diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index e8a1e8173e463..6b4f45fabc665 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -157,13 +157,13 @@ def is_scalar(val: object) -> bool:
 
     return (cnp.PyArray_IsAnyScalar(val)
             # PyArray_IsAnyScalar is always False for bytearrays on Py3
-            or isinstance(val, (Fraction, Number))
-            # We differ from numpy, which claims that None is not scalar;
-            # see np.isscalar
-            or val is None
             or PyDate_Check(val)
             or PyDelta_Check(val)
             or PyTime_Check(val)
+            # We differ from numpy, which claims that None is not scalar;
+            # see np.isscalar
+            or val is None
+            or isinstance(val, (Fraction, Number))
             or util.is_period_object(val)
             or is_decimal(val)
             or is_interval(val)
@@ -1192,7 +1192,9 @@ def infer_dtype(value: object, skipna: object=None) -> str:
         # e.g. categoricals
         try:
             values = getattr(value, '_values', getattr(value, 'values', value))
-        except:
+        except TypeError:
+            # This gets hit if we have an EA, since cython expects `values`
+            #  to be an ndarray
             value = _try_infer_map(value)
             if value is not None:
                 return value
@@ -1208,8 +1210,6 @@ def infer_dtype(value: object, skipna: object=None) -> str:
             construct_1d_object_array_from_listlike)
         values = construct_1d_object_array_from_listlike(value)
 
-    values = getattr(values, 'values', values)
-
     # make contiguous
     values = values.ravel()
 
diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py
index 0934d8529fdf7..bca33513b0069 100644
--- a/pandas/compat/pickle_compat.py
+++ b/pandas/compat/pickle_compat.py
@@ -6,7 +6,6 @@
 import pickle as pkl
 import sys
 
-import pandas  # noqa
 from pandas import Index
 
 
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 2c38e071d3d44..0cfa6df99ff97 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1977,12 +1977,6 @@ def diff(arr, n, axis=0):
             out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
 
     if is_timedelta:
-        from pandas import TimedeltaIndex
-
-        out_arr = (
-            TimedeltaIndex(out_arr.ravel().astype("int64"))
-            .asi8.reshape(out_arr.shape)
-            .astype("timedelta64[ns]")
-        )
+        out_arr = out_arr.astype("int64").view("timedelta64[ns]")
 
     return out_arr
diff --git a/pandas/core/arrays/array_.py b/pandas/core/arrays/array_.py
index 93ee570c1f971..314144db57712 100644
--- a/pandas/core/arrays/array_.py
+++ b/pandas/core/arrays/array_.py
@@ -212,7 +212,6 @@ def array(
     """
     from pandas.core.arrays import (
         period_array,
-        ExtensionArray,
         IntervalArray,
         PandasArray,
         DatetimeArray,
@@ -226,7 +225,7 @@ def array(
 
     data = extract_array(data, extract_numpy=True)
 
-    if dtype is None and isinstance(data, ExtensionArray):
+    if dtype is None and isinstance(data, ABCExtensionArray):
         dtype = data.dtype
 
     # this returns None for not-found dtypes.
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index df5cd12a479f0..0cbcbb1ce4ba4 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -6,7 +6,7 @@
 
 from pandas._config import get_option
 
-from pandas._libs import algos as libalgos, lib
+from pandas._libs import algos as libalgos, hashtable as htable, lib
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     Appender,
@@ -50,7 +50,14 @@
 from pandas.core import ops
 from pandas.core.accessor import PandasDelegate, delegate_names
 import pandas.core.algorithms as algorithms
-from pandas.core.algorithms import factorize, take, take_1d, unique1d
+from pandas.core.algorithms import (
+    _get_data_algo,
+    _hashtables,
+    factorize,
+    take,
+    take_1d,
+    unique1d,
+)
 from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
 import pandas.core.common as com
 from pandas.core.missing import interpolate_2d
@@ -1527,9 +1534,7 @@ def value_counts(self, dropna=True):
         See Also
         --------
         Series.value_counts
-
         """
-        from numpy import bincount
         from pandas import Series, CategoricalIndex
 
         code, cat = self._codes, self.categories
@@ -1538,9 +1543,9 @@ def value_counts(self, dropna=True):
 
         if dropna or clean:
             obs = code if clean else code[mask]
-            count = bincount(obs, minlength=ncat or 0)
+            count = np.bincount(obs, minlength=ncat or 0)
         else:
-            count = bincount(np.where(mask, code, ncat))
+            count = np.bincount(np.where(mask, code, ncat))
             ix = np.append(ix, -1)
 
         ix = self._constructor(ix, dtype=self.dtype, fastpath=True)
@@ -2329,9 +2334,6 @@ def mode(self, dropna=True):
         -------
         modes : `Categorical` (sorted)
         """
-
-        import pandas._libs.hashtable as htable
-
         codes = self._codes
         if dropna:
             good = self._codes != -1
@@ -2671,8 +2673,6 @@ def _get_codes_for_values(values, categories):
     """
     utility routine to turn values into codes given the specified categories
     """
-    from pandas.core.algorithms import _get_data_algo, _hashtables
-
     dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
 
     if dtype_equal:
@@ -2722,8 +2722,6 @@ def _recode_for_categories(codes, old_categories, new_categories):
     >>> _recode_for_categories(codes, old_cat, new_cat)
     array([ 1,  0,  0, -1])
     """
-    from pandas.core.algorithms import take_1d
-
     if len(old_categories) == 0:
         # All null anyway, so just retain the nulls
         return codes.copy()
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index df17388856117..98a745582e11b 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -34,7 +34,12 @@
     is_unsigned_integer_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndexClass,
+    ABCPeriodArray,
+    ABCSeries,
+)
 from pandas.core.dtypes.inference import is_array_like
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
 
@@ -1664,11 +1669,10 @@ def _ensure_datetimelike_to_i8(other, to_utc=False):
     i8 1d array
     """
     from pandas import Index
-    from pandas.core.arrays import PeriodArray
 
     if lib.is_scalar(other) and isna(other):
         return iNaT
-    elif isinstance(other, (PeriodArray, ABCIndexClass, DatetimeLikeArrayMixin)):
+    elif isinstance(other, (ABCPeriodArray, ABCIndexClass, DatetimeLikeArrayMixin)):
         # convert tz if needed
         if getattr(other, "tz", None) is not None:
             if to_utc:
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index 867122964fe59..62b1a8a184946 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -25,6 +25,7 @@
 from pandas.core.dtypes.missing import isna, notna
 
 from pandas.core import nanops, ops
+from pandas.core.algorithms import take
 from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
 from pandas.core.tools.numeric import to_numeric
 
@@ -420,8 +421,6 @@ def __iter__(self):
                 yield self._data[i]
 
     def take(self, indexer, allow_fill=False, fill_value=None):
-        from pandas.api.extensions import take
-
         # we always fill with 1 internally
         # to avoid upcasting
         data_fill_value = 1 if isna(fill_value) else fill_value
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index a0319fe96896a..7f1aad3ba3261 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -25,6 +25,7 @@
 from pandas.core.dtypes.dtypes import IntervalDtype
 from pandas.core.dtypes.generic import (
     ABCDatetimeIndex,
+    ABCIndexClass,
     ABCInterval,
     ABCIntervalIndex,
     ABCPeriodIndex,
@@ -35,7 +36,7 @@
 from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
 from pandas.core.arrays.categorical import Categorical
 import pandas.core.common as com
-from pandas.core.indexes.base import Index, ensure_index
+from pandas.core.indexes.base import ensure_index
 
 _VALID_CLOSED = {"left", "right", "both", "neither"}
 _interval_shared_docs = {}
@@ -510,7 +511,7 @@ def __getitem__(self, value):
         right = self.right[value]
 
         # scalar
-        if not isinstance(left, Index):
+        if not isinstance(left, ABCIndexClass):
             if isna(left):
                 return self._fill_value
             return Interval(left, right, self.closed)
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index 9f428a4ac10b2..77c9a3bc98690 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -11,10 +11,11 @@
 from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
 from pandas.core.dtypes.inference import is_array_like, is_list_like
+from pandas.core.dtypes.missing import isna
 
 from pandas import compat
 from pandas.core import nanops
-from pandas.core.algorithms import searchsorted
+from pandas.core.algorithms import searchsorted, take, unique
 from pandas.core.missing import backfill_1d, pad_1d
 
 from .base import ExtensionArray, ExtensionOpsMixin
@@ -249,8 +250,6 @@ def nbytes(self):
         return self._ndarray.nbytes
 
     def isna(self):
-        from pandas import isna
-
         return isna(self._ndarray)
 
     def fillna(self, value=None, method=None, limit=None):
@@ -281,8 +280,6 @@ def fillna(self, value=None, method=None, limit=None):
         return new_values
 
     def take(self, indices, allow_fill=False, fill_value=None):
-        from pandas.core.algorithms import take
-
         result = take(
             self._ndarray, indices, allow_fill=allow_fill, fill_value=fill_value
         )
@@ -298,8 +295,6 @@ def _values_for_factorize(self):
         return self._ndarray, -1
 
     def unique(self):
-        from pandas import unique
-
         return type(self)(unique(self._ndarray))
 
     # ------------------------------------------------------------------------
diff --git a/pandas/core/arrays/sparse.py b/pandas/core/arrays/sparse.py
index 65976021f5053..9376b49112f6f 100644
--- a/pandas/core/arrays/sparse.py
+++ b/pandas/core/arrays/sparse.py
@@ -105,8 +105,6 @@ class SparseDtype(ExtensionDtype):
     _metadata = ("_dtype", "_fill_value", "_is_na_fill_value")
 
     def __init__(self, dtype: Dtype = np.float64, fill_value: Any = None) -> None:
-        from pandas.core.dtypes.missing import na_value_for_dtype
-        from pandas.core.dtypes.common import pandas_dtype, is_string_dtype, is_scalar
 
         if isinstance(dtype, type(self)):
             if fill_value is None:
@@ -178,20 +176,14 @@ def fill_value(self):
 
     @property
     def _is_na_fill_value(self):
-        from pandas.core.dtypes.missing import isna
-
         return isna(self.fill_value)
 
     @property
     def _is_numeric(self):
-        from pandas.core.dtypes.common import is_object_dtype
-
         return not is_object_dtype(self.subtype)
 
     @property
     def _is_boolean(self):
-        from pandas.core.dtypes.common import is_bool_dtype
-
         return is_bool_dtype(self.subtype)
 
     @property
@@ -928,8 +920,6 @@ def values(self):
         return self.to_dense()
 
     def isna(self):
-        from pandas import isna
-
         # If null fill value, we want SparseDtype[bool, true]
         # to preserve the same memory usage.
         dtype = SparseDtype(bool, self._null_fill_value)
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 9480e2e425f79..f7b3fe723c28c 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -32,6 +32,7 @@
 
 from pandas.core import algorithms, common as com
 from pandas.core.accessor import DirNamesMixin
+from pandas.core.algorithms import duplicated, unique1d, value_counts
 from pandas.core.arrays import ExtensionArray
 import pandas.core.nanops as nanops
 
@@ -1381,8 +1382,6 @@ def value_counts(
         1.0    1
         dtype: int64
         """
-        from pandas.core.algorithms import value_counts
-
         result = value_counts(
             self,
             sort=sort,
@@ -1400,8 +1399,6 @@ def unique(self):
 
             result = values.unique()
         else:
-            from pandas.core.algorithms import unique1d
-
             result = unique1d(values)
 
         return result
@@ -1631,8 +1628,6 @@ def drop_duplicates(self, keep="first", inplace=False):
             return result
 
     def duplicated(self, keep="first"):
-        from pandas.core.algorithms import duplicated
-
         if isinstance(self, ABCIndexClass):
             if self.is_unique:
                 return np.zeros(len(self), dtype=np.bool)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index d2dd0d03d9425..f9a19291b8ad9 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -254,7 +254,6 @@ def asarray_tuplesafe(values, dtype=None):
 
     if result.ndim == 2:
         # Avoid building an array of arrays:
-        # TODO: verify whether any path hits this except #18819 (invalid)
         values = [tuple(x) for x in values]
         result = construct_1d_object_array_from_listlike(values)
 
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 44a3fefb1689a..220a02f2ca35b 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1368,7 +1368,7 @@ def maybe_cast_to_integer_array(arr, dtype, copy=False):
     arr = np.asarray(arr)
 
     if is_unsigned_integer_dtype(dtype) and (arr < 0).any():
-        raise OverflowError("Trying to coerce negative values " "to unsigned integers")
+        raise OverflowError("Trying to coerce negative values to unsigned integers")
 
     if is_integer_dtype(dtype) and (is_float_dtype(arr) or is_object_dtype(arr)):
         raise ValueError("Trying to coerce float values to integers")
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index ac74ad5726a99..ee5aa88cf2907 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -361,9 +361,7 @@ def _maybe_unwrap(x):
             new_codes = np.concatenate(codes)
 
         if sort_categories and not ignore_order and ordered:
-            raise TypeError(
-                "Cannot use sort_categories=True with " "ordered Categoricals"
-            )
+            raise TypeError("Cannot use sort_categories=True with ordered Categoricals")
 
         if sort_categories and not categories.is_monotonic_increasing:
             categories = categories.sort_values()
@@ -386,7 +384,7 @@ def _maybe_unwrap(x):
     else:
         # ordered - to show a proper error message
         if all(c.ordered for c in to_union):
-            msg = "to union ordered Categoricals, " "all categories must be the same"
+            msg = "to union ordered Categoricals, all categories must be the same"
             raise TypeError(msg)
         else:
             raise TypeError("Categorical.ordered must be the same")
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 6728d048efb79..bba551bd30a2d 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -12,7 +12,7 @@
 from pandas.core.dtypes.generic import ABCCategoricalIndex, ABCDateOffset, ABCIndexClass
 
 from .base import ExtensionDtype
-from .inference import is_list_like
+from .inference import is_bool, is_list_like
 
 str_type = str
 
@@ -149,7 +149,7 @@ def __repr__(self) -> str_type:
         return str(self)
 
     def __hash__(self) -> int:
-        raise NotImplementedError("sub-classes should implement an __hash__ " "method")
+        raise NotImplementedError("sub-classes should implement an __hash__ method")
 
     def __getstate__(self) -> Dict[str_type, Any]:
         # pickle support; we don't want to pickle the cache
@@ -320,7 +320,7 @@ def _from_values_or_dtype(
                     raise ValueError(msg.format(dtype=dtype))
             elif categories is not None or ordered is not None:
                 raise ValueError(
-                    "Cannot specify `categories` or `ordered` " "together with `dtype`."
+                    "Cannot specify `categories` or `ordered` together with `dtype`."
                 )
         elif is_categorical(values):
             # If no "dtype" was passed, use the one from "values", but honor
@@ -490,8 +490,6 @@ def validate_ordered(ordered: OrderedType) -> None:
         TypeError
             If 'ordered' is not a boolean.
         """
-        from pandas.core.dtypes.common import is_bool
-
         if not is_bool(ordered):
             raise TypeError("'ordered' must either be 'True' or 'False'")
 
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index bea73d72b91c9..6f599a6be6021 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -3,6 +3,8 @@
 """
 import numpy as np
 
+from pandas._config import get_option
+
 from pandas._libs import lib
 import pandas._libs.missing as libmissing
 from pandas._libs.tslibs import NaT, iNaT
@@ -203,8 +205,6 @@ def _use_inf_as_na(key):
     * http://stackoverflow.com/questions/4859217/
       programmatically-creating-variables-in-python/4859312#4859312
     """
-    from pandas._config import get_option
-
     flag = get_option(key)
     if flag:
         globals()["_isna"] = _isna_old
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index ce14cb22a88ce..d3dacee0468c6 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -33,8 +33,6 @@
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
 
-import pandas.core.common as com
-
 bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
 _BOTTLENECK_INSTALLED = bn is not None
 _USE_BOTTLENECK = False
@@ -281,12 +279,12 @@ def _get_values(
     mask = _maybe_get_mask(values, skipna, mask)
 
     if is_datetime64tz_dtype(values):
-        # com.values_from_object returns M8[ns] dtype instead of tz-aware,
+        # lib.values_from_object returns M8[ns] dtype instead of tz-aware,
         #  so this case must be handled separately from the rest
         dtype = values.dtype
         values = getattr(values, "_values", values)
     else:
-        values = com.values_from_object(values)
+        values = lib.values_from_object(values)
         dtype = values.dtype
 
     if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
@@ -742,7 +740,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
     >>> nanops.nanvar(s)
     1.0
     """
-    values = com.values_from_object(values)
+    values = lib.values_from_object(values)
     dtype = values.dtype
     mask = _maybe_get_mask(values, skipna, mask)
     if is_any_int_dtype(values):
@@ -943,7 +941,7 @@ def nanskew(values, axis=None, skipna=True, mask=None):
     >>> nanops.nanskew(s)
     1.7320508075688787
     """
-    values = com.values_from_object(values)
+    values = lib.values_from_object(values)
     mask = _maybe_get_mask(values, skipna, mask)
     if not is_float_dtype(values.dtype):
         values = values.astype("f8")
@@ -1022,7 +1020,7 @@ def nankurt(values, axis=None, skipna=True, mask=None):
     >>> nanops.nankurt(s)
     -1.2892561983471076
     """
-    values = com.values_from_object(values)
+    values = lib.values_from_object(values)
     mask = _maybe_get_mask(values, skipna, mask)
     if not is_float_dtype(values.dtype):
         values = values.astype("f8")
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 8850cddc45b0d..edb05360a832c 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -7,7 +7,7 @@
 import numpy as np
 
 from pandas._libs import lib
-from pandas._libs.tslibs import NaT, Timestamp
+from pandas._libs.tslibs import NaT, Period, Timestamp
 from pandas._libs.tslibs.frequencies import is_subperiod, is_superperiod
 from pandas._libs.tslibs.period import IncompatibleFrequency
 from pandas.compat.numpy import function as nv
@@ -16,7 +16,6 @@
 
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
-import pandas as pd
 import pandas.core.algorithms as algos
 from pandas.core.generic import _shared_docs
 from pandas.core.groupby.base import GroupByMixin
@@ -25,7 +24,7 @@
 from pandas.core.groupby.grouper import Grouper
 from pandas.core.groupby.ops import BinGrouper
 from pandas.core.indexes.datetimes import DatetimeIndex, date_range
-from pandas.core.indexes.period import PeriodIndex
+from pandas.core.indexes.period import PeriodIndex, period_range
 from pandas.core.indexes.timedeltas import TimedeltaIndex, timedelta_range
 
 from pandas.tseries.frequencies import to_offset
@@ -138,7 +137,7 @@ def _typ(self):
         """
         Masquerade for compat as a Series or a DataFrame.
         """
-        if isinstance(self._selected_obj, pd.Series):
+        if isinstance(self._selected_obj, ABCSeries):
             return "series"
         return "dataframe"
 
@@ -858,7 +857,9 @@ def size(self):
         # a copy of 0-len objects. GH14962
         result = self._downsample("size")
         if not len(self.ax) and isinstance(self._selected_obj, ABCDataFrame):
-            result = pd.Series([], index=result.index, dtype="int64")
+            from pandas import Series
+
+            result = Series([], index=result.index, dtype="int64")
         return result
 
     def quantile(self, q=0.5, **kwargs):
@@ -1559,9 +1560,7 @@ def _get_time_period_bins(self, ax):
             binner = labels = PeriodIndex(data=[], freq=freq, name=ax.name)
             return binner, [], labels
 
-        labels = binner = pd.period_range(
-            start=ax[0], end=ax[-1], freq=freq, name=ax.name
-        )
+        labels = binner = period_range(start=ax[0], end=ax[-1], freq=freq, name=ax.name)
 
         end_stamps = (labels + freq).asfreq(freq, "s").to_timestamp()
         if ax.tzinfo:
@@ -1604,11 +1603,11 @@ def _get_period_bins(self, ax):
             )
 
             # Get offset for bin edge (not label edge) adjustment
-            start_offset = pd.Period(start, self.freq) - pd.Period(p_start, self.freq)
+            start_offset = Period(start, self.freq) - Period(p_start, self.freq)
             bin_shift = start_offset.n % freq_mult
             start = p_start
 
-        labels = binner = pd.period_range(
+        labels = binner = period_range(
             start=start, end=end, freq=self.freq, name=ax.name
         )
 
@@ -1728,7 +1727,7 @@ def _get_period_range_edges(first, last, offset, closed="left", base=0):
     -------
     A tuple of length 2, containing the adjusted pd.Period objects.
     """
-    if not all(isinstance(obj, pd.Period) for obj in [first, last]):
+    if not all(isinstance(obj, Period) for obj in [first, last]):
         raise TypeError("'first' and 'last' must be instances of type Period")
 
     # GH 23882
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index c1a07c129f7cd..3a2e6a8b7ff62 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -1292,8 +1292,6 @@ def _get_join_indexers(left_keys, right_keys, sort=False, how="inner", **kwargs)
         indexers into the left_keys, right_keys
 
     """
-    from functools import partial
-
     assert len(left_keys) == len(
         right_keys
     ), "left_key and right_keys must be the same length"
@@ -1767,7 +1765,6 @@ def flip(xs):
 
 
 def _get_multiindex_indexer(join_keys, index, sort):
-    from functools import partial
 
     # bind `sort` argument
     fkeys = partial(_factorize_keys, sort=sort)
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index 65f6c374d0293..8d5b521ef7799 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -852,7 +852,6 @@ def get_dummies(
     2  0.0  0.0  1.0
     """
     from pandas.core.reshape.concat import concat
-    from itertools import cycle
 
     dtypes_to_encode = ["object", "category"]
 
@@ -881,7 +880,7 @@ def check_len(item, name):
         check_len(prefix_sep, "prefix_sep")
 
         if isinstance(prefix, str):
-            prefix = cycle([prefix])
+            prefix = itertools.cycle([prefix])
         if isinstance(prefix, dict):
             prefix = [prefix[col] for col in data_to_encode.columns]
 
@@ -890,7 +889,7 @@ def check_len(item, name):
 
         # validate separators
         if isinstance(prefix_sep, str):
-            prefix_sep = cycle([prefix_sep])
+            prefix_sep = itertools.cycle([prefix_sep])
         elif isinstance(prefix_sep, dict):
             prefix_sep = [prefix_sep[col] for col in data_to_encode.columns]
 
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index b188bd23cdf32..22b7435b5d29d 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -763,7 +763,7 @@ def _str_extract_noexpand(arr, pat, flags=0):
     Index.
 
     """
-    from pandas import DataFrame, Index
+    from pandas import DataFrame
 
     regex = re.compile(pat, flags=flags)
     groups_or_na = _groups_or_na_fun(regex)
@@ -772,7 +772,7 @@ def _str_extract_noexpand(arr, pat, flags=0):
         result = np.array([groups_or_na(val)[0] for val in arr], dtype=object)
         name = _get_single_group_name(regex)
     else:
-        if isinstance(arr, Index):
+        if isinstance(arr, ABCIndexClass):
             raise ValueError("only one regex group is supported with Index")
         name = None
         names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
@@ -2001,7 +2001,7 @@ def _wrap_result(
             # infer from ndim if expand is not specified
             expand = result.ndim != 1
 
-        elif expand is True and not isinstance(self._orig, Index):
+        elif expand is True and not isinstance(self._orig, ABCIndexClass):
             # required when expand=True is explicitly specified
             # not needed when inferred
 
@@ -2034,7 +2034,7 @@ def cons_row(x):
 
         # Wait until we are sure result is a Series or Index before
         # checking attributes (GH 12180)
-        if isinstance(self._orig, Index):
+        if isinstance(self._orig, ABCIndexClass):
             # if result is a boolean np.array, return the np.array
             # instead of wrapping it into a boolean Index (GH 8875)
             if is_bool_dtype(result):
@@ -2082,10 +2082,10 @@ def _get_series_list(self, others, ignore_index=False):
         # Once str.cat defaults to alignment, this function can be simplified;
         # will not need `ignore_index` and the second boolean output anymore
 
-        from pandas import Index, Series, DataFrame
+        from pandas import Series, DataFrame
 
         # self._orig is either Series or Index
-        idx = self._orig if isinstance(self._orig, Index) else self._orig.index
+        idx = self._orig if isinstance(self._orig, ABCIndexClass) else self._orig.index
 
         err_msg = (
             "others must be Series, Index, DataFrame, np.ndarray or "
@@ -2097,14 +2097,14 @@ def _get_series_list(self, others, ignore_index=False):
         # `idx` of the calling Series/Index - i.e. must have matching length.
         # Objects with an index (i.e. Series/Index/DataFrame) keep their own
         # index, *unless* ignore_index is set to True.
-        if isinstance(others, Series):
+        if isinstance(others, ABCSeries):
             warn = not others.index.equals(idx)
             # only reconstruct Series when absolutely necessary
             los = [
                 Series(others.values, index=idx) if ignore_index and warn else others
             ]
             return (los, warn)
-        elif isinstance(others, Index):
+        elif isinstance(others, ABCIndexClass):
             warn = not others.equals(idx)
             los = [Series(others.values, index=(idx if ignore_index else others))]
             return (los, warn)
@@ -2137,12 +2137,14 @@ def _get_series_list(self, others, ignore_index=False):
                     # only allowing Series/Index/np.ndarray[1-dim] will greatly
                     # simply this function post-deprecation.
                     if not (
-                        isinstance(nxt, (Series, Index))
+                        isinstance(nxt, (Series, ABCIndexClass))
                         or (isinstance(nxt, np.ndarray) and nxt.ndim == 1)
                     ):
                         depr_warn = True
 
-                    if not isinstance(nxt, (DataFrame, Series, Index, np.ndarray)):
+                    if not isinstance(
+                        nxt, (DataFrame, Series, ABCIndexClass, np.ndarray)
+                    ):
                         # safety for non-persistent list-likes (e.g. iterators)
                         # do not map indexed/typed objects; info needed below
                         nxt = list(nxt)
@@ -2150,7 +2152,7 @@ def _get_series_list(self, others, ignore_index=False):
                     # known types for which we can avoid deep inspection
                     no_deep = (
                         isinstance(nxt, np.ndarray) and nxt.ndim == 1
-                    ) or isinstance(nxt, (Series, Index))
+                    ) or isinstance(nxt, (Series, ABCIndexClass))
                     # nested list-likes are forbidden:
                     # -> elements of nxt must not be list-like
                     is_legal = (no_deep and nxt.dtype == object) or all(
@@ -2323,7 +2325,7 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
         if sep is None:
             sep = ""
 
-        if isinstance(self._orig, Index):
+        if isinstance(self._orig, ABCIndexClass):
             data = Series(self._orig, index=self._orig)
         else:  # Series
             data = self._orig
@@ -2409,7 +2411,7 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
             # no NaNs - can just concatenate
             result = cat_safe(all_cols, sep)
 
-        if isinstance(self._orig, Index):
+        if isinstance(self._orig, ABCIndexClass):
             # add dtype for case that result is all-NA
             result = Index(result, dtype=object, name=self._orig.name)
         else:  # Series
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index 07b9598f15902..960fdd89c4147 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -9,6 +9,7 @@
 from pandas._libs.tslibs import (
     NaT,
     OutOfBoundsDatetime,
+    Period,
     Timedelta,
     Timestamp,
     ccalendar,
@@ -33,7 +34,6 @@
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution, cache_readonly
 
-from pandas.core.dtypes.generic import ABCPeriod
 from pandas.core.dtypes.inference import is_list_like
 
 from pandas.core.tools.datetimes import to_datetime
@@ -2537,7 +2537,7 @@ def __add__(self, other):
                 return type(self)(self.n + other.n)
             else:
                 return _delta_to_tick(self.delta + other.delta)
-        elif isinstance(other, ABCPeriod):
+        elif isinstance(other, Period):
             return other + self
         try:
             return self.apply(other)