TYP: assorted

pandas/_libs/algos.pyi

@@ -3,6 +3,8 @@ from typing import Any
 
 import numpy as np
 
+from pandas._typing import npt
+
 class Infinity:
     """
     Provide a positive Infinity comparison method for ranking.
@@ -30,7 +32,7 @@ class NegInfinity:
 def unique_deltas(
     arr: np.ndarray,  # const int64_t[:]
 ) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
-def is_lexsorted(list_of_arrays: list[np.ndarray]) -> bool: ...
+def is_lexsorted(list_of_arrays: list[npt.NDArray[np.int64]]) -> bool: ...
 def groupsort_indexer(
     index: np.ndarray,  # const int64_t[:]
     ngroups: int,
@@ -146,18 +148,18 @@ def diff_2d(
     axis: int,
     datetimelike: bool = ...,
 ) -> None: ...
-def ensure_platform_int(arr: object) -> np.ndarray: ...
-def ensure_object(arr: object) -> np.ndarray: ...
-def ensure_float64(arr: object, copy=True) -> np.ndarray: ...
-def ensure_float32(arr: object, copy=True) -> np.ndarray: ...
-def ensure_int8(arr: object, copy=True) -> np.ndarray: ...
-def ensure_int16(arr: object, copy=True) -> np.ndarray: ...
-def ensure_int32(arr: object, copy=True) -> np.ndarray: ...
-def ensure_int64(arr: object, copy=True) -> np.ndarray: ...
-def ensure_uint8(arr: object, copy=True) -> np.ndarray: ...
-def ensure_uint16(arr: object, copy=True) -> np.ndarray: ...
-def ensure_uint32(arr: object, copy=True) -> np.ndarray: ...
-def ensure_uint64(arr: object, copy=True) -> np.ndarray: ...
+def ensure_platform_int(arr: object) -> npt.NDArray[np.intp]: ...
+def ensure_object(arr: object) -> npt.NDArray[np.object_]: ...
+def ensure_float64(arr: object, copy=True) -> npt.NDArray[np.float64]: ...
+def ensure_float32(arr: object, copy=True) -> npt.NDArray[np.float32]: ...
+def ensure_int8(arr: object, copy=True) -> npt.NDArray[np.int8]: ...
+def ensure_int16(arr: object, copy=True) -> npt.NDArray[np.int16]: ...
+def ensure_int32(arr: object, copy=True) -> npt.NDArray[np.int32]: ...
+def ensure_int64(arr: object, copy=True) -> npt.NDArray[np.int64]: ...
+def ensure_uint8(arr: object, copy=True) -> npt.NDArray[np.uint8]: ...
+def ensure_uint16(arr: object, copy=True) -> npt.NDArray[np.uint16]: ...
+def ensure_uint32(arr: object, copy=True) -> npt.NDArray[np.uint32]: ...
+def ensure_uint64(arr: object, copy=True) -> npt.NDArray[np.uint64]: ...
 def take_1d_int8_int8(
     values: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=...
 ) -> None: ...

pandas/_libs/algos.pyx

@@ -122,7 +122,7 @@ cpdef ndarray[int64_t, ndim=1] unique_deltas(const int64_t[:] arr):
 
     Parameters
     ----------
-    arr : ndarray[in64_t]
+    arr : ndarray[int64_t]
 
     Returns
     -------

pandas/_libs/hashing.pyi

@@ -1,7 +1,9 @@
 import numpy as np
 
+from pandas._typing import npt
+
 def hash_object_array(
-    arr: np.ndarray,  # np.ndarray[object]
+    arr: npt.NDArray[np.object_],
     key: str,
     encoding: str = ...,
-) -> np.ndarray: ...  # np.ndarray[np.uint64]
+) -> npt.NDArray[np.uint64]: ...

pandas/_libs/index.pyi

@@ -1,5 +1,7 @@
 import numpy as np
 
+from pandas._typing import npt
+
 class IndexEngine:
     over_size_threshold: bool
     def __init__(self, vgetter, n: int): ...
@@ -16,21 +18,18 @@ class IndexEngine:
     def is_monotonic_decreasing(self) -> bool: ...
     def get_backfill_indexer(
         self, other: np.ndarray, limit: int | None = ...
-    ) -> np.ndarray: ...
+    ) -> npt.NDArray[np.intp]: ...
     def get_pad_indexer(
         self, other: np.ndarray, limit: int | None = ...
-    ) -> np.ndarray: ...
+    ) -> npt.NDArray[np.intp]: ...
     @property
     def is_mapping_populated(self) -> bool: ...
     def clear_mapping(self): ...
-    def get_indexer(self, values: np.ndarray) -> np.ndarray: ...  # np.ndarray[np.intp]
+    def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ...
     def get_indexer_non_unique(
         self,
         targets: np.ndarray,
-    ) -> tuple[
-        np.ndarray,  # np.ndarray[np.intp]
-        np.ndarray,  # np.ndarray[np.intp]
-    ]: ...
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
 
 class Float64Engine(IndexEngine): ...
 class Float32Engine(IndexEngine): ...
@@ -58,8 +57,8 @@ class BaseMultiIndexCodesEngine:
     ): ...
     def get_indexer(
         self,
-        target: np.ndarray,  # np.ndarray[object]
-    ) -> np.ndarray: ...  # np.ndarray[np.intp]
+        target: npt.NDArray[np.object_],
+    ) -> npt.NDArray[np.intp]: ...
     def _extract_level_codes(self, target: object): ...
     def get_indexer_with_fill(
         self,

pandas/core/algorithms.py

@@ -433,7 +433,7 @@ def unique(values):
 unique1d = unique
 
 
-def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
+def isin(comps: AnyArrayLike, values: AnyArrayLike) -> npt.NDArray[np.bool_]:
     """
     Compute the isin boolean array.
 
@@ -903,7 +903,7 @@ def value_counts_arraylike(values, dropna: bool):
 
 def duplicated(
     values: ArrayLike, keep: Literal["first", "last", False] = "first"
-) -> np.ndarray:
+) -> npt.NDArray[np.bool_]:
     """
     Return boolean ndarray denoting duplicate values.
 
@@ -1032,8 +1032,8 @@ def rank(
 def checked_add_with_arr(
     arr: np.ndarray,
     b,
-    arr_mask: np.ndarray | None = None,
-    b_mask: np.ndarray | None = None,
+    arr_mask: npt.NDArray[np.bool_] | None = None,
+    b_mask: npt.NDArray[np.bool_] | None = None,
 ) -> np.ndarray:
     """
     Perform array addition that checks for underflow and overflow.

pandas/core/arrays/datetimelike.py

@@ -49,6 +49,7 @@
     DtypeObj,
     NpDtype,
     PositionalIndexer2D,
+    npt,
 )
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
@@ -274,7 +275,7 @@ def __iter__(self):
             return (self._box_func(v) for v in self.asi8)
 
     @property
-    def asi8(self) -> np.ndarray:
+    def asi8(self) -> npt.NDArray[np.int64]:
         """
         Integer representation of the values.
 
@@ -774,7 +775,7 @@ def map(self, mapper):
 
         return Index(self).map(mapper).array
 
-    def isin(self, values) -> np.ndarray:
+    def isin(self, values) -> npt.NDArray[np.bool_]:
         """
         Compute boolean array of whether each value is found in the
         passed set of values.
@@ -830,11 +831,11 @@ def isin(self, values) -> np.ndarray:
     # ------------------------------------------------------------------
     # Null Handling
 
-    def isna(self) -> np.ndarray:
+    def isna(self) -> npt.NDArray[np.bool_]:
         return self._isnan
 
     @property  # NB: override with cache_readonly in immutable subclasses
-    def _isnan(self) -> np.ndarray:
+    def _isnan(self) -> npt.NDArray[np.bool_]:
         """
         return if each value is nan
         """
@@ -1535,7 +1536,7 @@ class DatelikeOps(DatetimeLikeArrayMixin):
         URL="https://docs.python.org/3/library/datetime.html"
         "#strftime-and-strptime-behavior"
     )
-    def strftime(self, date_format: str) -> np.ndarray:
+    def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
         """
         Convert to Index using specified date_format.
 
@@ -1551,7 +1552,7 @@ def strftime(self, date_format: str) -> np.ndarray:
 
         Returns
         -------
-        ndarray
+        ndarray[object]
             NumPy ndarray of formatted strings.
 
         See Also

pandas/core/arrays/datetimes.py

@@ -38,6 +38,7 @@
     to_offset,
     tzconversion,
 )
+from pandas._typing import npt
 from pandas.errors import PerformanceWarning
 
 from pandas.core.dtypes.cast import astype_dt64_to_dt64tz
@@ -656,7 +657,7 @@ def astype(self, dtype, copy: bool = True):
     @dtl.ravel_compat
     def _format_native_types(
         self, na_rep="NaT", date_format=None, **kwargs
-    ) -> np.ndarray:
+    ) -> npt.NDArray[np.object_]:
         from pandas.io.formats.format import get_format_datetime64_from_values
 
         fmt = get_format_datetime64_from_values(self, date_format)
@@ -1045,7 +1046,7 @@ def tz_localize(self, tz, ambiguous="raise", nonexistent="raise") -> DatetimeArr
     # ----------------------------------------------------------------
     # Conversion Methods - Vectorized analogues of Timestamp methods
 
-    def to_pydatetime(self) -> np.ndarray:
+    def to_pydatetime(self) -> npt.NDArray[np.object_]:
         """
         Return Datetime Array/Index as object ndarray of datetime.datetime
         objects.
@@ -1262,7 +1263,7 @@ def day_name(self, locale=None):
         return result
 
     @property
-    def time(self) -> np.ndarray:
+    def time(self) -> npt.NDArray[np.object_]:
         """
         Returns numpy array of datetime.time. The time part of the Timestamps.
         """
@@ -1274,15 +1275,15 @@ def time(self) -> np.ndarray:
         return ints_to_pydatetime(timestamps, box="time")
 
     @property
-    def timetz(self) -> np.ndarray:
+    def timetz(self) -> npt.NDArray[np.object_]:
         """
         Returns numpy array of datetime.time also containing timezone
         information. The time part of the Timestamps.
         """
         return ints_to_pydatetime(self.asi8, self.tz, box="time")
 
     @property
-    def date(self) -> np.ndarray:
+    def date(self) -> npt.NDArray[np.object_]:
         """
         Returns numpy array of python datetime.date objects (namely, the date
         part of Timestamps without timezone information).

pandas/core/groupby/ops.py

@@ -947,9 +947,7 @@ def agg_series(
             out = npvalues
         return out
 
-    def _aggregate_series_fast(self, obj: Series, func: F) -> np.ndarray:
-        # -> np.ndarray[object]
-
+    def _aggregate_series_fast(self, obj: Series, func: F) -> npt.NDArray[np.object_]:
         # At this point we have already checked that
         #  - obj.index is not a MultiIndex
         #  - obj is backed by an ndarray, not ExtensionArray
@@ -967,8 +965,9 @@ def _aggregate_series_fast(self, obj: Series, func: F) -> np.ndarray:
         return result
 
     @final
-    def _aggregate_series_pure_python(self, obj: Series, func: F) -> np.ndarray:
-        # -> np.ndarray[object]
+    def _aggregate_series_pure_python(
+        self, obj: Series, func: F
+    ) -> npt.NDArray[np.object_]:
         ids, _, ngroups = self.group_info
 
         counts = np.zeros(ngroups, dtype=int)
@@ -1190,12 +1189,12 @@ def __init__(
         assert isinstance(axis, int), axis
 
     @cache_readonly
-    def slabels(self) -> np.ndarray:  # np.ndarray[np.intp]
+    def slabels(self) -> npt.NDArray[np.intp]:
         # Sorted labels
         return self.labels.take(self._sort_idx)
 
     @cache_readonly
-    def _sort_idx(self) -> np.ndarray:  # np.ndarray[np.intp]
+    def _sort_idx(self) -> npt.NDArray[np.intp]:
         # Counting sort indexer
         return get_group_index_sorter(self.labels, self.ngroups)
 

pandas/core/indexes/base.py

@@ -4450,7 +4450,9 @@ def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
         return join_index, left_indexer, right_indexer
 
     @final
-    def _join_monotonic(self, other: Index, how: str_t = "left"):
+    def _join_monotonic(
+        self, other: Index, how: str_t = "left"
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
         # We only get here with matching dtypes
         assert other.dtype == self.dtype
 

pandas/core/indexes/numeric.py

@@ -15,6 +15,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.util._decorators import (
     cache_readonly,
@@ -411,7 +412,7 @@ class IntegerIndex(NumericIndex):
     _is_backward_compat_public_numeric_index: bool = False
 
     @property
-    def asi8(self) -> np.ndarray:
+    def asi8(self) -> npt.NDArray[np.int64]:
         # do not cache or you'll create a memory leak
         warnings.warn(
             "Index.asi8 is deprecated and will be removed in a future version.",

pandas/core/missing.py

@@ -23,6 +23,7 @@
     ArrayLike,
     Axis,
     F,
+    npt,
 )
 from pandas.compat._optional import import_optional_dependency
 
@@ -57,7 +58,7 @@ def check_value_size(value, mask: np.ndarray, length: int):
     return value
 
 
-def mask_missing(arr: ArrayLike, values_to_mask) -> np.ndarray:
+def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
     """
     Return a masking array of same size/shape as arr
     with entries equaling any member of values_to_mask set to True