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5 Python Libraries for Data Visualization

Data visualization is the process of translating large data sets and metrics into charts, graphs and other visuals. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data.
5 Python Libraries for Data Visualization

Data is one of the most important parts of every research and exploration work, but it can’t give you many insights alone. Data visualization allows you to turn it into a visual representation that you can explore with your eyes, a sort of information map.

5 Python libraries for Data Visualization

  1. Matplotlip
  2. Seaborn
  3. Altair
  4. Plotly
  5. ggplot


Matplotlib is the most popular Python library for data visualization. It can be used in Python and IPython shells, Python scripts, Jupyter notebook, web application servers, etc.

With this library, with just a few lines of code, one can generate plots, bar charts, histograms, power spectra, stemplots, scatterplots, error charts, pie charts and many other types.



The Python data visualization library of Seaborn is a library based on Matplotlib. It provides a much more terse API for creating KDE-based visualizations. It provides a high-level interface for drawing attractive and informative statistical graphics. It is tightly integrated with PyData stack, including support for numpy and pandas data structures.



Top 5 Python Libraries For Data Visualization
Python has some of most interactive data visualisation tools. Here are the top Python visualization libraries. Data Visualization in python

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