Best Python Visualization Libraries for Data Science in 2018

July 16, 2018, 11:51 p.m. By: Kirti Bakshi

Python libraries

Python continues to take leading positions in solving data science tasks and challenges. Given below is a list of the best Python Visualization Libraries for Data Science in 2018:

1. Matplotlib:

Matplotlib is a 2D plotting library in Python that tries to make easy things easy and hard things as easy as possible and across platforms produces publication quality figures in a variety of hardcopy formats and interactive environments. Matplotlib can be used in web application servers, Python scripts, the Jupyter notebook, the Python and IPython shells, and four graphical user interface toolkits.

One can with just a few lines of code generate bar charts, plots, power spectra, histograms, errorcharts, scatterplots, etc.

The Pyplot module provides a MATLAB-like interface for simple plotting when combined with IPython particularly. For the power user, you have full control of font properties, line styles, axes properties, etc, via an object-oriented interface or via a set of functions familiar to MATLAB users.

More Information : Matplotlib

2. Seaborn:

Seaborn is a library in Python for making graphics that are attractive, informative as well as statistical. The library is built on top of matplotlib and is tightly integrated with the PyData stack.

The python library aims to make visualization a central part of exploring as well as understanding data. The plotting functions containing a whole dataset operate on data frames and arrays and to produce informative plots internally perform the necessary aggregation and statistical model-fitting.

Seaborn in a few lines of code harnesses the power of matplotlib to create beautiful charts. The default styles and colour palettes of Seaborn turn out to be the main difference, which is designed to be more pleasing and modern. As Seaborn is built on top of matplotlib, you’ll need to know matplotlib to know Seaborn’s defaults.

More Information : Seaborn

3. Plotly:

Plotly that at its core is a data visualization toolbox is an online collaborative data analysis and graphing tool. The Python API allows you to access all of the functionality of Plotly from Python. Plotly figures are edited, shared, and tracked all online and the data is always accessible from the graph.

We know Plotly as an online platform for data visualization, but a user can also access its capabilities from a Python notebook. Similar to Bokeh, the forte of Plotly is making interactive plots, but it offers some charts, like contour plots, dendograms, and 3D charts that are not found in most libraries.

More Information : Plotly

4. Bokeh:

Bokeh a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open source scientific computing community is an interactive visualization library that for presentation targets modern web browsers.

The goal of Bokeh is to provide elegant, concise construction of versatile graphics, and to over very large or streaming datasets extend this capability with high-performance interactivity. Bokeh is of use to anyone who would like to create data applications, dashboards, and interactive plots quickly and easily.

More Information : Bokeh

5. Pydot:

Pydot is basically an interface to Graphviz that: