我不确定你是否正在寻找这样的东西。由于您的示例数据不可复制，我从您共享的示例中创建了一个。

import pandas as pd

df = pd.DataFrame({
    'Col1': ['String' + str(i) for i in range(1, 21)],
    'Col2': range(1, 21),
    'Col3': np.random.rand(20)
})

conditions  = [df['Col2'].between(1,5), df['Col2'].between(6,10),
               df['Col2'].between(11,15), df['Col2'].between(16,20)]
choices     = [ "group1", "group2", "group3", "group4" ]
    
df["class"] = np.select(conditions, choices, default='random_group')
df["class_sum"] = df.groupby(['Col1' ,"class"])['Col3'].transform('sum')
print(df)

 Col1  Col2      Col3   class  class_sum
0    String1     1  0.509723  group1   0.509723
1    String2     2  0.387798  group1   0.387798
2    String3     3  0.106302  group1   0.106302
3    String4     4  0.576913  group1   0.576913
4    String5     5  0.068705  group1   0.068705
5    String6     6  0.802236  group2   0.802236
6    String7     7  0.511529  group2   0.511529
7    String8     8  0.846700  group2   0.846700
8    String9     9  0.785276  group2   0.785276
9   String10    10  0.912042  group2   0.912042
10  String11    11  0.607900  group3   0.607900
11  String12    12  0.842794  group3   0.842794
12  String13    13  0.779911  group3   0.779911
13  String14    14  0.964896  group3   0.964896
14  String15    15  0.983164  group3   0.983164
15  String16    16  0.753229  group4   0.753229
16  String17    17  0.739145  group4   0.739145
17  String18    18  0.915821  group4   0.915821
18  String19    19  0.338980  group4   0.338980
19  String20    20  0.698161  group4   0.698161

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您可以使用cut和groupby.sum：

MAX_SIZE = 10_000_000 # max chromosome size

bins = list(range(0, MAX_SIZE, 10_000))
bins[0] = 1
labels = [f'{a}-{b}' for a,b in zip(bins, bins[1:])]

group = pd.cut(df['position'], bins, labels=labels).astype(str)

out = (df.groupby(['chromosome', group, 'strand'])
       [['A', 'B', 'C', 'D']].sum().reset_index()
      )

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或者，使用floordiv计算组：

s = df['position'].floordiv(10_000).add(1)
group = s.astype(str)+'-'+s.mul(10_000).astype(str)

out = (df.groupby(['chromosome', group, 'strand'])
       [['A', 'B', 'C', 'D']].sum().reset_index()
      )

型
输出量：

chromosome position strand  A  B  C  D
0       ChrA  1-10000      F  4  5  2  8
1       ChrB  1-10000      F  1  1  0  4

型
使用的输入：

chromosome  position strand  A  B  C  D
0       ChrA       553      F  3  0  0  0
1       ChrA       834      F  0  3  1  0
2       ChrA       987      F  1  2  1  8
3       ChrB       348      F  1  1  0  4

型