[jira] [Created] (ARROW-12680) [Python] Pyarrow -Converting StructScalar Timestamp using .to_pandas() loses type information

["Tim Ryles (Jira)" <ji...@apache.org>]

Tim Ryles created ARROW-12680:
---------------------------------

             Summary: [Python] Pyarrow -Converting StructScalar Timestamp using .to_pandas() loses type information
                 Key: ARROW-12680
                 URL: https://issues.apache.org/jira/browse/ARROW-12680
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 3.0.0
            Reporter: Tim Ryles


Hi,

We're noticing an issue where we lose type and formatting on conversion to a pandas dataframe for a particular dataset we house, which contains a struct, and the underlying type of the child is Timestamp rather than datetime.datetime (which we believed synonymous from Pandas documentation).

 

Inside the StructArray we can see nicely formatted timestamp values, but when we call .to_pandas() on it, we end up with epoch stamps for the date child.
{code:java}
import pyarrow.parquet as pq

tbl=pq.read_table("part-00009-47f62157-cb6f-41a8-9ad6-ace65df94c6e-c000.snappy.parquet")

tbl.column("observations").chunk(0).values pyarrow.lib.StructArray object at 0x7fc8eb0cab40>
– is_valid: all not null
– child 0 type: timestamp[ns]
[
2000-01-01 00:00:00.000000000,
2001-01-01 00:00:00.000000000,
2002-01-01 00:00:00.000000000,
2003-01-01 00:00:00.000000000,
2004-01-01 00:00:00.000000000,
2005-01-01 00:00:00.000000000,
2006-01-01 00:00:00.000000000,
2007-01-01 00:00:00.000000000,
2008-01-01 00:00:00.000000000,
2009-01-01 00:00:00.000000000,
...
2018-07-01 00:00:00.000000000,
2018-10-01 00:00:00.000000000,
2019-01-01 00:00:00.000000000,
2019-04-01 00:00:00.000000000,
2019-07-01 00:00:00.000000000,
2019-10-01 00:00:00.000000000,
2020-01-01 00:00:00.000000000,
2020-04-01 00:00:00.000000000,
2020-07-01 00:00:00.000000000,
2020-10-01 00:00:00.000000000
]
– child 1 type: double
[
-2.69685,
9.27988,
7.26902,
-7.55753,
-1.62137,
6.84773,
-8.21204,
-8.97041,
-1.14405,
-0.710153,
...
2.1658,
3.05588,
2.3868,
2.10805,
2.39984,
2.54855,
-7.26804,
-2.35179,
-0.867518,
0.150593
]
{code}
{code:java}
 
tbl.to_pandas()['observations'] 

[{'date': 946684800000000000, 'value': -2.6968... 1 [{'date': 946684800000000000, 'value': 57.9608... 2 [{'date': 1483228800000000000, 'value': 95.904... 3 [{'date': 1214870400000000000, 'value': 19.021... 4 [{'date': 1199145600000000000, 'value': 1.2011... ... 636 [\{'date': 1072915200000000000, 'value': 5.418}... 637 [{'date': 946684800000000000, 'value': 110.695... 638 [{'date': 1009843200000000000, 'value': 3.0094... 639 [{'date': 1222819200000000000, 'value': 48.365... 640 [{'date': 1199145600000000000, 'value': 1.5600... Name: observations, Length: 641, dtype: object

In [12]: tbl.to_pandas()["observations"].iloc[0][0]
Out[12]: {'date': 1041379200000000000, 'value': 249.523242}
# date is now type Int{code}
 

We notice that if we take the same table, save it back out to a file first, and then check the chunk(0).values as above, the underlying type changes from *Timestamp* to *datetime.datetime*, and that will now convert .to_pandas() correctly.
{code:java}
pq.write_table(tbl, "output.parquet")

tbl2=pq.read_table("output.parquet")

tbl2.column("observations").chunk(0).values[0]
Out[17]: <pyarrow.StructScalar: {'date': datetime.datetime(2003, 1, 1, 0, 0), 'value': 249.523242}>

tbl2.column("observations").chunk(0).to_pandas()
Out[18]: 
0        [{'date': 2003-01-01 00:00:00, 'value': 249.52...
1        [{'date': 2008-01-01 00:00:00, 'value': 29.741...
2        [{'date': 2000-01-01 00:00:00, 'value': 2.3454...
3        [{'date': 2006-01-01 00:00:00, 'value': 1.2048...
4        [{'date': 2008-01-01 00:00:00, 'value': 196546...
                               ...                        
29489    [{'date': 2010-01-01 00:00:00, 'value': 19.155...
29490    [{'date': 2012-04-30 00:00:00, 'value': 0.0}, ...
29491    [{'date': 2012-04-30 00:00:00, 'value': 0.0}, ...
29492    [{'date': 2012-04-30 00:00:00, 'value': 0.0}, ...
29493    [{'date': 2012-04-30 00:00:00, 'value': 10.0},...
Length: 29494, dtype: object

tbl2.to_pandas()["observations"].iloc[0][0]
Out[8]: {'date': datetime.datetime(2003, 1, 1, 0, 0), 'value': 249.523242}

# date remains as datetime.datetime{code}




 

Thanks in advance, and apologies if I have not followed Issue protocol on this board.

Is there a parameter we need to pass into .to_pandas for this to take place (I can see there is date_as_object/timestamp_as_object, but these have no effect), we would like to know.



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