BUG: outer join out of order when joining multiple DataFrames

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Reproducible Example

import pandas as pd
a = pd.DataFrame(np.ones((4, 2)), index=[3,2,1,0])
b = pd.DataFrame(2*np.ones((2, 1)), columns=[2], index=[3,1])
c = pd.DataFrame(3*np.ones((2, 1)), columns=[3], index=[3,1])
a.join(b, how='outer') # correct, the index is [0,1,2,3]
b.join(a, how='outer') # correct, the index is [0,1,2,3]
a.join([b, c], how='outer') # incorrect, the index is [3,2,1,0]
b.join([a, c], how='outer') # incorrect, the index is [3,1,2,0]

Issue Description

The document of join says:

outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it. lexicographically.

But the behavior is not consistent. when joining more than 2 dataframes, the result remains unsorted, which is a bug.

Expected Behavior

The result should be sorted when outer joining multiple dataframes. This test should be passed for the example above:

assert a.join([b, c], how='outer').equals(a.join([b, c], how='outer', sort=True))

Installed Versions

1.5.0.dev0+471.g8e0baa2360

[hitvoice]

Would you be interested in submitting a PR to fix?

Sure. I’ll be glad to work on it.

[hitvoice]

Got it. Thanks!