11 Python Data Visualization Libraries Data Scientists should know

Python Data Visualization Libraries
Python Data Visualization Libraries


Data visualization is one of the most popular methods to represent data in easy to understand manner. It is not only useful in data exploratory phase of creating machine learning models but also helpful in showcasing the results and insights to non-technical people like business executives and decision-makers. In this article, we will explore various python data visualization libraries. We will learn about the features of each library and what are the specific tasks that can be accomplished using them.

Python Data Visualization Libraries


Pandas Visualization Library
Pandas Visualization Library (Source – Pandas Documentation)

Pandas is a python library widely used for the purposes of data handling and data preprocessing. However, along with this, it also has in-built functions to simple visualizations for representational and exploration purposes. Generally, Pandas  DataFrames can be quickly plugged into Pandas visualization functions for creating quick and detailed visualizations. The advantage over here is that during the data exploration process with Pandas it is not required to use other libraries for simple visualizations.


Maplotlib Visualization Library
Maplotlib Visualization Library (Matplotlib Documentation)

Matplotlib is one of the most popular python data visualization libraries that helps data scientists to produce some really useful visualizations. Using matplotlib, we can build simple to advance plots and graphs which scatter plot, box plot, bar charts, histograms, and many more. Along with this, it is very flexible to alter finer details of the plots like – font type and size, colors, transparency level, and orientation of plot axes labels, etc. In spite of its powerful functionality, the visualization it produces often lacks the appealing quotient.


Seaborn Visualization Library
Seaborn Visualization Library (Source – Seaborn Documentation)

Seaborn library is built on top of Matplotlib. Matplotlib provides highly customizable plots but it does not produce visually appealing graphs. This is where Seaborn helps in increasing the quality of the visualization. It can produce stunning visualizations with fewer lines of codes which is also another advantage. It can build a visualization in 2 lines of python code, whereas matplotlib may take 4-6 lines to create similar visualization.


Bokeh Visualization Library
Bokeh Visualization Library (Source – Bokeh documentation)

Bokeh has the unique feature of constructing interactive plots which can also be open in web browsers. Furthermore, Bokeh plots provide additional features like filtering, zoom and hover for its plots. In addition to this, Bokeh plots can be used as JSON objects, HTML documents, or web applications. Another important feature of Bokeh is its capability to use streaming and real-time data.


Plotly Visualization Library
Plotly Visualization Library (Source – Plotly Documentation)

Plotly is another library that offers browser-based visualization for analyzing data. Plotly actually has variants for different languages and one of those languages is Python. Furthermore, the interactive plots built using Plotly have tool-tips features like zooming effect, panning, selecting, auto-scaling, and many more. The interactive feature also allows us to alter the plots and find more information by clicking on some specific parts of the plots.


GMPlot Visualization Library
GMPlot Visualization Library (Source – GMPlot Documentation)

GMPlot has an interface similar to matplotlib. It mainly helps to render any kind of data over Google Maps with the help of HTML and JavaScript. GMPlot also provides different options for creating maps views with ease. If you are looking to create plots based on Maps then this python data visualization library is worth checking.


Geoplotlib Visualization Library (Source – Geoplotlib documentation)

Geoplotlib is a Python visualization library for plotting geographical data and creating maps. This library can be used for creating various kinds of maps like choropleths, heatmaps, density maps, etc. Geoplotlib has simplified the process of creating geographical visualizations as with its powerful bult-in features.


Altair Visualization Library
Altair Visualization Library (Source – Altair Documentation)

Altair is a statistical visualization library in python that uses the descriptive method for plotting the data. The descriptive method means that only the starting and ending points are provided for building the visualizations. To put in layman terms, you can pass on the data and it will try to come up with the best representation of the data on the plot.


Pygal Visualization Library
Pygal Visualization Library (source – Pygal Documentation)

Pygal is another Python visualization library whose visualization can be embedded in any web browser. Pygal has interactive features similar to Plotly and Bokeh, but the differentiating factor is that it can generate the output in the SVG (Scalable Vector Graphics) format.


MissingNo Visualization Library
MissingNo Visualization Library (Source – MissingNo Documentation)

When we perform data exploration and data preprocessing of a dataset, one of the aims is to find the missing values of the dataset. Generally, this is done by constructing tables which tell us about the missing values in various rows and columns.  Missingno library can make our work a bit easier by visualizing the missing values. This library can also visualize the data by filtering and sorting operations. Along with this heatmap and dendrogram can also be made for representational purposes using this library.


Cufflink Visualization Library
Cufflink Visualization Library (Source – Cufflink Documentation)

The cufflink library is useful in linking the Pandas DataFrames with the Plotly library. Since Pandas dataframes cannot be used with Plotly library directly, hence Cufflink creates a wrapper for integrating Pandas with Plotly. Using cufflink we can create various kinds of interactive graphs and charts on panda’s dataframe using the underlying plotly library.


So we have reached the end of this article, we covered plenty of python data visualization libraries. As we saw that there are many visualization libraries apart from the popular matplotlib library. These libraries can be used for building plots that can also be embedded in a web browser or plot geographical and map data. Therefore, using these libraries, we can apply our creativity with data visualization.


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