[
https://issues.apache.org/jira/browse/ARROW-3543?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Olaf updated ARROW-3543:
------------------------
Description:
Hello the dream team,
Pasting from [https://github.com/wesm/feather/issues/351]
Thanks for this wonderful package. I was playing with feather and some
timestamps and I noticed some dangerous behavior. Maybe it is a bug.
Consider this
{code:java}
import pandas as pd
import feather
import numpy as np
df = pd.DataFrame(
{'string_time_utc' : [pd.to_datetime('2018-02-01 14:00:00.531'),
pd.to_datetime('2018-02-01 14:01:00.456'), pd.to_datetime('2018-03-05
14:01:02.200')]}
)
df['timestamp_est'] =
pd.to_datetime(df.string_time_utc).dt.tz_localize('UTC').dt.tz_convert('US/Eastern').dt.tz_localize(None)
df
Out[17]:
string_time_utc timestamp_est
0 2018-02-01 14:00:00.531 2018-02-01 09:00:00.531
1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
{code}
Here I create the corresponding `EST` timestamp of my original timestamps (in
`UTC` time).
Now saving the dataframe to `csv` or to `feather` will generate two completely
different results.
{code:java}
df.to_csv('P://testing.csv')
df.to_feather('P://testing.feather')
{code}
Switching to R.
Using the good old `csv` gives me something a bit annoying, but expected. R
thinks my timezone is `UTC` by default, and wrongly attached this timezone to
`timestamp_est`. No big deal, I can always use `with_tz` or even better: import
as character and process as timestamp while in R.
{code:java}
> dataframe <- read_csv('P://testing.csv')
Parsed with column specification:
cols(
X1 = col_integer(),
string_time_utc = col_datetime(format = ""),
timestamp_est = col_datetime(format = "")
)
Warning message:
Missing column names filled in: 'X1' [1]
>
> dataframe %>% mutate(mytimezone = tz(timestamp_est))
A tibble: 3 x 4
X1 string_time_utc timestamp_est
<int> <dttm> <dttm>
1 0 2018-02-01 14:00:00.530 2018-02-01 09:00:00.530
2 1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
3 2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
mytimezone
<chr>
1 UTC
2 UTC
3 UTC
Now look at what happens with `feather`:{code}
{code:java}
> dataframe <- read_feather('P://testing.feather')
>
> dataframe %>% mutate(mytimezone = tz(timestamp_est))
A tibble: 3 x 3
string_time_utc timestamp_est mytimezone
<dttm> <dttm> <chr>
1 2018-02-01 09:00:00.531 2018-02-01 04:00:00.531 ""
2 2018-02-01 09:01:00.456 2018-02-01 04:01:00.456 ""
3 2018-03-05 09:01:02.200 2018-03-05 04:01:02.200 "" {code}
My timestamps have been converted!!! pure insanity.
Am I missing something here?
Thanks!!
was:
Hello the dream team,
Pasting from https://github.com/wesm/feather/issues/351
Thanks for this wonderful package. I was playing with feather and some
timestamps and I noticed some dangerous behavior. Maybe it is a bug.
Consider this
```
import pandas as pd
import feather
import numpy as np
df = pd.DataFrame({'string_time_utc' : [pd.to_datetime('2018-02-01
14:00:00.531'),
pd.to_datetime('2018-02-01 14:01:00.456'),
pd.to_datetime('2018-03-05 14:01:02.200')]})
df['timestamp_est'] =
pd.to_datetime(df.string_time_utc).dt.tz_localize('UTC').dt.tz_convert('US/Eastern').dt.tz_localize(None)
df
Out[17]:
string_time_utc timestamp_est
0 2018-02-01 14:00:00.531 2018-02-01 09:00:00.531
1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
```
Here I create the corresponding `EST` timestamp of my original timestamps (in
`UTC` time).
Now saving the dataframe to `csv` or to `feather` will generate two completely
different results.
```
df.to_csv('P://testing.csv')
df.to_feather('P://testing.feather')
```
Switching to R.
Using the good old `csv` gives me something a bit annoying, but expected. R
thinks my timezone is `UTC` by default, and wrongly attached this timezone to
`timestamp_est`. No big deal, I can always use `with_tz` or even better: import
as character and process as timestamp while in R.
```
> dataframe <- read_csv('P://testing.csv')
Parsed with column specification:
cols(
X1 = col_integer(),
string_time_utc = col_datetime(format = ""),
timestamp_est = col_datetime(format = "")
)
Warning message:
Missing column names filled in: 'X1' [1]
>
> dataframe %>% mutate(mytimezone = tz(timestamp_est))
# A tibble: 3 x 4
X1 string_time_utc timestamp_est
<int> <dttm> <dttm>
1 0 2018-02-01 14:00:00.530 2018-02-01 09:00:00.530
2 1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
3 2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
mytimezone
<chr>
1 UTC
2 UTC
3 UTC
```
Now look at what happens with `feather`:
```
> dataframe <- read_feather('P://testing.feather')
>
> dataframe %>% mutate(mytimezone = tz(timestamp_est))
# A tibble: 3 x 3
string_time_utc timestamp_est mytimezone
<dttm> <dttm> <chr>
1 2018-02-01 09:00:00.531 2018-02-01 04:00:00.531 ""
2 2018-02-01 09:01:00.456 2018-02-01 04:01:00.456 ""
3 2018-03-05 09:01:02.200 2018-03-05 04:01:02.200 ""
```
My timestamps have been converted!!! pure insanity.
Am I missing something here?
Thanks!!
> crazy timestamp bug in feather?
> --------------------------------
>
> Key: ARROW-3543
> URL: https://issues.apache.org/jira/browse/ARROW-3543
> Project: Apache Arrow
> Issue Type: Bug
> Reporter: Olaf
> Priority: Critical
>
> Hello the dream team,
> Pasting from [https://github.com/wesm/feather/issues/351]
> Thanks for this wonderful package. I was playing with feather and some
> timestamps and I noticed some dangerous behavior. Maybe it is a bug.
> Consider this
>
> {code:java}
> import pandas as pd
> import feather
> import numpy as np
> df = pd.DataFrame(
> {'string_time_utc' : [pd.to_datetime('2018-02-01 14:00:00.531'),
> pd.to_datetime('2018-02-01 14:01:00.456'), pd.to_datetime('2018-03-05
> 14:01:02.200')]}
> )
> df['timestamp_est'] =
> pd.to_datetime(df.string_time_utc).dt.tz_localize('UTC').dt.tz_convert('US/Eastern').dt.tz_localize(None)
> df
> Out[17]:
> string_time_utc timestamp_est
> 0 2018-02-01 14:00:00.531 2018-02-01 09:00:00.531
> 1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
> 2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
> {code}
> Here I create the corresponding `EST` timestamp of my original timestamps
> (in `UTC` time).
> Now saving the dataframe to `csv` or to `feather` will generate two
> completely different results.
>
> {code:java}
> df.to_csv('P://testing.csv')
> df.to_feather('P://testing.feather')
> {code}
> Switching to R.
> Using the good old `csv` gives me something a bit annoying, but expected. R
> thinks my timezone is `UTC` by default, and wrongly attached this timezone to
> `timestamp_est`. No big deal, I can always use `with_tz` or even better:
> import as character and process as timestamp while in R.
>
> {code:java}
> > dataframe <- read_csv('P://testing.csv')
> Parsed with column specification:
> cols(
> X1 = col_integer(),
> string_time_utc = col_datetime(format = ""),
> timestamp_est = col_datetime(format = "")
> )
> Warning message:
> Missing column names filled in: 'X1' [1]
> >
> > dataframe %>% mutate(mytimezone = tz(timestamp_est))
> A tibble: 3 x 4
> X1 string_time_utc timestamp_est
> <int> <dttm> <dttm>
> 1 0 2018-02-01 14:00:00.530 2018-02-01 09:00:00.530
> 2 1 2018-02-01 14:01:00.456 2018-02-01 09:01:00.456
> 3 2 2018-03-05 14:01:02.200 2018-03-05 09:01:02.200
> mytimezone
> <chr>
> 1 UTC
> 2 UTC
> 3 UTC
> Now look at what happens with `feather`:{code}
>
> {code:java}
> > dataframe <- read_feather('P://testing.feather')
> >
> > dataframe %>% mutate(mytimezone = tz(timestamp_est))
> A tibble: 3 x 3
> string_time_utc timestamp_est mytimezone
> <dttm> <dttm> <chr>
> 1 2018-02-01 09:00:00.531 2018-02-01 04:00:00.531 ""
> 2 2018-02-01 09:01:00.456 2018-02-01 04:01:00.456 ""
> 3 2018-03-05 09:01:02.200 2018-03-05 04:01:02.200 "" {code}
> My timestamps have been converted!!! pure insanity.
> Am I missing something here?
> Thanks!!
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