BUG: Regression adding Timedelta to DatetimeIndex, get ValueError: Inferred frequency None from passed values does not conform to passed frequency (original) (raw)

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

Details

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) in ----> 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 D

Problem 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()

Details

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