有效地按元素分组

可以说我有

lags = [0, 30, 60, 90, 120, 150, 180, np.inf]

和

list = [[500, 800, 1000, 200, 1500], [220, 450, 350, 1070, 1780], [900, 450, 1780, 1450, 100], 
        [340, 670, 830, 1370, 1420], [850, 630, 1230, 1670, 910]]

angle = [[50, 80, 100, 20, 150], [22, 45, 35, 107, 178], [90, 45, 178, 145, 10], 
        [34, 67, 83, 137, 142], [85, 63, 123, 167, 91]]

我想将每个元素放在列表中,并根据其值将其存储在不同的单独数组中;

for all list.values where angles.value is less than 30
list1 = [200, 220, 100]
for all list.values where angles.value is between 30 and 60
list2 = [500, 450, 350, 450, 340] 
for all list.values where angles.value is between 60 and 90
list3 = [800, 670, 830, 850, 630]

等等..

我做了这样的事情:

sortlist = defaultdict(list)
ulist = np.unique(list)
uangle = np.unique(angle)
for lag in lags:
    count += 1
    for k, dummy_val in enumerate(uangle):
        if lag <= uangle[k] < lag + 1:
            sortlist[count].append(ulist[k])

我想知道是否有一种pythonic /有效的方法来提高性能.

这是一个矢量化的方法 -

an = angle.ravel()
sidx = an.argsort()
cut_idx = np.searchsorted(an[sidx], lags)
out = np.split(list1.ravel()[sidx], cut_idx[1:-1])

样本输入,输出 -

In [97]: lags = np.array([0, 30, 60, 90, 120, 150, 180, np.inf])
    ...: 
    ...: list1 = np.array([[500, 800, 1000, 200, 1500], \
    ...:                   [220, 450, 350, 1070, 1780], \
    ...:                   [900, 450, 1780, 1450, 100], 
    ...:                   [340, 670, 830, 1370, 1420], \
    ...:                   [850, 630, 1230, 1670, 910]])
    ...: 
    ...: angle = np.array([[50, 80, 100, 20, 150],\
    ...:                   [22, 45, 35, 107, 178],\
    ...:                   [90, 45, 178, 145, 10], 
    ...:                   [34, 67, 83, 137, 142],\
    ...:                   [85, 63, 123, 167, 91]])
    ...: 

In [99]: out
Out[99]: 
[array([100, 200, 220]),            # <----- 0 to 30
 array([340, 350, 450, 450, 500]),  # <----- 30 to 60
 array([630, 670, 800, 830, 850]),  # <----- 60 to 90
 array([ 900,  910, 1000, 1070]),   # <----- 90 to 120
 array([1230, 1370, 1420, 1450]),   # <----- 120 to 150
 array([1500, 1670, 1780, 1780]),   # <----- 150 to 180
 array([], dtype=int64)]            # <----- 180 to Inf