Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas.
Code Sample, a copy-pastable example
import pandas as pd
pd.date_range(start=pd.Timestamp('2019-03-26 00:00:00-0400', tz='Canada/Eastern'),
end=pd.Timestamp('2020-10-17 00:00:00-0400', tz='Canada/Eastern'),
freq='D') + pd.Timedelta('1D')Traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _validate_frequency(cls, index, freq, **kwargs)
1156 if not np.array_equal(index.asi8, on_freq.asi8):
-> 1157 raise ValueError
1158 except ValueError as e:
ValueError:
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-2-d8f454fa3ee3> in <module>
----> 1 pd.date_range(start=pd.Timestamp('2019-03-26 00:00:00-0400', tz='Canada/Eastern'), end=pd.Timestamp('2020-10-17 00:00:00-0400', tz='Canada/Eastern'), freq='D') + pd.Timedelta('1D')
~/.miniconda/lib/python3.7/site-packages/pandas/core/indexes/extension.py in method(self, other)
145 return NotImplemented
146 meth = getattr(self._data, opname)
--> 147 result = meth(_maybe_unwrap_index(other))
148 return _wrap_arithmetic_op(self, other, result)
149
~/.miniconda/lib/python3.7/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in __add__(self, other)
1399 result = self._add_nat()
1400 elif isinstance(other, (Tick, timedelta, np.timedelta64)):
-> 1401 result = self._add_timedeltalike_scalar(other)
1402 elif isinstance(other, BaseOffset):
1403 # specifically _not_ a Tick
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _add_timedeltalike_scalar(self, other)
1254 new_freq = self.freq
1255
-> 1256 return type(self)(new_values, dtype=self.dtype, freq=new_freq)
1257
1258 def _add_timedelta_arraylike(self, other):
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in __init__(self, values, dtype, freq, copy)
282
283 if inferred_freq is None and freq is not None:
--> 284 type(self)._validate_frequency(self, freq)
285
286 @classmethod
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _validate_frequency(cls, index, freq, **kwargs)
1169 f"Inferred frequency {inferred} from passed values "
1170 f"does not conform to passed frequency {freq.freqstr}"
-> 1171 ) from e
1172
1173 # monotonicity/uniqueness properties are called via frequencies.infer_freq,
ValueError: Inferred frequency None from passed values does not conform to passed frequency DProblem description
Unexpected ValueError when adding a Timedelta to a timezone-aware DatetimeIndex. It appears using timezone-aware Timestamp objects is necessary to reproduce - simple date strings won't.
Expected Output
The output from the same code sample in v0.25.3 is:
DatetimeIndex(['2019-03-27 00:00:00-04:00', '2019-03-28 00:00:00-04:00',
'2019-03-29 00:00:00-04:00', '2019-03-30 00:00:00-04:00',
'2019-03-31 00:00:00-04:00', '2019-04-01 00:00:00-04:00',
'2019-04-02 00:00:00-04:00', '2019-04-03 00:00:00-04:00',
'2019-04-04 00:00:00-04:00', '2019-04-05 00:00:00-04:00',
...
'2020-10-09 00:00:00-04:00', '2020-10-10 00:00:00-04:00',
'2020-10-11 00:00:00-04:00', '2020-10-12 00:00:00-04:00',
'2020-10-13 00:00:00-04:00', '2020-10-14 00:00:00-04:00',
'2020-10-15 00:00:00-04:00', '2020-10-16 00:00:00-04:00',
'2020-10-17 00:00:00-04:00', '2020-10-18 00:00:00-04:00'],
dtype='datetime64[ns, Canada/Eastern]', length=572, freq=None)don't know about later versions, before v1.1.3.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : db08276
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
Version : Darwin Kernel Version 16.7.0: Thu Dec 20 21:53:35 PST 2018; root:xnu-3789.73.31~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : 0.29.20
pytest : 6.1.1
hypothesis : None
sphinx : 1.8.5
blosc : 1.9.2
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.5.2
html5lib : 0.9999999
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2