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REGRESSION: Merging on vectors looks broken #19038

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gfyoung opened this issue Jan 2, 2018 · 0 comments
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REGRESSION: Merging on vectors looks broken #19038

gfyoung opened this issue Jan 2, 2018 · 0 comments
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Regression Functionality that used to work in a prior pandas version Reshaping Concat, Merge/Join, Stack/Unstack, Explode
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gfyoung commented Jan 2, 2018

Here is the code:

import pandas as pd

df = pd.DataFrame([1, 2, 3], ['2016-01-01', '2017-01-01', '2018-01-01'], columns=['a'])
df.index = pd.to_datetime(df.index)

df.merge(df, on=['a', df.index.year])

This works in v0.19.0, but in v0.22.0 fails with:

KeyError: "Int64Index([2016, 2017, 2018], dtype='int64') not in index"

In v0.19.0, the merge produces a result (not sure if I agree with it though):

  a key_1
0 1 2016
1 2 2017
2 3 2018
@gfyoung gfyoung added Regression Functionality that used to work in a prior pandas version Reshaping Concat, Merge/Join, Stack/Unstack, Explode labels Jan 2, 2018
@gfyoung gfyoung added this to the 0.23.0 milestone Jan 2, 2018
@gfyoung gfyoung changed the title BUG: Merging on vectors looks broken REGRESSION: Merging on vectors looks broken Jan 2, 2018
gfyoung added a commit to forking-repos/pandas that referenced this issue Jan 4, 2018
This behavior used to work in v0.19.0 and
is consistent with the documentation.

Closes pandas-devgh-19038
gfyoung added a commit to forking-repos/pandas that referenced this issue Jan 5, 2018
This behavior used to work in v0.19.0 and
is consistent with the documentation.

Closes pandas-devgh-19038
gfyoung added a commit to forking-repos/pandas that referenced this issue Jan 6, 2018
This behavior used to work in v0.19.0 and
is consistent with the documentation.

Closes pandas-devgh-19038
jreback pushed a commit that referenced this issue Jan 6, 2018
* BUG: Allow merging on Index vectors

This behavior used to work in v0.19.0 and
is consistent with the documentation.

Closes gh-19038

* ENH: Add is_array_like method

Used for abstracting checks in DataFrame.merge,
but the function itself can be quite useful.
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