numpy.fabs — NumPy v1.13 Manual (original) (raw)

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

Compute the absolute values element-wise.

This function returns the absolute values (positive magnitude) of the data in x. Complex values are not handled, use absolute to find the absolute values of complex data.

Parameters:	x : array_like The array of numbers for which the absolute values are required. If_x_ is a scalar, the result y will also be a scalar. 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:	y : ndarray or scalar The absolute values of x, the returned values are always floats.

Parameters:

x : array_like The array of numbers for which the absolute values are required. If_x_ is a scalar, the result y will also be a scalar. 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:

y : ndarray or scalar The absolute values of x, the returned values are always floats.

Examples

np.fabs(-1) 1.0 np.fabs([-1.2, 1.2]) array([ 1.2, 1.2])