Pandas Dataframe:将具有列表的行扩展为具有所有列所需索引的多行 [英] Pandas Dataframe: Expand rows with lists to multiple row with desired indexing for all columns

问题描述

我在pandas数据框中有时间序列数据，在测量开始时的索引为时间，在列中以固定的采样率记录了值列表(连续索引的差异/列表中元素的数量)

I have time series data in pandas dataframe with index as time at the start of measurement and columns with list of values recorded at a fixed sampling rate (difference in consecutive index/number of elements in the list)

这是它的样子:

Time         A                   B                   .......  Z
0    [1, 2, 3, 4]      [1, 2, 3, 4]
2    [5, 6, 7, 8]      [5, 6, 7, 8]
4    [9, 10, 11, 12]   [9, 10, 11, 12]
6    [13, 14, 15, 16]  [13, 14, 15, 16 ] 
...

我想将所有列中的每一行扩展为多行，以便:

I want to expand each row in all the columns to multiple rows such that:

Time       A           B  .... Z
0          1           1
0.5        2           2
1          3           3
1.5        4           4
2          5           5 
2.5        6           6
.......

到目前为止，我正在按照以下思路考虑(代码无法正常运行):

So far I am thinking along these lines (code doesn't wok):

def expand_row(dstruc):
    for i in range (len(dstruc)):
        for j in range (1,len(dstruc[i])):
            dstruc.loc[i+j/len(dstruc[i])] = dstruc[i][j]

    dstruc.loc[i] = dstruc[i][0]
    return dstruc

expanded = testdf.apply(expand_row)

我也尝试同时使用split('，')和stack()，但无法正确修复索引.

I also tried using split(',') and stack() together but I am not able to fix my indexing appropriately.

推荐答案

可能不理想，但这可以使用groupby完成，并应用一个函数，该函数返回每一行的扩展DataFrame(此处假定时差为固定为2.0):

Probably not ideal, but this can be done using groupby and apply a function which returns the expanded DataFrame for each row (here the time difference is assumed to be fixed at 2.0):

def expand(x):
    data = {c: x[c].iloc[0] for c in x if c != 'Time'}
    n = len(data['A'])
    step = 2.0 / n;
    data['Time'] = [x['Time'].iloc[0] + i*step for i in range(n)]
    return pd.DataFrame(data)

print df.groupby('Time').apply(expand).set_index('Time', drop=True)

输出:

       A   B
Time        
0.0    1   1
0.5    2   2
1.0    3   3
1.5    4   4
2.0    5   5
2.5    6   6
3.0    7   7
3.5    8   8
4.0    9   9
4.5   10  10
5.0   11  11
5.5   12  12
6.0   13  13
6.5   14  14
7.0   15  15
7.5   16  16

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