Pandas can be regarded as a "wonder tool" when it comes to applications like data manipulation, data cleaning, or handling time series data. It is extremely fast and efficient, and it is powerful enough to handle small to intermediate datasets. The best part is that the use of pandas is not restricted just to Python. There are methods enabling the supremacy of pandas to be utilized in other frameworks, like R, Julia, Azure ML Studio and H20.ai. These methods of using the benefits of a superior framework from another tool is called cross-tooling and is frequently applied. One of the main reasons for this to exist is that it is almost impossible for one tool to have all the functionalities. Suppose one task has two sub-tasks: sub-task 1 can be done well in R while the sub-task 2 in Python. One can handle this by doing sub-task 1 in R and sub-task 2 by calling Python code from R or doing sub-task 2 in Python and sub-task 1 by calling R code from Python.

This option makes pandas even more powerful. Let's see how pandas methods and /or Python code in general can be used with other tools.