Download here > [Bokeh](https://docs.bokeh.org/en/latest/)

**Plotly**
The Plotly library is an online platform for data visualization and it can be used in making interactive plots that are not possible using other Python libraries. Few such plots include dendrograms, contour plots, and 3D charts. Other than these graphics, some basic visualization graphs such as area charts, bar charts, box plots, histograms, polar charts, and bubble charts can also be created using the Plotly library. One interesting fact about Plotly is that the graphs are not saved as images but rather serialized as JSON, because of which the graphs can be opened and viewed with other applications such as R, Julia, and MATLAB.
Download here > [Plotly](https://plotly.com)

**Pygal**
The Pygal library creates interactive plots that can be embedded in the web browser. It also has the ability to output charts as Scalable Vector Graphics (SVGs). All the chart types created using pygalare packaged into a method that makes it easy to create an artistic chart in a few lines of code. For instance, to create a bar chart, simply import the pygal library and then create a variable to assign the value of pygal.Bar().The graph created can finally be saved in .svg extension to get stylized CSS formatting.
Download here > [Pygal](https://www.pygal.org/en/stable/)

**geoplotlib**
The geoplotlib is a toolbox for designing maps and plotting geographical data. A few of the map types that can be created are heatmaps, dot-density maps, and choropleths. In order to use geoplotlib, one has to also install Pyglet, an object-oriented programming interface. This library is mainly used for drawing maps as no other Python libraries are meant for creating graphics for maps.
Download here > [geoplotlib](https://pypi.org/project/geoplotlib/#files)

**gleam**
The gleam library is similar to the R’s Shiny package. It converts analyses into interactive web apps using Python scripts. It creates a web interface that lets anyone play with the data in real-time. One interesting capability of this library is that fields can be created on top of the graphic and users can filter and sort data by choosing appropriate fields.
Download here > [gleam](https://github.com/dgrtwo/gleam)

**missingno**
The missingno library can deal with missing data and can quickly measure the wholeness of a dataset with a visual summary, instead of managing through a table. The data can be filtered and arranged based on completion or spot correlations with a dendrogram or heatmap.
Download here > [missingno](https://github.com/ResidentMario/missingno)

**Leather**
Christopher Groskopf, the Creator of Leather, has stated that Leather is the Python charting library for those who need charts now and don’t care if they’re perfect. This library is designed to work with all data types and create charts as SVGs. This library is relatively new and the charts that can be made using this library are the very basics.
Download here > [Leather](https://pypi.org/project/leather/)

Which of these is your preferred library? Do comment and let us know.

Hope this was helpful.