code: from datetime import datetime, timedelta
  import functools
  import inspect
  import re
  from typing import Any, List
  import warnings
  
  import numpy as np
  
  from pandas._libs import NaT, algos as libalgos, lib, tslib, writers
  from pandas._libs.index import convert_scalar
  import pandas._libs.internals as libinternals
  from pandas._libs.tslibs import Timedelta, conversion
  from pandas._libs.tslibs.timezones import tz_compare
  from pandas.util._validators import validate_bool_kwarg
  
  from pandas.core.dtypes.cast import (
      astype_nansafe,
      find_common_type,
      infer_dtype_from,
      infer_dtype_from_scalar,
      maybe_downcast_numeric,
      maybe_downcast_to_dtype,
      maybe_infer_dtype_type,
      maybe_promote,
      maybe_upcast,
      soft_convert_objects,
  )
  from pandas.core.dtypes.common import (
      _NS_DTYPE,
      _TD_DTYPE,
      ensure_platform_int,
      is_bool_dtype,
      is_categorical,
      is_categorical_dtype,
      is_datetime64_dtype,
      is_datetime64tz_dtype,
      is_dtype_equal,
      is_extension_array_dtype,
      is_float_dtype,
      is_integer,
      is_integer_dtype,
      is_interval_dtype,
      is_list_like,
      is_object_dtype,
      is_period_dtype,
      is_re,
      is_re_compilable,
      is_sparse,
      is_timedelta64_dtype,
      pandas_dtype,
  )
  from pandas.core.dtypes.concat import concat_categorical, concat_datetime
  from pandas.core.dtypes.dtypes import CategoricalDtype, ExtensionDtype
  from pandas.core.dtypes.generic import (
      ABCDataFrame,
      ABCExtensionArray,
      ABCPandasArray,
      ABCSeries,
  )
  from pandas.core.dtypes.missing import (
      _isna_compat,
      array_equivalent,
      is_valid_nat_for_dtype,
      isna,
  )
  
  import pandas.core.algorithms as algos
  from pandas.core.arrays import Categorical, DatetimeArray, PandasDtype, TimedeltaArray
  from pandas.core.base import PandasObject
  import pandas.core.common as com
  from pandas.core.construction import extract_array
  from pandas.core.indexers import (
      check_setitem_lengths,
      is_empty_indexer,
      is_scalar_indexer,
  )
  import pandas.core.missing as missing
  from pandas.core.nanops import nanpercentile
  
  from pandas.io.formats.printing import pprint_thing
  
  
  class Block(PandasObject):
      """
      Canonical n-dimensional unit of homogeneous dtype contained in a pandas
      data structure
  
      Index-ignorant; let the container take care of that
      """
  
      __slots__ = ["_mgr_locs", "values", "ndim"]
      is_numeric = False
      is_float = False
      is_integer = False
      is_complex = False
      is_datetime = False
      is_datetimetz = False
      is_timedelta = False
      is_bool = False
      is_object = False
      is_categorical = False
      is_extension = False
      _can_hold_na = False
      _can_consolidate = True
      _verify_integrity = True
      _validate_ndim = True
      _ftype = "dense"
      _concatenator = staticmethod(np.concatenate)
  
      def __init__(self, values, placement, ndim=None):
          self.ndim = self._check_ndim(values, ndim)
          self.mgr_locs = placement
          self.values = values
  
          if self._validate_ndim and self.ndim and len(self.mgr_locs) != len(self.values):
              raise ValueError(
                  f"Wrong number of items passed {len(self.values)}, "
                  f"placement implies {len(self.mgr_locs)}"
              )
  
      def _check_ndim(self, values, ndim):
          """
          ndim inference and validation.
  
          Infers ndim from 'values' if not provided to __init__.
          Validates that values.ndim and ndim are consistent if and only if
          the class variable '_validate_ndim' is True.
  
          Parameters
          ----------
          values : array-like
          ndim : int or None
  
          Returns
          -------
          ndim : int
  
          Raises
          ------
          ValueError : the number of dimensions do not match
          """
          if ndim is None:
              ndim = values.ndim
  
          if self._validate_ndim and values.ndim != ndim:
              raise ValueError(
                  "Wrong number of dimensions. "
                  f"values.ndim != ndim [{values.ndim} != {ndim}]"
              )
          return ndim
  
      @property
      def _holder(self):
          """The array-like that can hold the underlying values.
  
          None for 'Block', overridden by subclasses that don't
          use an ndarray.
          """
          return None
  
      @property
      def _consolidate_key(self):
          return (self._can_consolidate, self.dtype.name)
  
      @property
      def _is_single_block(self):
          return self.ndim == 1
  
      @property
      def is_view(self):
          """ return a boolean if I am possibly a view """
          return self.values.base is not None
  
      @property
      def is_datelike(self):
          """ return True if I am a non-datelike """
          return self.is_datetime or self.is_timedelta
  
      def is_categorical_astype(self, dtype):
          """
          validate that we have a astypeable to categorical,
          returns a boolean if we are a categorical
          """
          if dtype is Categorical or dtype is CategoricalDtype:
              # this is a pd.Categorical, but is not
              # a valid type for astypeing
              raise TypeError(f"invalid type {dtype} for astype")
  
          elif is_categorical_dtype(dtype):
              return True
  
          return False
  
      def external_values(self, dtype=None):
          """ return an outside world format, currently just the ndarray """
          return self.values
  
      def internal_values(self, dtype=None):
          """ return an internal format, currently just the ndarray
          this should be the pure internal API format
          """
          return self.values
  
      def get_values(self, dtype=None):
          """
          return an internal format, currently just the ndarray
          this is often overridden to handle to_dense like operations
          """
          if is_object_dtype(dtype):
              return self.values.astype(object)
          return self.values
  
      def get_block_values(self, dtype=None):
          """
          This is used in the JSON C code
          """
          return self.get_values(dtype=dtype)
  
      def to_dense(self):
          return self.values.view()
  
      @property
      def fill_value(self):
          return np.nan
  
      @property
      def mgr_locs(self):
          return self._mgr_locs
  
      @mgr_locs.setter
      def mgr_locs(self, new_mgr_locs):
          if not isinstance(new_mgr_locs, libinternals.BlockPlacement):
              new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)
  
          self._mgr_locs = new_mgr_locs
  
      @property
      def array_dtype(self):
          """ the dtype to return if I want to construct this block as an
          array
          """
          return self.dtype
  
      def make_block(self, values, placement=None) -> "Block":
          """
          Create a new block, with type inference propagate any values that are
          not specified
          """
          if placement is None:
              placement = self.mgr_locs
  
          return make_block(values, placement=placement, ndim=self.ndim)
  
      def make_block_same_class(self, values, placement=None, ndim=None):
          """ Wrap given values in a block of same type as self. """
          if placement is None:
              placement = self.mgr_locs
          if ndim is None:
              ndim = self.ndim
          return make_block(values, placement=placement, ndim=ndim, klass=type(self))
  
      def __repr__(self) -> str:
          # don't want to print out all of the items here
          name = type(self).__name__
          if self._is_single_block:
  
              result = f"{name}: {len(self)} dtype: {self.dtype}"
  
          else:
  
              shape = " x ".join(pprint_thing(s) for s in self.shape)
              result = (
                  f"{name}: {pprint_thing(self.mgr_locs.indexer)}, "
                  f"{shape}, dtype: {self.dtype}"
              )
  
          return result
  
      def __len__(self) -> int:
          return len(self.values)
  
      def __getstate__(self):
          return self.mgr_locs.indexer, self.values
  
      def __setstate__(self, state):
          self.mgr_locs = libinternals.BlockPlacement(state[0])
          self.values = state[1]
          self.ndim = self.values.ndim
  
      def _slice(self, slicer):
          """ return a slice of my values """
          return self.values[slicer]
  
      def getitem_block(self, slicer, new_mgr_locs=None):
          """
          Perform __getitem__-like, return result as block.
  
          As of now, only supports slices that preserve dimensionality.
          """
          if new_mgr_locs is None:
              if isinstance(slicer, tuple):
                  axis0_slicer = slicer[0]
              else:
                  axis0_slicer = slicer
              new_mgr_locs = self.mgr_locs[axis0_slicer]
  
          new_values = self._slice(slicer)
  
          if self._validate_ndim and new_values.ndim != self.ndim:
              raise ValueError("Only same dim slicing is allowed")
  
          return self.make_block_same_class(new_values, new_mgr_locs)
  
      @property
      def shape(self):
          return self.values.shape
  
      @property
      def dtype(self):
          return self.values.dtype
  
      @property
      def ftype(self):
          if getattr(self.values, "_pandas_ftype", False):
              dtype = self.dtype.subtype
          else:
              dtype = self.dtype
          return f"{dtype}:{self._ftype}"
  
      def merge(self, other):
          return _merge_blocks([self, other])
  
      def concat_same_type(self, to_concat, placement=None):
          """
          Concatenate list of single blocks of the same type.
          """
          values = self._concatenator(
              [blk.values for blk in to_concat], axis=self.ndim - 1
          )
          return self.make_block_same_class(
              values, placement=placement or slice(0, len(values), 1)
          )
  
      def iget(self, i):
          return self.values[i]
  
      def set(self, locs, values):
          """
          Modify Block in-place with new item value
  
          Returns
          -------
          None
          """
          self.values[locs] = values
  
      def delete(self, loc):
          """
          Delete given loc(-s) from block in-place.
          """
          self.values = np.delete(self.values, loc, 0)
          self.mgr_locs = self.mgr_locs.delete(loc)
  
      def apply(self, func, **kwargs):
          """ apply the function to my values; return a block if we are not
          one
          """
          with np.errstate(all="ignore"):
              result = func(self.values, **kwargs)
  
          if is_extension_array_dtype(result) and result.ndim > 1:
              # if we get a 2D ExtensionArray, we need to split it into 1D pieces
              nbs = []
              for i, loc in enumerate(self.mgr_locs):
                  vals = result[i]
                  nv = _block_shape(vals, ndim=self.ndim)
                  block = self.make_block(values=nv, placement=[loc])
                  nbs.append(block)
              return nbs
  
          if not isinstance(result, Block):
              result = self.make_block(values=_block_shape(result, ndim=self.ndim))
  
          return result
  
      def fillna(self, value, limit=None, inplace=False, downcast=None):
          """ fillna on the block with the value. If we fail, then convert to
          ObjectBlock and try again
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          mask = isna(self.values)
          if limit is not None:
              limit = libalgos._validate_limit(None, limit=limit)
              mask[mask.cumsum(self.ndim - 1) > limit] = False
  
          if not self._can_hold_na:
              if inplace:
                  return self
              else:
                  return self.copy()
  
          if self._can_hold_element(value):
              # equivalent: _try_coerce_args(value) would not raise
              blocks = self.putmask(mask, value, inplace=inplace)
              return self._maybe_downcast(blocks, downcast)
  
          # we can't process the value, but nothing to do
          if not mask.any():
              return self if inplace else self.copy()
  
          # operate column-by-column
          def f(mask, val, idx):
              block = self.coerce_to_target_dtype(value)
  
              # slice out our block
              if idx is not None:
                  # i.e. self.ndim == 2
                  block = block.getitem_block(slice(idx, idx + 1))
              return block.fillna(value, limit=limit, inplace=inplace, downcast=None)
  
          return self.split_and_operate(None, 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
          masking which will not change a block unless needed.
  
          Parameters
          ----------
          mask : 2-d boolean mask
          f : callable accepting (1d-mask, 1d values, indexer)
          inplace : boolean
  
          Returns
          -------
          list of blocks
          """
  
          if mask is None:
              mask = np.broadcast_to(True, shape=self.shape)
  
          new_values = self.values
  
          def make_a_block(nv, ref_loc):
              if isinstance(nv, list):
                  assert len(nv) == 1, nv
                  assert isinstance(nv[0], Block)
                  block = nv[0]
              else:
                  # Put back the dimension that was taken from it and make
                  # a block out of the result.
                  nv = _block_shape(nv, ndim=self.ndim)
                  block = self.make_block(values=nv, placement=ref_loc)
              return block
  
          # ndim == 1
          if self.ndim == 1:
              if mask.any():
                  nv = f(mask, new_values, None)
              else:
                  nv = new_values if inplace else new_values.copy()
              block = make_a_block(nv, self.mgr_locs)
              return [block]
  
          # ndim > 1
          new_blocks = []
          for i, ref_loc in enumerate(self.mgr_locs):
              m = mask[i]
              v = new_values[i]
  
              # need a new block
              if m.any():
                  nv = f(m, v, i)
              else:
                  nv = v if inplace else v.copy()
  
              block = make_a_block(nv, [ref_loc])
              new_blocks.append(block)
  
          return new_blocks
  
      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 or self.is_timedelta or self.is_datetime
          ):
              return blocks
  
          return _extend_blocks([b.downcast(downcast) for b in blocks])
  
      def downcast(self, dtypes=None):
          """ try to downcast each item to the dict of dtypes if present """
  
          # turn it off completely
          if dtypes is False:
              return self
  
          values = self.values
  
          # single block handling
          if self._is_single_block:
  
              # try to cast all non-floats here
              if dtypes is None:
                  dtypes = "infer"
  
              nv = maybe_downcast_to_dtype(values, dtypes)
              return self.make_block(nv)
  
          # ndim > 1
          if dtypes is None:
              return self
  
          if not (dtypes == "infer" or isinstance(dtypes, dict)):
              raise ValueError(
                  "downcast must have a dictionary or 'infer' as its argument"
              )
          elif dtypes != "infer":
              raise AssertionError("dtypes as dict is not supported yet")
  
          # operate column-by-column
          # this is expensive as it splits the blocks items-by-item
          def f(mask, val, idx):
              val = maybe_downcast_to_dtype(val, dtype="infer")
              return val
  
          return self.split_and_operate(None, f, False)
  
      def astype(self, dtype, copy: bool = False, errors: str = "raise"):
          """
          Coerce to the new dtype.
  
          Parameters
          ----------
          dtype : str, dtype convertible
          copy : bool, default False
              copy if indicated
          errors : str, {'raise', 'ignore'}, default 'ignore'
              - ``raise`` : allow exceptions to be raised
              - ``ignore`` : suppress exceptions. On error return original object
  
          Returns
          -------
          Block
          """
          errors_legal_values = ("raise", "ignore")
  
          if errors not in errors_legal_values:
              invalid_arg = (
                  "Expected value of kwarg 'errors' to be one of "
                  f"{list(errors_legal_values)}. Supplied value is '{errors}'"
              )
              raise ValueError(invalid_arg)
  
          if inspect.isclass(dtype) and issubclass(dtype, ExtensionDtype):
              msg = (
                  f"Expected an instance of {dtype.__name__}, "
                  "but got the class instead. Try instantiating 'dtype'."
              )
              raise TypeError(msg)
  
          # may need to convert to categorical
          if self.is_categorical_astype(dtype):
  
              if is_categorical_dtype(self.values):
                  # GH 10696/18593: update an existing categorical efficiently
                  return self.make_block(self.values.astype(dtype, copy=copy))
  
              return self.make_block(Categorical(self.values, dtype=dtype))
  
          dtype = pandas_dtype(dtype)
  
          # astype processing
          if is_dtype_equal(self.dtype, dtype):
              if copy:
                  return self.copy()
              return self
  
          # force the copy here
          if self.is_extension:
              # TODO: Should we try/except this astype?
              values = self.values.astype(dtype)
          else:
              if issubclass(dtype.type, str):
  
                  # use native type formatting for datetime/tz/timedelta
                  if self.is_datelike:
                      values = self.to_native_types()
  
                  # astype formatting
                  else:
                      values = self.get_values()
  
              else:
                  values = self.get_values(dtype=dtype)
  
              # _astype_nansafe works fine with 1-d only
              vals1d = values.ravel()
              try:
                  values = astype_nansafe(vals1d, dtype, copy=True)
              except (ValueError, TypeError):
                  # e.g. astype_nansafe can fail on object-dtype of strings
                  #  trying to convert to float
                  if errors == "raise":
                      raise
                  newb = self.copy() if copy else self
                  return newb
  
          # TODO(extension)
          # should we make this attribute?
          if isinstance(values, np.ndarray):
              values = values.reshape(self.shape)
  
          newb = make_block(values, placement=self.mgr_locs, ndim=self.ndim)
  
          if newb.is_numeric and self.is_numeric:
              if newb.shape != self.shape:
                  raise TypeError(
                      f"cannot set astype for copy = [{copy}] for dtype "
                      f"({self.dtype.name} [{self.shape}]) to different shape "
                      f"({newb.dtype.name} [{newb.shape}])"
                  )
          return newb
  
      def convert(
          self,
          copy: bool = True,
          datetime: bool = True,
          numeric: bool = True,
          timedelta: bool = True,
          coerce: bool = False,
      ):
          """ attempt to coerce any object types to better types return a copy
          of the block (if copy = True) by definition we are not an ObjectBlock
          here!
          """
  
          return self.copy() if copy else self
  
      def _can_hold_element(self, element: Any) -> bool:
          """ require the same dtype as ourselves """
          dtype = self.values.dtype.type
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return issubclass(tipo.type, dtype)
          return isinstance(element, dtype)
  
      def to_native_types(self, slicer=None, na_rep="nan", quoting=None, **kwargs):
          """ convert to our native types format, slicing if desired """
          values = self.get_values()
  
          if slicer is not None:
              values = values[:, slicer]
          mask = isna(values)
          itemsize = writers.word_len(na_rep)
  
          if not self.is_object and not quoting and itemsize:
              values = values.astype(f"<U{itemsize}")
          else:
              values = np.array(values, dtype="object")
  
          values[mask] = na_rep
          return values
  
      # block actions #
      def copy(self, deep=True):
          """ copy constructor """
          values = self.values
          if deep:
              values = values.copy()
          return self.make_block_same_class(values, ndim=self.ndim)
  
      def replace(
          self, to_replace, value, inplace=False, filter=None, regex=False, convert=True
      ):
          """replace the to_replace value with value, possible to create new
          blocks here this is just a call to putmask. regex is not used here.
          It is used in ObjectBlocks.  It is here for API compatibility.
          """
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          original_to_replace = to_replace
  
          # If we cannot replace with own dtype, convert to ObjectBlock and
          # retry
          if not self._can_hold_element(to_replace):
              if not isinstance(to_replace, list):
                  if inplace:
                      return [self]
                  return [self.copy()]
  
              to_replace = [x for x in to_replace if self._can_hold_element(x)]
              if not len(to_replace):
                  # GH#28084 avoid costly checks since we can infer
                  #  that there is nothing to replace in this block
                  if inplace:
                      return [self]
                  return [self.copy()]
  
              if len(to_replace) == 1:
                  # _can_hold_element checks have reduced this back to the
                  #  scalar case and we can avoid a costly object cast
                  return self.replace(
                      to_replace[0],
                      value,
                      inplace=inplace,
                      filter=filter,
                      regex=regex,
                      convert=convert,
                  )
  
              # GH 22083, TypeError or ValueError occurred within error handling
              # causes infinite loop. Cast and retry only if not objectblock.
              if is_object_dtype(self):
                  raise AssertionError
  
              # try again with a compatible block
              block = self.astype(object)
              return block.replace(
                  to_replace=to_replace,
                  value=value,
                  inplace=inplace,
                  filter=filter,
                  regex=regex,
                  convert=convert,
              )
  
          values = self.values
          if lib.is_scalar(to_replace) and isinstance(values, np.ndarray):
              # The only non-DatetimeLike class that also has a non-trivial
              #  try_coerce_args is ObjectBlock, but that overrides replace,
              #  so does not get here.
              to_replace = convert_scalar(values, to_replace)
  
          mask = missing.mask_missing(values, to_replace)
          if filter is not None:
              filtered_out = ~self.mgr_locs.isin(filter)
              mask[filtered_out.nonzero()[0]] = False
  
          if not mask.any():
              if inplace:
                  return [self]
              return [self.copy()]
  
          try:
              blocks = self.putmask(mask, value, inplace=inplace)
              # Note: it is _not_ the case that self._can_hold_element(value)
              #  is always true at this point.  In particular, that can fail
              #  for:
              #   "2u" with bool-dtype, float-dtype
              #   0.5 with int64-dtype
              #   np.nan with int64-dtype
          except (TypeError, ValueError):
              # GH 22083, TypeError or ValueError occurred within error handling
              # causes infinite loop. Cast and retry only if not objectblock.
              if is_object_dtype(self):
                  raise
  
              assert not self._can_hold_element(value), value
  
              # try again with a compatible block
              block = self.astype(object)
              return block.replace(
                  to_replace=original_to_replace,
                  value=value,
                  inplace=inplace,
                  filter=filter,
                  regex=regex,
                  convert=convert,
              )
          if convert:
              blocks = [b.convert(numeric=False, copy=not inplace) for b in blocks]
          return blocks
  
      def _replace_single(self, *args, **kwargs):
          """ no-op on a non-ObjectBlock """
          return self if kwargs["inplace"] else self.copy()
  
      def setitem(self, indexer, value):
          """
          Set the value inplace, returning a a maybe different typed block.
  
          Parameters
          ----------
          indexer : tuple, list-like, array-like, slice
              The subset of self.values to set
          value : object
              The value being set
  
          Returns
          -------
          Block
  
          Notes
          -----
          `indexer` is a direct slice/positional indexer. `value` must
          be a compatible shape.
          """
          transpose = self.ndim == 2
  
          # coerce None values, if appropriate
          if value is None:
              if self.is_numeric:
                  value = np.nan
  
          # coerce if block dtype can store value
          values = self.values
          if self._can_hold_element(value):
              # We only get here for non-Extension Blocks, so _try_coerce_args
              #  is only relevant for DatetimeBlock and TimedeltaBlock
              if lib.is_scalar(value):
                  value = convert_scalar(values, value)
  
          else:
              # current dtype cannot store value, coerce to common dtype
              find_dtype = False
  
              if hasattr(value, "dtype"):
                  dtype = value.dtype
                  find_dtype = True
  
              elif lib.is_scalar(value) and not isna(value):
                  dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
                  find_dtype = True
  
              if find_dtype:
                  dtype = find_common_type([values.dtype, dtype])
                  if not is_dtype_equal(self.dtype, dtype):
                      b = self.astype(dtype)
                      return b.setitem(indexer, value)
  
          # value must be storeable at this moment
          if is_extension_array_dtype(getattr(value, "dtype", None)):
              # We need to be careful not to allow through strings that
              #  can be parsed to EADtypes
              arr_value = value
          else:
              arr_value = np.array(value)
  
          # cast the values to a type that can hold nan (if necessary)
          if not self._can_hold_element(value):
              dtype, _ = maybe_promote(arr_value.dtype)
              values = values.astype(dtype)
  
          if transpose:
              values = values.T
  
          # length checking
          check_setitem_lengths(indexer, value, values)
  
          if is_empty_indexer(indexer, arr_value):
              # GH#8669 empty indexers
              pass
  
          elif is_scalar_indexer(indexer, arr_value):
              # setting a single element for each dim and with a rhs that could
              #  be e.g. a list; see GH#6043
              values[indexer] = value
  
          # if we are an exact match (ex-broadcasting),
          # then use the resultant dtype
          elif (
              len(arr_value.shape)
              and arr_value.shape[0] == values.shape[0]
              and arr_value.size == values.size
          ):
              values[indexer] = value
              try:
                  values = values.astype(arr_value.dtype)
              except ValueError:
                  pass
  
          # set
          else:
              values[indexer] = value
  
          if transpose:
              values = values.T
          block = self.make_block(values)
          return block
  
      def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False):
          """ putmask the data to the block; it is possible that we may create a
          new dtype of block
  
          return the resulting block(s)
  
          Parameters
          ----------
          mask  : the condition to respect
          new : a ndarray/object
          align : boolean, perform alignment on other/cond, default is True
          inplace : perform inplace modification, default is False
          axis : int
          transpose : boolean
              Set to True if self is stored with axes reversed
  
          Returns
          -------
          a list of new blocks, the result of the putmask
          """
  
          new_values = self.values if inplace else self.values.copy()
  
          new = getattr(new, "values", new)
          mask = getattr(mask, "values", mask)
  
          # if we are passed a scalar None, convert it here
          if not is_list_like(new) and isna(new) and not self.is_object:
              # FIXME: make sure we have compatible NA
              new = self.fill_value
  
          if self._can_hold_element(new):
              # We only get here for non-Extension Blocks, so _try_coerce_args
              #  is only relevant for DatetimeBlock and TimedeltaBlock
              if lib.is_scalar(new):
                  new = convert_scalar(new_values, new)
  
              if transpose:
                  new_values = new_values.T
  
              # If the default repeat behavior in np.putmask would go in the
              # wrong direction, then explicitly repeat and reshape new instead
              if getattr(new, "ndim", 0) >= 1:
                  if self.ndim - 1 == new.ndim and axis == 1:
                      new = np.repeat(new, new_values.shape[-1]).reshape(self.shape)
                  new = new.astype(new_values.dtype)
  
              # we require exact matches between the len of the
              # values we are setting (or is compat). np.putmask
              # doesn't check this and will simply truncate / pad
              # the output, but we want sane error messages
              #
              # TODO: this prob needs some better checking
              # for 2D cases
              if (
                  is_list_like(new)
                  and np.any(mask[mask])
                  and getattr(new, "ndim", 1) == 1
              ):
                  if mask[mask].shape[-1] == len(new):
                      # GH 30567
                      # If length of ``new`` is less than the length of ``new_values``,
                      # `np.putmask` would first repeat the ``new`` array and then
                      # assign the masked values hence produces incorrect result.
                      # `np.place` on the other hand uses the ``new`` values at it is
                      # to place in the masked locations of ``new_values``
                      np.place(new_values, mask, new)
                  elif mask.shape[-1] == len(new) or len(new) == 1:
                      np.putmask(new_values, mask, new)
                  else:
                      raise ValueError("cannot assign mismatch length to masked array")
              else:
                  np.putmask(new_values, mask, new)
  
          # maybe upcast me
          elif mask.any():
              if transpose:
                  mask = mask.T
                  if isinstance(new, np.ndarray):
                      new = new.T
                  axis = new_values.ndim - axis - 1
  
              # Pseudo-broadcast
              if getattr(new, "ndim", 0) >= 1:
                  if self.ndim - 1 == new.ndim:
                      new_shape = list(new.shape)
                      new_shape.insert(axis, 1)
                      new = new.reshape(tuple(new_shape))
  
              # operate column-by-column
              def f(mask, val, idx):
  
                  if idx is None:
                      # ndim==1 case.
                      n = new
                  else:
  
                      if isinstance(new, np.ndarray):
                          n = np.squeeze(new[idx % new.shape[0]])
                      else:
                          n = np.array(new)
  
                      # type of the new block
                      dtype, _ = maybe_promote(n.dtype)
  
                      # we need to explicitly astype here to make a copy
                      n = n.astype(dtype)
  
                  nv = _putmask_smart(val, mask, n)
                  return nv
  
              new_blocks = self.split_and_operate(mask, f, inplace)
              return new_blocks
  
          if inplace:
              return [self]
  
          if transpose:
              new_values = new_values.T
  
          return [self.make_block(new_values)]
  
      def coerce_to_target_dtype(self, other):
          """
          coerce the current block to a dtype compat for other
          we will return a block, possibly object, and not raise
  
          we can also safely try to coerce to the same dtype
          and will receive the same block
          """
  
          # if we cannot then coerce to object
          dtype, _ = infer_dtype_from(other, pandas_dtype=True)
  
          if is_dtype_equal(self.dtype, dtype):
              return self
  
          if self.is_bool or is_object_dtype(dtype) or is_bool_dtype(dtype):
              # we don't upcast to bool
              return self.astype(object)
  
          elif (self.is_float or self.is_complex) and (
              is_integer_dtype(dtype) or is_float_dtype(dtype)
          ):
              # don't coerce float/complex to int
              return self
  
          elif (
              self.is_datetime
              or is_datetime64_dtype(dtype)
              or is_datetime64tz_dtype(dtype)
          ):
  
              # not a datetime
              if not (
                  (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype))
                  and self.is_datetime
              ):
                  return self.astype(object)
  
              # don't upcast timezone with different timezone or no timezone
              mytz = getattr(self.dtype, "tz", None)
              othertz = getattr(dtype, "tz", None)
  
              if not tz_compare(mytz, othertz):
                  return self.astype(object)
  
              raise AssertionError(
                  f"possible recursion in coerce_to_target_dtype: {self} {other}"
              )
  
          elif self.is_timedelta or is_timedelta64_dtype(dtype):
  
              # not a timedelta
              if not (is_timedelta64_dtype(dtype) and self.is_timedelta):
                  return self.astype(object)
  
              raise AssertionError(
                  f"possible recursion in coerce_to_target_dtype: {self} {other}"
              )
  
          try:
              return self.astype(dtype)
          except (ValueError, TypeError, OverflowError):
              return self.astype(object)
  
      def interpolate(
          self,
          method="pad",
          axis=0,
          index=None,
          values=None,
          inplace=False,
          limit=None,
          limit_direction="forward",
          limit_area=None,
          fill_value=None,
          coerce=False,
          downcast=None,
          **kwargs,
      ):
  
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          def check_int_bool(self, inplace):
              # Only FloatBlocks will contain NaNs.
              # timedelta subclasses IntBlock
              if (self.is_bool or self.is_integer) and not self.is_timedelta:
                  if inplace:
                      return self
                  else:
                      return self.copy()
  
          # a fill na type method
          try:
              m = missing.clean_fill_method(method)
          except ValueError:
              m = None
  
          if m is not None:
              r = check_int_bool(self, inplace)
              if r is not None:
                  return r
              return self._interpolate_with_fill(
                  method=m,
                  axis=axis,
                  inplace=inplace,
                  limit=limit,
                  fill_value=fill_value,
                  coerce=coerce,
                  downcast=downcast,
              )
          # validate the interp method
          m = missing.clean_interp_method(method, **kwargs)
  
          r = check_int_bool(self, inplace)
          if r is not None:
              return r
          return self._interpolate(
              method=m,
              index=index,
              values=values,
              axis=axis,
              limit=limit,
              limit_direction=limit_direction,
              limit_area=limit_area,
              fill_value=fill_value,
              inplace=inplace,
              downcast=downcast,
              **kwargs,
          )
  
      def _interpolate_with_fill(
          self,
          method="pad",
          axis=0,
          inplace=False,
          limit=None,
          fill_value=None,
          coerce=False,
          downcast=None,
      ):
          """ fillna but using the interpolate machinery """
  
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          # if we are coercing, then don't force the conversion
          # if the block can't hold the type
          if coerce:
              if not self._can_hold_na:
                  if inplace:
                      return [self]
                  else:
                      return [self.copy()]
  
          values = self.values if inplace else self.values.copy()
  
          # We only get here for non-ExtensionBlock
          fill_value = convert_scalar(self.values, fill_value)
  
          values = missing.interpolate_2d(
              values,
              method=method,
              axis=axis,
              limit=limit,
              fill_value=fill_value,
              dtype=self.dtype,
          )
  
          blocks = [self.make_block_same_class(values, ndim=self.ndim)]
          return self._maybe_downcast(blocks, downcast)
  
      def _interpolate(
          self,
          method=None,
          index=None,
          values=None,
          fill_value=None,
          axis=0,
          limit=None,
          limit_direction="forward",
          limit_area=None,
          inplace=False,
          downcast=None,
          **kwargs,
      ):
          """ interpolate using scipy wrappers """
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          data = self.values if inplace else self.values.copy()
  
          # only deal with floats
          if not self.is_float:
              if not self.is_integer:
                  return self
              data = data.astype(np.float64)
  
          if fill_value is None:
              fill_value = self.fill_value
  
          if method in ("krogh", "piecewise_polynomial", "pchip"):
              if not index.is_monotonic:
                  raise ValueError(
                      f"{method} interpolation requires that the index be monotonic."
                  )
          # process 1-d slices in the axis direction
  
          def func(x):
  
              # process a 1-d slice, returning it
              # should the axis argument be handled below in apply_along_axis?
              # i.e. not an arg to missing.interpolate_1d
              return missing.interpolate_1d(
                  index,
                  x,
                  method=method,
                  limit=limit,
                  limit_direction=limit_direction,
                  limit_area=limit_area,
                  fill_value=fill_value,
                  bounds_error=False,
                  **kwargs,
              )
  
          # interp each column independently
          interp_values = np.apply_along_axis(func, axis, data)
  
          blocks = [self.make_block_same_class(interp_values)]
          return self._maybe_downcast(blocks, downcast)
  
      def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None):
          """
          Take values according to indexer and return them as a block.bb
  
          """
  
          # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock
          # so need to preserve types
          # sparse is treated like an ndarray, but needs .get_values() shaping
  
          values = self.values
  
          if fill_tuple is None:
              fill_value = self.fill_value
              allow_fill = False
          else:
              fill_value = fill_tuple[0]
              allow_fill = True
  
          new_values = algos.take_nd(
              values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value
          )
  
          # Called from three places in managers, all of which satisfy
          #  this assertion
          assert not (axis == 0 and new_mgr_locs is None)
          if new_mgr_locs is None:
              new_mgr_locs = self.mgr_locs
  
          if not is_dtype_equal(new_values.dtype, self.dtype):
              return self.make_block(new_values, new_mgr_locs)
          else:
              return self.make_block_same_class(new_values, new_mgr_locs)
  
      def diff(self, n: int, axis: int = 1) -> List["Block"]:
          """ return block for the diff of the values """
          new_values = algos.diff(self.values, n, axis=axis)
          return [self.make_block(values=new_values)]
  
      def shift(self, periods, axis=0, fill_value=None):
          """ shift the block by periods, possibly upcast """
  
          # convert integer to float if necessary. need to do a lot more than
          # that, handle boolean etc also
          new_values, fill_value = maybe_upcast(self.values, fill_value)
  
          # make sure array sent to np.roll is c_contiguous
          f_ordered = new_values.flags.f_contiguous
          if f_ordered:
              new_values = new_values.T
              axis = new_values.ndim - axis - 1
  
          if np.prod(new_values.shape):
              new_values = np.roll(new_values, ensure_platform_int(periods), axis=axis)
  
          axis_indexer = [slice(None)] * self.ndim
          if periods > 0:
              axis_indexer[axis] = slice(None, periods)
          else:
              axis_indexer[axis] = slice(periods, None)
          new_values[tuple(axis_indexer)] = fill_value
  
          # restore original order
          if f_ordered:
              new_values = new_values.T
  
          return [self.make_block(new_values)]
  
      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
  
          Parameters
          ----------
          other : a ndarray/object
          cond  : the condition to respect
          align : boolean, perform alignment on other/cond
          errors : str, {'raise', 'ignore'}, default 'raise'
              - ``raise`` : allow exceptions to be raised
              - ``ignore`` : suppress exceptions. On error return original object
          axis : int
  
          Returns
          -------
          a new block(s), the result of the func
          """
          import pandas.core.computation.expressions as expressions
  
          assert errors in ["raise", "ignore"]
          transpose = self.ndim == 2
  
          values = self.values
          orig_other = other
          if transpose:
              values = values.T
  
          other = getattr(other, "_values", getattr(other, "values", other))
          cond = getattr(cond, "values", cond)
  
          # If the default broadcasting would go in the wrong direction, then
          # explicitly reshape other instead
          if getattr(other, "ndim", 0) >= 1:
              if values.ndim - 1 == other.ndim and axis == 1:
                  other = other.reshape(tuple(other.shape + (1,)))
              elif transpose and values.ndim == self.ndim - 1:
                  cond = cond.T
  
          if not hasattr(cond, "shape"):
              raise ValueError("where must have a condition that is ndarray like")
  
          # our where function
          def func(cond, values, other):
  
              if not (
                  (self.is_integer or self.is_bool)
                  and lib.is_float(other)
                  and np.isnan(other)
              ):
                  # np.where will cast integer array to floats in this case
                  if not self._can_hold_element(other):
                      raise TypeError
                  if lib.is_scalar(other) and isinstance(values, np.ndarray):
                      other = convert_scalar(values, other)
  
              fastres = expressions.where(cond, values, other)
              return fastres
  
          if cond.ravel().all():
              result = values
          else:
              # see if we can operate on the entire block, or need item-by-item
              # or if we are a single block (ndim == 1)
              try:
                  result = func(cond, values, other)
              except TypeError:
  
                  # we cannot coerce, return a compat dtype
                  # we are explicitly ignoring errors
                  block = self.coerce_to_target_dtype(other)
                  blocks = block.where(
                      orig_other,
                      cond,
                      align=align,
                      errors=errors,
                      try_cast=try_cast,
                      axis=axis,
                  )
                  return self._maybe_downcast(blocks, "infer")
  
          if self._can_hold_na or self.ndim == 1:
  
              if transpose:
                  result = result.T
  
              return [self.make_block(result)]
  
          # might need to separate out blocks
          axis = cond.ndim - 1
          cond = cond.swapaxes(axis, 0)
          mask = np.array([cond[i].all() for i in range(cond.shape[0])], dtype=bool)
  
          result_blocks = []
          for m in [mask, ~mask]:
              if m.any():
                  taken = result.take(m.nonzero()[0], axis=axis)
                  r = maybe_downcast_numeric(taken, self.dtype)
                  nb = self.make_block(r.T, placement=self.mgr_locs[m])
                  result_blocks.append(nb)
  
          return result_blocks
  
      def equals(self, other) -> bool:
          if self.dtype != other.dtype or self.shape != other.shape:
              return False
          return array_equivalent(self.values, other.values)
  
      def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
          """Return a list of unstacked blocks of self
  
          Parameters
          ----------
          unstacker_func : callable
              Partially applied unstacker.
          new_columns : Index
              All columns of the unstacked BlockManager.
          n_rows : int
              Only used in ExtensionBlock._unstack
          fill_value : int
              Only used in ExtensionBlock._unstack
  
          Returns
          -------
          blocks : list of Block
              New blocks of unstacked values.
          mask : array_like of bool
              The mask of columns of `blocks` we should keep.
          """
          unstacker = unstacker_func(self.values.T)
          new_items = unstacker.get_new_columns()
          new_placement = new_columns.get_indexer(new_items)
          new_values, mask = unstacker.get_new_values()
  
          mask = mask.any(0)
          new_values = new_values.T[mask]
          new_placement = new_placement[mask]
  
          blocks = [make_block(new_values, placement=new_placement)]
          return blocks, mask
  
      def quantile(self, qs, interpolation="linear", axis=0):
          """
          compute the quantiles of the
  
          Parameters
          ----------
          qs: a scalar or list of the quantiles to be computed
          interpolation: type of interpolation, default 'linear'
          axis: axis to compute, default 0
  
          Returns
          -------
          Block
          """
          # We should always have ndim == 2 because Series dispatches to DataFrame
          assert self.ndim == 2
  
          values = self.get_values()
  
          is_empty = values.shape[axis] == 0
          orig_scalar = not is_list_like(qs)
          if orig_scalar:
              # make list-like, unpack later
              qs = [qs]
  
          if is_empty:
              # create the array of na_values
              # 2d len(values) * len(qs)
              result = np.repeat(
                  np.array([self.fill_value] * len(qs)), len(values)
              ).reshape(len(values), len(qs))
          else:
              # asarray needed for Sparse, see GH#24600
              mask = np.asarray(isna(values))
              result = nanpercentile(
                  values,
                  np.array(qs) * 100,
                  axis=axis,
                  na_value=self.fill_value,
                  mask=mask,
                  ndim=values.ndim,
                  interpolation=interpolation,
              )
  
              result = np.array(result, copy=False)
              result = result.T
  
          if orig_scalar and not lib.is_scalar(result):
              # result could be scalar in case with is_empty and self.ndim == 1
              assert result.shape[-1] == 1, result.shape
              result = result[..., 0]
              result = lib.item_from_zerodim(result)
  
          ndim = np.ndim(result)
          return make_block(result, placement=np.arange(len(result)), ndim=ndim)
  
      def _replace_coerce(
          self, to_replace, value, inplace=True, regex=False, convert=False, mask=None
      ):
          """
          Replace value corresponding to the given boolean array with another
          value.
  
          Parameters
          ----------
          to_replace : object or pattern
              Scalar to replace or regular expression to match.
          value : object
              Replacement object.
          inplace : bool, default False
              Perform inplace modification.
          regex : bool, default False
              If true, perform regular expression substitution.
          convert : bool, default True
              If true, try to coerce any object types to better types.
          mask : array-like of bool, optional
              True indicate corresponding element is ignored.
  
          Returns
          -------
          A new block if there is anything to replace or the original block.
          """
  
          if mask.any():
              if not regex:
                  self = self.coerce_to_target_dtype(value)
                  return self.putmask(mask, value, inplace=inplace)
              else:
                  return self._replace_single(
                      to_replace,
                      value,
                      inplace=inplace,
                      regex=regex,
                      convert=convert,
                      mask=mask,
                  )
          return self
  
  
  class NonConsolidatableMixIn:
      """ hold methods for the nonconsolidatable blocks """
  
      _can_consolidate = False
      _verify_integrity = False
      _validate_ndim = False
  
      def __init__(self, values, placement, ndim=None):
          """Initialize a non-consolidatable block.
  
          'ndim' may be inferred from 'placement'.
  
          This will call continue to call __init__ for the other base
          classes mixed in with this Mixin.
          """
          # Placement must be converted to BlockPlacement so that we can check
          # its length
          if not isinstance(placement, libinternals.BlockPlacement):
              placement = libinternals.BlockPlacement(placement)
  
          # Maybe infer ndim from placement
          if ndim is None:
              if len(placement) != 1:
                  ndim = 1
              else:
                  ndim = 2
          super().__init__(values, placement, ndim=ndim)
  
      @property
      def shape(self):
          if self.ndim == 1:
              return ((len(self.values)),)
          return (len(self.mgr_locs), len(self.values))
  
      def iget(self, col):
  
          if self.ndim == 2 and isinstance(col, tuple):
              col, loc = col
              if not com.is_null_slice(col) and col != 0:
                  raise IndexError(f"{self} only contains one item")
              elif isinstance(col, slice):
                  if col != slice(None):
                      raise NotImplementedError(col)
                  return self.values[[loc]]
              return self.values[loc]
          else:
              if col != 0:
                  raise IndexError(f"{self} only contains one item")
              return self.values
  
      def should_store(self, value):
          return isinstance(value, self._holder)
  
      def set(self, locs, values, check=False):
          assert locs.tolist() == [0]
          self.values = values
  
      def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False):
          """
          putmask the data to the block; we must be a single block and not
          generate other blocks
  
          return the resulting block
  
          Parameters
          ----------
          mask  : the condition to respect
          new : a ndarray/object
          align : boolean, perform alignment on other/cond, default is True
          inplace : perform inplace modification, default is False
  
          Returns
          -------
          a new block, the result of the putmask
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          # use block's copy logic.
          # .values may be an Index which does shallow copy by default
          new_values = self.values if inplace else self.copy().values
  
          if isinstance(new, np.ndarray) and len(new) == len(mask):
              new = new[mask]
  
          mask = _safe_reshape(mask, new_values.shape)
  
          new_values[mask] = new
          return [self.make_block(values=new_values)]
  
      def _get_unstack_items(self, unstacker, new_columns):
          """
          Get the placement, values, and mask for a Block unstack.
  
          This is shared between ObjectBlock and ExtensionBlock. They
          differ in that ObjectBlock passes the values, while ExtensionBlock
          passes the dummy ndarray of positions to be used by a take
          later.
  
          Parameters
          ----------
          unstacker : pandas.core.reshape.reshape._Unstacker
          new_columns : Index
              All columns of the unstacked BlockManager.
  
          Returns
          -------
          new_placement : ndarray[int]
              The placement of the new columns in `new_columns`.
          new_values : Union[ndarray, ExtensionArray]
              The first return value from _Unstacker.get_new_values.
          mask : ndarray[bool]
              The second return value from _Unstacker.get_new_values.
          """
          # shared with ExtensionBlock
          new_items = unstacker.get_new_columns()
          new_placement = new_columns.get_indexer(new_items)
          new_values, mask = unstacker.get_new_values()
  
          mask = mask.any(0)
          return new_placement, new_values, mask
  
  
  class ExtensionBlock(NonConsolidatableMixIn, Block):
      """Block for holding extension types.
  
      Notes
      -----
      This holds all 3rd-party extension array types. It's also the immediate
      parent class for our internal extension types' blocks, CategoricalBlock.
  
      ExtensionArrays are limited to 1-D.
      """
  
      is_extension = True
  
      def __init__(self, values, placement, ndim=None):
          values = self._maybe_coerce_values(values)
          super().__init__(values, placement, ndim)
  
      def _maybe_coerce_values(self, values):
          """
          Unbox to an extension array.
  
          This will unbox an ExtensionArray stored in an Index or Series.
          ExtensionArrays pass through. No dtype coercion is done.
  
          Parameters
          ----------
          values : Index, Series, ExtensionArray
  
          Returns
          -------
          ExtensionArray
          """
          return extract_array(values)
  
      @property
      def _holder(self):
          # For extension blocks, the holder is values-dependent.
          return type(self.values)
  
      @property
      def fill_value(self):
          # Used in reindex_indexer
          return self.values.dtype.na_value
  
      @property
      def _can_hold_na(self):
          # The default ExtensionArray._can_hold_na is True
          return self._holder._can_hold_na
  
      @property
      def is_view(self):
          """Extension arrays are never treated as views."""
          return False
  
      @property
      def is_numeric(self):
          return self.values.dtype._is_numeric
  
      def setitem(self, indexer, value):
          """Set the value inplace, returning a same-typed block.
  
          This differs from Block.setitem by not allowing setitem to change
          the dtype of the Block.
  
          Parameters
          ----------
          indexer : tuple, list-like, array-like, slice
              The subset of self.values to set
          value : object
              The value being set
  
          Returns
          -------
          Block
  
          Notes
          -----
          `indexer` is a direct slice/positional indexer. `value` must
          be a compatible shape.
          """
          if isinstance(indexer, tuple):
              # we are always 1-D
              indexer = indexer[0]
  
          check_setitem_lengths(indexer, value, self.values)
          self.values[indexer] = value
          return self
  
      def get_values(self, dtype=None):
          # ExtensionArrays must be iterable, so this works.
          values = np.asarray(self.values)
          if values.ndim == self.ndim - 1:
              values = values.reshape((1,) + values.shape)
          return values
  
      def to_dense(self):
          return np.asarray(self.values)
  
      def to_native_types(self, slicer=None, na_rep="nan", quoting=None, **kwargs):
          """override to use ExtensionArray astype for the conversion"""
          values = self.values
          if slicer is not None:
              values = values[slicer]
          mask = isna(values)
  
          values = np.asarray(values.astype(object))
          values[mask] = na_rep
  
          # we are expected to return a 2-d ndarray
          return values.reshape(1, len(values))
  
      def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
          """
          Take values according to indexer and return them as a block.
          """
          if fill_tuple is None:
              fill_value = None
          else:
              fill_value = fill_tuple[0]
  
          # axis doesn't matter; we are really a single-dim object
          # but are passed the axis depending on the calling routing
          # if its REALLY axis 0, then this will be a reindex and not a take
          new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
  
          # Called from three places in managers, all of which satisfy
          #  this assertion
          assert not (self.ndim == 1 and new_mgr_locs is None)
          if new_mgr_locs is None:
              new_mgr_locs = self.mgr_locs
  
          return self.make_block_same_class(new_values, new_mgr_locs)
  
      def _can_hold_element(self, element: Any) -> bool:
          # XXX: We may need to think about pushing this onto the array.
          # We're doing the same as CategoricalBlock here.
          return True
  
      def _slice(self, slicer):
          """ return a slice of my values """
  
          # slice the category
          # return same dims as we currently have
  
          if isinstance(slicer, tuple) and len(slicer) == 2:
              if not com.is_null_slice(slicer[0]):
                  raise AssertionError("invalid slicing for a 1-ndim categorical")
              slicer = slicer[1]
  
          return self.values[slicer]
  
      def concat_same_type(self, to_concat, placement=None):
          """
          Concatenate list of single blocks of the same type.
          """
          values = self._holder._concat_same_type([blk.values for blk in to_concat])
          placement = placement or slice(0, len(values), 1)
          return self.make_block_same_class(values, ndim=self.ndim, placement=placement)
  
      def fillna(self, value, limit=None, inplace=False, downcast=None):
          values = self.values if inplace else self.values.copy()
          values = values.fillna(value=value, limit=limit)
          return [
              self.make_block_same_class(
                  values=values, placement=self.mgr_locs, ndim=self.ndim
              )
          ]
  
      def interpolate(
          self, method="pad", axis=0, inplace=False, limit=None, fill_value=None, **kwargs
      ):
  
          values = self.values if inplace else self.values.copy()
          return self.make_block_same_class(
              values=values.fillna(value=fill_value, method=method, limit=limit),
              placement=self.mgr_locs,
          )
  
      def shift(
          self,
          periods: int,
          axis: libinternals.BlockPlacement = 0,
          fill_value: Any = None,
      ) -> List["ExtensionBlock"]:
          """
          Shift the block by `periods`.
  
          Dispatches to underlying ExtensionArray and re-boxes in an
          ExtensionBlock.
          """
          return [
              self.make_block_same_class(
                  self.values.shift(periods=periods, fill_value=fill_value),
                  placement=self.mgr_locs,
                  ndim=self.ndim,
              )
          ]
  
      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.
              assert other.shape[1] == 1
              other = other.iloc[:, 0]
  
          other = extract_array(other, extract_numpy=True)
  
          if isinstance(cond, ABCDataFrame):
              assert cond.shape[1] == 1
              cond = cond.iloc[:, 0]
  
          cond = extract_array(cond, extract_numpy=True)
  
          if lib.is_scalar(other) and isna(other):
              # The default `other` for Series / Frame is np.nan
              # we want to replace that with the correct NA value
              # for the type
              other = self.dtype.na_value
  
          if is_sparse(self.values):
              # TODO(SparseArray.__setitem__): remove this if condition
              # We need to re-infer the type of the data after doing the
              # where, for cases where the subtypes don't match
              dtype = None
          else:
              dtype = self.dtype
  
          result = self.values.copy()
          icond = ~cond
          if lib.is_scalar(other):
              set_other = other
          else:
              set_other = other[icond]
          try:
              result[icond] = set_other
          except (NotImplementedError, TypeError):
              # NotImplementedError for class not implementing `__setitem__`
              # TypeError for SparseArray, which implements just to raise
              # a TypeError
              result = self._holder._from_sequence(
                  np.where(cond, self.values, other), dtype=dtype
              )
  
          return [self.make_block_same_class(result, placement=self.mgr_locs)]
  
      @property
      def _ftype(self):
          return getattr(self.values, "_pandas_ftype", Block._ftype)
  
      def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
          # ExtensionArray-safe unstack.
          # We override ObjectBlock._unstack, which unstacks directly on the
          # values of the array. For EA-backed blocks, this would require
          # converting to a 2-D ndarray of objects.
          # Instead, we unstack an ndarray of integer positions, followed by
          # a `take` on the actual values.
          dummy_arr = np.arange(n_rows)
          dummy_unstacker = functools.partial(unstacker_func, fill_value=-1)
          unstacker = dummy_unstacker(dummy_arr)
  
          new_placement, new_values, mask = self._get_unstack_items(
              unstacker, new_columns
          )
  
          blocks = [
              self.make_block_same_class(
                  self.values.take(indices, allow_fill=True, fill_value=fill_value),
                  [place],
              )
              for indices, place in zip(new_values.T, new_placement)
          ]
          return blocks, mask
  
  
  class ObjectValuesExtensionBlock(ExtensionBlock):
      """
      Block providing backwards-compatibility for `.values`.
  
      Used by PeriodArray and IntervalArray to ensure that
      Series[T].values is an ndarray of objects.
      """
  
      def external_values(self, dtype=None):
          return self.values.astype(object)
  
  
  class NumericBlock(Block):
      __slots__ = ()
      is_numeric = True
      _can_hold_na = True
  
  
  class FloatOrComplexBlock(NumericBlock):
      __slots__ = ()
  
      def equals(self, other) -> bool:
          if self.dtype != other.dtype or self.shape != other.shape:
              return False
          left, right = self.values, other.values
          return ((left == right) | (np.isnan(left) & np.isnan(right))).all()
  
  
  class FloatBlock(FloatOrComplexBlock):
      __slots__ = ()
      is_float = True
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return issubclass(tipo.type, (np.floating, np.integer)) and not issubclass(
                  tipo.type, (np.datetime64, np.timedelta64)
              )
          return isinstance(
              element, (float, int, np.floating, np.int_)
          ) and not isinstance(
              element,
              (bool, np.bool_, datetime, timedelta, np.datetime64, np.timedelta64),
          )
  
      def to_native_types(
          self,
          slicer=None,
          na_rep="",
          float_format=None,
          decimal=".",
          quoting=None,
          **kwargs,
      ):
          """ convert to our native types format, slicing if desired """
  
          values = self.values
          if slicer is not None:
              values = values[:, slicer]
  
          # see gh-13418: no special formatting is desired at the
          # output (important for appropriate 'quoting' behaviour),
          # so do not pass it through the FloatArrayFormatter
          if float_format is None and decimal == ".":
              mask = isna(values)
  
              if not quoting:
                  values = values.astype(str)
              else:
                  values = np.array(values, dtype="object")
  
              values[mask] = na_rep
              return values
  
          from pandas.io.formats.format import FloatArrayFormatter
  
          formatter = FloatArrayFormatter(
              values,
              na_rep=na_rep,
              float_format=float_format,
              decimal=decimal,
              quoting=quoting,
              fixed_width=False,
          )
          return formatter.get_result_as_array()
  
      def should_store(self, value):
          # when inserting a column should not coerce integers to floats
          # unnecessarily
          return issubclass(value.dtype.type, np.floating) and value.dtype == self.dtype
  
  
  class ComplexBlock(FloatOrComplexBlock):
      __slots__ = ()
      is_complex = True
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return issubclass(tipo.type, (np.floating, np.integer, np.complexfloating))
          return isinstance(
              element, (float, int, complex, np.float_, np.int_)
          ) and not isinstance(element, (bool, np.bool_))
  
      def should_store(self, value):
          return issubclass(value.dtype.type, np.complexfloating)
  
  
  class IntBlock(NumericBlock):
      __slots__ = ()
      is_integer = True
      _can_hold_na = False
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return (
                  issubclass(tipo.type, np.integer)
                  and not issubclass(tipo.type, (np.datetime64, np.timedelta64))
                  and self.dtype.itemsize >= tipo.itemsize
              )
          return is_integer(element)
  
      def should_store(self, value):
          return is_integer_dtype(value) and value.dtype == self.dtype
  
  
  class DatetimeLikeBlockMixin:
      """Mixin class for DatetimeBlock, DatetimeTZBlock, and TimedeltaBlock."""
  
      @property
      def _holder(self):
          return DatetimeArray
  
      @property
      def fill_value(self):
          return np.datetime64("NaT", "ns")
  
      def get_values(self, dtype=None):
          """
          return object dtype as boxed values, such as Timestamps/Timedelta
          """
          if is_object_dtype(dtype):
              values = self.values.ravel()
              result = self._holder(values).astype(object)
              return result.reshape(self.values.shape)
          return self.values
  
  
  class DatetimeBlock(DatetimeLikeBlockMixin, Block):
      __slots__ = ()
      is_datetime = True
  
      def __init__(self, values, placement, ndim=None):
          values = self._maybe_coerce_values(values)
          super().__init__(values, placement=placement, ndim=ndim)
  
      @property
      def _can_hold_na(self):
          return True
  
      def _maybe_coerce_values(self, values):
          """
          Input validation for values passed to __init__. Ensure that
          we have datetime64ns, coercing if necessary.
  
          Parameters
          ----------
          values : array-like
              Must be convertible to datetime64
  
          Returns
          -------
          values : ndarray[datetime64ns]
  
          Overridden by DatetimeTZBlock.
          """
          if values.dtype != _NS_DTYPE:
              values = conversion.ensure_datetime64ns(values)
  
          if isinstance(values, DatetimeArray):
              values = values._data
  
          assert isinstance(values, np.ndarray), type(values)
          return values
  
      def astype(self, dtype, copy: bool = False, errors: str = "raise"):
          """
          these automatically copy, so copy=True has no effect
          raise on an except if raise == True
          """
          dtype = pandas_dtype(dtype)
  
          # if we are passed a datetime64[ns, tz]
          if is_datetime64tz_dtype(dtype):
              values = self.values
              if getattr(values, "tz", None) is None:
                  values = DatetimeArray(values).tz_localize("UTC")
              values = values.tz_convert(dtype.tz)
              return self.make_block(values)
  
          # delegate
          return super().astype(dtype=dtype, copy=copy, errors=errors)
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              if self.is_datetimetz:
                  # require exact match, since non-nano does not exist
                  return is_dtype_equal(tipo, self.dtype) or is_valid_nat_for_dtype(
                      element, self.dtype
                  )
  
              # GH#27419 if we get a non-nano datetime64 object
              return is_datetime64_dtype(tipo)
          elif element is NaT:
              return True
          elif isinstance(element, datetime):
              if self.is_datetimetz:
                  return tz_compare(element.tzinfo, self.dtype.tz)
              return element.tzinfo is None
  
          return is_valid_nat_for_dtype(element, self.dtype)
  
      def to_native_types(
          self, slicer=None, na_rep=None, date_format=None, quoting=None, **kwargs
      ):
          """ convert to our native types format, slicing if desired """
  
          values = self.values
          i8values = self.values.view("i8")
  
          if slicer is not None:
              values = values[..., slicer]
              i8values = i8values[..., slicer]
  
          from pandas.io.formats.format import _get_format_datetime64_from_values
  
          fmt = _get_format_datetime64_from_values(values, date_format)
  
          result = tslib.format_array_from_datetime(
              i8values.ravel(),
              tz=getattr(self.values, "tz", None),
              format=fmt,
              na_rep=na_rep,
          ).reshape(i8values.shape)
          return np.atleast_2d(result)
  
      def should_store(self, value):
          return (
              issubclass(value.dtype.type, np.datetime64)
              and not is_datetime64tz_dtype(value)
              and not is_extension_array_dtype(value)
          )
  
      def set(self, locs, values):
          """
          Modify Block in-place with new item value
  
          Returns
          -------
          None
          """
          values = conversion.ensure_datetime64ns(values, copy=False)
  
          self.values[locs] = values
  
      def external_values(self):
          return np.asarray(self.values.astype("datetime64[ns]", copy=False))
  
  
  class DatetimeTZBlock(ExtensionBlock, DatetimeBlock):
      """ implement a datetime64 block with a tz attribute """
  
      __slots__ = ()
      is_datetimetz = True
      is_extension = True
  
      _can_hold_element = DatetimeBlock._can_hold_element
      to_native_types = DatetimeBlock.to_native_types
      fill_value = np.datetime64("NaT", "ns")
  
      @property
      def _holder(self):
          return DatetimeArray
  
      def _maybe_coerce_values(self, values):
          """Input validation for values passed to __init__. Ensure that
          we have datetime64TZ, coercing if necessary.
  
          Parameters
          ----------
          values : array-like
              Must be convertible to datetime64
  
          Returns
          -------
          values : DatetimeArray
          """
          if not isinstance(values, self._holder):
              values = self._holder(values)
  
          if values.tz is None:
              raise ValueError("cannot create a DatetimeTZBlock without a tz")
  
          return values
  
      @property
      def is_view(self):
          """ return a boolean if I am possibly a view """
          # check the ndarray values of the DatetimeIndex values
          return self.values._data.base is not None
  
      def get_values(self, dtype=None):
          """
          Returns an ndarray of values.
  
          Parameters
          ----------
          dtype : np.dtype
              Only `object`-like dtypes are respected here (not sure
              why).
  
          Returns
          -------
          values : ndarray
              When ``dtype=object``, then and object-dtype ndarray of
              boxed values is returned. Otherwise, an M8[ns] ndarray
              is returned.
  
              DatetimeArray is always 1-d. ``get_values`` will reshape
              the return value to be the same dimensionality as the
              block.
          """
          values = self.values
          if is_object_dtype(dtype):
              values = values.astype(object)
  
          values = np.asarray(values)
  
          if self.ndim == 2:
              # Ensure that our shape is correct for DataFrame.
              # ExtensionArrays are always 1-D, even in a DataFrame when
              # the analogous NumPy-backed column would be a 2-D ndarray.
              values = values.reshape(1, -1)
          return values
  
      def to_dense(self):
          # we request M8[ns] dtype here, even though it discards tzinfo,
          # as lots of code (e.g. anything using values_from_object)
          # expects that behavior.
          return np.asarray(self.values, dtype=_NS_DTYPE)
  
      def _slice(self, slicer):
          """ return a slice of my values """
          if isinstance(slicer, tuple):
              col, loc = slicer
              if not com.is_null_slice(col) and col != 0:
                  raise IndexError(f"{self} only contains one item")
              return self.values[loc]
          return self.values[slicer]
  
      def diff(self, n: int, axis: int = 0) -> List["Block"]:
          """
          1st discrete difference.
  
          Parameters
          ----------
          n : int
              Number of periods to diff.
          axis : int, default 0
              Axis to diff upon.
  
          Returns
          -------
          A list with a new TimeDeltaBlock.
  
          Notes
          -----
          The arguments here are mimicking shift so they are called correctly
          by apply.
          """
          if axis == 0:
              # Cannot currently calculate diff across multiple blocks since this
              # function is invoked via apply
              raise NotImplementedError
          new_values = (self.values - self.shift(n, axis=axis)[0].values).asi8
  
          # Reshape the new_values like how algos.diff does for timedelta data
          new_values = new_values.reshape(1, len(new_values))
          new_values = new_values.astype("timedelta64[ns]")
          return [TimeDeltaBlock(new_values, placement=self.mgr_locs.indexer)]
  
      def concat_same_type(self, to_concat, placement=None):
          # need to handle concat([tz1, tz2]) here, since DatetimeArray
          # only handles cases where all the tzs are the same.
          # Instead of placing the condition here, it could also go into the
          # is_uniform_join_units check, but I'm not sure what is better.
          if len({x.dtype for x in to_concat}) > 1:
              values = concat_datetime([x.values for x in to_concat])
              placement = placement or slice(0, len(values), 1)
  
              if self.ndim > 1:
                  values = np.atleast_2d(values)
              return ObjectBlock(values, ndim=self.ndim, placement=placement)
          return super().concat_same_type(to_concat, placement)
  
      def fillna(self, value, limit=None, inplace=False, downcast=None):
          # We support filling a DatetimeTZ with a `value` whose timezone
          # is different by coercing to object.
          if self._can_hold_element(value):
              return super().fillna(value, limit, inplace, downcast)
  
          # different timezones, or a non-tz
          return self.astype(object).fillna(
              value, limit=limit, inplace=inplace, downcast=downcast
          )
  
      def setitem(self, indexer, value):
          # https://github.com/pandas-dev/pandas/issues/24020
          # Need a dedicated setitem until #24020 (type promotion in setitem
          # for extension arrays) is designed and implemented.
          if self._can_hold_element(value) or (
              isinstance(indexer, np.ndarray) and indexer.size == 0
          ):
              return super().setitem(indexer, value)
  
          obj_vals = self.values.astype(object)
          newb = make_block(
              obj_vals, placement=self.mgr_locs, klass=ObjectBlock, ndim=self.ndim
          )
          return newb.setitem(indexer, value)
  
      def equals(self, other) -> bool:
          # override for significant performance improvement
          if self.dtype != other.dtype or self.shape != other.shape:
              return False
          return (self.values.view("i8") == other.values.view("i8")).all()
  
      def quantile(self, qs, interpolation="linear", axis=0):
          naive = self.values.view("M8[ns]")
  
          # kludge for 2D block with 1D values
          naive = naive.reshape(self.shape)
  
          blk = self.make_block(naive)
          res_blk = blk.quantile(qs, interpolation=interpolation, axis=axis)
  
          # ravel is kludge for 2D block with 1D values, assumes column-like
          aware = self._holder(res_blk.values.ravel(), dtype=self.dtype)
          return self.make_block_same_class(aware, ndim=res_blk.ndim)
  
  
  class TimeDeltaBlock(DatetimeLikeBlockMixin, IntBlock):
      __slots__ = ()
      is_timedelta = True
      _can_hold_na = True
      is_numeric = False
      fill_value = np.timedelta64("NaT", "ns")
  
      def __init__(self, values, placement, ndim=None):
          if values.dtype != _TD_DTYPE:
              values = conversion.ensure_timedelta64ns(values)
          if isinstance(values, TimedeltaArray):
              values = values._data
          assert isinstance(values, np.ndarray), type(values)
          super().__init__(values, placement=placement, ndim=ndim)
  
      @property
      def _holder(self):
          return TimedeltaArray
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return issubclass(tipo.type, np.timedelta64)
          elif element is NaT:
              return True
          elif isinstance(element, (timedelta, np.timedelta64)):
              return True
          return is_valid_nat_for_dtype(element, self.dtype)
  
      def fillna(self, value, **kwargs):
  
          # allow filling with integers to be
          # interpreted as nanoseconds
          if is_integer(value):
              # Deprecation GH#24694, GH#19233
              raise TypeError(
                  "Passing integers to fillna for timedelta64[ns] dtype is no "
                  "longer supported.  To obtain the old behavior, pass "
                  "`pd.Timedelta(seconds=n)` instead."
              )
          return super().fillna(value, **kwargs)
  
      def should_store(self, value):
          return issubclass(
              value.dtype.type, np.timedelta64
          ) and not is_extension_array_dtype(value)
  
      def to_native_types(self, slicer=None, na_rep=None, quoting=None, **kwargs):
          """ convert to our native types format, slicing if desired """
  
          values = self.values
          if slicer is not None:
              values = values[:, slicer]
          mask = isna(values)
  
          rvalues = np.empty(values.shape, dtype=object)
          if na_rep is None:
              na_rep = "NaT"
          rvalues[mask] = na_rep
          imask = (~mask).ravel()
  
          # FIXME:
          # should use the formats.format.Timedelta64Formatter here
          # to figure what format to pass to the Timedelta
          # e.g. to not show the decimals say
          rvalues.flat[imask] = np.array(
              [Timedelta(val)._repr_base(format="all") for val in values.ravel()[imask]],
              dtype=object,
          )
          return rvalues
  
      def external_values(self, dtype=None):
          return np.asarray(self.values.astype("timedelta64[ns]", copy=False))
  
  
  class BoolBlock(NumericBlock):
      __slots__ = ()
      is_bool = True
      _can_hold_na = False
  
      def _can_hold_element(self, element: Any) -> bool:
          tipo = maybe_infer_dtype_type(element)
          if tipo is not None:
              return issubclass(tipo.type, np.bool_)
          return isinstance(element, (bool, np.bool_))
  
      def should_store(self, value):
          return issubclass(value.dtype.type, np.bool_) and not is_extension_array_dtype(
              value
          )
  
      def replace(
          self, to_replace, value, inplace=False, filter=None, regex=False, convert=True
      ):
          inplace = validate_bool_kwarg(inplace, "inplace")
          to_replace_values = np.atleast_1d(to_replace)
          if not np.can_cast(to_replace_values, bool):
              return self
          return super().replace(
              to_replace,
              value,
              inplace=inplace,
              filter=filter,
              regex=regex,
              convert=convert,
          )
  
  
  class ObjectBlock(Block):
      __slots__ = ()
      is_object = True
      _can_hold_na = True
  
      def __init__(self, values, placement=None, ndim=2):
          if issubclass(values.dtype.type, str):
              values = np.array(values, dtype=object)
  
          super().__init__(values, ndim=ndim, placement=placement)
  
      @property
      def is_bool(self):
          """ we can be a bool if we have only bool values but are of type
          object
          """
          return lib.is_bool_array(self.values.ravel())
  
      def convert(
          self,
          copy: bool = True,
          datetime: bool = True,
          numeric: bool = True,
          timedelta: bool = True,
          coerce: bool = False,
      ):
          """ attempt to coerce any object types to better types return a copy of
          the block (if copy = True) by definition we ARE an ObjectBlock!!!!!
  
          can return multiple blocks!
          """
  
          # operate column-by-column
          def f(mask, val, idx):
              shape = val.shape
              values = soft_convert_objects(
                  val.ravel(),
                  datetime=datetime,
                  numeric=numeric,
                  timedelta=timedelta,
                  coerce=coerce,
                  copy=copy,
              )
              if isinstance(values, np.ndarray):
                  # TODO: allow EA once reshape is supported
                  values = values.reshape(shape)
  
              values = _block_shape(values, ndim=self.ndim)
              return values
  
          if self.ndim == 2:
              blocks = self.split_and_operate(None, f, False)
          else:
              values = f(None, self.values.ravel(), None)
              blocks = [make_block(values, ndim=self.ndim, placement=self.mgr_locs)]
  
          return blocks
  
      def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
  
          if downcast is not None:
              return blocks
  
          # split and convert the blocks
          return _extend_blocks([b.convert(datetime=True, numeric=False) for b in blocks])
  
      def _can_hold_element(self, element: Any) -> bool:
          return True
  
      def should_store(self, value):
          return not (
              issubclass(
                  value.dtype.type,
                  (np.integer, np.floating, np.complexfloating, np.datetime64, np.bool_),
              )
              or is_extension_array_dtype(value)
          )
  
      def replace(
          self, to_replace, value, inplace=False, filter=None, regex=False, convert=True
      ):
          to_rep_is_list = is_list_like(to_replace)
          value_is_list = is_list_like(value)
          both_lists = to_rep_is_list and value_is_list
          either_list = to_rep_is_list or value_is_list
  
          result_blocks = []
          blocks = [self]
  
          if not either_list and is_re(to_replace):
              return self._replace_single(
                  to_replace,
                  value,
                  inplace=inplace,
                  filter=filter,
                  regex=True,
                  convert=convert,
              )
          elif not (either_list or regex):
              return super().replace(
                  to_replace,
                  value,
                  inplace=inplace,
                  filter=filter,
                  regex=regex,
                  convert=convert,
              )
          elif both_lists:
              for to_rep, v in zip(to_replace, value):
                  result_blocks = []
                  for b in blocks:
                      result = b._replace_single(
                          to_rep,
                          v,
                          inplace=inplace,
                          filter=filter,
                          regex=regex,
                          convert=convert,
                      )
                      result_blocks = _extend_blocks(result, result_blocks)
                  blocks = result_blocks
              return result_blocks
  
          elif to_rep_is_list and regex:
              for to_rep in to_replace:
                  result_blocks = []
                  for b in blocks:
                      result = b._replace_single(
                          to_rep,
                          value,
                          inplace=inplace,
                          filter=filter,
                          regex=regex,
                          convert=convert,
                      )
                      result_blocks = _extend_blocks(result, result_blocks)
                  blocks = result_blocks
              return result_blocks
  
          return self._replace_single(
              to_replace,
              value,
              inplace=inplace,
              filter=filter,
              convert=convert,
              regex=regex,
          )
  
      def _replace_single(
          self,
          to_replace,
          value,
          inplace=False,
          filter=None,
          regex=False,
          convert=True,
          mask=None,
      ):
          """
          Replace elements by the given value.
  
          Parameters
          ----------
          to_replace : object or pattern
              Scalar to replace or regular expression to match.
          value : object
              Replacement object.
          inplace : bool, default False
              Perform inplace modification.
          filter : list, optional
          regex : bool, default False
              If true, perform regular expression substitution.
          convert : bool, default True
              If true, try to coerce any object types to better types.
          mask : array-like of bool, optional
              True indicate corresponding element is ignored.
  
          Returns
          -------
          a new block, the result after replacing
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          # to_replace is regex compilable
          to_rep_re = regex and is_re_compilable(to_replace)
  
          # regex is regex compilable
          regex_re = is_re_compilable(regex)
  
          # only one will survive
          if to_rep_re and regex_re:
              raise AssertionError(
                  "only one of to_replace and regex can be regex compilable"
              )
  
          # if regex was passed as something that can be a regex (rather than a
          # boolean)
          if regex_re:
              to_replace = regex
  
          regex = regex_re or to_rep_re
  
          # try to get the pattern attribute (compiled re) or it's a string
          if is_re(to_replace):
              pattern = to_replace.pattern
          else:
              pattern = to_replace
  
          # if the pattern is not empty and to_replace is either a string or a
          # regex
          if regex and pattern:
              rx = re.compile(to_replace)
          else:
              # if the thing to replace is not a string or compiled regex call
              # the superclass method -> to_replace is some kind of object
              return super().replace(
                  to_replace, value, inplace=inplace, filter=filter, regex=regex
              )
  
          new_values = self.values if inplace else self.values.copy()
  
          # deal with replacing values with objects (strings) that match but
          # whose replacement is not a string (numeric, nan, object)
          if isna(value) or not isinstance(value, str):
  
              def re_replacer(s):
                  if is_re(rx) and isinstance(s, str):
                      return value if rx.search(s) is not None else s
                  else:
                      return s
  
          else:
              # value is guaranteed to be a string here, s can be either a string
              # or null if it's null it gets returned
              def re_replacer(s):
                  if is_re(rx) and isinstance(s, str):
                      return rx.sub(value, s)
                  else:
                      return s
  
          f = np.vectorize(re_replacer, otypes=[self.dtype])
  
          if filter is None:
              filt = slice(None)
          else:
              filt = self.mgr_locs.isin(filter).nonzero()[0]
  
          if mask is None:
              new_values[filt] = f(new_values[filt])
          else:
              new_values[filt][mask] = f(new_values[filt][mask])
  
          # convert
          block = self.make_block(new_values)
          if convert:
              block = block.convert(numeric=False)
          return block
  
      def _replace_coerce(
          self, to_replace, value, inplace=True, regex=False, convert=False, mask=None
      ):
          """
          Replace value corresponding to the given boolean array with another
          value.
  
          Parameters
          ----------
          to_replace : object or pattern
              Scalar to replace or regular expression to match.
          value : object
              Replacement object.
          inplace : bool, default False
              Perform inplace modification.
          regex : bool, default False
              If true, perform regular expression substitution.
          convert : bool, default True
              If true, try to coerce any object types to better types.
          mask : array-like of bool, optional
              True indicate corresponding element is ignored.
  
          Returns
          -------
          A new block if there is anything to replace or the original block.
          """
          if mask.any():
              block = super()._replace_coerce(
                  to_replace=to_replace,
                  value=value,
                  inplace=inplace,
                  regex=regex,
                  convert=convert,
                  mask=mask,
              )
              if convert:
                  block = [b.convert(numeric=False, copy=True) for b in block]
              return block
          if convert:
              return [self.convert(numeric=False, copy=True)]
          return self
  
  
  class CategoricalBlock(ExtensionBlock):
      __slots__ = ()
      is_categorical = True
      _verify_integrity = True
      _can_hold_na = True
      _concatenator = staticmethod(concat_categorical)
  
      def __init__(self, values, placement, ndim=None):
          # coerce to categorical if we can
          values = extract_array(values)
          assert isinstance(values, Categorical), type(values)
          super().__init__(values, placement=placement, ndim=ndim)
  
      @property
      def _holder(self):
          return Categorical
  
      @property
      def array_dtype(self):
          """ the dtype to return if I want to construct this block as an
          array
          """
          return np.object_
  
      def to_dense(self):
          # Categorical.get_values returns a DatetimeIndex for datetime
          # categories, so we can't simply use `np.asarray(self.values)` like
          # other types.
          return self.values._internal_get_values()
  
      def to_native_types(self, slicer=None, na_rep="", quoting=None, **kwargs):
          """ convert to our native types format, slicing if desired """
  
          values = self.values
          if slicer is not None:
              # Categorical is always one dimension
              values = values[slicer]
          mask = isna(values)
          values = np.array(values, dtype="object")
          values[mask] = na_rep
  
          # we are expected to return a 2-d ndarray
          return values.reshape(1, len(values))
  
      def concat_same_type(self, to_concat, placement=None):
          """
          Concatenate list of single blocks of the same type.
  
          Note that this CategoricalBlock._concat_same_type *may* not
          return a CategoricalBlock. When the categories in `to_concat`
          differ, this will return an object ndarray.
  
          If / when we decide we don't like that behavior:
  
          1. Change Categorical._concat_same_type to use union_categoricals
          2. Delete this method.
          """
          values = self._concatenator(
              [blk.values for blk in to_concat], axis=self.ndim - 1
          )
          # not using self.make_block_same_class as values can be object dtype
          return make_block(
              values, placement=placement or slice(0, len(values), 1), ndim=self.ndim
          )
  
      def replace(
          self,
          to_replace,
          value,
          inplace: bool = False,
          filter=None,
          regex: bool = False,
          convert: bool = True,
      ):
          inplace = validate_bool_kwarg(inplace, "inplace")
          result = self if inplace else self.copy()
          if filter is None:  # replace was called on a series
              result.values.replace(to_replace, value, inplace=True)
              if convert:
                  return result.convert(numeric=False, copy=not inplace)
              else:
                  return result
          else:  # replace was called on a DataFrame
              if not isna(value):
                  result.values.add_categories(value, inplace=True)
              return super(CategoricalBlock, result).replace(
                  to_replace, value, inplace, filter, regex, convert
              )
  
  
  # -----------------------------------------------------------------
  # Constructor Helpers
  
  
  def get_block_type(values, dtype=None):
      """
      Find the appropriate Block subclass to use for the given values and dtype.
  
      Parameters
      ----------
      values : ndarray-like
      dtype : numpy or pandas dtype
  
      Returns
      -------
      cls : class, subclass of Block
      """
      dtype = dtype or values.dtype
      vtype = dtype.type
  
      if is_sparse(dtype):
          # Need this first(ish) so that Sparse[datetime] is sparse
          cls = ExtensionBlock
      elif is_categorical(values):
          cls = CategoricalBlock
      elif issubclass(vtype, np.datetime64):
          assert not is_datetime64tz_dtype(values)
          cls = DatetimeBlock
      elif is_datetime64tz_dtype(values):
          cls = DatetimeTZBlock
      elif is_interval_dtype(dtype) or is_period_dtype(dtype):
          cls = ObjectValuesExtensionBlock
      elif is_extension_array_dtype(values):
          cls = ExtensionBlock
      elif issubclass(vtype, np.floating):
          cls = FloatBlock
      elif issubclass(vtype, np.timedelta64):
          assert issubclass(vtype, np.integer)
          cls = TimeDeltaBlock
      elif issubclass(vtype, np.complexfloating):
          cls = ComplexBlock
      elif issubclass(vtype, np.integer):
          cls = IntBlock
      elif dtype == np.bool_:
          cls = BoolBlock
      else:
          cls = ObjectBlock
      return cls
  
  
  def make_block(values, placement, klass=None, ndim=None, dtype=None):
      # Ensure that we don't allow PandasArray / PandasDtype in internals.
      # For now, blocks should be backed by ndarrays when possible.
      if isinstance(values, ABCPandasArray):
          values = values.to_numpy()
          if ndim and ndim > 1:
              values = np.atleast_2d(values)
  
      if isinstance(dtype, PandasDtype):
          dtype = dtype.numpy_dtype
  
      if klass is None:
          dtype = dtype or values.dtype
          klass = get_block_type(values, dtype)
  
      elif klass is DatetimeTZBlock and not is_datetime64tz_dtype(values):
          # TODO: This is no longer hit internally; does it need to be retained
          #  for e.g. pyarrow?
          values = DatetimeArray._simple_new(values, dtype=dtype)
  
      return klass(values, ndim=ndim, placement=placement)
  
  
  # -----------------------------------------------------------------
  
  
  def _extend_blocks(result, blocks=None):
      """ return a new extended blocks, given the result """
      from pandas.core.internals import BlockManager
  
      if blocks is None:
          blocks = []
      if isinstance(result, list):
          for r in result:
              if isinstance(r, list):
                  blocks.extend(r)
              else:
                  blocks.append(r)
      elif isinstance(result, BlockManager):
          blocks.extend(result.blocks)
      else:
          blocks.append(result)
      return blocks
  
  
  def _block_shape(values, ndim=1, shape=None):
      """ guarantee the shape of the values to be at least 1 d """
      if values.ndim < ndim:
          if shape is None:
              shape = values.shape
          if not is_extension_array_dtype(values):
              # TODO: https://github.com/pandas-dev/pandas/issues/23023
              # block.shape is incorrect for "2D" ExtensionArrays
              # We can't, and don't need to, reshape.
              values = values.reshape(tuple((1,) + shape))
      return values
  
  
  def _merge_blocks(blocks, dtype=None, _can_consolidate=True):
  
      if len(blocks) == 1:
          return blocks[0]
  
      if _can_consolidate:
  
          if dtype is None:
              if len({b.dtype for b in blocks}) != 1:
                  raise AssertionError("_merge_blocks are invalid!")
  
          # FIXME: optimization potential in case all mgrs contain slices and
          # combination of those slices is a slice, too.
          new_mgr_locs = np.concatenate([b.mgr_locs.as_array for b in blocks])
          new_values = np.vstack([b.values for b in blocks])
  
          argsort = np.argsort(new_mgr_locs)
          new_values = new_values[argsort]
          new_mgr_locs = new_mgr_locs[argsort]
  
          return make_block(new_values, placement=new_mgr_locs)
  
      # no merge
      return blocks
  
  
  def _safe_reshape(arr, new_shape):
      """
      If possible, reshape `arr` to have shape `new_shape`,
      with a couple of exceptions (see gh-13012):
  
      1) If `arr` is a ExtensionArray or Index, `arr` will be
         returned as is.
      2) If `arr` is a Series, the `_values` attribute will
         be reshaped and returned.
  
      Parameters
      ----------
      arr : array-like, object to be reshaped
      new_shape : int or tuple of ints, the new shape
      """
      if isinstance(arr, ABCSeries):
          arr = arr._values
      if not isinstance(arr, ABCExtensionArray):
          arr = arr.reshape(new_shape)
      return arr
  
  
  def _putmask_smart(v, mask, n):
      """
      Return a new ndarray, try to preserve dtype if possible.
  
      Parameters
      ----------
      v : `values`, updated in-place (array like)
      mask : np.ndarray
          Applies to both sides (array like).
      n : `new values` either scalar or an array like aligned with `values`
  
      Returns
      -------
      values : ndarray with updated values
          this *may* be a copy of the original
  
      See Also
      --------
      ndarray.putmask
      """
  
      # we cannot use np.asarray() here as we cannot have conversions
      # that numpy does when numeric are mixed with strings
  
      # n should be the length of the mask or a scalar here
      if not is_list_like(n):
          n = np.repeat(n, len(mask))
  
      # see if we are only masking values that if putted
      # will work in the current dtype
      try:
          nn = n[mask]
      except TypeError:
          # TypeError: only integer scalar arrays can be converted to a scalar index
          pass
      else:
          # make sure that we have a nullable type
          # if we have nulls
          if not _isna_compat(v, nn[0]):
              pass
          elif not (is_float_dtype(nn.dtype) or is_integer_dtype(nn.dtype)):
              # only compare integers/floats
              pass
          elif not (is_float_dtype(v.dtype) or is_integer_dtype(v.dtype)):
              # only compare integers/floats
              pass
          else:
  
              # we ignore ComplexWarning here
              with warnings.catch_warnings(record=True):
                  warnings.simplefilter("ignore", np.ComplexWarning)
                  nn_at = nn.astype(v.dtype)
  
              comp = nn == nn_at
              if is_list_like(comp) and comp.all():
                  nv = v.copy()
                  nv[mask] = nn_at
                  return nv
  
      n = np.asarray(n)
  
      def _putmask_preserve(nv, n):
          try:
              nv[mask] = n[mask]
          except (IndexError, ValueError):
              nv[mask] = n
          return nv
  
      # preserves dtype if possible
      if v.dtype.kind == n.dtype.kind:
          return _putmask_preserve(v, n)
  
      # change the dtype if needed
      dtype, _ = maybe_promote(n.dtype)
  
      if is_extension_array_dtype(v.dtype) and is_object_dtype(dtype):
          v = v._internal_get_values(dtype)
      else:
          v = v.astype(dtype)
  
      return _putmask_preserve(v, n)
  
apis: numpy.any numpy.asarray pandas._libs.lib.is_float pandas.core.dtypes.common.is_categorical pandas._libs.tslibs.Timedelta numpy.datetime64 pandas.core.dtypes.cast.infer_dtype_from_scalar pandas._libs.index.convert_scalar pandas.io.formats.format.FloatArrayFormatter pandas.core.computation.expressions.where numpy.timedelta64 pandas.core.dtypes.cast.maybe_downcast_numeric numpy.atleast_2d pandas.core.dtypes.common.is_period_dtype pandas._libs.tslibs.timezones.tz_compare pandas.core.missing.interpolate_2d numpy.repeat numpy.putmask pandas.core.dtypes.cast.find_common_type pandas._libs.algos._validate_limit pandas.core.indexers.is_empty_indexer pandas.core.missing.interpolate_1d pandas.core.algorithms.diff numpy.errstate numpy.array pandas.core.dtypes.missing.array_equivalent pandas.core.missing.clean_interp_method pandas._libs.lib.is_scalar numpy.vectorize pandas.core.arrays.DatetimeArray pandas.core.dtypes.common.is_integer pandas.core.dtypes.missing._isna_compat numpy.vstack pandas._libs.internals.BlockPlacement pandas.io.formats.printing.pprint_thing pandas._libs.tslibs.conversion.ensure_timedelta64ns numpy.delete pandas.core.indexers.is_scalar_indexer pandas.core.dtypes.common.is_timedelta64_dtype pandas._libs.lib.item_from_zerodim pandas.core.dtypes.common.ensure_platform_int pandas.core.dtypes.missing.isna pandas.core.dtypes.common.is_datetime64tz_dtype pandas.core.dtypes.common.is_object_dtype pandas.core.dtypes.cast.maybe_upcast numpy.concatenate pandas.core.dtypes.common.is_integer_dtype numpy.argsort pandas.core.arrays.Categorical numpy.apply_along_axis pandas.core.construction.extract_array pandas.core.dtypes.common.is_list_like pandas.core.missing.clean_fill_method pandas.core.dtypes.common.is_categorical_dtype pandas.core.dtypes.common.is_re_compilable numpy.isnan pandas._libs.writers.word_len numpy.can_cast pandas.core.dtypes.cast.maybe_infer_dtype_type numpy.arange numpy.ndim numpy.prod pandas.io.formats.format._get_format_datetime64_from_values pandas.core.missing.mask_missing numpy.atleast_1d pandas.core.dtypes.common.is_sparse pandas.util._validators.validate_bool_kwarg pandas.core.dtypes.common.is_re pandas.core.indexers.check_setitem_lengths pandas.core.dtypes.common.is_float_dtype pandas.core.dtypes.common.pandas_dtype pandas.core.common.is_null_slice pandas.core.dtypes.concat.concat_datetime pandas.core.dtypes.missing.is_valid_nat_for_dtype pandas.core.dtypes.cast.astype_nansafe pandas.core.dtypes.cast.maybe_downcast_to_dtype numpy.where pandas.core.dtypes.common.is_datetime64_dtype pandas.core.dtypes.common.is_bool_dtype pandas.core.algorithms.take_nd pandas.core.dtypes.common.is_interval_dtype numpy.broadcast_to pandas.core.dtypes.common.is_extension_array_dtype numpy.empty pandas.core.dtypes.cast.maybe_promote numpy.squeeze pandas.core.dtypes.cast.infer_dtype_from pandas.core.dtypes.common.is_dtype_equal pandas.core.arrays.DatetimeArray._simple_new numpy.place pandas._libs.tslibs.conversion.ensure_datetime64ns