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up vote 2 down vote favorite. 1. Python has a lot of convenient data structures (lists, tuples, dicts, sets, etc) which can be used to make other 'conventional' data structures (Eg, I can use a Python list to create a stack and a collections.dequeue to make a queue, dicts to make trees and graphs, etc).p vote 2 down vote favorite. 1. Python has a lot of convenient data structures (lists, tuples, dicts, sets, etc) which can be used to make other 'conventional' data structures (Eg, I can use a Python list to create a stack and a collections.dequeue to make a queue, dicts to make trees and graphs, etc).
3. Strings, Lists, Arrays, and DictionariesÂ¶. The most import data structure for scientific computing in Python is the NumPy array. NumPy arrays are used to store lists of numerical data and to represent vectors, matrices, and even tensors. NumPy arrays are designed to handle large data sets efficiently and with a minimum of fuss.