From dc77ed43d2fefe51f249872ed12030f7cb678bca Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Sun, 28 Jul 2019 09:15:33 -0700
Subject: [PATCH 1/5] cleanup

---
 pandas/core/arrays/base.py               |  13 +-
 pandas/core/arrays/interval.py           |   5 +-
 pandas/core/base.py                      |   4 +-
 pandas/core/dtypes/cast.py               | 152 ++++++++++++++---------
 pandas/core/generic.py                   |  21 ++--
 pandas/core/internals/blocks.py          |  50 ++++++--
 pandas/core/resample.py                  |  10 +-
 pandas/core/window.py                    |   2 +
 pandas/tests/groupby/test_categorical.py |   2 +-
 9 files changed, 149 insertions(+), 110 deletions(-)

diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index ee796f9896b52..29b3efd99089e 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -14,14 +14,17 @@
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution
+from pandas.util._validators import validate_fillna_kwargs
 
-from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.common import is_array_like, is_list_like
 from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndexClass, ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas._typing import ArrayLike
 from pandas.core import ops
+from pandas.core.algorithms import _factorize_array, unique
+from pandas.core.missing import backfill_1d, pad_1d
 from pandas.core.sorting import nargsort
 
 _not_implemented_message = "{} does not implement {}."
@@ -484,10 +487,6 @@ def fillna(self, value=None, method=None, limit=None):
         -------
         filled : ExtensionArray with NA/NaN filled
         """
-        from pandas.api.types import is_array_like
-        from pandas.util._validators import validate_fillna_kwargs
-        from pandas.core.missing import pad_1d, backfill_1d
-
         value, method = validate_fillna_kwargs(value, method)
 
         mask = self.isna()
@@ -584,8 +583,6 @@ def unique(self):
         -------
         uniques : ExtensionArray
         """
-        from pandas import unique
-
         uniques = unique(self.astype(object))
         return self._from_sequence(uniques, dtype=self.dtype)
 
@@ -700,8 +697,6 @@ def factorize(self, na_sentinel: int = -1) -> Tuple[np.ndarray, ABCExtensionArra
         #    original ExtensionArray.
         # 2. ExtensionArray.factorize.
         #    Complete control over factorization.
-        from pandas.core.algorithms import _factorize_array
-
         arr, na_value = self._values_for_factorize()
 
         labels, uniques = _factorize_array(
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 2a0d2c8770063..e87c472dbe88e 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -33,6 +33,7 @@
 )
 from pandas.core.dtypes.missing import isna, notna
 
+from pandas.core.algorithms import take, value_counts
 from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
 from pandas.core.arrays.categorical import Categorical
 import pandas.core.common as com
@@ -796,8 +797,6 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
             When `indices` contains negative values other than ``-1``
             and `allow_fill` is True.
         """
-        from pandas.core.algorithms import take
-
         nv.validate_take(tuple(), kwargs)
 
         fill_left = fill_right = fill_value
@@ -843,8 +842,6 @@ def value_counts(self, dropna=True):
         Series.value_counts
         """
         # TODO: implement this is a non-naive way!
-        from pandas.core.algorithms import value_counts
-
         return value_counts(np.asarray(self), dropna=dropna)
 
     # Formatting
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 89a3d9cfea5ab..29fd7fe096e02 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -4,7 +4,7 @@
 import builtins
 from collections import OrderedDict
 import textwrap
-from typing import Optional
+from typing import Dict, Optional
 import warnings
 
 import numpy as np
@@ -37,7 +37,7 @@
 from pandas.core.arrays import ExtensionArray
 import pandas.core.nanops as nanops
 
-_shared_docs = dict()
+_shared_docs = dict()  # type: Dict[str, str]
 _indexops_doc_kwargs = dict(
     klass="IndexOpsMixin",
     inplace="",
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index fd8536e38eee7..2be910f0d389c 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -95,13 +95,11 @@ def maybe_downcast_to_dtype(result, dtype):
     """ try to cast to the specified dtype (e.g. convert back to bool/int
     or could be an astype of float64->float32
     """
+    do_round = False
 
     if is_scalar(result):
         return result
 
-    def trans(x):
-        return x
-
     if isinstance(dtype, str):
         if dtype == "infer":
             inferred_type = lib.infer_dtype(ensure_object(result.ravel()), skipna=False)
@@ -118,83 +116,115 @@ def trans(x):
             elif inferred_type == "floating":
                 dtype = "int64"
                 if issubclass(result.dtype.type, np.number):
-
-                    def trans(x):  # noqa
-                        return x.round()
+                    do_round = True
 
             else:
                 dtype = "object"
 
-    if isinstance(dtype, str):
         dtype = np.dtype(dtype)
 
-    try:
+    converted = maybe_downcast_numeric(result, dtype, do_round)
+    if converted is not result:
+        return converted
+
+    # a datetimelike
+    # GH12821, iNaT is casted to float
+    if dtype.kind in ["M", "m"] and result.dtype.kind in ["i", "f"]:
+        try:
+            result = result.astype(dtype)
+        except Exception:
+            if dtype.tz:
+                # convert to datetime and change timezone
+                from pandas import to_datetime
+
+                result = to_datetime(result).tz_localize("utc")
+                result = result.tz_convert(dtype.tz)
+
+    elif dtype.type == Period:
+        # TODO(DatetimeArray): merge with previous elif
+        from pandas.core.arrays import PeriodArray
+
+        try:
+            return PeriodArray(result, freq=dtype.freq)
+        except TypeError:
+            # e.g. TypeError: int() argument must be a string, a
+            #  bytes-like object or a number, not 'Period
+            pass
+
+    return result
+
+
+def maybe_downcast_numeric(result, dtype, do_round: bool = False):
+    """
+    Subset of maybe_downcast_to_dtype restricted to numeric dtypes.
+
+    Parameters
+    ----------
+    result : ndarray or ExtensionArray
+    dtype : np.dtype or ExtensionDtype
+    do_round : bool
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    if not isinstance(dtype, np.dtype):
+        # e.g. SparseDtype has no itemsize attr
+        return result
+
+    if isinstance(result, list):
+        # reached via groupoby.agg _ohlc; really this should be handled
+        #  earlier
+        result = np.array(result)
+
+    def trans(x):
+        if do_round:
+            return x.round()
+        return x
 
+    if dtype.kind == result.dtype.kind:
         # don't allow upcasts here (except if empty)
-        if dtype.kind == result.dtype.kind:
-            if result.dtype.itemsize <= dtype.itemsize and np.prod(result.shape):
-                return result
+        if result.dtype.itemsize <= dtype.itemsize and result.size:
+            return result
 
-        if is_bool_dtype(dtype) or is_integer_dtype(dtype):
+    if is_bool_dtype(dtype) or is_integer_dtype(dtype):
 
+        if not result.size:
             # if we don't have any elements, just astype it
-            if not np.prod(result.shape):
-                return trans(result).astype(dtype)
+            return trans(result).astype(dtype)
 
-            # do a test on the first element, if it fails then we are done
-            r = result.ravel()
-            arr = np.array([r[0]])
+        # do a test on the first element, if it fails then we are done
+        r = result.ravel()
+        arr = np.array([r[0]])
 
+        if isna(arr).any() or not np.allclose(arr, trans(arr).astype(dtype), rtol=0):
             # if we have any nulls, then we are done
-            if isna(arr).any() or not np.allclose(
-                arr, trans(arr).astype(dtype), rtol=0
-            ):
-                return result
+            return result
 
+        elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, float, bool)):
             # a comparable, e.g. a Decimal may slip in here
-            elif not isinstance(
-                r[0], (np.integer, np.floating, np.bool, int, float, bool)
-            ):
-                return result
+            return result
 
-            if (
-                issubclass(result.dtype.type, (np.object_, np.number))
-                and notna(result).all()
-            ):
-                new_result = trans(result).astype(dtype)
-                try:
-                    if np.allclose(new_result, result, rtol=0):
-                        return new_result
-                except Exception:
-
-                    # comparison of an object dtype with a number type could
-                    # hit here
-                    if (new_result == result).all():
-                        return new_result
-        elif issubclass(dtype.type, np.floating) and not is_bool_dtype(result.dtype):
-            return result.astype(dtype)
-
-        # a datetimelike
-        # GH12821, iNaT is casted to float
-        elif dtype.kind in ["M", "m"] and result.dtype.kind in ["i", "f"]:
+        if (
+            issubclass(result.dtype.type, (np.object_, np.number))
+            and notna(result).all()
+        ):
+            new_result = trans(result).astype(dtype)
             try:
-                result = result.astype(dtype)
+                if np.allclose(new_result, result, rtol=0):
+                    return new_result
             except Exception:
-                if dtype.tz:
-                    # convert to datetime and change timezone
-                    from pandas import to_datetime
-
-                    result = to_datetime(result).tz_localize("utc")
-                    result = result.tz_convert(dtype.tz)
-
-        elif dtype.type == Period:
-            # TODO(DatetimeArray): merge with previous elif
-            from pandas.core.arrays import PeriodArray
-
-            return PeriodArray(result, freq=dtype.freq)
-
-    except Exception:
-        pass
+                # comparison of an object dtype with a number type could
+                #  hit here
+                if (new_result == result).all():
+                    return new_result
+
+    elif (
+        issubclass(dtype.type, np.floating)
+        and not is_bool_dtype(result.dtype)
+        and not is_string_dtype(result.dtype)
+    ):
+        return result.astype(dtype)
 
     return result
 
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 97a0b04146297..eecb3d1bee0ce 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -5,6 +5,7 @@
 import json
 import operator
 import pickle
+import re
 from textwrap import dedent
 from typing import Callable, Dict, FrozenSet, List, Optional, Set
 import warnings
@@ -4615,8 +4616,6 @@ def filter(self, items=None, like=None, regex=None, axis=None):
                  one  two  three
         rabbit    4    5      6
         """
-        import re
-
         nkw = com.count_not_none(items, like, regex)
         if nkw > 1:
             raise TypeError(
@@ -6193,8 +6192,6 @@ def fillna(
             axis = 0
         axis = self._get_axis_number(axis)
 
-        from pandas import DataFrame
-
         if value is None:
 
             if self._is_mixed_type and axis == 1:
@@ -6257,7 +6254,7 @@ def fillna(
                 new_data = self._data.fillna(
                     value=value, limit=limit, inplace=inplace, downcast=downcast
                 )
-            elif isinstance(value, DataFrame) and self.ndim == 2:
+            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
                 new_data = self.where(self.notna(), value)
             else:
                 raise ValueError("invalid fill value with a %s" % type(value))
@@ -7146,9 +7143,7 @@ def asof(self, where, subset=None):
         2018-02-27 09:04:30   40.0 NaN
         """
         if isinstance(where, str):
-            from pandas import to_datetime
-
-            where = to_datetime(where)
+            where = Timestamp(where)
 
         if not self.index.is_monotonic:
             raise ValueError("asof requires a sorted index")
@@ -8720,12 +8715,10 @@ def align(
         fill_axis=0,
         broadcast_axis=None,
     ):
-        from pandas import DataFrame, Series
-
         method = missing.clean_fill_method(method)
 
         if broadcast_axis == 1 and self.ndim != other.ndim:
-            if isinstance(self, Series):
+            if isinstance(self, ABCSeries):
                 # this means other is a DataFrame, and we need to broadcast
                 # self
                 cons = self._constructor_expanddim
@@ -8743,7 +8736,7 @@ def align(
                     limit=limit,
                     fill_axis=fill_axis,
                 )
-            elif isinstance(other, Series):
+            elif isinstance(other, ABCSeries):
                 # this means self is a DataFrame, and we need to broadcast
                 # other
                 cons = other._constructor_expanddim
@@ -8764,7 +8757,7 @@ def align(
 
         if axis is not None:
             axis = self._get_axis_number(axis)
-        if isinstance(other, DataFrame):
+        if isinstance(other, ABCDataFrame):
             return self._align_frame(
                 other,
                 join=join,
@@ -8776,7 +8769,7 @@ def align(
                 limit=limit,
                 fill_axis=fill_axis,
             )
-        elif isinstance(other, Series):
+        elif isinstance(other, ABCSeries):
             return self._align_series(
                 other,
                 join=join,
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 4ca867b1088e7..8cb55af4b61f5 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -437,7 +437,7 @@ def f(m, v, i):
 
         return self.split_and_operate(mask, f, inplace)
 
-    def split_and_operate(self, mask, f, inplace):
+    def split_and_operate(self, mask, f, inplace: bool):
         """
         split the block per-column, and apply the callable f
         per-column, return a new block for each. Handle
@@ -496,17 +496,17 @@ def make_a_block(nv, ref_loc):
 
         return new_blocks
 
-    def _maybe_downcast(self, blocks, downcast=None):
+    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
 
         # no need to downcast our float
         # unless indicated
-        if downcast is None and self.is_float:
-            return blocks
-        elif downcast is None and (self.is_timedelta or self.is_datetime):
+        assert isinstance(blocks, list), blocks
+
+        if downcast is None and (
+            self.is_float or self.is_timedelta or self.is_datetime
+        ):
             return blocks
 
-        if not isinstance(blocks, list):
-            blocks = [blocks]
         return _extend_blocks([b.downcast(downcast) for b in blocks])
 
     def downcast(self, dtypes=None):
@@ -1355,7 +1355,15 @@ def shift(self, periods, axis=0, fill_value=None):
 
         return [self.make_block(new_values)]
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         """
         evaluate the block; return result block(s) from the result
 
@@ -1454,7 +1462,7 @@ def func(cond, values, other):
             if try_cast:
                 result = self._try_cast_result(result)
 
-            return self.make_block(result)
+            return [self.make_block(result)]
 
         # might need to separate out blocks
         axis = cond.ndim - 1
@@ -1964,7 +1972,15 @@ def shift(
             )
         ]
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         if isinstance(other, ABCDataFrame):
             # ExtensionArrays are 1-D, so if we get here then
             # `other` should be a DataFrame with a single column.
@@ -2009,7 +2025,7 @@ def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0)
                 np.where(cond, self.values, other), dtype=dtype
             )
 
-        return self.make_block_same_class(result, placement=self.mgr_locs)
+        return [self.make_block_same_class(result, placement=self.mgr_locs)]
 
     @property
     def _ftype(self):
@@ -2767,7 +2783,7 @@ def f(m, v, i):
 
         return blocks
 
-    def _maybe_downcast(self, blocks, downcast=None):
+    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
 
         if downcast is not None:
             return blocks
@@ -3102,7 +3118,15 @@ def concat_same_type(self, to_concat, placement=None):
             values, placement=placement or slice(0, len(values), 1), ndim=self.ndim
         )
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         # TODO(CategoricalBlock.where):
         # This can all be deleted in favor of ExtensionBlock.where once
         # we enforce the deprecation.
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 66878c3b1026c..b9ee2001eaaac 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1630,15 +1630,13 @@ def _get_period_bins(self, ax):
 
 
 def _take_new_index(obj, indexer, new_index, axis=0):
-    from pandas.core.api import Series, DataFrame
-
-    if isinstance(obj, Series):
+    if isinstance(obj, ABCSeries):
         new_values = algos.take_1d(obj.values, indexer)
-        return Series(new_values, index=new_index, name=obj.name)
-    elif isinstance(obj, DataFrame):
+        return obj._constructor(new_values, index=new_index, name=obj.name)
+    elif isinstance(obj, ABCDataFrame):
         if axis == 1:
             raise NotImplementedError("axis 1 is not supported")
-        return DataFrame(
+        return obj._constructor(
             obj._data.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
         )
     else:
diff --git a/pandas/core/window.py b/pandas/core/window.py
index 4721d6cfc6dda..e9e69c6dd8faf 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -255,6 +255,8 @@ def _wrap_result(self, result, block=None, obj=None):
             # coerce if necessary
             if block is not None:
                 if is_timedelta64_dtype(block.values.dtype):
+                    # TODO: do we know what result.dtype is at this point?
+                    #  i.e. can we just do an astype?
                     from pandas import to_timedelta
 
                     result = to_timedelta(result.ravel(), unit="ns").values.reshape(
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index 486b3b28b29a3..c2c0af78aa518 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -508,7 +508,7 @@ def test_datetime():
     desc_result = grouped.describe()
 
     idx = cats.codes.argsort()
-    ord_labels = cats.take_nd(idx)
+    ord_labels = cats.take(idx)  # TODO: deprecate take<->take_nd alias
     ord_data = data.take(idx)
     expected = ord_data.groupby(ord_labels, observed=False).describe()
     assert_frame_equal(desc_result, expected)

From 7aa52ef8e90fad7d9d884b4eefe079568062a845 Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Sun, 28 Jul 2019 16:47:48 -0700
Subject: [PATCH 2/5] cleanup

---
 pandas/core/missing.py | 8 ++++----
 pandas/core/sorting.py | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pandas/core/missing.py b/pandas/core/missing.py
index 8f0abc91f7aef..5edf9504157c7 100644
--- a/pandas/core/missing.py
+++ b/pandas/core/missing.py
@@ -119,7 +119,7 @@ def clean_interp_method(method, **kwargs):
         "from_derivatives",
     ]
     if method in ("spline", "polynomial") and order is None:
-        raise ValueError("You must specify the order of the spline or " "polynomial.")
+        raise ValueError("You must specify the order of the spline or polynomial.")
     if method not in valid:
         raise ValueError(
             "method must be one of {valid}. Got '{method}' "
@@ -176,7 +176,7 @@ def interpolate_1d(
     valid_limit_directions = ["forward", "backward", "both"]
     limit_direction = limit_direction.lower()
     if limit_direction not in valid_limit_directions:
-        msg = "Invalid limit_direction: expecting one of {valid!r}, " "got {invalid!r}."
+        msg = "Invalid limit_direction: expecting one of {valid!r}, got {invalid!r}."
         raise ValueError(
             msg.format(valid=valid_limit_directions, invalid=limit_direction)
         )
@@ -322,7 +322,7 @@ def _interpolate_scipy_wrapper(
             alt_methods["pchip"] = interpolate.pchip_interpolate
         except AttributeError:
             raise ImportError(
-                "Your version of Scipy does not support " "PCHIP interpolation."
+                "Your version of Scipy does not support PCHIP interpolation."
             )
     elif method == "akima":
         alt_methods["akima"] = _akima_interpolate
@@ -470,7 +470,7 @@ def interpolate_2d(
     ndim = values.ndim
     if values.ndim == 1:
         if axis != 0:  # pragma: no cover
-            raise AssertionError("cannot interpolate on a ndim == 1 with " "axis != 0")
+            raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
         values = values.reshape(tuple((1,) + values.shape))
 
     if fill_value is None:
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 46e8e97c7de8a..5db31fe6664ea 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -436,7 +436,7 @@ def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False, verify=T
     """
     if not is_list_like(values):
         raise TypeError(
-            "Only list-like objects are allowed to be passed to" "safe_sort as values"
+            "Only list-like objects are allowed to be passed to safe_sort as values"
         )
 
     if not isinstance(values, np.ndarray) and not is_extension_array_dtype(values):

From 93159296a5384847597d4d281678a72d9345381f Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Fri, 2 Aug 2019 08:27:40 -0700
Subject: [PATCH 3/5] revert unrelated changes

---
 pandas/core/generic.py                   |  4 +-
 pandas/core/internals/blocks.py          | 50 ++++++------------------
 pandas/core/resample.py                  | 10 +++--
 pandas/core/window.py                    |  2 -
 pandas/tests/groupby/test_categorical.py |  2 +-
 5 files changed, 23 insertions(+), 45 deletions(-)

diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 68481fc410c70..821c35e0cce2f 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -6190,6 +6190,8 @@ def fillna(
             axis = 0
         axis = self._get_axis_number(axis)
 
+        from pandas import DataFrame
+
         if value is None:
 
             if self._is_mixed_type and axis == 1:
@@ -6252,7 +6254,7 @@ def fillna(
                 new_data = self._data.fillna(
                     value=value, limit=limit, inplace=inplace, downcast=downcast
                 )
-            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
+            elif isinstance(value, DataFrame) and self.ndim == 2:
                 new_data = self.where(self.notna(), value)
             else:
                 raise ValueError("invalid fill value with a %s" % type(value))
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index c7cc9c92e7aa7..6d70fcfb62d52 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -437,7 +437,7 @@ def f(m, v, i):
 
         return self.split_and_operate(mask, f, inplace)
 
-    def split_and_operate(self, mask, f, inplace: bool):
+    def split_and_operate(self, mask, f, inplace):
         """
         split the block per-column, and apply the callable f
         per-column, return a new block for each. Handle
@@ -496,17 +496,17 @@ def make_a_block(nv, ref_loc):
 
         return new_blocks
 
-    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
+    def _maybe_downcast(self, blocks, downcast=None):
 
         # no need to downcast our float
         # unless indicated
-        assert isinstance(blocks, list), blocks
-
-        if downcast is None and (
-            self.is_float or self.is_timedelta or self.is_datetime
-        ):
+        if downcast is None and self.is_float:
+            return blocks
+        elif downcast is None and (self.is_timedelta or self.is_datetime):
             return blocks
 
+        if not isinstance(blocks, list):
+            blocks = [blocks]
         return _extend_blocks([b.downcast(downcast) for b in blocks])
 
     def downcast(self, dtypes=None):
@@ -1351,15 +1351,7 @@ def shift(self, periods, axis=0, fill_value=None):
 
         return [self.make_block(new_values)]
 
-    def where(
-        self,
-        other,
-        cond,
-        align=True,
-        errors="raise",
-        try_cast: bool = False,
-        axis: int = 0,
-    ) -> List["Block"]:
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         """
         evaluate the block; return result block(s) from the result
 
@@ -1458,7 +1450,7 @@ def func(cond, values, other):
             if try_cast:
                 result = self._try_cast_result(result)
 
-            return [self.make_block(result)]
+            return self.make_block(result)
 
         # might need to separate out blocks
         axis = cond.ndim - 1
@@ -1954,15 +1946,7 @@ def shift(
             )
         ]
 
-    def where(
-        self,
-        other,
-        cond,
-        align=True,
-        errors="raise",
-        try_cast: bool = False,
-        axis: int = 0,
-    ) -> List["Block"]:
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         if isinstance(other, ABCDataFrame):
             # ExtensionArrays are 1-D, so if we get here then
             # `other` should be a DataFrame with a single column.
@@ -2007,7 +1991,7 @@ def where(
                 np.where(cond, self.values, other), dtype=dtype
             )
 
-        return [self.make_block_same_class(result, placement=self.mgr_locs)]
+        return self.make_block_same_class(result, placement=self.mgr_locs)
 
     @property
     def _ftype(self):
@@ -2759,7 +2743,7 @@ def f(m, v, i):
 
         return blocks
 
-    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
+    def _maybe_downcast(self, blocks, downcast=None):
 
         if downcast is not None:
             return blocks
@@ -3094,15 +3078,7 @@ def concat_same_type(self, to_concat, placement=None):
             values, placement=placement or slice(0, len(values), 1), ndim=self.ndim
         )
 
-    def where(
-        self,
-        other,
-        cond,
-        align=True,
-        errors="raise",
-        try_cast: bool = False,
-        axis: int = 0,
-    ) -> List["Block"]:
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         # TODO(CategoricalBlock.where):
         # This can all be deleted in favor of ExtensionBlock.where once
         # we enforce the deprecation.
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index b9ee2001eaaac..66878c3b1026c 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1630,13 +1630,15 @@ def _get_period_bins(self, ax):
 
 
 def _take_new_index(obj, indexer, new_index, axis=0):
-    if isinstance(obj, ABCSeries):
+    from pandas.core.api import Series, DataFrame
+
+    if isinstance(obj, Series):
         new_values = algos.take_1d(obj.values, indexer)
-        return obj._constructor(new_values, index=new_index, name=obj.name)
-    elif isinstance(obj, ABCDataFrame):
+        return Series(new_values, index=new_index, name=obj.name)
+    elif isinstance(obj, DataFrame):
         if axis == 1:
             raise NotImplementedError("axis 1 is not supported")
-        return obj._constructor(
+        return DataFrame(
             obj._data.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
         )
     else:
diff --git a/pandas/core/window.py b/pandas/core/window.py
index a7425bc1466c3..4b6a1cf2e9a04 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -265,8 +265,6 @@ def _wrap_result(self, result, block=None, obj=None):
             # coerce if necessary
             if block is not None:
                 if is_timedelta64_dtype(block.values.dtype):
-                    # TODO: do we know what result.dtype is at this point?
-                    #  i.e. can we just do an astype?
                     from pandas import to_timedelta
 
                     result = to_timedelta(result.ravel(), unit="ns").values.reshape(
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index c2c0af78aa518..486b3b28b29a3 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -508,7 +508,7 @@ def test_datetime():
     desc_result = grouped.describe()
 
     idx = cats.codes.argsort()
-    ord_labels = cats.take(idx)  # TODO: deprecate take<->take_nd alias
+    ord_labels = cats.take_nd(idx)
     ord_data = data.take(idx)
     expected = ord_data.groupby(ord_labels, observed=False).describe()
     assert_frame_equal(desc_result, expected)

From 92f04fbcb0c2cdd8a361624cb925000b4ffdfebe Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Fri, 2 Aug 2019 09:02:16 -0700
Subject: [PATCH 4/5] doctest fixup

---
 pandas/core/dtypes/cast.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 2be910f0d389c..af64650cf91da 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -46,6 +46,7 @@
 )
 from .dtypes import DatetimeTZDtype, ExtensionDtype, PeriodDtype
 from .generic import (
+    ABCDataFrame,
     ABCDatetimeArray,
     ABCDatetimeIndex,
     ABCPeriodArray,
@@ -99,6 +100,9 @@ def maybe_downcast_to_dtype(result, dtype):
 
     if is_scalar(result):
         return result
+    elif isinstance(result, ABCDataFrame):
+        # occurs in pivot_table doctest
+        return result
 
     if isinstance(dtype, str):
         if dtype == "infer":

From 44bf1e464bb8dec082deff3bfa5d2f9e8c88513f Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Fri, 2 Aug 2019 09:50:09 -0700
Subject: [PATCH 5/5] == -> is

---
 pandas/core/dtypes/cast.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index af64650cf91da..4bb1deffd9524 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -144,7 +144,7 @@ def maybe_downcast_to_dtype(result, dtype):
                 result = to_datetime(result).tz_localize("utc")
                 result = result.tz_convert(dtype.tz)
 
-    elif dtype.type == Period:
+    elif dtype.type is Period:
         # TODO(DatetimeArray): merge with previous elif
         from pandas.core.arrays import PeriodArray