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Pandas is a library that permits using data frame (stylized DataFrame) structures and includes a suite of I/O and data manipulation tools. Unlike NumPy, Pandas allows you to reference named columns instead of using indices. With Pandas, you can perform the same kinds of essential tasks that are available in spreadsheet programs (but now automated and with fewer mouse clicks!). For those who are familiar with R programming language, Pandas mimics the R data.frame function.

A limitation of Pandas is that it can only operate with 2D data structures. More recently, the xarray package has been developed to handle higher‐dimensional datasets. In addition, Pandas can be somewhat inefficient because the library is technically a wrapper for NumPy, so it can consume up to three times as much memory, particularly in Jupyter Notebook. For larger row operations (500K rows or greater), the differences can even out. (Goutham, 2017).