Most Efficient Way to Reverse a Numpy Array

Most efficient way to reverse a numpy array

reversed_arr = arr[::-1]

gives a reversed view into the original array arr. Any changes made to the original array arr will also be immediately visible in reversed_arr. The underlying data buffers for arr and reversed_arr are shared, so creating this view is always instantaneous, and does not require any additional memory allocation or copying for the array contents.

See also, this discussion on NumPy views: How do I create a view onto a NumPy array?

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Possible solutions to performance problems regarding views

Are you re-creating the view more often than you need to? You should be able to do something like this:

arr = np.array(some_sequence)
reversed_arr = arr[::-1]

do_something(arr)
look_at(reversed_arr)
do_something_else(arr)
look_at(reversed_arr)

I'm not a numpy expert, but this seems like it would be the fastest way to do things in numpy. If this is what you are already doing, I don't think you can improve on it.

Efficiently sorting a numpy array in descending order?

temp[::-1].sort() sorts the array in place, whereas np.sort(temp)[::-1] creates a new array.

In [25]: temp = np.random.randint(1,10, 10)

In [26]: temp
Out[26]: array([5, 2, 7, 4, 4, 2, 8, 6, 4, 4])

In [27]: id(temp)
Out[27]: 139962713524944

In [28]: temp[::-1].sort()

In [29]: temp
Out[29]: array([8, 7, 6, 5, 4, 4, 4, 4, 2, 2])

In [30]: id(temp)
Out[30]: 139962713524944

Most efficient ways to compare arrays in numpy?

From the answer: Finding the (x,y) indexes of specific (R,G,B) color values from images stored in NumPy ndarrays

The solution to your problem would be:

cords = list(zip(*np.where(np.all(np_pixels == torso, axis=-1))))

Writing an efficient code for applying a reverse operation using masks in NumPy

You can use boolean indexing:

b = np.zeros_like(m, dtype=float) # change dtype to `int` if that's what you want.

b[m] = c

Output

array([1., 2., 3., 4., 0., 0., 7., 8., 9.])

What is the most efficient way of finding the ranks of one numpy array in another?

Use searchsorted

ind = np.argsort(y)
np.searchsorted(y, x, sorter=ind)
# array([1, 3, 2, 1, 0], dtype=int64)

If y has duplicates, you may have to tinker with the side argument.

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