Description
Pandas version checks
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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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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
df = pd.DataFrame({
'ts': [datetime(2005, 10, 9, 21)],
'values': [10.]
})
df['ts'] = df['ts'].dt.tz_localize('Chile/Continental')
df.resample('W-Mon', on='ts', closed='left', label='left')Issue Description
This throws
NonExistentTimeError: 2005-10-09 00:00:00I don't see why it should need to though - the window it needs to create is [2005-10-03, 2005-10-10), so why would it matter that there's a non-existent time in that interval?
Expected Behavior
ts values
0 2005-10-03 00:00:00-04:00 10.0For reference, polars would give that output:
In [35]: (
...: pl.from_pandas(df)
...: .with_columns(pl.col("ts").set_sorted())
...: .groupby_dynamic("ts", every="1w", start_by="monday")
...: .agg(pl.col("values").sum())
...: )
Out[35]:
shape: (1, 2)
┌─────────────────────────────────┬────────┐
│ ts ┆ values │
│ --- ┆ --- │
│ datetime[ns, Chile/Continental] ┆ f64 │
╞═════════════════════════════════╪════════╡
│ 2005-10-03 00:00:00 -04 ┆ 10.0 │
└─────────────────────────────────┴────────┘Installed Versions
INSTALLED VERSIONS
commit : 965ceca
python : 3.10.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 2.0.2
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.6.1
pip : 23.0.1
Cython : None
pytest : 7.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None