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Tanya Schlusser commented on ARROW-3543: ---------------------------------------- I can confirm this bug is still present. Most recent commit in my pull is c0ac97f126c98fb29e81d6544adfea9d4ab74aff For others, the R libraries needed to re-run Olaf's code (in addition to arrow) are readr, dplyr, and lubridate. I will mess around but won't be hurt if a stronger R coder takes this before I finish. > [R] Time zone adjustment issue when reading Feather file written by Python > -------------------------------------------------------------------------- > > Key: ARROW-3543 > URL: https://issues.apache.org/jira/browse/ARROW-3543 > Project: Apache Arrow > Issue Type: Bug > Reporter: Olaf > Priority: Critical > Fix For: 0.12.0 > > > 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 {code} > {code:java} > #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 "" {code} > My timestamps have been converted!!! pure insanity. > Am I missing something here? > Thanks!! -- This message was sent by Atlassian JIRA (v7.6.3#76005)