Over time, there is more and more data in the world, which is causing an increasing need for data analysts, scientists, and engineers to make sense of the data, and use it in a meaningful way. This is where Pandas comes in, which allows people to perform analysis on and work with data.
The main steps of the workflow for data analysts, scientists, and engineers, usually is as follows:
Munging and cleaning data. (Munging means to convert data, usually raw data, from one format to another, that is clearer for us to work with)
Analyzing and modeling the data.
Organizing the results in a usable form to plot or store in a table.
Pandas allows us to do all the steps of this workflow.
In addition, Pandas is used in many fields, including finance, mathematics, web development, and many other areas where data is an important factor.
Search didn’t bring up any DIRECT results to the query, but after reading basic functionalities of pandas and dplyr, I feel they have both got a lot in common, and both at the very least help in bringing SQL-Like functionality to python and R respectively (like joins, etc)
for that.