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Correct assert_frame_equal doc string #22552

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Merged
merged 7 commits into from
Sep 3, 2018
Merged

Correct assert_frame_equal doc string #22552

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Correct assert_frame_equal doc string
pablojim Aug 31, 2018
234cb53
DOC: correct assert_frame_equal docstring
pablojim Aug 31, 2018
62f29a0
DOC: update assert_frame_equal docstring
pablojim Aug 31, 2018
0f8cb08
DOC: update assert_frame_equal docstring updates
pablojim Sep 2, 2018
0019855
DOC: update assert_frame_equal, fix line length
pablojim Sep 2, 2018
3c532ce
DOC: update assert_frame_equal equiv formatting
pablojim Sep 3, 2018
6a861d7
DOC: update assert_frame_equal equiv further updates
pablojim Sep 3, 2018
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6 changes: 3 additions & 3 deletions pandas/util/testing.py
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Expand Up @@ -1314,13 +1314,13 @@ def assert_frame_equal(left, right, check_dtype=True,
right : DataFrame
check_dtype : bool, default True
Whether to check the DataFrame dtype is identical.
check_index_type : bool / string {'equiv'}, default False
check_index_type : bool / string {'equiv'}, default 'equiv'
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I think what we've been using in these cases is {'equiv'} or bool

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IMO that is more confusing. e.g. it might imply I pass a set. I'm not sure it's worth a special case for when there is only one possible string value.

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what is your suggestion, just 'equiv' or bool, default 'equiv' or something else? Besides being more consistent for the user, using the curly brackets in all cases would simplify parsing the types and adding validation and extracting stats. But if you are strongly in favor of not using them, I'm happy to merge this with it now, and see later on what's best when we implement that validation.

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I think as it is above is good. It's maybe a little verbose but is very clear.

check_index_type : bool / string {'equiv'}, default 'equiv'

Happy to revisit if a standard emerges.

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You have the standard here: https://pandas.pydata.org/pandas-docs/stable/contributing_docstring.html#parameter-types

If you can use the first format I suggested, that would be great.

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Thanks. I have used your suggestion.

Whether to check the Index class, dtype and inferred_type
are identical.
check_column_type : bool / string {'equiv'}, default False
check_column_type : bool / string {'equiv'}, default 'equiv'
Whether to check the columns class, dtype and inferred_type
are identical.
check_frame_type : bool, default False
check_frame_type : bool, default True
Whether to check the DataFrame class is identical.
check_less_precise : bool or int, default False
Specify comparison precision. Only used when check_exact is False.
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