pandas如何使用pd.cut（）

test[‘range’] = pd.cut(test.days, [0,30,60], include_lowest=True)
print (test)
daysrange
0 0 (-0.001, 30.0]
1 31 (30.0, 60.0]
2 45 (30.0, 60.0]

看区别：

test = pd.Dataframe({'days': [0,20,30,31,45,60]})test['range1'] = pd.cut(test.days, [0,30,60], include_lowest=True)#30 value is in [30, 60) grouptest['range2'] = pd.cut(test.days, [0,30,60], right=False)#30 value is in (0, 30] grouptest['range3'] = pd.cut(test.days, [0,30,60])print (test)   days          range1    range2    range30     0  (-0.001, 30.0]   [0, 30)       NaN1    20  (-0.001, 30.0]   [0, 30)   (0, 30]2    30  (-0.001, 30.0]  [30, 60)   (0, 30]3    31    (30.0, 60.0]  [30, 60)  (30, 60]4    45    (30.0, 60.0]  [30, 60)  (30, 60]5    60    (30.0, 60.0]       NaN  (30, 60]

或使用

numpy.searchsorted

days

arr = np.array([0,30,60])test['range1'] = arr.searchsorted(test.days)test['range2'] = arr.searchsorted(test.days, side='right') - 1print (test)   days  range1  range20     0       0       01    20       1       02    30       1       13    31       2       14    45       2       15    60       2       2