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How to vectorize nested nested for loops in python?


Asked by Malani Howard on Dec 08, 2021 FAQ



We can also gradually build the three ranges corresponding to the shape parameters and perform the subtraction against the three elements of roi on the fly without actually creating the meshes as done earlier with np.mgrid. This would be benefited by the use of broadcasting for efficiency purposes. The implementation would look like this -
Indeed,
Python Nested for Loop 1 The outer for loop uses the range () function to iterate over the first ten numbers 2 The inner for loop will execute ten times for each outer number 3 In the body of the inner loop, we will print the multiplication of the outer number and current number 4 The inner loop is nothing but a body of an outer loop.
Also, List Comprehensions are one of the most amazing features of Python. It is a smart and concise way of creating lists by iterating over an iterable object. Nested List Comprehensions are nothing but a list comprehension within another list comprehension which is quite similar to nested for loops.
Just so,
And it turns out one can easily vectorize simple blocks of conditional loops by first turning them into functions and then using numpy.vectorize method. In my previous article I showed an order of magnitude speed boost for numpy vectorization of simple mathematical transformation.
Consequently,
What is Vectorization ? Vectorization is used to speed up the Python code without using loop. Using such a function can help in minimizing the running time of code efficiently.