filter: Try converting all queried columns to numerical type #1268
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Codecov ReportPatch coverage:
Additional details and impacted files@@ Coverage Diff @@
## master #1268 +/- ##
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+ Coverage 69.36% 69.67% +0.31%
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Files 66 67 +1
Lines 7024 7104 +80
Branches 1708 1727 +19
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+ Hits 4872 4950 +78
- Misses 1847 1848 +1
- Partials 305 306 +1
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The dtype inference in augur.io.read_metadata does not support numerical columns with empty values (because it calls pandas.read_csv with na_filter=False¹). This gets around that limitation by converting columns before querying. I also considered infer_objects and convert_dtypes, but those are not useful here since they only support soft (not hard) conversions². ¹ a1bfce4 ² https://stackoverflow.com/a/60278450
In the end, it's only worth calling to_numeric on the columns used for numerical comparison. This gets us halfway there, since in most cases, only a small subset of metadata columns are used in the query. This is a hacky approach, but it is more computationally efficient.
Since this is now being used in multiple places of the file.
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victorlin
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Jul 28, 2023
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| # Try converting all queried columns to numeric. | ||
| for column in extract_variables(query).intersection(metadata.columns): | ||
| metadata[column] = pd.to_numeric(metadata[column], errors='ignore') |
|
Merging pre-review since this is a functional improvement, as noted on Slack. |
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Description of proposed changes
The dtype inference in augur.io.read_metadata does not support numerical columns with empty values (because it calls pandas.read_csv with na_filter=False¹). This gets around that limitation by converting columns before querying.
I also considered infer_objects and convert_dtypes, but those are not useful here since they only support soft (not hard) conversions².
¹ a1bfce4
² https://stackoverflow.com/a/60278450
Related issue(s)
Fixes #1269
Addresses #1252 (comment)
Prompted by in-lab discussion with a user.
Testing
Checklist