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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).
A typical task you come around when analyzing data with Python is to run a computation line or column wise on a numpy array and store the results in a new one. Ok - sounds not too bad: Initialize an empty array to store the results in. Create a for-loop looping over the lines/columns of the data array Inside the loop: Do the computation. Append the result array.