Understand the Pandas Melt Function

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Learn how to use Pandas' melt function to help you separate information and make it more visual and easier to work with.

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Understand the Pandas Melt Function
In today's class, I will cover Pandas' melt function, a function that will help you when separating information, making it more visual and easier to work with.

This function allows us to transform a wide format DataFrame into a long DataFrame. Basically, the idea is to take a table with multiple columns and transform those columns into rows.

Often, when we work with data, we will come across databases that are not ready for immediate use. Therefore, we need to do some processing and transformations before starting data analysis.

The melt method is one of these viber data possible transformations. In addition to it, I will also show you how to reorganize the data and use the rsplit method to prepare your database for a complete analysis.

Download the available material and come with me to learn how to use the Pandas melt function.

Viewing the Database
In the material available for download, you will find the database that we will use throughout this class.

In it, we have fictitious data that simulates the record of customer satisfaction with the company. This data includes overall satisfaction, product satisfaction and service satisfaction over a three-month period.

We can visualize this data by generating the DataFrame with Pandas and checking the information present.

The information collected is organized so that each line represents a customer and their responses for each satisfaction category in different months.

Note that the data is structured in a wide DataFrame and , for this analysis and visualization of the information, it will be better if it is arranged in a long format . For this, the melt function will be essential.