5 Best Python Libraries For Data Visualization in 2023

1

Matplotlib

You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.

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2

Seaborn

Seaborn is working hard to make visualization a central part of understanding and exploring data. Its dataset-oriented plotting functions run on data frames carrying whole datasets. Seaborn internally performs the necessary semantic mapping and statistical aggregation to provide informative plots. Lastly, Seaborn is fully integrated with the PyData stack including support for NumPy and pandas data structures.

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3

Altair

It’s a declarative statistical library built on top of the popular Vega-Lite visualization grammar. Altair is friendly, consistent, and comes with a simple API. Using this data visualization library, you can create beautiful and effective visualizations with a minimal amount of code. If you don’t know, a declarative library needed to be mentioned only one link between the data columns to the encoding channels, and the rest will be handled automatically. You can check the official repository of Altair on Github.

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4

Plotly

Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.

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5

ggplot2

ggplot is a system for creating graphics declaratively. It’s based on the Grammar of Graphics of R programming language and is tightly integrated with Pandas. ggplot just requires you to declare how to map the variables to aesthetics and primitives to use and handles the rest automatically. Remember, ggplot is not recommended for creating highly customized graphics.

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