numpy中的where()函数

test_data=np.where(df_test>120,1,0)

where算是三目运算符，where(条件，真，假)

Return elements, either from x or y, depending on condition.

If only condition is given, return condition.nonzero().

Parameters:

condition : array_like, bool

When True, yield x, otherwise yield y.

x, y : array_like, optional

Values from which to choose. x, y and condition need to bebroadcastable to some shape.

Returns:

out : ndarray or tuple of ndarrays

If both x and y are specified, the output array containselements ofx where condition is True, and elements fromy elsewhere.

If only condition is given, return the tuplecondition.nonzero(), the indices wherecondition is True.

Notes

If x and y are given and input arrays are 1-D,where isequivalent to:

[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]

Examples

>>> np.where([[True, False], [True, True]],...          [[1, 2], [3, 4]],...          [[9, 8], [7, 6]])array([[1, 8],       [3, 4]])

>>> np.where([[0, 1], [1, 0]])(array([0, 1]), array([1, 0]))

>>> x = np.arange(9.).reshape(3, 3)>>> np.where( x > 5 )(array([2, 2, 2]), array([0, 1, 2]))>>> x[np.where( x > 3.0 )]               # Note: result is 1D.array([ 4.,  5.,  6.,  7.,  8.])>>> np.where(x < 5, x, -1)               # Note: broadcasting.array([[ 0.,  1.,  2.],       [ 3.,  4., -1.],       [-1., -1., -1.]])

Find the indices of elements of x that are in goodvalues.

>>> goodvalues = [3, 4, 7]>>> ix = np.isin(x, goodvalues)>>> ixarray([[False, False, False],       [ True,  True, False],       [False,  True, False]], dtype=bool)>>> np.where(ix)(array([1, 1, 2]), array([0, 1, 1]))