将分隔符上的多列拆分为pandas数据框中的行 [英] Splitting multiple columns on a delimiter into rows in pandas dataframe

问题描述

我有一个熊猫数据框，如下所示:

I have a pandas dataframe as shown here:

id    pos      value                sent
1     a/b/c    test/test2/test3     21
2     d/a      test/test5           21

我想拆分(= explode)df['pos']和df['token']，以便数据框看起来像这样:

I would like to split (=explode)df['pos'] and df['token'] so that the dataframe looks like this:

id      pos          value         sent
1       a            test          21
1       b            test2         21 
1       c            test3         21 
2       d            test          21
2       a            test5         21

如果我拆分每列然后将它们合并在一起，那是行不通的

It doesn't work if I split each column and then concat them à la

pos = df.token.str.split('/', expand=True).stack().str.strip().reset_index(level=1, drop=True)

df1 = pd.concat([pos,value], axis=1, keys=['pos','value'])

有什么想法吗?我真的很感激.

Any ideas? I'd really appreciate it.

我尝试在此处使用此解决方案: https://stackoverflow.com/a/40449726/4219498

I tried using this solution here : https://stackoverflow.com/a/40449726/4219498

但是出现以下错误: TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

But I get the following error: TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

我不确定这是一个与numpy相关的问题，尽管我不确定这是怎么发生的.我正在使用Python 2.7.14

I suppose this is a numpy related issue although I'm not sure how this happens. I'm using Python 2.7.14

推荐答案

我倾向于避免使用stack魔术，而是从头开始构建新的数据框.通常这也更有效.下面是一种方法.

I tend to avoid the stack magic in favour of building a new dataframe from scratch. This is usually also more efficient. Below is one way.

import numpy as np
from itertools import chain

lens = list(map(len, df['pos'].str.split('/')))

res = pd.DataFrame({'id': np.repeat(df['id'], lens),
                    'pos': list(chain.from_iterable(df['pos'].str.split('/'))),
                    'value': list(chain.from_iterable(df['value'].str.split('/'))),
                    'sent': np.repeat(df['sent'], lens)})

print(res)

   id pos  sent  value
0   1   a    21   test
0   1   b    21  test2
0   1   c    21  test3
1   2   d    21   test
1   2   a    21  test5

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