Code Sample, a copy-pastable example if possible

import pandas as pd
pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').floor('T')

Traceback (most recent call last):
  File "/venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 3265, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-2-42bf9ce66d1d>", line 1, in <module>
    pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').floor('T')
  File "pandas/_libs/tslibs/timestamps.pyx", line 696, in pandas._libs.tslibs.timestamps.Timestamp.floor
  File "pandas/_libs/tslibs/timestamps.pyx", line 667, in pandas._libs.tslibs.timestamps.Timestamp._round
  File "pandas/_libs/tslibs/timestamps.pyx", line 903, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
  File "pandas/_libs/tslibs/conversion.pyx", line 963, in pandas._libs.tslibs.conversion.tz_localize_to_utc
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from '2018-11-04 01:55:00', try using the 'ambiguous' argument

Problem description

AmbiguousTimeError thrown on floor of timestamp that leads to time within dst change.

Similar issues happen on ceil and round:

pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').ceil('T')
pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').round('T')

Expected Output

Timestamp('2018-11-04 01:55:00-0500', tz='America/New_York')

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.6.final.0 python-bits: 64 OS: Linux OS-release: 4.15.0-38-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.23.4 pytest: None pip: 10.0.1 setuptools: 39.1.0 Cython: None numpy: 1.15.2 scipy: None pyarrow: None xarray: None IPython: 7.0.1 sphinx: None patsy: None dateutil: 2.7.3 pytz: 2018.5 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

Comment From: Safrone

A workaround I found:

import pandas as pd
import pytz
ts = pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York')
ts.astimezone(pytz.UTC).floor('T').astimezone(ts.tzinfo)

Timestamp('2018-11-04 01:55:00-0500', tz='America/New_York')

Comment From: gfyoung

cc @mroeschke

I believe we might have fixed this in #22647?

@Safrone : Thanks for filing this! Can you installing the master branch an re-running your code?

Comment From: Safrone

I cloned the latest version and am still having issues:

>>> import pandas as pd
>>> pd.__version__
'0.24.0.dev0+917.gf0877eccc'
>>> pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').floor('T')

Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "pandas/_libs/tslibs/timestamps.pyx", line 824, in pandas._libs.tslibs.timestamps.Timestamp.floor
    return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent)
  File "pandas/_libs/tslibs/timestamps.pyx", line 751, in pandas._libs.tslibs.timestamps.Timestamp._round
    result = result.tz_localize(
  File "pandas/_libs/tslibs/timestamps.pyx", line 1096, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
    value = tz_localize_to_utc(np.array([self.value], dtype='i8'), tz,
  File "pandas/_libs/tslibs/conversion.pyx", line 1019, in pandas._libs.tslibs.conversion.tz_localize_to_utc
    raise pytz.AmbiguousTimeError(
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from %r, try using the 'ambiguous' argument
INSTALLED VERSIONS ------------------ commit: f0877eccc080cb38afa092436f09a79a65269d8c python: 3.5.6.final.0 python-bits: 64 OS: Linux OS-release: 4.15.0-38-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.24.0.dev0+917.gf0877eccc pytest: None pip: 10.0.1 setuptools: 39.1.0 Cython: 0.29 numpy: 1.15.4 scipy: None pyarrow: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.7.5 pytz: 2018.7 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None gcsfs: None

Comment From: mroeschke

As the error notes, you need to specify the ambiguous argument in floor as these were added in #22647

In [1]: pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').f
   ...: loor('T', ambiguous=True)
Out[1]: Timestamp('2018-11-04 01:55:00-0400', tz='America/New_York')

In [2]: pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').f
   ...: loor('T', ambiguous=False)
Out[2]: Timestamp('2018-11-04 01:55:00-0500', tz='America/New_York')

In [3]: pd.Timestamp('2018-11-04 01:55:17.869342-0500', tz='America/New_York').f
   ...: loor('T', ambiguous='NaT')
Out[3]: NaT

Comment From: Safrone

Ah yes you are correct, thank you.

Comment From: adamkpickering

I think that the problems here are deeper, and cannot be solved by tacking the ambiguous keyword argument onto floor and others.

In [106]: a = pd.Timestamp('2018-11-04T7:31:33Z')
In [107]: a
Out[107]: Timestamp('2018-11-04 07:31:33+0000', tz='UTC')
In [108]: b = a.tz_convert('America/Edmonton')
In [109]: b
Out[109]: Timestamp('2018-11-04 01:31:33-0600', tz='America/Edmonton')
In [110]: b.floor('H')
---------------------------------------------------------------------------
AmbiguousTimeError                        Traceback (most recent call last)
<ipython-input-110-7a7b2ce5091f> in <module>()
----> 1 b.floor('H')
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp.floor()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp._round()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp.tz_localize()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/conversion.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.conversion.tz_localize_to_utc()
AmbiguousTimeError: Cannot infer dst time from '2018-11-04 01:00:00', try using the 'ambiguous' argument

In [127]: c = pd.Timestamp('2018-11-04T8:31:33Z')
In [128]: c
Out[128]: Timestamp('2018-11-04 08:31:33+0000', tz='UTC')
In [129]: d = c.tz_convert('America/Edmonton')
In [130]: d
Out[130]: Timestamp('2018-11-04 01:31:33-0700', tz='America/Edmonton')
In [131]: d.floor('H')
---------------------------------------------------------------------------
AmbiguousTimeError                        Traceback (most recent call last)
<ipython-input-131-ebab98f5821c> in <module>()
----> 1 d.floor('H')
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp.floor()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp._round()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/timestamps.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.timestamps.Timestamp.tz_localize()
/usr/local/lib/python3.5/dist-packages/pandas/_libs/tslibs/conversion.cpython-35m-x86_64-linux-gnu.so in pandas._libs.tslibs.conversion.tz_localize_to_utc()
AmbiguousTimeError: Cannot infer dst time from '2018-11-04 01:00:00', try using the 'ambiguous' argument

The thing is: 2018-11-04 01:31:33-0700 and 2018-11-04 01:31:33-0600 are not ambiguous times, so it makes no sense to require the programmer to use the ambiguous keyword to specify whether they are in standard or daylight savings time. If given a UTC offset and a location like America/Edmonton or America/New_York it is possible to infer whether that time is in standard or daylight savings time. If you look at the traceback from above:

AmbiguousTimeError: Cannot infer dst time from '2018-11-04 01:00:00', try using the 'ambiguous' argument

it seems that pandas strips the time offset while processing the time, which is very strange since the offset is crucial information.

Comment From: snewhouse

Hi All - is this still an issue?

It is for us.

This only occurs on our Windows machines though.

I do not get this error on Linux (Ubuntu 20)

Comment From: ulmag

Can someone please explain why this has been closed ? It still seems to be an issue (at least, on Windows). In my view, adamkpickering's https://github.com/pandas-dev/pandas/issues/23521#issuecomment-441087553 perfectly describes the issue - and is still valid.

Comment From: torfsen

I also still run into this on Pandas 1.5.3 (on Linux). I've posted a potential workaround in #49199.