Python 需要将dataframe列中的可变长度数据拆分为多个列

我有两列数据框，如下所示：

ITEM        REFNUMS
1   00000299    0036701923024762922029229294652954429569295832...
2   00000655    NaN
24  00001791    00016027123076000158004563065131972
25  00001805    00016027123076000158004563065131972
26  00001813    00016027123076000158004563065131972
27  00001821    00016027123076000158004563065131972
28  00001937    0142530521316303164702509000510012201310027820...

我希望将REFNUMS列拆分为可分割的部分，并尽可能添加到现有的数据框架中，因为我需要保留行索引和匹配项。REFNUMS中的数据长度可以被5整除，如果不是NaN，那么例如第1行等于78组5

data_len = (data['REFNUMS'].str.len())/5 

然后

感谢您对如何执行此操作的建议。

IIUC，您可以使用str.extractall获取5位数的组，清理列，然后加入：

In [168]: r = df.REFNUMS.str.extractall("(\d{1,5})").unstack()

In [169]: r.columns = r.columns.droplevel(0)

In [170]: df.join(r)
Out[170]: 
    ITEM                                            REFNUMS      0      1      2      3      4      5      6      7      8     9
1    299  0036701923024762922029229294652954429569295832...  00367  01923  02476  29220  29229  29465  29544  29569  29583     2
2    655                                                NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   NaN
24  1791                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
25  1805                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
26  1813                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
27  1821                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
28  1937  0142530521316303164702509000510012201310027820...  01425  30521  31630  31647  02509  00051  00122  01310  02782     0

---

你的预期产出是什么？太棒了，太棒了。我甚至都没想过。

In [168]: r = df.REFNUMS.str.extractall("(\d{1,5})").unstack()

In [169]: r.columns = r.columns.droplevel(0)

In [170]: df.join(r)
Out[170]: 
    ITEM                                            REFNUMS      0      1      2      3      4      5      6      7      8     9
1    299  0036701923024762922029229294652954429569295832...  00367  01923  02476  29220  29229  29465  29544  29569  29583     2
2    655                                                NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   NaN
24  1791                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
25  1805                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
26  1813                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
27  1821                00016027123076000158004563065131972  00016  02712  30760  00158  00456  30651  31972   None   None  None
28  1937  0142530521316303164702509000510012201310027820...  01425  30521  31630  31647  02509  00051  00122  01310  02782     0