Exploratory Data Analysis is the process of inspecting the data in order to understand what the data is all about. It is generally a visual method where we create different plots and graphs to understand what patterns, anomalies and outliers do data have. It is an important step because it helps us analyze the relationship between different attributes within themselves, also it is helpful in analyzing the properties of different attributes of the dataset.
Being an important step in analyzing what data is all about Exploratory Data Analysis generally takes a lot of time because we need to write code for analyzing and visualizing data. What if we can automate this process of visualizing and analyzing data?
Pandas Visual Analysis is an open-source python library which is used to visually analyze the data and that too in just a single line of code. It creates a user interface that can be used to create different plots and graphs taking different attributes. It supports a large variety of graphs and plots, also all the graphs are created using Plotly so that they are highly interactive, visually appealing, and easily downloadable.
In this article, we will see how easily and effortlessly we can automate the process of visual analysis using Pandas Visual Analysis.o…
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