ARROW-16838: [Python] Improve schema inference for pandas indexes with extension dtypes #14080
Conversation
|
|
I'm not quite sure what to do here. I don't see a "Start Progress" link here or over in the corresponding JIRA issue. Can somebody clarify? |
|
Hrm it looks like the failing Travis CI build happened when building CXX objects -- so presumably unrelated to the changes in this PR. Not sure if this is a known issue or not |
|
Travis: that does tend to be a little flaky. JIRA: you need to be signed in to JIRA (it's basically asking you to make sure the ticket is assigned to yourself) |
|
Gotcha -- thanks @lidavidm I've gone ahead and assigned the ticket to myself in JIRA 👍 |
|
@jorisvandenbossche are you perhaps able to take a look here? |
python/pyarrow/pandas_compat.py
Outdated
| @@ -541,7 +541,7 @@ def dataframe_to_types(df, preserve_index, columns=None): | |||
| if _pandas_api.is_categorical(values): | |||
| type_ = pa.array(c, from_pandas=True).type | |||
| elif _pandas_api.is_extension_array_dtype(values): | |||
| type_ = pa.array(c.head(0), from_pandas=True).type | |||
| type_ = pa.array(c[:0], from_pandas=True).type | |||
There was a problem hiding this comment.
So for the case that c is a Series and not Index, this [:0] currently is always positional (so that's fine). But to be sure I was just checking on pandas main, and this now gives a warning that this will change in the future:
In [9]: s = pd.Series([1, 2], index=[1, 2])
In [10]: s[:0]
<ipython-input-10-48e1b387a0b4>:1: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
s[:0]
Out[10]: Series([], dtype: int64)
So we should avoid running into this warning.
I am thinking that we could maybe also use values[:0] instead?
There was a problem hiding this comment.
I am thinking that we could maybe also use values[:0] instead?
But, that then will loose some information in case of tz-aware datetimes I think.
Maybe a "dumb" c.head(0) if isinstance(c, Series) else c[:0] might be the easiest after all.
There was a problem hiding this comment.
Ah, good catch. Just updated to avoid that deprecation with the if isinstance(c, Series) check
|
Hrm, the linting errors in CI appear to be unrelated to the changes in this PR. Maybe #14113 fixes them? Merging |
|
The Travis CI failures is, I think, unrelated to the changes in this PR. @jorisvandenbossche let me know if there are any other changes you'd like to see |
|
Just wanted to check in here -- are there any additional comment, question, or concerns on this PR? |
|
No, looks all good, thanks for the update! (I was just away for a few days) |
|
Benchmark runs are scheduled for baseline = 8ad5e59 and contender = afd3c40. afd3c40 is a master commit associated with this PR. Results will be available as each benchmark for each run completes. |
|
['Python', 'R'] benchmarks have high level of regressions. |
|
Thanks @jorisvandenbossche! |
…h extension dtypes (apache#14080) Possible fix for https://issues.apache.org/jira/browse/ARROW-16838. `pd.Index` objects don't have a `.head` method, while `pd.DataFrame`, `pd.Series`, and `pd.Index` all support indexing with `[:0]` to return a empty object of the same type. Authored-by: James Bourbeau <jrbourbeau@gmail.com> Signed-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>
…h extension dtypes (apache#14080) Possible fix for https://issues.apache.org/jira/browse/ARROW-16838. `pd.Index` objects don't have a `.head` method, while `pd.DataFrame`, `pd.Series`, and `pd.Index` all support indexing with `[:0]` to return a empty object of the same type. Authored-by: James Bourbeau <jrbourbeau@gmail.com> Signed-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>
Possible fix for https://issues.apache.org/jira/browse/ARROW-16838.
pd.Indexobjects don't have a.headmethod, whilepd.DataFrame,pd.Series, andpd.Indexall support indexing with[:0]to return a empty object of the same type.