Top 10 Python libraries for Data Visualization

Data collecting techniques are a vital tool for any modern organisation. All of that data, though, is of no use unless you know how to interpret it. The key to extracting the most value from your business data is data visualisation. It is a critical component of data analysis. Data visualisation charts such as bar charts, scatterplots, line charts, geographical maps, and so on are essential because humans are visual creatures. Data visualisation combines various data sets and produces a visual representation of the data using diagrams, charts, and graphs. Many modern computer programming languages may produce data visualisation results. 

Python is a widely used programming language for data analytics and data visualisation. Python has several excellent graphing libraries that aid in creating interactive displays and highly customisable plots.

List of Python data visualisation libraries

Enlisted below are the ten best Python libraries commonly used for data visualisation. 

Matplotlib: This data visualisation library is the oldest and most widely used library for 2-D plotting in the Python community. It has an interactive environment that may be used on a variety of platforms. It provides an object-oriented API for embedding charts into applications utilising GUI toolkits such as Tkinter, wxPython, Qt, or GTK. It is very versatile, and with minimal coding, Matplotlib allows you to create plots, bar charts, histograms, pie charts, error charts, stemplots, scatterplots, power spectra, and any other visualisation chart you want!

Plotly: It is a web-based toolkit for data visualisation. It is a free, open-source library and can also be used offline. It can be accessed through a Python notebook and has a great API. With unique functionality, such as 3D charts, dendrograms can create scatter plots, line charts, bar charts, box plots, error bars, histograms, multiple axes, subplots, and many others. It also includes contour plots, which are uncommon in data visualisation libraries.

Seaborn: Matplotlib serves as the foundation for this Python data visualisation library. It offers a much more powerful API for creating KDE-based visualisations. It provides an advanced interface for creating visually appealing and informative statistical graphics. It is tightly integrated with the NumPy data stack and Panda data structures. It aims to make visualisation a central part of exploring and understanding data. Seaborn provides a plethora of dataset-oriented plotting routines that work with data frames and arrays containing entire datasets. Then it executes the required statistical aggregation and mapping tasks to build the user’s desired informative visualisations. Seaborn’s standard styles and colour palettes are intended to be more aesthetically pleasing and modern. 

GGplot: It is a Python implementation for the grammar of graphics in the R programming language. It has a high-level API for creating bar charts, pie charts, histograms, error charts, scatter plots, etc. It also enables you to include various types of data visualisation components or layers in a single visualisation. It can create the desired plots with minimal user efforts required in creating them.

Bokeh: Bokeh is a data visualisation library that creates interactive and detailed graphics across various datasets, large and small. Its strength is its ability to generate interactive web-ready plots that can be easily exported as JSON objects, HTML documents, or interactive web applications. This also supports streaming and real-time data. It provides three interfaces with varying levels of controls to accommodate different user types. The highest level is for data analysts to create charts such as bar plots and histograms with no pre-set defaults. The middle level controls the plots’ basic building blocks. The first/basic level is for programmers to create quick data plots.

Pygal: This data visualisation library offers interactive plots that can be embedded in the web browser. Its primary distinguishing feature is generating charts as SVGs (Scalable Vector Graphics), and it works with smaller datasets. Even when scaled, these SVGs ensure that you can view charts and plots without ambiguity. However, SVGs are only helpful for smaller datasets.

Geoplotlib: This data visualisation library is an essential and excellent Python library as it supports the creation of maps and the plotting of geographical data. It makes it easier to create hardware-accelerated interactive visualisations in Python and includes spatial graphs, shapefiles, dot maps, kernel density estimation, Voronoi tessellation, and many other standard spatial visualisations. You can use it to create map types like heat maps, symbol maps, choropleths, and dot-density maps. 

Altair: It is a statistical visualisation library with a simple API. It is based on Vega and Vega-Lite, which are declarative languages for creating, saving, and sharing interactive data visualisation designs. Altair provides a powerful and concise visualisation grammar that quickly creates a wide range of statistical visualisations. Altair will enable you to devote more time and effort to your data – understanding, analysing, and visualising it – rather than the code required to do so.

Gleam: Gleam allows you to create interactive web visualisations of data using only Python. No HTML or JS knowledge is required! You can select any number of inputs that your users can control and then use any Python graphing library to generate plots based on those inputs. Gleam combines it all to create a web interface that allows anyone to interact with your data in real-time. It makes it easy for others to comprehend your data.

Missingno: Missing data can be the most challenging aspect of any project. Missingno Python library allows you to quickly access the completeness of a data set with the visual summary. Rather than sorting through rows and rows of numbers, you can filter and sort the data based on completion and correlation between variables. The Missingno library provides an excellent way to visualise the distribution of NaN values.

These Python libraries for data visualisation are excellent choices for creating informative and visually appealing graphs and charts. A detailed understanding of their advantages and disadvantages can help you select the best one for your data visualisation or project. To understand them in greater depth, you can opt for the Python based data science courses available online or offline by many institutes in India or worldwide.

Conclusion

Data visualisation is an integral part of data science. Careers in data visualisation provide opportunities to earn competitive salaries while working in an industry expected to grow significantly in the coming decades. Data visualisation jobs are concerned with taking complex data and communicating it in ways that non-experts can understand. Professionals with a degree or a diploma in this field can work as data visualisation specialists, data engineers, data analysts, and even data scientists. They also benefit from opportunities available in a variety of industries. 

To make a career in data visualisation, you need to upgrade yourself with the required knowledge of data visualisation solutions. With a degree or a diploma in data visualisation, you can gain the analysis, management, and design skills required to translate complex datasets into simple visual representations. 

There are institutes in India and worldwide that offer post-graduation in data science, data visualisation programs or certificate courses. There are many Python based data science online courses available for you to pursue to advance your career in this field.