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…patibility with pandas >=1.3.5
standage
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Nov 7, 2025
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| # for some reason some int columns are behaving as floats -- this converts them | ||
| inDf = inDf.apply(pd.to_numeric, errors="ignore", downcast="integer") | ||
| inDf = inDf.apply(pd.to_numeric, errors="coerce", downcast="integer").fillna(inDf) |
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The errors="ignore" mode is deprecated in v2 and will be removed in v3. Its behavior can be replicated with "coerce" mode which inserts an NA in case of error, followed by filling NA values with the original dataframe.
| df = df.merge(orient, on="Type", how="left") | ||
| df["Type"] = df["Type"].str.replace("_", " ") | ||
| df["has_orientation"] = df["has_orientation"].fillna(value=False).infer_objects() | ||
| df["has_orientation"] = df["has_orientation"].astype(bool).fillna(value=False) |
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A couple places now require a bit more explicit handling of data types.
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| infernal["sframe"] = infernal["sframe"].replace(["-", "+"], [-1, 1]) | ||
| infernal["sframe"] = infernal["sframe"].replace({"-": "-1", "+": "1"}).astype("int16") |
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A couple places now require a bit more explicit handling of data types.
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| # adds a FeatureLocation object so it can be used in gbk construction | ||
| inDf["feat loc"] = inDf.apply(FeatureLocation_smart, axis=1) | ||
| if inDf.empty: | ||
| inDf = pd.DataFrame(columns=DF_COLS) | ||
| else: | ||
| # adds a FeatureLocation object so it can be used in gbk construction | ||
| inDf["feat loc"] = inDf.apply(FeatureLocation_smart, axis=1) |
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The changes I made to this file were not necessary for pandas v2 support. I only encountered errors when I downgraded to pandas v1.3.5 for testing. But the updated code should work across all supported versions.
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Superseded by #59. |
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The PR makes a few marginal changes to unlock support for pandas version 2. I ensured that all changes were compatible with pandas 1.3.5, currently the minimal supported version.
Partially addresses #56.