使用列表为DataFrame编制索引，并为熊猫的越界索引返回NaN？

Question

Normally when I index a DataFrame (or a Series) with a list of integer indices, I get back a subset of the rows, unless some of my indices are out of bounds, in which case I get an IndexError :

s = pd.Series(range(4))
0    0
1    1
2    2
3    3

s.iloc[[1,3]]
1    1
3    3

s.iloc[[1,3,5]]
IndexError

But I'd like to get back a DataFrame (or Series) having an index identical to the list I queried with (ie, parallel to the query list), with (the rows corresponding to) any out-of-bounds indices filled in with NaN :

s.something[[1,3,5]]
1    1
3    3
5    NaN

I don't think join tricks work because those want to operate on the DataFrame index (or columns). As far as I can tell there's not even an " iget " integer-based get method if I wanted to manually loop over the indices myself. That leaves something like:

indices = [1,3,5]
pd.Series([s.iloc[i] if 0 <= i < len(s) else np.nan for i in indices], index=indices)

Is that the best Pandas 0.18 can do?

Answer 1

You can use reindex to achieve this:

In [119]:
s.reindex([1,3,5])

Out[119]:
1     1
3     3
5   NaN
dtype: float64

This will use the passed index and return existing values or NaN

Answer 2

感谢@EdChum的启发，一般的解决方案是：

s.reset_index(drop=True).reindex([1,3,5])