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- Describe output time index after resampling in docs / docstring · 2 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1480973335 | https://github.com/pydata/xarray/issues/7662#issuecomment-1480973335 | https://api.github.com/repos/pydata/xarray/issues/7662 | IC_kwDOAMm_X85YRdwX | jules-ch 43635101 | 2023-03-23T10:51:27Z | 2023-03-23T10:51:27Z | NONE | Values outside of the month are filled with NaN when aggregating after your month filter. It seems to be a side effect of grouping with a frequency. |
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Describe output time index after resampling in docs / docstring 1636435706 | |
| 1480964097 | https://github.com/pydata/xarray/issues/7662#issuecomment-1480964097 | https://api.github.com/repos/pydata/xarray/issues/7662 | IC_kwDOAMm_X85YRbgB | jules-ch 43635101 | 2023-03-23T10:44:26Z | 2023-03-23T10:46:22Z | NONE | Idk if this comes from xarray, pandas seems to have the same behaviour: ```python import xarray as xr import pandas as pd import numpy as np time = pd.date_range("2000-01-01", "2022-01-01", freq="1h") ds = xr.Dataset({"time": time, "v": ("time",np.random.rand(len(time)))}) ds df = ds.to_dataframe() df = df[df.index.month.isin([6,7,8])] df.groupby(pd.Grouper(freq="D")).mean()
time [7762 rows x 1 columns] ``` You can select your months after performing aggregation and you will have the correct number of rows |
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Describe output time index after resampling in docs / docstring 1636435706 |
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