您可以从itertools模块使用groupby：

lst = []
for mask, grp in groupby(zip(np.arange(len(x)), np.isnan(x)), key=lambda x: x[1]):
    if mask == True:  # only for NaN
        idx = [idx for idx, _ in grp]
        lst.append([idx[0], idx[-1]] if len(idx) > 1 else idx[0])

输出：

>>> lst
[0, [3, 5], 8, 11]

您可以使用布尔运算将np.isnan输出在左/右移位：

# if the value a NaN?
m = np.isnan(x)
# is the preceding value not a NaN?
m2 = np.r_[False, ~m[:-1]]
# is the following value not a NaN?
m3 = np.r_[~m[1:], False]

out = np.where((m&m2)|(m&m3))[0]

输出：

array([ 0,  3,  5,  8, 11])