From NumPy, you get fast (vectorized) operations on large multidimensional arrays of numbers. This bit not be so useful outside of a data-science context but it

From NumPy, you get fast (vectorized) operations on large multidimensional arrays of numbers. This bit not be so useful outside of a data-science context but it depends on what it is you _are_ doing. Most graphics stuff is also arrays of numbers, for example. Also lots of useful indexing/slicing/masking/filtering on those structures. You can also have non-numeric multidimensional arrays, but the performance part might not be there depending on the type. Pandas adds utility methods like join, groupby, and fromsql. Also hierarchical indexing, and a bunch of other stuff that I don't know much about - I'm not in data-science either, but I work with some of them. Overall, lots of useful stuff for processing tables locally once you've fetched them from SQL or similar. Also, the simple fact of having a tabular data structure without needing to define a custom Row class. Lists of dicts or tuples have a lot of overhead by comparison.