Python supports a variety of data types such as numeric (integers, floats, complex), string, boolean, list, tuple, and dictionary. Each data type has its own unique set of properties and methods.

Pandas makes it easy to quickly load, manipulate, align, merge, and even visualize data tables directly in Python.

However, Python’s methods for parallelizing operations often require data to be serialized and deserialized between threads or nodes, while Julia’s parallelization is more refined.