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Update merging.rst #17556

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80a1104
Update merging.rst
1kastner Sep 16, 2017
0b6e692
add commas before and.
1kastner Sep 17, 2017
265403e
Change documentation of ignore_index according to manual.
1kastner Sep 17, 2017
74372db
Merge pull request #1 from 1kastner/patch-4
1kastner Sep 17, 2017
7721220
PEP8 conformity of documentation.
1kastner Sep 17, 2017
9c12863
PEP8 conformity.
1kastner Sep 17, 2017
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12 changes: 9 additions & 3 deletions doc/source/merging.rst
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Expand Up @@ -86,12 +86,18 @@ some configurable handling of "what to do with the other axes":
it is passed, in which case the values will be selected (see below). Any None
objects will be dropped silently unless they are all None in which case a
ValueError will be raised.
- ``axis`` : {0, 1, ...}, default 0. The axis to concatenate along.
- ``axis`` : {0, 1, ...}, default 0. The axis to concatenate along. 0 is the index
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make this more similar to the doc-string of pd.concat (you can change either), but it should follow our existing conventions, and this is not it

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I am not sure what you ask me to do when you say "make more similar". The styles slightly differ and I tried to follow the styles I found.

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reread my comment

similar to the doc string as i said

axis, 1 the column axis etc.
- ``join`` : {'inner', 'outer'}, default 'outer'. How to handle indexes on
other axis(es). Outer for union and inner for intersection.
- ``ignore_index`` : boolean, default False. If True, do not use the index
values on the concatenation axis. The resulting axis will be labeled 0, ...,
n - 1. This is useful if you are concatenating objects where the
values on the concatenation axis. If the concatenation axis is 0, it is the
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this is too much text

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What makes you think that? For me there is a great difference between an index column and column names so that I wanted to stress this. How would you want to shorten it?

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you have a bunch of text here
it is too dense to read

index axis, and the index column is ignored. If the concatenation axis is 1,
it is the column axis, and the column names are ignored.
The resulting axis after concatenation will be labeled 0, ..., n - 1.
That means that either the indices in the index column are replaced by
new indices, column names are replaced by numbers etc.
This is useful if you are concatenating objects where the
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you might simply be able to use the pd.concat.__doc__ here directly, it should render

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What I did was copy and paste and adjust to the respective required format. How should I be able to use pd.concat.__doc__? It was not used somewhere around I guess so that I was not aware of that feature.

concatenation axis does not have meaningful indexing information. Note
the index values on the other axes are still respected in the join.
- ``join_axes`` : list of Index objects. Specific indexes to use for the other
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15 changes: 10 additions & 5 deletions pandas/core/reshape/concat.py
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Expand Up @@ -44,11 +44,16 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
Specific indexes to use for the other n - 1 axes instead of performing
inner/outer set logic
ignore_index : boolean, default False
If True, do not use the index values along the concatenation axis. The
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
concatenating objects where the concatenation axis does not have
meaningful indexing information. Note the index values on the other
axes are still respected in the join.
If True, do not use the index values along the concatenation axis.
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same.

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do you mean the pd.concat.__doc__ issue?

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as i said

If the concatenation axis is 0, it is the index axis, and the index
column is ignored. If the concatenation axis is 1, it is the column
axis, and the column names are ignored. The resulting axis will be
labeled 0, ..., n - 1. That means that either the indices in the
index column are replaced by new indices, column names are replaced
by numbers etc. This is useful if you are concatenating objects
where the concatenation axis does not have meaningful indexing
information. Note the index values on the other axes are still
respected in the join.
keys : sequence, default None
If multiple levels passed, should contain tuples. Construct
hierarchical index using the passed keys as the outermost level
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