numpy.less — NumPy v1.15 Manual (original) (raw)

numpy. less(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, _extobj_]) = <ufunc 'less'>¶

Return the truth value of (x1 < x2) element-wise.

Parameters:	x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.
Returns:	out : ndarray or scalar Output array, element-wise comparison of x1 and x2. Typically of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.

Parameters:

x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

out : ndarray or scalar Output array, element-wise comparison of x1 and x2. Typically of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.

Examples

np.less([1, 2], [2, 2]) array([ True, False])