以下是我正在使用的更大/复杂数据帧的微小/玩具版本：
>>> A
key u v w x
0 a 0.757954 0.258917 0.404934 0.303313
1 b 0.583382 0.504687 NaN 0.618369
2 c NaN 0.982785 0.902166 NaN
3 d 0.898838 0.472143 NaN 0.610887
4 e 0.966606 0.865310 NaN 0.548699
5 f NaN 0.398824 0.668153 NaN
>>> B
key y z
0 a 0.867603 NaN
1 b NaN 0.191067
2 c 0.238616 0.803179
3 p 0.080446 NaN
4 q 0.932834 NaN
5 r 0.706561 0.814467
（FWIW，在这篇文章的末尾，我提供了生成这些数据帧的代码。）
I want to produce an outer join of these dataframes on the key column1, in such a way that the new positions induced by the outer join get default value 0.0. IOW, the desired result looks like this
key u v w x y z
0 a 0.757954 0.258917 0.404934 0.303313 0.867603 NaN
1 b 0.583382 0.504687 NaN 0.618369 NaN 0.191067
2 c NaN 0.982785 0.902166 NaN 0.238616 0.803179
3 d 0.898838 0.472143 NaN 0.610887 0.000000 0.000000
4 e 0.966606 0.86531 NaN 0.548699 0.000000 0.000000
5 f NaN 0.398824 0.668153 NaN 0.000000 0.000000
6 p 0.000000 0.000000 0.000000 0.000000 0.080446 NaN
7 q 0.000000 0.000000 0.000000 0.000000 0.932834 NaN
8 r 0.000000 0.000000 0.000000 0.000000 0.706561 0.814467
（请注意，这个所需的输出包含一些 NaN，即那些已经存在于A or B.)
The merge方法让我到达那里，但填充的默认值是 NaN，而不是 0.0：
>>> C = pandas.DataFrame.merge(A, B, how='outer', on='key')
>>> C
key u v w x y z
0 a 0.757954 0.258917 0.404934 0.303313 0.867603 NaN
1 b 0.583382 0.504687 NaN 0.618369 NaN 0.191067
2 c NaN 0.982785 0.902166 NaN 0.238616 0.803179
3 d 0.898838 0.472143 NaN 0.610887 NaN NaN
4 e 0.966606 0.865310 NaN 0.548699 NaN NaN
5 f NaN 0.398824 0.668153 NaN NaN NaN
6 p NaN NaN NaN NaN 0.080446 NaN
7 q NaN NaN NaN NaN 0.932834 NaN
8 r NaN NaN NaN NaN 0.706561 0.814467
The fillna方法无法产生所需的输出，因为它修改了一些应保持不变的位置：
>>> C.fillna(0.0)
key u v w x y z
0 a 0.757954 0.258917 0.404934 0.303313 0.867603 0.000000
1 b 0.583382 0.504687 0.000000 0.618369 0.000000 0.191067
2 c 0.000000 0.982785 0.902166 0.000000 0.238616 0.803179
3 d 0.898838 0.472143 0.000000 0.610887 0.000000 0.000000
4 e 0.966606 0.865310 0.000000 0.548699 0.000000 0.000000
5 f 0.000000 0.398824 0.668153 0.000000 0.000000 0.000000
6 p 0.000000 0.000000 0.000000 0.000000 0.080446 0.000000
7 q 0.000000 0.000000 0.000000 0.000000 0.932834 0.000000
8 r 0.000000 0.000000 0.000000 0.000000 0.706561 0.814467
如何高效地达到预期的输出？ （性能在这里很重要，因为我打算在比此处显示的数据帧大得多的数据帧上执行此操作。）
FWIW，下面是生成示例数据帧的代码A and B.
from pandas import DataFrame
from collections import OrderedDict
from random import random, seed
def make_dataframe(rows, colnames):
return DataFrame(OrderedDict([(n, [row[i] for row in rows])
for i, n in enumerate(colnames)]))
maybe_nan = lambda: float('nan') if random() < 0.4 else random()
seed(0)
A = make_dataframe([['a', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()],
['b', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()],
['c', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()],
['d', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()],
['e', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()],
['f', maybe_nan(), maybe_nan(), maybe_nan(), maybe_nan()]],
('key', 'u', 'v', 'w', 'x'))
B = make_dataframe([['a', maybe_nan(), maybe_nan()],
['b', maybe_nan(), maybe_nan()],
['c', maybe_nan(), maybe_nan()],
['p', maybe_nan(), maybe_nan()],
['q', maybe_nan(), maybe_nan()],
['r', maybe_nan(), maybe_nan()]],
('key', 'y', 'z'))
1For for case of multi-key outer joins, see here https://stackoverflow.com/q/39751636/559827.
