CLN: runtime imports

pandas/core/dtypes/cast.py

@@ -7,15 +7,17 @@
 
 import numpy as np
 
-from pandas._libs import lib, tslib, tslibs
+from pandas._libs import lib, tslib
 from pandas._libs.tslibs import (
     NaT,
     OutOfBoundsDatetime,
     Period,
     Timedelta,
     Timestamp,
+    conversion,
     iNaT,
     ints_to_pydatetime,
+    ints_to_pytimedelta,
 )
 from pandas._libs.tslibs.timezones import tz_compare
 from pandas._typing import ArrayLike, Dtype, DtypeObj
@@ -552,7 +554,7 @@ def maybe_promote(dtype, fill_value=np.nan):
             dtype = np.dtype(np.object_)
         else:
             try:
-                fill_value = tslibs.Timestamp(fill_value).to_datetime64()
+                fill_value = Timestamp(fill_value).to_datetime64()
             except (TypeError, ValueError):
                 dtype = np.dtype(np.object_)
     elif issubclass(dtype.type, np.timedelta64):
@@ -565,7 +567,7 @@ def maybe_promote(dtype, fill_value=np.nan):
             dtype = np.dtype(np.object_)
         else:
             try:
-                fv = tslibs.Timedelta(fill_value)
+                fv = Timedelta(fill_value)
             except ValueError:
                 dtype = np.dtype(np.object_)
             else:
@@ -738,8 +740,8 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> Tuple[DtypeObj,
         dtype = np.dtype(object)
 
     elif isinstance(val, (np.datetime64, datetime)):
-        val = tslibs.Timestamp(val)
-        if val is tslibs.NaT or val.tz is None:
+        val = Timestamp(val)
+        if val is NaT or val.tz is None:
             dtype = np.dtype("M8[ns]")
         else:
             if pandas_dtype:
@@ -750,7 +752,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> Tuple[DtypeObj,
         val = val.value
 
     elif isinstance(val, (np.timedelta64, timedelta)):
-        val = tslibs.Timedelta(val).value
+        val = Timedelta(val).value
         dtype = np.dtype("m8[ns]")
 
     elif is_bool(val):
@@ -941,9 +943,9 @@ def conv(r, dtype):
         if np.any(isna(r)):
             pass
         elif dtype == DT64NS_DTYPE:
-            r = tslibs.Timestamp(r)
+            r = Timestamp(r)
         elif dtype == TD64NS_DTYPE:
-            r = tslibs.Timedelta(r)
+            r = Timedelta(r)
         elif dtype == np.bool_:
             # messy. non 0/1 integers do not get converted.
             if is_integer(r) and r not in [0, 1]:
@@ -1006,7 +1008,7 @@ def astype_nansafe(arr, dtype, copy: bool = True, skipna: bool = False):
 
     elif is_timedelta64_dtype(arr):
         if is_object_dtype(dtype):
-            return tslibs.ints_to_pytimedelta(arr.view(np.int64))
+            return ints_to_pytimedelta(arr.view(np.int64))
         elif dtype == np.int64:
             if isna(arr).any():
                 raise ValueError("Cannot convert NaT values to integer")
@@ -1317,8 +1319,6 @@ def try_datetime(v):
             # we might have a sequence of the same-datetimes with tz's
             # if so coerce to a DatetimeIndex; if they are not the same,
             # then these stay as object dtype, xref GH19671
-            from pandas._libs.tslibs import conversion
-
             from pandas import DatetimeIndex
 
             try:
@@ -1492,7 +1492,7 @@ def maybe_cast_to_datetime(value, dtype, errors: str = "raise"):
             dtype = value.dtype
 
             if dtype.kind == "M" and dtype != DT64NS_DTYPE:
-                value = tslibs.conversion.ensure_datetime64ns(value)
+                value = conversion.ensure_datetime64ns(value)
 
             elif dtype.kind == "m" and dtype != TD64NS_DTYPE:
                 value = to_timedelta(value)

pandas/core/dtypes/dtypes.py

@@ -406,8 +406,6 @@ def __repr__(self) -> str_type:
 
     @staticmethod
     def _hash_categories(categories, ordered: Ordered = True) -> int:
-        from pandas.core.dtypes.common import DT64NS_DTYPE, is_datetime64tz_dtype
-
         from pandas.core.util.hashing import (
             combine_hash_arrays,
             hash_array,
@@ -430,9 +428,9 @@ def _hash_categories(categories, ordered: Ordered = True) -> int:
                     hashed = hash((tuple(categories), ordered))
                     return hashed
 
-            if is_datetime64tz_dtype(categories.dtype):
+            if DatetimeTZDtype.is_dtype(categories.dtype):
                 # Avoid future warning.
-                categories = categories.astype(DT64NS_DTYPE)
+                categories = categories.astype("datetime64[ns]")
 
             cat_array = hash_array(np.asarray(categories), categorize=False)
         if ordered:
@@ -1011,11 +1009,7 @@ class IntervalDtype(PandasExtensionDtype):
     _cache: Dict[str_type, PandasExtensionDtype] = {}
 
     def __new__(cls, subtype=None):
-        from pandas.core.dtypes.common import (
-            is_categorical_dtype,
-            is_string_dtype,
-            pandas_dtype,
-        )
+        from pandas.core.dtypes.common import is_string_dtype, pandas_dtype
 
         if isinstance(subtype, IntervalDtype):
             return subtype
@@ -1038,7 +1032,7 @@ def __new__(cls, subtype=None):
             except TypeError as err:
                 raise TypeError("could not construct IntervalDtype") from err
 
-        if is_categorical_dtype(subtype) or is_string_dtype(subtype):
+        if CategoricalDtype.is_dtype(subtype) or is_string_dtype(subtype):
             # GH 19016
             msg = (
                 "category, object, and string subtypes are not supported "

pandas/core/frame.py

@@ -147,6 +147,7 @@
 )
 from pandas.core.reshape.melt import melt
 from pandas.core.series import Series
+from pandas.core.sorting import get_group_index, lexsort_indexer, nargsort
 
 from pandas.io.common import get_filepath_or_buffer
 from pandas.io.formats import console, format as fmt
@@ -5251,8 +5252,6 @@ def duplicated(
         """
         from pandas._libs.hashtable import SIZE_HINT_LIMIT, duplicated_int64
 
-        from pandas.core.sorting import get_group_index
-
         if self.empty:
             return self._constructor_sliced(dtype=bool)
 
@@ -5315,7 +5314,6 @@ def sort_values(  # type: ignore[override]
                 f"Length of ascending ({len(ascending)}) != length of by ({len(by)})"
             )
         if len(by) > 1:
-            from pandas.core.sorting import lexsort_indexer
 
             keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
 
@@ -5328,7 +5326,6 @@ def sort_values(  # type: ignore[override]
             )
             indexer = ensure_platform_int(indexer)
         else:
-            from pandas.core.sorting import nargsort
 
             by = by[0]
             k = self._get_label_or_level_values(by, axis=axis)

pandas/core/generic.py

@@ -34,7 +34,7 @@
 from pandas._config import config
 
 from pandas._libs import lib
-from pandas._libs.tslibs import Tick, Timestamp, to_offset
+from pandas._libs.tslibs import Period, Tick, Timestamp, to_offset
 from pandas._typing import (
     Axis,
     CompressionOptions,
@@ -86,17 +86,21 @@
 from pandas.core.dtypes.missing import isna, notna
 
 import pandas as pd
-from pandas.core import missing, nanops
+from pandas.core import indexing, missing, nanops
 import pandas.core.algorithms as algos
 from pandas.core.base import PandasObject, SelectionMixin
 import pandas.core.common as com
 from pandas.core.construction import create_series_with_explicit_dtype
 from pandas.core.flags import Flags
 from pandas.core.indexes import base as ibase
-from pandas.core.indexes.api import Index, MultiIndex, RangeIndex, ensure_index
-from pandas.core.indexes.datetimes import DatetimeIndex
-from pandas.core.indexes.period import Period, PeriodIndex
-import pandas.core.indexing as indexing
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    ensure_index,
+)
 from pandas.core.internals import BlockManager
 from pandas.core.missing import find_valid_index
 from pandas.core.ops import align_method_FRAME

pandas/core/indexes/base.py

@@ -23,15 +23,13 @@
 from pandas._libs import algos as libalgos, index as libindex, lib
 import pandas._libs.join as libjoin
 from pandas._libs.lib import is_datetime_array, no_default
-from pandas._libs.tslibs import OutOfBoundsDatetime, Timestamp
-from pandas._libs.tslibs.period import IncompatibleFrequency
+from pandas._libs.tslibs import IncompatibleFrequency, OutOfBoundsDatetime, Timestamp
 from pandas._libs.tslibs.timezones import tz_compare
 from pandas._typing import AnyArrayLike, Dtype, DtypeObj, Label
 from pandas.compat.numpy import function as nv
 from pandas.errors import DuplicateLabelError, InvalidIndexError
 from pandas.util._decorators import Appender, cache_readonly, doc
 
-from pandas.core.dtypes import concat as _concat
 from pandas.core.dtypes.cast import (
     maybe_cast_to_integer_array,
     validate_numeric_casting,
@@ -77,7 +75,7 @@
 )
 from pandas.core.dtypes.missing import array_equivalent, isna
 
-from pandas.core import ops
+from pandas.core import missing, ops
 from pandas.core.accessor import CachedAccessor
 import pandas.core.algorithms as algos
 from pandas.core.arrays import Categorical, ExtensionArray
@@ -86,7 +84,6 @@
 import pandas.core.common as com
 from pandas.core.indexers import deprecate_ndim_indexing
 from pandas.core.indexes.frozen import FrozenList
-import pandas.core.missing as missing
 from pandas.core.ops import get_op_result_name
 from pandas.core.ops.invalid import make_invalid_op
 from pandas.core.sorting import ensure_key_mapped, nargsort
@@ -4205,7 +4202,7 @@ def _concat(self, to_concat, name):
         """
         to_concat = [x._values if isinstance(x, Index) else x for x in to_concat]
 
-        result = _concat.concat_compat(to_concat)
+        result = concat_compat(to_concat)
         return Index(result, name=name)
 
     def putmask(self, mask, value):

pandas/core/indexes/multi.py

@@ -3093,7 +3093,6 @@ def get_locs(self, seq):
         >>> mi.get_locs([[True, False, True], slice('e', 'f')])  # doctest: +SKIP
         array([2], dtype=int64)
         """
-        from pandas.core.indexes.numeric import Int64Index
 
         # must be lexsorted to at least as many levels
         true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]

pandas/core/internals/blocks.py

@@ -6,8 +6,7 @@
 
 import numpy as np
 
-from pandas._libs import NaT, algos as libalgos, lib, writers
-import pandas._libs.internals as libinternals
+from pandas._libs import NaT, algos as libalgos, internals as libinternals, lib, writers
 from pandas._libs.internals import BlockPlacement
 from pandas._libs.tslibs import conversion
 from pandas._libs.tslibs.timezones import tz_compare

pandas/core/reshape/merge.py

@@ -11,8 +11,7 @@
 
 import numpy as np
 
-from pandas._libs import Timedelta, hashtable as libhashtable, lib
-import pandas._libs.join as libjoin
+from pandas._libs import Timedelta, hashtable as libhashtable, join as libjoin, lib
 from pandas._typing import ArrayLike, FrameOrSeries, FrameOrSeriesUnion
 from pandas.errors import MergeError
 from pandas.util._decorators import Appender, Substitution

pandas/core/series.py

@@ -67,7 +67,6 @@
     remove_na_arraylike,
 )
 
-import pandas as pd
 from pandas.core import algorithms, base, generic, nanops, ops
 from pandas.core.accessor import CachedAccessor
 from pandas.core.aggregation import aggregate, transform
@@ -76,6 +75,7 @@
 from pandas.core.arrays.sparse import SparseAccessor
 import pandas.core.common as com
 from pandas.core.construction import (
+    array as pd_array,
     create_series_with_explicit_dtype,
     extract_array,
     is_empty_data,
@@ -4193,7 +4193,7 @@ def f(x):
         if len(mapped) and isinstance(mapped[0], Series):
             # GH 25959 use pd.array instead of tolist
             # so extension arrays can be used
-            return self._constructor_expanddim(pd.array(mapped), index=self.index)
+            return self._constructor_expanddim(pd_array(mapped), index=self.index)
         else:
             return self._constructor(mapped, index=self.index).__finalize__(
                 self, method="apply"