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ahuang11
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Mar 19, 2026
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Tests need some improvements. In general, thank you for making this fix!
xarray_sql/sql_test.py
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| def test_max_ignores_nan(self, nan_ds): | ||
| ctx = XarrayContext() | ||
| ctx.from_dataset("data", nan_ds) | ||
| result = ctx.sql("SELECT MAX(temp) AS mx FROM data").to_pandas() | ||
| assert result["mx"].iloc[0] == 8.0 | ||
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| def test_min_ignores_nan(self, nan_ds): | ||
| ctx = XarrayContext() | ||
| ctx.from_dataset("data", nan_ds) | ||
| result = ctx.sql("SELECT MIN(temp) AS mn FROM data").to_pandas() | ||
| assert result["mn"].iloc[0] == 1.0 | ||
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| def test_avg_ignores_nan(self, nan_ds): | ||
| import numpy as np | ||
| ctx = XarrayContext() | ||
| ctx.from_dataset("data", nan_ds) | ||
| result = ctx.sql("SELECT AVG(temp) AS avg FROM data").to_pandas() | ||
| expected = np.nanmean([1.0, 2.0, 4.0, 5.0, 7.0, 8.0]) | ||
| assert abs(result["avg"].iloc[0] - expected) < 1e-6 |
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I think these could be melded into one test -- or better yet, only one of these should imply the others.
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Made changes!
Playing devil's advocate, I'm used to having pandas df and xarray ds be returned, but this can be discussed more in #148 |
Signed-off-by: Andrew <15331990+ahuang11@users.noreply.github.com>
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LGTM. Thank for this critical fix!!
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Closes #144 After attempting this, I realized it did not feel like a good first issue~ Note I used Claude Opus to plan and write this. Two tiers based on calendar type: 1. Gregorian-like (standard, noleap, all_leap) → pa.timestamp('us'). String SQL filters work naturally (WHERE time > '1980-01-01'). Microsecond resolution covers ±292k years, avoiding the nanosecond 1678–2262 overflow. 2. Non-Gregorian (360_day, julian) → pa.int64() with xarray:units/xarray:calendar metadata on the Arrow field. Lossless — a 360-day "Feb 30th" isn't forced into Gregorian. Auto-registered cftime() UDF provides ergonomic filtering: WHERE time > cftime('2000-07-01'). Now this runs: ``` import xarray as xr from metpy.cbook import get_test_data from xarray_sql import XarrayContext ds = xr.tutorial.open_dataset("rasm") context = XarrayContext().from_dataset("rasm", ds, chunks={"time": 2}) context.sql("SELECT * FROM rasm LIMIT 1000") ``` <img width="230" height="279" alt="image" src="https://github.com/user-attachments/assets/5f7ff224-d4b1-429f-a3b3-51e017475c21" /> However as you can see, there are a lot of NaNs; to be fixed in a separate PR in #151 --------- Co-authored-by: Alex Merose <al@merose.com>
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I noticed that the values were all NaN. This fixes it. Used Claude Opus during planning and execution.