Sorting, searching, and counting

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NumPy API Reference: Sorting, searching, and counting

Sorting

sort(a[, axis, kind])	Returns a sorted copy of an array with a stable sorting algorithm.
lexsort(keys)	Perform an indirect sort using an array of keys.
argsort(a[, axis, kind])	Returns the indices that would sort an array with a stable sorting.
msort(a)	Returns a copy of an array sorted along the first axis.
sort_complex(a)	Sort a complex array using the real part first, then the imaginary part.
partition(a, kth[, axis])	Returns a partitioned copy of an array.
argpartition(a, kth[, axis])	Returns the indices that would partially sort an array.

See also

cupy.ndarray.sort()

Searching

argmax(a[, axis, dtype, out, keepdims])	Returns the indices of the maximum along an axis.
nanargmax(a[, axis, dtype, out, keepdims])	Return the indices of the maximum values in the specified axis ignoring NaNs.
argmin(a[, axis, dtype, out, keepdims])	Returns the indices of the minimum along an axis.
nanargmin(a[, axis, dtype, out, keepdims])	Return the indices of the minimum values in the specified axis ignoring NaNs.
argwhere(a)	Return the indices of the elements that are non-zero.
nonzero(a)	Return the indices of the elements that are non-zero.
flatnonzero(a)	Return indices that are non-zero in the flattened version of a.
where(condition[, x, y])	Return elements, either from x or y, depending on condition.
searchsorted(a, v[, side, sorter])	Finds indices where elements should be inserted to maintain order.
extract(condition, a)	Return the elements of an array that satisfy some condition.

Counting

count_nonzero(a[, axis])

Counts the number of non-zero values in the array.