python - 如何在 Pandas 中的同一数据框中组合/合并列？

Question

我有一个与此类似的数据框:

       0    1   2   3           4   5
0   1001    1   176 REMAINING   US  SOUTH
1   1002    1   176 REMAINING   US  SOUTH

我想要做的是合并第 3,4 和 5 列，以创建包含第 3,4 和 5 列中所有数据的列。

期望的输出:

       0    1   2   3           
0   1001    1   176 REMAINING US SOUTH
1   1002    1   176 REMAINING US SOUTH

我已经尝试过

hbadef['6'] = hbadef[['3', '4', '5']].apply(lambda x: ''.join(x), axis=1)

但这并没有成功。

这是我实现时的堆栈跟踪

 hbadef['3'] = hbadef['3'] + ' ' +  hbadef['4'] + ' ' + hbadef['5']

堆栈跟踪:

TypeError                                 Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

TypeError: an integer is required

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2524             try:
-> 2525                 return self._engine.get_loc(key)
   2526             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

KeyError: '3'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

TypeError: an integer is required

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-62-2da6c35d6e89> in <module>()
----> 1 hbadef['3'] = hbadef['3'] + ' ' +  hbadef['4'] + ' ' + hbadef['5']
      2 # hbadef.drop(['4', '5'], axis=1)
      3 # hbadef.columns = ['MKTcode', 'Region']
      4 
      5 # pd.concat(

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2137             return self._getitem_multilevel(key)
   2138         else:
-> 2139             return self._getitem_column(key)
   2140 
   2141     def _getitem_column(self, key):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
   2144         # get column
   2145         if self.columns.is_unique:
-> 2146             return self._get_item_cache(key)
   2147 
   2148         # duplicate columns & possible reduce dimensionality

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
   1840         res = cache.get(item)
   1841         if res is None:
-> 1842             values = self._data.get(item)
   1843             res = self._box_item_values(item, values)
   1844             cache[item] = res

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
   3841 
   3842             if not isna(item):
-> 3843                 loc = self.items.get_loc(item)
   3844             else:
   3845                 indexer = np.arange(len(self.items))[isna(self.items)]

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2525                 return self._engine.get_loc(key)
   2526             except KeyError:
-> 2527                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2528 
   2529         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

KeyError: '3'

我尝试删除 NaN 值，但得到了类似的结果。我很困惑为什么这么简单的功能不能正常工作。

我将接受答案，以便我们可以“结束”这个问题。这两个答案都是可以接受的并解决了问题，我遇到的问题可能是应用程序错误，我必须独立于这个问题来解决。

Answer 1

使用concat + agg

pd.concat(
    [df.iloc[:, :3], df.iloc[:, 3:].agg(' '.join, axis=1)], 
    axis=1, 
    ignore_index=True
)

      0  1    2                   3
0  1001  1  176  REMAINING US SOUTH
1  1002  1  176  REMAINING US SOUTH