更新非时间数据

Question

是否有更好的方法根据每一行中包含的开始和停止条件对一系列浮点数进行抽样？下面是我开始讲的一个例子：

borehole   top  bottom  lithology
0     AP-2  94.6    95.1   dolomite
1     AP-2  95.1    96.7  limestone
2     AP-2  96.7    97.0   dolomite
3     AP-2  97.0    97.5  limestone
4     AP-2  97.5    97.8  limestone
5     AP-3  87.4    87.7  limestone
6     AP-3  87.7    88.1  limestone
7     AP-3  88.1    88.5   dolomite
8     AP-3  88.5    89.1  limestone

对于每一行，我希望使用选择的增量在顶部和底部之间添加行，并向前填充钻孔和岩性值。增量在dataframe中是常量。这里是我希望增加0.1的填充数据。(这仅适用于上述dataframe中的前两行：)

   borehole   Top       Lith
0      AP-2  94.6   dolomite
1      AP-2  94.7   dolomite
2      AP-2  94.8   dolomite
3      AP-2  94.9   dolomite
4      AP-2  95.0   dolomite
5      AP-2  95.1  limestone
6      AP-2  95.2  limestone
7      AP-2  95.3  limestone
8      AP-2  95.4  limestone
9      AP-2  95.5  limestone
10     AP-2  95.6  limestone
11     AP-2  95.7  limestone
12     AP-2  95.8  limestone
13     AP-2  95.9  limestone
14     AP-2  96.0  limestone
15     AP-2  96.1  limestone
16     AP-2  96.2  limestone
17     AP-2  96.3  limestone
18     AP-2  96.4  limestone
19     AP-2  96.5  limestone
20     AP-2  96.6  limestone
21     AP-2  96.7  limestone

这是我用过的代码，它起作用了，但是当我在熊猫中做一个循环时，我想知道我是不是遗漏了一些明显的东西。pd.DataFrame.resample()很吸引人，但我想不出如何使它与非时间数据一起工作。

import pandas as pd
import numpy as np

liths = pd.DataFrame(
{'borehole': {0: 'AP-2',
  1: 'AP-2',
  2: 'AP-2',
  3: 'AP-2',
  4: 'AP-2',
  5: 'AP-3',
  6: 'AP-3',
  7: 'AP-3',
  8: 'AP-3'},
 'bottom': {0: 95.099999999999994,
  1: 96.700000000000003,
  2: 97.0,
  3: 97.5,
  4: 97.799999999999997,
  5: 87.700000000000003,
  6: 88.099999999999994,
  7: 88.5,
  8: 89.099999999999994},
 'lithology': {0: 'dolomite',
  1: 'limestone',
  2: 'dolomite',
  3: 'limestone',
  4: 'limestone',
  5: 'limestone',
  6: 'limestone',
  7: 'dolomite',
  8: 'limestone'},
 'top': {0: 94.599999999999994,
  1: 95.099999999999994,
  2: 96.700000000000003,
  3: 97.0,
  4: 97.5,
  5: 87.400000000000006,
  6: 87.700000000000003,
  7: 88.099999999999994,
  8: 88.5}}
)

filled = []
increment = 0.1
for row in liths.itertuples():
    start = row.top
    end = row.bottom
    for i in np.arange(start, end, increment):
        filled.append([row.borehole, i, row.lithology])
filled = pd.DataFrame(filled, columns=['borehole', 'Top', 'Lith']); filled

Answer 1

我将从每一行构造一个新的数据帧并将它们连接起来。

def expand(r):
    a = np.arange(r.top, r.bottom, .1)
    n = len(a)
    return pd.DataFrame(dict(
            borehole=[r.borehole] * n,
            lithology=[r.lithology] * n,
            top=a
        ))

pd.concat([expand(r) for r in df.itertuples()], ignore_index=True)

   borehole  lithology   top
0      AP-2   dolomite  94.6
1      AP-2   dolomite  94.7
2      AP-2   dolomite  94.8
3      AP-2   dolomite  94.9
4      AP-2   dolomite  95.0
5      AP-2  limestone  95.1
6      AP-2  limestone  95.2
7      AP-2  limestone  95.3
8      AP-2  limestone  95.4
9      AP-2  limestone  95.5
10     AP-2  limestone  95.6
11     AP-2  limestone  95.7
12     AP-2  limestone  95.8
13     AP-2  limestone  95.9
14     AP-2  limestone  96.0
15     AP-2  limestone  96.1
16     AP-2  limestone  96.2
17     AP-2  limestone  96.3
18     AP-2  limestone  96.4
19     AP-2  limestone  96.5
20     AP-2  limestone  96.6
21     AP-2  limestone  96.7
22     AP-2   dolomite  96.7
23     AP-2   dolomite  96.8
24     AP-2   dolomite  96.9
25     AP-2  limestone  97.0
26     AP-2  limestone  97.1
27     AP-2  limestone  97.2
28     AP-2  limestone  97.3
29     AP-2  limestone  97.4
30     AP-2  limestone  97.5
31     AP-2  limestone  97.6
32     AP-2  limestone  97.7
33     AP-3  limestone  87.4
34     AP-3  limestone  87.5
35     AP-3  limestone  87.6
36     AP-3  limestone  87.7
37     AP-3  limestone  87.8
38     AP-3  limestone  87.9
39     AP-3  limestone  88.0
40     AP-3   dolomite  88.1
41     AP-3   dolomite  88.2
42     AP-3   dolomite  88.3
43     AP-3   dolomite  88.4
44     AP-3   dolomite  88.5
45     AP-3  limestone  88.5
46     AP-3  limestone  88.6
47     AP-3  limestone  88.7
48     AP-3  limestone  88.8
49     AP-3  limestone  88.9
50     AP-3  limestone  89.0