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DEPR: move NumericIndex._convert_tolerance to Index #50932

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Merged
merged 2 commits into from
Jan 25, 2023
Merged

DEPR: move NumericIndex._convert_tolerance to Index #50932

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d50c82d
DEPR: move NumericIndex._convert_tolerance to Index
topper-123 Jan 22, 2023
92c2c7a
fix error message
topper-123 Jan 24, 2023
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11 changes: 11 additions & 0 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3780,6 +3780,17 @@ def _convert_tolerance(self, tolerance, target: np.ndarray | Index) -> np.ndarra
tolerance = np.asarray(tolerance)
if target.size != tolerance.size and tolerance.size > 1:
raise ValueError("list-like tolerance size must match target index size")
elif is_numeric_dtype(self) and not np.issubdtype(tolerance.dtype, np.number):
if tolerance.ndim > 0:
raise ValueError(
f"tolerance argument for {type(self).__name__} must contain "
"numeric elements if it is list type"
)

raise ValueError(
f"tolerance argument for {type(self).__name__} must be numeric "
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should type(self).__name__ here be changed to refer to dtype instead of type?

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@topper-123 topper-123 Jan 24, 2023 •
edited
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Yeah, now that Index can handle a lot of different dtypes (numeric and non-numeric) that makes sense. I'll update.

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Error messages updated.

f"if it is a scalar: {repr(tolerance)}"
)
return tolerance

@final
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16 changes: 0 additions & 16 deletions pandas/core/indexes/numeric.py
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Expand Up @@ -221,22 +221,6 @@ def _maybe_cast_slice_bound(self, label, side: str):

# ----------------------------------------------------------------

def _convert_tolerance(self, tolerance, target):
tolerance = super()._convert_tolerance(tolerance, target)

if not np.issubdtype(tolerance.dtype, np.number):
if tolerance.ndim > 0:
raise ValueError(
f"tolerance argument for {type(self).__name__} must contain "
"numeric elements if it is list type"
)

raise ValueError(
f"tolerance argument for {type(self).__name__} must be numeric "
f"if it is a scalar: {repr(tolerance)}"
)
return tolerance

@classmethod
def _assert_safe_casting(cls, data: np.ndarray, subarr: np.ndarray) -> None:
"""
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