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# Iterator Use

A good abstraction proves its worth when it makes coding simpler under diverse conditions, or when it ends up being useful in ways that were originally unintended. Both of these affirmations of value are definitely true of the NumPy iterator object. With only slight modifications, the original simple NumPy iterator has become a workhorse for implementing other NumPy features, such as iteration over all but one dimension and iteration over multiple arrays at the same time. In addition, when we had to quickly add

some enhancements to the code for generating random numbers and for broadcast-based copying, the existence of the iterator and its extensions made implementation much easier.

## Iteration Over All But One Dimension

A common motif in NumPy is to gain speed by concentrating optimizations on the loop over a single dimension where simple striding can be assumed. Then an iteration strategy that iterates over all but the last dimension is used. This was the approach introduced by NumPy's predecessor, Numeric, to implement the math functionality.

In NumPy, a slight modification to the NumPy iterator makes it possible to use this basic strategy in any code. The modified iterator is returned from the constructor as follows:

it = PyArray_IterAllButAxis(array, &dim).

The PyArray_IterAllButAxis function takes a NumPy array and the address of an integer representing the dimension to remove from the iteration. The integer is passed by reference (the & operator) because if the dimension ...

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