python – 有效地按元素分组

概述可以说我有 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, 12 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],[900,1780,1450,100],[340,670,830,1370,1420],[850,630,1230,1670,910]]angle = [[50,80,100,20,150],[22,45,35,107,178],[90,178,145,10],[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 30List1 = [200,220,100]for all List.values where angles.value is between 30 and 60List2 = [500,340] for all List.values where angles.value is between 60 and 90List3 = [800,850,630]

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.

解决方法 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,np.inf])    ...:     ...: List1 = np.array([[500,\    ...:                   [220,\    ...:                   [900,...:                   [340,\    ...:                   [850,910]])    ...:     ...: angle = np.array([[50,\    ...:                   [22,\    ...:                   [90,...:                   [34,\    ...:                   [85,91]])    ...: In [99]: outOut[99]: [array([100,220]),# <----- 0 to 30 array([340,500]),# <----- 30 to 60 array([630,850]),# <----- 60 to 90 array([ 900,910,1070]),# <----- 90 to 120 array([1230,1420,1450]),# <----- 120 to 150 array([1500,1780]),# <----- 150 to 180 array([],dtype=int64)]            # <----- 180 to Inf

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