Github user HyukjinKwon commented on the issue:
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Thanks. I was just worried if I missed any discussion somewhere and wanted
to double check.
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Github user ueshin commented on the issue:
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Ah, yes, I think so.
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Hm .. I see. but is this something we should fix though ideally? I am
asking this because I am checking R related codes now ..
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Github user ueshin commented on the issue:
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Maybe we need at least 2 external libraries like `dateutil` and `pytz` to
handle timezones properly, which Pandas uses, but I have no idea to handle
timezones out of Pandas properly for now.
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Github user HyukjinKwon commented on the issue:
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Yup it should be separate. I meant to file another JIRA while we are here
if it is something we need to fix before we fix before forgetting. If
`df.collect()` is not meant to be fixed, I think I
Github user ueshin commented on the issue:
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Unfortunately, `df.collect()` is out of scope of this pr. Its timestamp
values will respect python system timezone.
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Github user HyukjinKwon commented on the issue:
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Sorry, but does anyone remember how we are going to deal with
`df.collect()` in PySpark? R fix should be more like `df.collect()`. It should
be good to file a JIRA for `df.collect()` in PySpark
Github user HyukjinKwon commented on the issue:
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Yup, I was testing and trying to produce details. Let me describe this in
the JIRA, not here :D.
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Github user cloud-fan commented on the issue:
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LGTM, merging to master!
Let's fix the R timestamp issue in a new ticket.
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Yup, I think we should take a look between POSIXct / POSIXlt in R and
timestamp within Spark too. Seems not respecting the session timezone in a
quick look.
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Github user felixcheung commented on the issue:
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I guess we should look at R to see if it should behavior similarly? WDYT
@HyukjinKwon ?
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LGTM
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retest this please
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Github user HyukjinKwon commented on the issue:
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> I tried to find out a workaround for old Pandas, but I haven't done yet.
I haven't looked at this closely yet but will definitely try to take a look
and help soon together. I would app
Github user HyukjinKwon commented on the issue:
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Yea, that was my proposal. If anything is blocked by this, I think we
should bump it up as, IMHO, technically the fixed version specification was not
yet released and published.
^ cc @c
Github user ueshin commented on the issue:
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@BryanCutler I guess the oldest version of Pandas is `0.13.0` currently
according to #18403, cc @HyukjinKwon.
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Github user BryanCutler commented on the issue:
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Hi @ueshin , what is the oldest version of Pandas that's required to
support and what exactly wasn't working with it?
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@BryanCutler I'm fixing the behavior of `toPandas()` and pandas udfs as we
discussed in #18664 but I guess we still need to support old Pandas as well.
I tried to find out a workaround for old Pan
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Jenkins, retest this please.
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