Pandas 2.2.0 #359
Pandas 2.2.0 #359
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…quite happy with the latter).
| def _calculate_bins(self, data, bins): | ||
| nd_values = data.infer_objects(copy=False)._get_numeric_data() | ||
| values = np.ravel(nd_values) | ||
| values = values[~isna(values)] |
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I'm not happy with repeating this a third time, and I haven't checked if the weights definitely need checking for nans
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Why not work with data.to_numpy().ravel() in quantile below? seems simpler to me...
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Essentially because I'm attempting to replicate the pandas versions as closely as possible
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Well, considering that Hist1dPlot was created for a WeightedSeries, we could simplify things here.
Even simpler than my previous suggestion:
xmin, xmax = data.quantile(q).to_numpy().ravel()Not sure what is easier for maintenance in the long run, though, so happy with either approach.
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That's too tidy to resist!
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #359 +/- ##
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Coverage 100.00% 100.00%
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Files 36 36
Lines 3043 3032 -11
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- Hits 3043 3032 -11 ☔ View full report in Codecov by Sentry. |
…are the problem???)" This reverts commit 1f9857b.
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I think that's all the new deprecation warnings, aside from the warnings about constructing from a |
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PR here which may clean up some of the BlockManager mess, pandas-dev/pandas#57553 Edit: I've just run anesthetic with this branch of pandas, and all the BlockManager warnings seem to have gone away, so I'm not going to worry about them any further |
…red after changes to defaults in pandas
| def _calculate_bins(self, data, bins): | ||
| nd_values = data.infer_objects(copy=False)._get_numeric_data() | ||
| values = np.ravel(nd_values) | ||
| values = values[~isna(values)] |
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Why not work with data.to_numpy().ravel() in quantile below? seems simpler to me...
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Is there anything else that needs to be taken into account for this?
If not, I am happy with this. Thanks for catching this so quickly @AdamOrmondroyd!
Feel free to handle my inline reply as you see fit.
… after cloning a fresh repo
Description
Pandas 2.2.0 just released, with a flurry of changes to the plotting to become more stateless (though not even close to achieving it), and a whole flood of deprecations ready for pandas 3.0 (!!!!!!!)
Unfortunately, the changes to the plotting are incompatible with the old
I'm yet to do anything about the warnings
Fixes # (issue)
Checklist:
flake8 anesthetic tests)pydocstyle --convention=numpy anesthetic)python -m pytest)