Top 8 Python Libraries for Data Visualization

1

Matplotlib

Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, etc. It can be used to embed plots into applications using various GUI toolkits like Tkinter, GTK+, wxPython, Qt, etc. So you can use Matplotlib to create plots, bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, and whatever other visualization charts you want! The Pyplot module also provides a MATLAB-like interface that is just as versatile and useful as MATLAB while being free and open source.

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2

Plotly

Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique chart types like scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. Plotly also provides contour plots, which are not that common in other data visualization libraries. In addition to all this, Plotly can be used offline with no internet connection.

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3

Seaborn

Seaborn is a Python data visualization library that is based on Matplotlib and closely integrated with the NumPy and pandas data structures. Seaborn has various dataset-oriented plotting functions that operate on data frames and arrays that have whole datasets within them. Then it internally performs the necessary statistical aggregation and mapping functions to create informative plots that the user desires. It is a high-level interface for creating beautiful and informative statistical graphics that are integral to exploring and understanding data. The Seaborn data graphics can include bar charts, pie charts, histograms, scatterplots, error charts, etc. Seaborn also has various tools for choosing color palettes that can reveal patterns in the data.

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4

ggplot2

Ggplot is a Python data visualization library that is based on the implementation of ggplot2 which is created for the programming language R. Ggplot can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API. It also allows you to add different types of data visualization components or layers in a single visualization. Once ggplot has been told which variables to map to which aesthetics in the plot, it does the rest of the work so that the user can focus on interpreting the visualizations and take less time in creating them. But this also means that it is not possible to create highly customized graphics in ggplot. Ggplot is also deeply connected with pandas so it is best to keep the data in DataFrames.

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5

Altair

Altair is a statistical data visualization library in Python. It is based on Vega and Vega-Lite which are a sort of declarative language for creating, saving, and sharing data visualization designs that are also interactive. Altair can be used to create beautiful data visualizations of plots such as bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, etc. using a minimal amount of coding. Altair has dependencies which include python 3.6, entrypoints, jsonschema, NumPy, Pandas, and Toolz which are automatically installed with the Altair installation commands. You can open Jupyter Notebook or JupyterLab and execute any of the code to obtain that data visualizations in Altair. Currently, the source for Altair is available on GitHub.

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6

Bokeh

Pygal is a Python data visualization library that is made for creating sexy charts! (According to their website!) While Pygal is similar to Plotly or Bokeh in that it creates data visualization charts that can be embedded into web pages and accessed using a web browser, a primary difference is that it can output charts in the form of SVG’s or Scalable Vector Graphics. These SVG’s ensure that you can observe your charts clearly without losing any of the quality even if you scale them. However, SVG’s are only useful with smaller datasets as too many data points are difficult to render and the charts can become sluggish.

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In conclusion, all these Python Libraries for Data Visualization are great options for creating beautiful and informative data visualizations. Each of these has its strong points and advantages so you can select the one that is perfect for your data visualization or project. For example, Matplotlib is extremely popular and well suited to general 2-D plots while Geoplotlib is uniquely suite to geographical visualizations. So go on and choose your library to create a stunning visualization in Python!

In conclusion, all these Python Libraries for Data Visualization are great options for creating beautiful and informative data visualizations. Each of these has its strong points and advantages so you can select the one that is perfect for your data visualization or project. For example, Matplotlib is extremely popular and well suited to general 2-D plots while Geoplotlib is uniquely suite to geographical visualizations. So go on and choose your library to create a stunning visualization in Python!