You'll have pictures, maps, and graphs that help the human brain in processing and understanding any given data easily. So, with the help of data visualization, you will be able to get the summary of your data visually. That way, you would be able to expose patterns, correlations, and trends from the data that you would not have been able to do if it was presented to you in a tabular format. To understand what your data conveys, and the stories it contains, and for being able to clean it properly for models as well, it must be first visualized and represented in a pictorial format. With such large amounts of data being generated every day, it is obvious that you would never be able to understand it, or summarize it (if asked) if it is given to you in a tabular format or in any raw format. Introduction to Data Visualization in PythonĮvery day, data is generated in zettabytes, where 1 zettabyte is 1 0 0 0 7 1000^ 1 0 0 0 7 bytes. The article does not go into the details of every function and library used for data visualization in Python.It contains a short description of libraries and their usage of them to create scatter, line, bar, and histogram graphs along with code.This article discusses the basics of data visualization in python.Some of the prominent libraries are - Matplotlib, Seaborn, Bokeh, and Plotly. Python provides a plethora of libraries for data visualization. This representation of your data, in pictorial formats such as graphs is known as data visualization.
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