在MultiIndex DataFrame / Series中添加一行 [英] adding a row to a MultiIndex DataFrame/Series
问题描述
我想知道是否有一种等效方法可以使用MultiIndex向Series或DataFrame添加一行,因为只有一个索引,即使用.ix或.loc?
I was wondering if there is an equivalent way to add a row to a Series or DataFrame with a MultiIndex as there is with a single index, i.e. using .ix or .loc?
我认为自然的方式就像
row_to_add = pd.MultiIndex.from_tuples()
df.ix[row_to_add] = my_row
但是会引发KeyError。我知道我可以使用.append(),但我觉得使用.ix []或.loc []更加整洁。
but that raises a KeyError. I know I can use .append(), but I would find it much neater to use .ix[] or .loc[].
这里有一个例子:
>>> df = pd.DataFrame({'Time': [dt.datetime(2013,2,3,9,0,1), dt.datetime(2013,2,3,9,0,1)], 'hsec': [1,25], 'vals': [45,46]})
>>> df
Time hsec vals
0 2013-02-03 09:00:01 1 45
1 2013-02-03 09:00:01 25 46
[2 rows x 3 columns]
>>> df.set_index(['Time','hsec'],inplace=True)
>>> ind = pd.MultiIndex.from_tuples([(dt.datetime(2013,2,3,9,0,2),0)],names=['Time','hsec'])
>>> df.ix[ind] = 5
Traceback (most recent call last):
File "<pyshell#201>", line 1, in <module>
df.ix[ind] = 5
File "C:\Program Files\Python27\lib\site-packages\pandas\core\indexing.py", line 96, in __setitem__
indexer = self._convert_to_indexer(key, is_setter=True)
File "C:\Program Files\Python27\lib\site-packages\pandas\core\indexing.py", line 967, in _convert_to_indexer
raise KeyError('%s not in index' % objarr[mask])
KeyError: "[(Timestamp('2013-02-03 09:00:02', tz=None), 0L)] not in index"
推荐答案
你必须为多索引指定一个元组才能工作(你必须完全指定所有的轴,例如:
是必要的)
You have to specify a tuple for the multi-indexing to work (AND you have to fully specify all axes, e.g. the :
is necessary)
In [26]: df.ix[(dt.datetime(2013,2,3,9,0,2),0),:] = 5
In [27]: df
Out[27]:
vals
Time hsec
2013-02-03 09:00:01 1 45
25 46
2013-02-03 09:00:02 0 5
更容易重新索引和/或连接/添加一个新的数据帧。通常设置(使用这种放大),只有在使用少量值进行设置时才有意义。当你这样做时,这就是副本。
Easier to reindex and/or concat/append a new dataframe though. Generally setting (with this kind of enlargement), only makes sense if you are doing it with a small number of values. As this makes a copy when you do this.
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