REF: share Index/Block get_values_for_csv (#55485)

* REF: share Index/Block get_values_for_csv

* mypy fixup

pandas/core/indexes/base.py

@@ -30,6 +30,7 @@
     algos as libalgos,
     index as libindex,
     lib,
+    writers,
 )
 from pandas._libs.internals import BlockValuesRefs
 import pandas._libs.join as libjoin
@@ -97,7 +98,6 @@
     is_bool_dtype,
     is_ea_or_datetimelike_dtype,
     is_float,
-    is_float_dtype,
     is_hashable,
     is_integer,
     is_iterator,
@@ -119,6 +119,7 @@
     ExtensionDtype,
     IntervalDtype,
     PeriodDtype,
+    SparseDtype,
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -151,7 +152,9 @@
     ArrowExtensionArray,
     BaseMaskedArray,
     Categorical,
+    DatetimeArray,
     ExtensionArray,
+    TimedeltaArray,
 )
 from pandas.core.arrays.string_ import StringArray
 from pandas.core.base import (
@@ -199,7 +202,10 @@
         MultiIndex,
         Series,
     )
-    from pandas.core.arrays import PeriodArray
+    from pandas.core.arrays import (
+        IntervalArray,
+        PeriodArray,
+    )
 
 __all__ = ["Index"]
 
@@ -1403,7 +1409,7 @@ def _format_with_header(self, *, header: list[str_t], na_rep: str_t) -> list[str
         result = trim_front(formatted)
         return header + result
 
-    def _format_native_types(
+    def _get_values_for_csv(
         self,
         *,
         na_rep: str_t = "",
@@ -1412,30 +1418,14 @@ def _format_native_types(
         date_format=None,
         quoting=None,
     ) -> npt.NDArray[np.object_]:
-        """
-        Actually format specific types of the index.
-        """
-        from pandas.io.formats.format import FloatArrayFormatter
-
-        if is_float_dtype(self.dtype) and not isinstance(self.dtype, ExtensionDtype):
-            formatter = FloatArrayFormatter(
-                self._values,
-                na_rep=na_rep,
-                float_format=float_format,
-                decimal=decimal,
-                quoting=quoting,
-                fixed_width=False,
-            )
-            return formatter.get_result_as_array()
-
-        mask = isna(self)
-        if self.dtype != object and not quoting:
-            values = np.asarray(self).astype(str)
-        else:
-            values = np.array(self, dtype=object, copy=True)
-
-        values[mask] = na_rep
-        return values
+        return get_values_for_csv(
+            self._values,
+            na_rep=na_rep,
+            decimal=decimal,
+            float_format=float_format,
+            date_format=date_format,
+            quoting=quoting,
+        )
 
     def _summary(self, name=None) -> str_t:
         """
@@ -7629,3 +7619,113 @@ def _maybe_try_sort(result: Index | ArrayLike, sort: bool | None):
                 stacklevel=find_stack_level(),
             )
     return result
+
+
+def get_values_for_csv(
+    values: ArrayLike,
+    *,
+    date_format,
+    na_rep: str = "nan",
+    quoting=None,
+    float_format=None,
+    decimal: str = ".",
+) -> npt.NDArray[np.object_]:
+    """
+    Convert to types which can be consumed by the standard library's
+    csv.writer.writerows.
+    """
+    if isinstance(values, Categorical) and values.categories.dtype.kind in "Mm":
+        # GH#40754 Convert categorical datetimes to datetime array
+        values = algos.take_nd(
+            values.categories._values,
+            ensure_platform_int(values._codes),
+            fill_value=na_rep,
+        )
+
+    values = ensure_wrapped_if_datetimelike(values)
+
+    if isinstance(values, (DatetimeArray, TimedeltaArray)):
+        if values.ndim == 1:
+            result = values._format_native_types(na_rep=na_rep, date_format=date_format)
+            result = result.astype(object, copy=False)
+            return result
+
+        # GH#21734 Process every column separately, they might have different formats
+        results_converted = []
+        for i in range(len(values)):
+            result = values[i, :]._format_native_types(
+                na_rep=na_rep, date_format=date_format
+            )
+            results_converted.append(result.astype(object, copy=False))
+        return np.vstack(results_converted)
+
+    elif isinstance(values.dtype, PeriodDtype):
+        # TODO: tests that get here in column path
+        values = cast("PeriodArray", values)
+        res = values._format_native_types(na_rep=na_rep, date_format=date_format)
+        return res
+
+    elif isinstance(values.dtype, IntervalDtype):
+        # TODO: tests that get here in column path
+        values = cast("IntervalArray", values)
+        mask = values.isna()
+        if not quoting:
+            result = np.asarray(values).astype(str)
+        else:
+            result = np.array(values, dtype=object, copy=True)
+
+        result[mask] = na_rep
+        return result
+
+    elif values.dtype.kind == "f" and not isinstance(values.dtype, SparseDtype):
+        # see GH#13418: no special formatting is desired at the
+        # output (important for appropriate 'quoting' behaviour),
+        # so do not pass it through the FloatArrayFormatter
+        if float_format is None and decimal == ".":
+            mask = isna(values)
+
+            if not quoting:
+                values = values.astype(str)
+            else:
+                values = np.array(values, dtype="object")
+
+            values[mask] = na_rep
+            values = values.astype(object, copy=False)
+            return values
+
+        from pandas.io.formats.format import FloatArrayFormatter
+
+        formatter = FloatArrayFormatter(
+            values,
+            na_rep=na_rep,
+            float_format=float_format,
+            decimal=decimal,
+            quoting=quoting,
+            fixed_width=False,
+        )
+        res = formatter.get_result_as_array()
+        res = res.astype(object, copy=False)
+        return res
+
+    elif isinstance(values, ExtensionArray):
+        mask = isna(values)
+
+        new_values = np.asarray(values.astype(object))
+        new_values[mask] = na_rep
+        return new_values
+
+    else:
+        mask = isna(values)
+        itemsize = writers.word_len(na_rep)
+
+        if values.dtype != _dtype_obj and not quoting and itemsize:
+            values = values.astype(str)
+            if values.dtype.itemsize / np.dtype("U1").itemsize < itemsize:
+                # enlarge for the na_rep
+                values = values.astype(f"<U{itemsize}")
+        else:
+            values = np.array(values, dtype="object")
+
+        values[mask] = na_rep
+        values = values.astype(object, copy=False)
+        return values

pandas/core/indexes/datetimelike.py

@@ -219,7 +219,7 @@ def _format_with_header(
         # TODO: not reached in tests 2023-10-11
         # matches base class except for whitespace padding and date_format
         return header + list(
-            self._format_native_types(na_rep=na_rep, date_format=date_format)
+            self._get_values_for_csv(na_rep=na_rep, date_format=date_format)
         )
 
     @property

pandas/core/indexes/datetimes.py

@@ -118,7 +118,6 @@ def _new_DatetimeIndex(cls, d):
         "tzinfo",
         "dtype",
         "to_pydatetime",
-        "_format_native_types",
         "date",
         "time",
         "timetz",

pandas/core/indexes/multi.py

@@ -1384,15 +1384,15 @@ def _formatter_func(self, tup):
         formatter_funcs = [level._formatter_func for level in self.levels]
         return tuple(func(val) for func, val in zip(formatter_funcs, tup))
 
-    def _format_native_types(
+    def _get_values_for_csv(
         self, *, na_rep: str = "nan", **kwargs
     ) -> npt.NDArray[np.object_]:
         new_levels = []
         new_codes = []
 
         # go through the levels and format them
         for level, level_codes in zip(self.levels, self.codes):
-            level_strs = level._format_native_types(na_rep=na_rep, **kwargs)
+            level_strs = level._get_values_for_csv(na_rep=na_rep, **kwargs)
             # add nan values, if there are any
             mask = level_codes == -1
             if mask.any():
@@ -1408,7 +1408,7 @@ def _format_native_types(
 
         if len(new_levels) == 1:
             # a single-level multi-index
-            return Index(new_levels[0].take(new_codes[0]))._format_native_types()
+            return Index(new_levels[0].take(new_codes[0]))._get_values_for_csv()
         else:
             # reconstruct the multi-index
             mi = MultiIndex(

pandas/core/indexes/period.py

@@ -80,7 +80,7 @@ def _new_PeriodIndex(cls, **d):
     PeriodArray,
     wrap=True,
 )
-@inherit_names(["is_leap_year", "_format_native_types"], PeriodArray)
+@inherit_names(["is_leap_year"], PeriodArray)
 class PeriodIndex(DatetimeIndexOpsMixin):
     """
     Immutable ndarray holding ordinal values indicating regular periods in time.

pandas/core/indexes/timedeltas.py

@@ -48,7 +48,6 @@
         "sum",
         "std",
         "median",
-        "_format_native_types",
     ],
     TimedeltaArray,
 )

pandas/core/internals/array_manager.py

@@ -68,6 +68,7 @@
     Index,
     ensure_index,
 )
+from pandas.core.indexes.base import get_values_for_csv
 from pandas.core.internals.base import (
     DataManager,
     SingleDataManager,
@@ -79,7 +80,6 @@
     ensure_block_shape,
     external_values,
     extract_pandas_array,
-    get_values_for_csv,
     maybe_coerce_values,
     new_block,
 )