Python本地最小值最大值，而bin没有改变

下面有两种方法可以解决您的问题，一种是使用pandas，另一种是使用numpy(UPDATED反映了OP在关于基于连续分组的bin个值进行装箱的 comments 中的澄清):

res = df.assign(bin_index = (df.bin != df.bin.shift()).cumsum())

dfAggs = res[['butter', 'bin', 'bin_index']].groupby(['bin', 'bin_index']).agg([min, max])
dfAggs.columns = dfAggs.columns.droplevel()
res = res.join(dfAggs, on=['bin', 'bin_index']).drop(columns='bin_index')
print("", "pandas:", res, sep="\n")

a = df.copy().to_numpy()
print("", "input as numpy 2d array", a, sep="\n")
bin_index = a[:,2:3] != np.concatenate((np.full((1, 1), np.NaN), a[:-1,2:3]), axis = 0)
bin_index = np.cumsum(bin_index)
bins = np.unique(bin_index)
aggs = np.empty((a.shape[0], 2))
for b in bins:
    mask = bin_index==b
    aggs[mask, :] = (a[mask, 1].min(), a[mask, 1].max())
res = np.concatenate((a, aggs), axis=1)
print("", "numpy:", res, sep="\n")

输出:

input as pandas dataframe
                         time    butter  bin
0   2022-07-04 17:33:45+00:00  1.041967    3
1   2022-07-04 17:34:00+00:00  1.041967    4
2   2022-07-04 17:34:15+00:00  1.041966    4
3   2022-07-04 17:34:30+00:00  1.041967    4
4   2022-07-04 17:34:45+00:00  1.041968    4
5   2022-07-04 17:35:00+00:00  1.041969    4
6   2022-07-04 17:35:15+00:00  1.041971    4
7   2022-07-04 17:35:30+00:00  1.041973    4
8   2022-07-04 17:35:45+00:00  1.041975    4
9   2022-07-04 17:36:00+00:00  1.041977    5
10  2022-07-04 17:36:15+00:00  1.041979    5
11  2022-07-04 17:36:30+00:00  1.041981    5
12  2022-07-04 17:36:45+00:00  1.041983    5
13  2022-07-04 17:37:00+00:00  1.041985    5
14  2022-07-04 17:37:15+00:00  1.041986    6
15  2022-07-04 17:37:30+00:00  1.041987    6
16  2022-07-04 17:37:45+00:00  1.041988    6
17  2022-07-04 17:38:00+00:00  1.041989    6
18  2022-07-04 17:38:15+00:00  1.041990    4
19  2022-07-04 17:38:30+00:00  1.041995    4

pandas:
                         time    butter  bin       min       max
0   2022-07-04 17:33:45+00:00  1.041967    3  1.041967  1.041967
1   2022-07-04 17:34:00+00:00  1.041967    4  1.041966  1.041975
2   2022-07-04 17:34:15+00:00  1.041966    4  1.041966  1.041975
3   2022-07-04 17:34:30+00:00  1.041967    4  1.041966  1.041975
4   2022-07-04 17:34:45+00:00  1.041968    4  1.041966  1.041975
5   2022-07-04 17:35:00+00:00  1.041969    4  1.041966  1.041975
6   2022-07-04 17:35:15+00:00  1.041971    4  1.041966  1.041975
7   2022-07-04 17:35:30+00:00  1.041973    4  1.041966  1.041975
8   2022-07-04 17:35:45+00:00  1.041975    4  1.041966  1.041975
9   2022-07-04 17:36:00+00:00  1.041977    5  1.041977  1.041985
10  2022-07-04 17:36:15+00:00  1.041979    5  1.041977  1.041985
11  2022-07-04 17:36:30+00:00  1.041981    5  1.041977  1.041985
12  2022-07-04 17:36:45+00:00  1.041983    5  1.041977  1.041985
13  2022-07-04 17:37:00+00:00  1.041985    5  1.041977  1.041985
14  2022-07-04 17:37:15+00:00  1.041986    6  1.041986  1.041989
15  2022-07-04 17:37:30+00:00  1.041987    6  1.041986  1.041989
16  2022-07-04 17:37:45+00:00  1.041988    6  1.041986  1.041989
17  2022-07-04 17:38:00+00:00  1.041989    6  1.041986  1.041989
18  2022-07-04 17:38:15+00:00  1.041990    4  1.041990  1.041995
19  2022-07-04 17:38:30+00:00  1.041995    4  1.041990  1.041995

input as numpy 2d array
[['2022-07-04 17:33:45+00:00' 1.041967 3]
 ['2022-07-04 17:34:00+00:00' 1.041967 4]
 ['2022-07-04 17:34:15+00:00' 1.041966 4]
 ['2022-07-04 17:34:30+00:00' 1.041967 4]
 ['2022-07-04 17:34:45+00:00' 1.041968 4]
 ['2022-07-04 17:35:00+00:00' 1.041969 4]
 ['2022-07-04 17:35:15+00:00' 1.041971 4]
 ['2022-07-04 17:35:30+00:00' 1.041973 4]
 ['2022-07-04 17:35:45+00:00' 1.041975 4]
 ['2022-07-04 17:36:00+00:00' 1.041977 5]
 ['2022-07-04 17:36:15+00:00' 1.041979 5]
 ['2022-07-04 17:36:30+00:00' 1.041981 5]
 ['2022-07-04 17:36:45+00:00' 1.041983 5]
 ['2022-07-04 17:37:00+00:00' 1.041985 5]
 ['2022-07-04 17:37:15+00:00' 1.041986 6]
 ['2022-07-04 17:37:30+00:00' 1.041987 6]
 ['2022-07-04 17:37:45+00:00' 1.041988 6]
 ['2022-07-04 17:38:00+00:00' 1.041989 6]
 ['2022-07-04 17:38:15+00:00' 1.04199 4]
 ['2022-07-04 17:38:30+00:00' 1.041995 4]]

numpy:
[['2022-07-04 17:33:45+00:00' 1.041967 3 1.041967 1.041967]
 ['2022-07-04 17:34:00+00:00' 1.041967 4 1.041966 1.041975]
 ['2022-07-04 17:34:15+00:00' 1.041966 4 1.041966 1.041975]
 ['2022-07-04 17:34:30+00:00' 1.041967 4 1.041966 1.041975]
 ['2022-07-04 17:34:45+00:00' 1.041968 4 1.041966 1.041975]
 ['2022-07-04 17:35:00+00:00' 1.041969 4 1.041966 1.041975]
 ['2022-07-04 17:35:15+00:00' 1.041971 4 1.041966 1.041975]
 ['2022-07-04 17:35:30+00:00' 1.041973 4 1.041966 1.041975]
 ['2022-07-04 17:35:45+00:00' 1.041975 4 1.041966 1.041975]
 ['2022-07-04 17:36:00+00:00' 1.041977 5 1.041977 1.041985]
 ['2022-07-04 17:36:15+00:00' 1.041979 5 1.041977 1.041985]
 ['2022-07-04 17:36:30+00:00' 1.041981 5 1.041977 1.041985]
 ['2022-07-04 17:36:45+00:00' 1.041983 5 1.041977 1.041985]
 ['2022-07-04 17:37:00+00:00' 1.041985 5 1.041977 1.041985]
 ['2022-07-04 17:37:15+00:00' 1.041986 6 1.041986 1.041989]
 ['2022-07-04 17:37:30+00:00' 1.041987 6 1.041986 1.041989]
 ['2022-07-04 17:37:45+00:00' 1.041988 6 1.041986 1.041989]
 ['2022-07-04 17:38:00+00:00' 1.041989 6 1.041986 1.041989]
 ['2022-07-04 17:38:15+00:00' 1.04199 4 1.04199 1.041995]
 ['2022-07-04 17:38:30+00:00' 1.041995 4 1.04199 1.041995]]

Pandas 说明:

• 创建bin_index列，该列检测bin中的更改，并 for each 这样的行增加一个id值
• 使用DataFrame.groupby()基于bin_index执行聚合(minmax)
• 使用DataFrame.join()(对聚合数据帧aggs进行预处理以删除其名为butter的多索引的级别)向原始数据帧添加min和max列.

Numpy解释:

• 创建bin_index个数组，该数组检测bin中的变化，并 for each 这样的行增加一个id值
• 准备aggs作为形状为a.shape[0], 2的数组，用于接收输入数组a中对应bin值的min和max列
• 对bin_index中的每个唯一bin值使用布尔掩码，在a的butter列的相应行上执行聚合，并将这两个值放置在aggs列中，用于这些相同的行
• 使用numpy.concatenate()将a和aggs水平粘合在一起.