DEPR: remove DTA.__init__, TDA.__init__

ci/deps/actions-310.yaml

@@ -26,7 +26,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

ci/deps/actions-311-downstream_compat.yaml

@@ -28,7 +28,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

ci/deps/actions-311.yaml

@@ -26,7 +26,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

ci/deps/actions-312.yaml

@@ -26,7 +26,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

ci/deps/actions-39-minimum_versions.yaml

@@ -29,7 +29,7 @@ dependencies:
   - beautifulsoup4=4.11.2
   - blosc=1.21.3
   - bottleneck=1.3.6
-  - fastparquet=2023.04.0
+  - fastparquet=2023.10.0
   - fsspec=2022.11.0
   - html5lib=1.1
   - hypothesis=6.46.1

ci/deps/actions-39.yaml

@@ -26,7 +26,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

ci/deps/circle-310-arm64.yaml

@@ -27,7 +27,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

doc/source/getting_started/install.rst

@@ -361,7 +361,7 @@ Dependency                Minimum Version    pip extra        Notes
 PyTables                  3.8.0              hdf5             HDF5-based reading / writing
 blosc                     1.21.3             hdf5             Compression for HDF5; only available on ``conda``
 zlib                                         hdf5             Compression for HDF5
-fastparquet               2023.04.0          -                Parquet reading / writing (pyarrow is default)
+fastparquet               2023.10.0          -                Parquet reading / writing (pyarrow is default)
 pyarrow                   10.0.1             parquet, feather Parquet, ORC, and feather reading / writing
 pyreadstat                1.2.0              spss             SPSS files (.sav) reading
 odfpy                     1.4.1              excel            Open document format (.odf, .ods, .odt) reading / writing

doc/source/whatsnew/v3.0.0.rst

@@ -134,7 +134,7 @@ Optional libraries below the lowest tested version may still work, but are not c
 +------------------------+---------------------+
 | Package                | New Minimum Version |
 +========================+=====================+
-| fastparquet            | 2023.04.0           |
+| fastparquet            | 2023.10.0           |
 +------------------------+---------------------+
 | adbc-driver-postgresql | 0.10.0              |
 +------------------------+---------------------+

environment.yml

@@ -30,7 +30,7 @@ dependencies:
   - beautifulsoup4>=4.11.2
   - blosc
   - bottleneck>=1.3.6
-  - fastparquet>=2023.04.0
+  - fastparquet>=2023.10.0
   - fsspec>=2022.11.0
   - html5lib>=1.1
   - hypothesis>=6.46.1

pandas/compat/_optional.py

@@ -25,7 +25,7 @@
     "bs4": "4.11.2",
     "blosc": "1.21.3",
     "bottleneck": "1.3.6",
-    "fastparquet": "2023.04.0",
+    "fastparquet": "2023.10.0",
     "fsspec": "2022.11.0",
     "html5lib": "1.1",
     "hypothesis": "6.46.1",

pandas/core/arrays/datetimelike.py

@@ -26,7 +26,6 @@
     algos,
     lib,
 )
-from pandas._libs.arrays import NDArrayBacked
 from pandas._libs.tslibs import (
     BaseOffset,
     IncompatibleFrequency,
@@ -1936,100 +1935,6 @@ class TimelikeOps(DatetimeLikeArrayMixin):
     Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
     """
 
-    _default_dtype: np.dtype
-
-    def __init__(
-        self, values, dtype=None, freq=lib.no_default, copy: bool = False
-    ) -> None:
-        warnings.warn(
-            # GH#55623
-            f"{type(self).__name__}.__init__ is deprecated and will be "
-            "removed in a future version. Use pd.array instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        if dtype is not None:
-            dtype = pandas_dtype(dtype)
-
-        values = extract_array(values, extract_numpy=True)
-        if isinstance(values, IntegerArray):
-            values = values.to_numpy("int64", na_value=iNaT)
-
-        inferred_freq = getattr(values, "_freq", None)
-        explicit_none = freq is None
-        freq = freq if freq is not lib.no_default else None
-
-        if isinstance(values, type(self)):
-            if explicit_none:
-                # don't inherit from values
-                pass
-            elif freq is None:
-                freq = values.freq
-            elif freq and values.freq:
-                freq = to_offset(freq)
-                freq = _validate_inferred_freq(freq, values.freq)
-
-            if dtype is not None and dtype != values.dtype:
-                # TODO: we only have tests for this for DTA, not TDA (2022-07-01)
-                raise TypeError(
-                    f"dtype={dtype} does not match data dtype {values.dtype}"
-                )
-
-            dtype = values.dtype
-            values = values._ndarray
-
-        elif dtype is None:
-            if isinstance(values, np.ndarray) and values.dtype.kind in "Mm":
-                dtype = values.dtype
-            else:
-                dtype = self._default_dtype
-                if isinstance(values, np.ndarray) and values.dtype == "i8":
-                    values = values.view(dtype)
-
-        if not isinstance(values, np.ndarray):
-            raise ValueError(
-                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
-                f"{type(self).__name__}, ndarray, or Series or Index "
-                "containing one of those."
-            )
-        if values.ndim not in [1, 2]:
-            raise ValueError("Only 1-dimensional input arrays are supported.")
-
-        if values.dtype == "i8":
-            # for compat with datetime/timedelta/period shared methods,
-            #  we can sometimes get here with int64 values.  These represent
-            #  nanosecond UTC (or tz-naive) unix timestamps
-            if dtype is None:
-                dtype = self._default_dtype
-                values = values.view(self._default_dtype)
-            elif lib.is_np_dtype(dtype, "mM"):
-                values = values.view(dtype)
-            elif isinstance(dtype, DatetimeTZDtype):
-                kind = self._default_dtype.kind
-                new_dtype = f"{kind}8[{dtype.unit}]"
-                values = values.view(new_dtype)
-
-        dtype = self._validate_dtype(values, dtype)
-
-        if freq == "infer":
-            raise ValueError(
-                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
-                "Use 'pd.array()' instead."
-            )
-
-        if copy:
-            values = values.copy()
-        if freq:
-            freq = to_offset(freq)
-            if values.dtype.kind == "m" and not isinstance(freq, Tick):
-                raise TypeError("TimedeltaArray/Index freq must be a Tick")
-
-        NDArrayBacked.__init__(self, values=values, dtype=dtype)
-        self._freq = freq
-
-        if inferred_freq is None and freq is not None:
-            type(self)._validate_frequency(self, freq)
-
     @classmethod
     def _validate_dtype(cls, values, dtype):
         raise AbstractMethodError(cls)

pandas/core/arrays/datetimes.py

@@ -186,7 +186,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):  # type: ignore[misc]
 
     Parameters
     ----------
-    values : Series, Index, DatetimeArray, ndarray
+    data : Series, Index, DatetimeArray, ndarray
         The datetime data.
 
         For DatetimeArray `values` (or a Series or Index boxing one),
@@ -287,7 +287,6 @@ def _scalar_type(self) -> type[Timestamp]:
 
     _dtype: np.dtype[np.datetime64] | DatetimeTZDtype
     _freq: BaseOffset | None = None
-    _default_dtype = DT64NS_DTYPE  # used in TimeLikeOps.__init__
 
     @classmethod
     def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:

pandas/core/arrays/timedeltas.py

@@ -113,7 +113,7 @@ class TimedeltaArray(dtl.TimelikeOps):
 
     Parameters
     ----------
-    values : array-like
+    data : array-like
         The timedelta data.
 
     dtype : numpy.dtype
@@ -196,7 +196,6 @@ def dtype(self) -> np.dtype[np.timedelta64]:  # type: ignore[override]
     # Constructors
 
     _freq = None
-    _default_dtype = TD64NS_DTYPE  # used in TimeLikeOps.__init__
 
     @classmethod
     def _validate_dtype(cls, values, dtype):

pandas/tests/arrays/datetimes/test_constructors.py

@@ -16,34 +16,6 @@ def test_from_sequence_invalid_type(self):
         with pytest.raises(TypeError, match="Cannot create a DatetimeArray"):
             DatetimeArray._from_sequence(mi, dtype="M8[ns]")
 
-    def test_only_1dim_accepted(self):
-        arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
-
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Only 1-dimensional"):
-                # 3-dim, we allow 2D to sneak in for ops purposes GH#29853
-                DatetimeArray(arr.reshape(2, 2, 1))
-
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Only 1-dimensional"):
-                # 0-dim
-                DatetimeArray(arr[[0]].squeeze())
-
-    def test_freq_validation(self):
-        # GH#24623 check that invalid instances cannot be created with the
-        #  public constructor
-        arr = np.arange(5, dtype=np.int64) * 3600 * 10**9
-
-        msg = (
-            "Inferred frequency h from passed values does not "
-            "conform to passed frequency W-SUN"
-        )
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match=msg):
-                DatetimeArray(arr, freq="W")
-
     @pytest.mark.parametrize(
         "meth",
         [
@@ -76,42 +48,9 @@ def test_from_pandas_array(self):
         expected = pd.date_range("1970-01-01", periods=5, freq="h")._data
         tm.assert_datetime_array_equal(result, expected)
 
-    def test_mismatched_timezone_raises(self):
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            arr = DatetimeArray(
-                np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"),
-                dtype=DatetimeTZDtype(tz="US/Central"),
-            )
-        dtype = DatetimeTZDtype(tz="US/Eastern")
-        msg = r"dtype=datetime64\[ns.*\] does not match data dtype datetime64\[ns.*\]"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(TypeError, match=msg):
-                DatetimeArray(arr, dtype=dtype)
-
-        # also with mismatched tzawareness
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(TypeError, match=msg):
-                DatetimeArray(arr, dtype=np.dtype("M8[ns]"))
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(TypeError, match=msg):
-                DatetimeArray(arr.tz_localize(None), dtype=arr.dtype)
-
-    def test_non_array_raises(self):
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="list"):
-                DatetimeArray([1, 2, 3])
-
     def test_bool_dtype_raises(self):
         arr = np.array([1, 2, 3], dtype="bool")
 
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        msg = "Unexpected value for 'dtype': 'bool'. Must be"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match=msg):
-                DatetimeArray(arr)
-
         msg = r"dtype bool cannot be converted to datetime64\[ns\]"
         with pytest.raises(TypeError, match=msg):
             DatetimeArray._from_sequence(arr, dtype="M8[ns]")
@@ -122,41 +61,6 @@ def test_bool_dtype_raises(self):
         with pytest.raises(TypeError, match=msg):
             pd.to_datetime(arr)
 
-    def test_incorrect_dtype_raises(self):
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
-                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
-
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
-                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="m8[s]")
-
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
-                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="M8[D]")
-
-    def test_mismatched_values_dtype_units(self):
-        arr = np.array([1, 2, 3], dtype="M8[s]")
-        dtype = np.dtype("M8[ns]")
-        msg = "Values resolution does not match dtype."
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match=msg):
-                DatetimeArray(arr, dtype=dtype)
-
-        dtype2 = DatetimeTZDtype(tz="UTC", unit="ns")
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match=msg):
-                DatetimeArray(arr, dtype=dtype2)
-
-    def test_freq_infer_raises(self):
-        depr_msg = "DatetimeArray.__init__ is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-            with pytest.raises(ValueError, match="Frequency inference"):
-                DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer")
-
     def test_copy(self):
         data = np.array([1, 2, 3], dtype="M8[ns]")
         arr = DatetimeArray._from_sequence(data, copy=False)

pandas/tests/arrays/test_datetimelike.py

@@ -1320,12 +1320,6 @@ def test_from_pandas_array(dtype):
 
     cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype]
 
-    depr_msg = f"{cls.__name__}.__init__ is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=depr_msg):
-        result = cls(arr)
-        expected = cls(data)
-    tm.assert_extension_array_equal(result, expected)
-
     result = cls._from_sequence(arr, dtype=dtype)
     expected = cls._from_sequence(data, dtype=dtype)
     tm.assert_extension_array_equal(result, expected)