Corrwith when "other" has different columns
It would be great if `corrwith` could calculate correlations between dataframes that have different column names, but the same index.

For example, take the two (10, 5) `df1` and `df2` dataframes below.

```
import pandas as pd
import numpy as np
import string

nrow = 10
ncol = 5

axis = 0

index = list(string.ascii_lowercase[:nrow])
columns = list(string.ascii_uppercase[:ncol])

df1 = pd.DataFrame(np.random.randn(nrow, ncol), index=index, columns=columns)
df2 = pd.DataFrame(np.random.randn(nrow, ncol), index=index)
```

`df1` and `df2` have different columns, and I'd like to create a 5x5 matrix of the correlations of their columns, on the values in each row. 

I've implemented a stopgap measure here: https://github.com/YeoLab/flotilla/blob/d9e53c219320c5d5dbbbfa41769abb2ab6f25574/flotilla/compute/generic.py#L429

Is this a planned feature for future releases?

Probably also related to the `method` issue: https://github.com/pydata/pandas/issues/9490
