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https://issues.apache.org/jira/browse/ARROW-7747?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17029761#comment-17029761
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Joris Van den Bossche commented on ARROW-7747:
----------------------------------------------

bq. For that from_pandas call, the error is expected I think, although we 
currently don't have an option to allow this (to indicate you are OK with 
losing data). 

Actually, I am mistaken here, and we already have a {{safe}} option here:

{code:python}
>>> table = pa.Table.from_pandas(df, schema=pyarrow_schema, safe=False) 
>>> table                                                                       
>>>                                                                             
>>>                                                   
pyarrow.Table
datetime_ms: timestamp[ms]
metadata
--------
{b'pandas': ...

>>> table.to_pandas()  
              datetime_ms
0 2019-06-21 22:13:02.901
{code}

> [Python] coerce_timestamps + allow_truncated_timestamps does not work as 
> expected with nanoseconds 
> ---------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-7747
>                 URL: https://issues.apache.org/jira/browse/ARROW-7747
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.15.1
>            Reporter: Théophile Chevalier
>            Priority: Major
>
> Hi,
> I've encountered what seems to me a bug using
> {noformat}
> pyarrow==0.15.1
> pandas==0.25.3
> numpy==1.18.1{noformat}
>  
> I'm trying to write a table containing nanosecond timestamps to a millisecond 
> schema. Here is a minimal example:
> {code:python}
> import pyarrow as pa
> import pyarrow.parquet as pq
> import pandas as pd
> import numpy as np
> pyarrow_schema = pa.schema([pa.field("datetime_ms", pa.timestamp("ms"))])
> timestamp = np.datetime64("2019-06-21T22:13:02.901123")
> d = {"datetime_ms": timestamp}
> df = pd.DataFrame(d, index=range(1))
> table = pa.Table.from_pandas(df, schema=pyarrow_schema)
> pq.write_table(
>     table,
>     "test.parquet",
>     coerce_timestamps="ms",
>     allow_truncated_timestamps=True,
> )
> {code}
> {noformat}
> pyarrow.lib.ArrowInvalid: ('Casting from timestamp[ns] to timestamp[ms] would 
> lose data: 1561155182901123000', 'Conversion failed for column datetime_ms 
> with type datetime64[ns]'){noformat}
> From my understanding, the expected behaviour shoud be arrow allowing the 
> conversion anyway, even if loosing some data.
> Related discussions:
> - https://github.com/apache/arrow/issues/1920
> - https://issues.apache.org/jira/browse/ARROW-2555
> This test 
> https://github.com/apache/arrow/blob/f70dbd1dbdb51a47e6a8a8aac8efd40ccf4d44f2/python/pyarrow/tests/test_parquet.py#L846
>  does not explicitely check for nanosecond timestamps.
> To be honest I've not checked at the code yet, so let me know whether I 
> missed something. I'd be happy to fix it if it's really a bug.



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