pandas:如何将一列中的文本拆分为多行? [英] pandas: How do I split text in a column into multiple rows?

问题描述

我正在处理一个大型 csv 文件，最后一列的下一列有一个我想用特定分隔符拆分的文本字符串.我想知道是否有一种简单的方法可以使用 Pandas 或 Python 来做到这一点?

I'm working with a large csv file and the next to last column has a string of text that I want to split by a specific delimiter. I was wondering if there is a simple way to do this using pandas or python?

CustNum  CustomerName     ItemQty  Item   Seatblocks                 ItemExt
32363    McCartney, Paul      3     F04    2:218:10:4,6                   60
31316    Lennon, John        25     F01    1:13:36:1,12 1:13:37:1,13     300

我想在 Seatblocks 列中用空格(' ') 和冒号(':') 分开，但是每个单元格将导致不同数量的列.我有一个重新排列列的功能，因此 Seatblocks 列位于工作表的末尾，但我不确定从那里开始做什么.我可以使用内置的 text-to-columns 函数和一个快速宏在 excel 中完成，但我的数据集有太多记录，excel 无法处理.

I want to split by the space(' ') and then the colon(':') in the Seatblocks column, but each cell would result in a different number of columns. I have a function to rearrange the columns so the Seatblocks column is at the end of the sheet, but I'm not sure what to do from there. I can do it in excel with the built in text-to-columns function and a quick macro, but my dataset has too many records for excel to handle.

最终，我想记录 John Lennon 的记录并创建多行，将每组座位的信息放在单独的行上.

Ultimately, I want to take records such John Lennon's and create multiple lines, with the info from each set of seats on a separate line.

推荐答案

这将按空间拆分座垫，并为每个座垫分配自己的行.

This splits the Seatblocks by space and gives each its own row.

In [43]: df
Out[43]: 
   CustNum     CustomerName  ItemQty Item                 Seatblocks  ItemExt
0    32363  McCartney, Paul        3  F04               2:218:10:4,6       60
1    31316     Lennon, John       25  F01  1:13:36:1,12 1:13:37:1,13      300

In [44]: s = df['Seatblocks'].str.split(' ').apply(Series, 1).stack()

In [45]: s.index = s.index.droplevel(-1) # to line up with df's index

In [46]: s.name = 'Seatblocks' # needs a name to join

In [47]: s
Out[47]: 
0    2:218:10:4,6
1    1:13:36:1,12
1    1:13:37:1,13
Name: Seatblocks, dtype: object

In [48]: del df['Seatblocks']

In [49]: df.join(s)
Out[49]: 
   CustNum     CustomerName  ItemQty Item  ItemExt    Seatblocks
0    32363  McCartney, Paul        3  F04       60  2:218:10:4,6
1    31316     Lennon, John       25  F01      300  1:13:36:1,12
1    31316     Lennon, John       25  F01      300  1:13:37:1,13

或者，在其自己的列中给出每个以冒号分隔的字符串:

Or, to give each colon-separated string in its own column:

In [50]: df.join(s.apply(lambda x: Series(x.split(':'))))
Out[50]: 
   CustNum     CustomerName  ItemQty Item  ItemExt  0    1   2     3
0    32363  McCartney, Paul        3  F04       60  2  218  10   4,6
1    31316     Lennon, John       25  F01      300  1   13  36  1,12
1    31316     Lennon, John       25  F01      300  1   13  37  1,13

这有点难看，但也许有人会提出更漂亮的解决方案.

This is a little ugly, but maybe someone will chime in with a prettier solution.

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