- What are Python-based Data Visualization Libraries?
Simply put, data visualization is the graphical representation of data.
Data visualization tools help in creating visual elements like graphs, charts and maps to understand patterns, trends or currents in data.
Data visualization technologies are making life easier for millions of data professionals analysing massive data sets to extract relevant information. The benefits of having data visualization lie in human psychology.
Humans are well-versed with colours, shapes, and patterns since birth. So, we can quickly identify and differentiate between different colour schemes, patterns, sizes, etc.
Data visualization is likewise, and stacking data into an art form creates interest. Some can even say it is Data Storytelling. We can differentiate the data sets easily if they are arranged to put out a visual impact.
Data visualization tools are helping in managing massive data sets into a visually engaging story. The most-used data visualization tools are Excel, R, Python, and BI.
In this blog, we will look at the top Python data visualization libraries.
What are Python-based Data Visualization Libraries?
Python is continuing to be the top-most programming language used in Data Science! So it is pretty apparent that the number of data visualization libraries based on Python are on the rise. Most of these libraries are open source; they are free to download and also free to use.
By definition, “Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.” Thus, we might say that matplotlib is the Originals of Python data visualizations libraries! It was initially released in 2003. John Hunter developed it, and since then, many other contributors have poured immense time and effort into running this software, which is used by thousands of scientists worldwide!
Matplotlib was designed to resemble MATLAB. Since it was the first developed Python data visualization library, many other libraries are built and designed on top of matplotlib! Libraries like pandas or seaborn allow you to access the number of matplotlib’s practices with lesser codes! Matplotlib is a potent tool but very complex to use.
Learn More – matplotlib.org.
Seaborn runs on the power of matplotlib to create aesthetically pleasing charts with just minimal coding. It was designed to give out beautiful and impactful visuals, which was just lacking on matplotlib’s side.
Learn more – seaborn.pydata.org
ggplot is a Python version of the ggplot2 based on the R plotting system and concepts from The Grammar of Graphics. In such a sense, ggplot works differently than matplotlib. To create a complete plot, you need to layer the components. ggplot is tied tightly with Pandas, where you can quickly build visualizations using your Pandas dataframe.
Bokeh Python is also based on concepts of The Grammar of Graphics. The only difference between ggplot and bokeh is that bokeh is Python-based and not derived from R. This library was made to generate visualizations for web interfaces and browsers. It can create interactive plots that can deliver outputs as HTML documents, JSON objects, and web applications. In addition, bokeh can be used to generate real-time data and supports streaming.
Learn More – Bokeh
Altair is a declarative library that is based on Vega-Lite. Now, Vega-Lite is high-level grammar for interactive and statistical graphics. This makes Altair ideal for plots that require a lot of statistical transformation! But, on the other hand, it is effortless and produces compelling visualizations with minimum coding.
Learn More – Github
Plotly is Python’s graphing library. It is easy to create interactive publication-quality graphs on Plotly. Moreover, it is effortless to build interactive dashboards. The speciality of this library is charts like contour plots, dendrograms, and 3D charts.
Learn More – Plotly
Geoplotlib is a library that focuses on creating maps and plotting geographical data. The speciality of having Geoplotlib is that most of the Python libraries don’t have options to create maps. It is effortless to develop various map types such as choropleths, dot-density maps, and heat maps.
Learn More – Github
In this blog, we have taken brief notes on the most popular Python data visualization libraries. Each library has a unique speciality of its own. Data visualization is just a form of detailed information into a picture. What do you think is your favourite data visualization library?
Special Courtesy –
- Towards Data Science
- Better Programming
- Analytics Vidhya
- Yhat Github
- Docs bokeh