managers.py

from collections import defaultdict
import itertools
import operator
import re
from typing import DefaultDict, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
import warnings

import numpy as np

from pandas._libs import internals as libinternals, lib
from pandas._typing import ArrayLike, DtypeObj, Label, Scalar
from pandas.util._validators import validate_bool_kwarg

from pandas.core.dtypes.cast import (
    find_common_type,
    infer_dtype_from_scalar,
    maybe_promote,
)
from pandas.core.dtypes.common import (
    DT64NS_DTYPE,
    is_datetimelike_v_numeric,
    is_extension_array_dtype,
    is_list_like,
    is_numeric_v_string_like,
    is_scalar,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.dtypes import ExtensionDtype
from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
from pandas.core.dtypes.missing import isna

import pandas.core.algorithms as algos
from pandas.core.arrays.sparse import SparseDtype
from pandas.core.base import PandasObject
import pandas.core.common as com
from pandas.core.construction import extract_array
from pandas.core.indexers import maybe_convert_indices
from pandas.core.indexes.api import Index, ensure_index
from pandas.core.internals.blocks import (
    Block,
    CategoricalBlock,
    DatetimeTZBlock,
    ExtensionBlock,
    ObjectValuesExtensionBlock,
    _extend_blocks,
    _safe_reshape,
    get_block_type,
    make_block,
)

# TODO: flexible with index=None and/or items=None

T = TypeVar("T", bound="BlockManager")


class BlockManager(PandasObject):
    """
    Core internal data structure to implement DataFrame, Series, etc.

    Manage a bunch of labeled 2D mixed-type ndarrays. Essentially it's a
    lightweight blocked set of labeled data to be manipulated by the DataFrame
    public API class

    Attributes
    ----------
    shape
    ndim
    axes
    values
    items

    Methods
    -------
    set_axis(axis, new_labels)
    copy(deep=True)

    get_dtype_counts
    get_dtypes

    apply(func, axes, block_filter_fn)

    get_bool_data
    get_numeric_data

    get_slice(slice_like, axis)
    get(label)
    iget(loc)

    take(indexer, axis)
    reindex_axis(new_labels, axis)
    reindex_indexer(new_labels, indexer, axis)

    delete(label)
    insert(loc, label, value)
    set(label, value)

    Parameters
    ----------
    blocks: Sequence of Block
    axes: Sequence of Index
    do_integrity_check: bool, default True

    Notes
    -----
    This is *not* a public API class
    """

    __slots__ = [
        "axes",
        "blocks",
        "_known_consolidated",
        "_is_consolidated",
        "_blknos",
        "_blklocs",
    ]

    _blknos: np.ndarray
    _blklocs: np.ndarray

    def __init__(
        self,
        blocks: Sequence[Block],
        axes: Sequence[Index],
        do_integrity_check: bool = True,
    ):
        self.axes = [ensure_index(ax) for ax in axes]
        self.blocks: Tuple[Block, ...] = tuple(blocks)

        for block in blocks:
            if self.ndim != block.ndim:
                raise AssertionError(
                    f"Number of Block dimensions ({block.ndim}) must equal "
                    f"number of axes ({self.ndim})"
                )

        if do_integrity_check:
            self._verify_integrity()

        # Populate known_consolidate, blknos, and blklocs lazily
        self._known_consolidated = False
        self._blknos = None
        self._blklocs = None

    @classmethod
    def from_blocks(cls, blocks: List[Block], axes: List[Index]):
        """
        Constructor for BlockManager and SingleBlockManager with same signature.
        """
        return cls(blocks, axes, do_integrity_check=False)

    @property
    def blknos(self):
        """
        Suppose we want to find the array corresponding to our i'th column.

        blknos[i] identifies the block from self.blocks that contains this column.

        blklocs[i] identifies the column of interest within
        self.blocks[self.blknos[i]]
        """
        if self._blknos is None:
            # Note: these can be altered by other BlockManager methods.
            self._rebuild_blknos_and_blklocs()

        return self._blknos

    @property
    def blklocs(self):
        """
        See blknos.__doc__
        """
        if self._blklocs is None:
            # Note: these can be altered by other BlockManager methods.
            self._rebuild_blknos_and_blklocs()

        return self._blklocs

    def make_empty(self: T, axes=None) -> T:
        """ return an empty BlockManager with the items axis of len 0 """
        if axes is None:
            axes = [Index([])] + self.axes[1:]

        # preserve dtype if possible
        if self.ndim == 1:
            assert isinstance(self, SingleBlockManager)  # for mypy
            blk = self.blocks[0]
            arr = blk.values[:0]
            nb = blk.make_block_same_class(arr, placement=slice(0, 0), ndim=1)
            blocks = [nb]
        else:
            blocks = []
        return type(self).from_blocks(blocks, axes)

    def __nonzero__(self) -> bool:
        return True

    # Python3 compat
    __bool__ = __nonzero__

    @property
    def shape(self) -> Tuple[int, ...]:
        return tuple(len(ax) for ax in self.axes)

    @property
    def ndim(self) -> int:
        return len(self.axes)

    def set_axis(self, axis: int, new_labels: Index) -> None:
        # Caller is responsible for ensuring we have an Index object.
        old_len = len(self.axes[axis])
        new_len = len(new_labels)

        if new_len != old_len:
            raise ValueError(
                f"Length mismatch: Expected axis has {old_len} elements, new "
                f"values have {new_len} elements"
            )

        self.axes[axis] = new_labels

    @property
    def _is_single_block(self) -> bool:
        if self.ndim == 1:
            return True

        if len(self.blocks) != 1:
            return False

        blk = self.blocks[0]
        return blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice == slice(
            0, len(self), 1
        )

    def _rebuild_blknos_and_blklocs(self) -> None:
        """
        Update mgr._blknos / mgr._blklocs.
        """
        new_blknos = np.empty(self.shape[0], dtype=np.int64)
        new_blklocs = np.empty(self.shape[0], dtype=np.int64)
        new_blknos.fill(-1)
        new_blklocs.fill(-1)

        for blkno, blk in enumerate(self.blocks):
            rl = blk.mgr_locs
            new_blknos[rl.indexer] = blkno
            new_blklocs[rl.indexer] = np.arange(len(rl))

        if (new_blknos == -1).any():
            # TODO: can we avoid this?  it isn't cheap
            raise AssertionError("Gaps in blk ref_locs")

        self._blknos = new_blknos
        self._blklocs = new_blklocs

    @property
    def items(self) -> Index:
        return self.axes[0]

    def _get_counts(self, f):
        """ return a dict of the counts of the function in BlockManager """
        self._consolidate_inplace()
        counts = dict()
        for b in self.blocks:
            v = f(b)
            counts[v] = counts.get(v, 0) + b.shape[0]
        return counts

    def get_dtype_counts(self):
        return self._get_counts(lambda b: b.dtype.name)

    def get_dtypes(self):
        dtypes = np.array([blk.dtype for blk in self.blocks])
        return algos.take_1d(dtypes, self.blknos, allow_fill=False)

    def __getstate__(self):
        block_values = [b.values for b in self.blocks]
        block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]
        axes_array = list(self.axes)

        extra_state = {
            "0.14.1": {
                "axes": axes_array,
                "blocks": [
                    dict(values=b.values, mgr_locs=b.mgr_locs.indexer)
                    for b in self.blocks
                ],
            }
        }

        # First three elements of the state are to maintain forward
        # compatibility with 0.13.1.
        return axes_array, block_values, block_items, extra_state

    def __setstate__(self, state):
        def unpickle_block(values, mgr_locs):
            return make_block(values, placement=mgr_locs)

        if isinstance(state, tuple) and len(state) >= 4 and "0.14.1" in state[3]:
            state = state[3]["0.14.1"]
            self.axes = [ensure_index(ax) for ax in state["axes"]]
            self.blocks = tuple(
                unpickle_block(b["values"], b["mgr_locs"]) for b in state["blocks"]
            )
        else:
            raise NotImplementedError("pre-0.14.1 pickles are no longer supported")

        self._post_setstate()

    def _post_setstate(self) -> None:
        self._is_consolidated = False
        self._known_consolidated = False
        self._rebuild_blknos_and_blklocs()

    def __len__(self) -> int:
        return len(self.items)

    def __repr__(self) -> str:
        output = type(self).__name__
        for i, ax in enumerate(self.axes):
            if i == 0:
                output += f"\nItems: {ax}"
            else:
                output += f"\nAxis {i}: {ax}"

        for block in self.blocks:
            output += f"\n{block}"
        return output

    def _verify_integrity(self) -> None:
        mgr_shape = self.shape
        tot_items = sum(len(x.mgr_locs) for x in self.blocks)
        for block in self.blocks:
            if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
                raise construction_error(tot_items, block.shape[1:], self.axes)
        if len(self.items) != tot_items:
            raise AssertionError(
                "Number of manager items must equal union of "
                f"block items\n# manager items: {len(self.items)}, # "
                f"tot_items: {tot_items}"
            )

    def reduce(self, func, *args, **kwargs):
        # If 2D, we assume that we're operating column-wise
        if self.ndim == 1:
            # we'll be returning a scalar
            blk = self.blocks[0]
            return func(blk.values, *args, **kwargs)

        res = {}
        for blk in self.blocks:
            bres = func(blk.values, *args, **kwargs)

            if np.ndim(bres) == 0:
                # EA
                assert blk.shape[0] == 1
                new_res = zip(blk.mgr_locs.as_array, [bres])
            else:
                assert bres.ndim == 1, bres.shape
                assert blk.shape[0] == len(bres), (blk.shape, bres.shape, args, kwargs)
                new_res = zip(blk.mgr_locs.as_array, bres)

            nr = dict(new_res)
            assert not any(key in res for key in nr)
            res.update(nr)

        return res

    def apply(self: T, f, align_keys=None, **kwargs) -> T:
        """
        Iterate over the blocks, collect and create a new BlockManager.

        Parameters
        ----------
        f : str or callable
            Name of the Block method to apply.

        Returns
        -------
        BlockManager
        """
        assert "filter" not in kwargs

        align_keys = align_keys or []
        result_blocks: List[Block] = []
        # fillna: Series/DataFrame is responsible for making sure value is aligned

        self._consolidate_inplace()

        align_copy = False
        if f == "where":
            align_copy = True

        aligned_args = {k: kwargs[k] for k in align_keys}

        for b in self.blocks:

            if aligned_args:
                b_items = self.items[b.mgr_locs.indexer]

                for k, obj in aligned_args.items():
                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
                        axis = obj._info_axis_number
                        kwargs[k] = obj.reindex(
                            b_items, axis=axis, copy=align_copy
                        )._values
                    else:
                        # otherwise we have an ndarray
                        kwargs[k] = obj[b.mgr_locs.indexer]

            if callable(f):
                applied = b.apply(f, **kwargs)
            else:
                applied = getattr(b, f)(**kwargs)
            result_blocks = _extend_blocks(applied, result_blocks)

        if len(result_blocks) == 0:
            return self.make_empty(self.axes)

        return type(self).from_blocks(result_blocks, self.axes)

    def quantile(
        self,
        axis: int = 0,
        consolidate: bool = True,
        transposed: bool = False,
        interpolation="linear",
        qs=None,
        numeric_only=None,
    ) -> "BlockManager":
        """
        Iterate over blocks applying quantile reduction.
        This routine is intended for reduction type operations and
        will do inference on the generated blocks.

        Parameters
        ----------
        axis: reduction axis, default 0
        consolidate: bool, default True. Join together blocks having same
            dtype
        transposed: bool, default False
            we are holding transposed data
        interpolation : type of interpolation, default 'linear'
        qs : a scalar or list of the quantiles to be computed
        numeric_only : ignored

        Returns
        -------
        BlockManager
        """
        # Series dispatches to DataFrame for quantile, which allows us to
        #  simplify some of the code here and in the blocks
        assert self.ndim >= 2

        if consolidate:
            self._consolidate_inplace()

        def get_axe(block, qs, axes):
            # Because Series dispatches to DataFrame, we will always have
            #  block.ndim == 2
            from pandas import Float64Index

            if is_list_like(qs):
                ax = Float64Index(qs)
            else:
                ax = axes[0]
            return ax

        axes, blocks = [], []
        for b in self.blocks:
            block = b.quantile(axis=axis, qs=qs, interpolation=interpolation)

            axe = get_axe(b, qs, axes=self.axes)

            axes.append(axe)
            blocks.append(block)

        # note that some DatetimeTZ, Categorical are always ndim==1
        ndim = {b.ndim for b in blocks}
        assert 0 not in ndim, ndim

        if 2 in ndim:

            new_axes = list(self.axes)

            # multiple blocks that are reduced
            if len(blocks) > 1:
                new_axes[1] = axes[0]

                # reset the placement to the original
                for b, sb in zip(blocks, self.blocks):
                    b.mgr_locs = sb.mgr_locs

            else:
                new_axes[axis] = Index(np.concatenate([ax.values for ax in axes]))

            if transposed:
                new_axes = new_axes[::-1]
                blocks = [
                    b.make_block(b.values.T, placement=np.arange(b.shape[1]))
                    for b in blocks
                ]

            return type(self)(blocks, new_axes)

        # single block, i.e. ndim == {1}
        values = concat_compat([b.values for b in blocks])

        # compute the orderings of our original data
        if len(self.blocks) > 1:

            indexer = np.empty(len(self.axes[0]), dtype=np.intp)
            i = 0
            for b in self.blocks:
                for j in b.mgr_locs:
                    indexer[j] = i
                    i = i + 1

            values = values.take(indexer)

        return SingleBlockManager(
            make_block(values, ndim=1, placement=np.arange(len(values))), axes[0],
        )

    def isna(self, func) -> "BlockManager":
        return self.apply("apply", func=func)

    def where(
        self, other, cond, align: bool, errors: str, try_cast: bool, axis: int
    ) -> "BlockManager":
        if align:
            align_keys = ["other", "cond"]
        else:
            align_keys = ["cond"]
            other = extract_array(other, extract_numpy=True)

        return self.apply(
            "where",
            align_keys=align_keys,
            other=other,
            cond=cond,
            errors=errors,
            try_cast=try_cast,
            axis=axis,
        )

    def setitem(self, indexer, value) -> "BlockManager":
        return self.apply("setitem", indexer=indexer, value=value)

    def putmask(
        self, mask, new, align: bool = True, axis: int = 0,
    ):
        transpose = self.ndim == 2

        if align:
            align_keys = ["new", "mask"]
        else:
            align_keys = ["mask"]
            new = extract_array(new, extract_numpy=True)

        return self.apply(
            "putmask",
            align_keys=align_keys,
            mask=mask,
            new=new,
            inplace=True,
            axis=axis,
            transpose=transpose,
        )

    def diff(self, n: int, axis: int) -> "BlockManager":
        return self.apply("diff", n=n, axis=axis)

    def interpolate(self, **kwargs) -> "BlockManager":
        return self.apply("interpolate", **kwargs)

    def shift(self, periods: int, axis: int, fill_value) -> "BlockManager":
        return self.apply("shift", periods=periods, axis=axis, fill_value=fill_value)

    def fillna(self, value, limit, inplace: bool, downcast) -> "BlockManager":
        return self.apply(
            "fillna", value=value, limit=limit, inplace=inplace, downcast=downcast
        )

    def downcast(self) -> "BlockManager":
        return self.apply("downcast")

    def astype(
        self, dtype, copy: bool = False, errors: str = "raise"
    ) -> "BlockManager":
        return self.apply("astype", dtype=dtype, copy=copy, errors=errors)

    def convert(
        self,
        copy: bool = True,
        datetime: bool = True,
        numeric: bool = True,
        timedelta: bool = True,
        coerce: bool = False,
    ) -> "BlockManager":
        return self.apply(
            "convert",
            copy=copy,
            datetime=datetime,
            numeric=numeric,
            timedelta=timedelta,
            coerce=coerce,
        )

    def replace(self, value, **kwargs) -> "BlockManager":
        assert np.ndim(value) == 0, value
        return self.apply("replace", value=value, **kwargs)

    def replace_list(
        self, src_list, dest_list, inplace: bool = False, regex: bool = False
    ) -> "BlockManager":
        """ do a list replace """
        inplace = validate_bool_kwarg(inplace, "inplace")

        # figure out our mask a-priori to avoid repeated replacements
        values = self.as_array()

        def comp(s, regex=False):
            """
            Generate a bool array by perform an equality check, or perform
            an element-wise regular expression matching
            """
            if isna(s):
                return isna(values)

            s = com.maybe_box_datetimelike(s)
            return _compare_or_regex_search(values, s, regex)

        masks = [comp(s, regex) for s in src_list]

        result_blocks = []
        src_len = len(src_list) - 1
        for blk in self.blocks:

            # its possible to get multiple result blocks here
            # replace ALWAYS will return a list
            rb = [blk if inplace else blk.copy()]
            for i, (s, d) in enumerate(zip(src_list, dest_list)):
                new_rb: List[Block] = []
                for b in rb:
                    m = masks[i][b.mgr_locs.indexer]
                    convert = i == src_len  # only convert once at the end
                    result = b._replace_coerce(
                        mask=m,
                        to_replace=s,
                        value=d,
                        inplace=inplace,
                        convert=convert,
                        regex=regex,
                    )
                    if m.any() or convert:
                        new_rb = _extend_blocks(result, new_rb)
                    else:
                        new_rb.append(b)
                rb = new_rb
            result_blocks.extend(rb)

        bm = type(self).from_blocks(result_blocks, self.axes)
        bm._consolidate_inplace()
        return bm

    def is_consolidated(self) -> bool:
        """
        Return True if more than one block with the same dtype
        """
        if not self._known_consolidated:
            self._consolidate_check()
        return self._is_consolidated

    def _consolidate_check(self) -> None:
        dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]
        self._is_consolidated = len(dtypes) == len(set(dtypes))
        self._known_consolidated = True

    @property
    def is_mixed_type(self) -> bool:
        # Warning, consolidation needs to get checked upstairs
        self._consolidate_inplace()
        return len(self.blocks) > 1

    @property
    def is_numeric_mixed_type(self) -> bool:
        # Warning, consolidation needs to get checked upstairs
        self._consolidate_inplace()
        return all(block.is_numeric for block in self.blocks)

    @property
    def any_extension_types(self) -> bool:
        """Whether any of the blocks in this manager are extension blocks"""
        return any(block.is_extension for block in self.blocks)

    @property
    def is_view(self) -> bool:
        """ return a boolean if we are a single block and are a view """
        if len(self.blocks) == 1:
            return self.blocks[0].is_view

        # It is technically possible to figure out which blocks are views
        # e.g. [ b.values.base is not None for b in self.blocks ]
        # but then we have the case of possibly some blocks being a view
        # and some blocks not. setting in theory is possible on the non-view
        # blocks w/o causing a SettingWithCopy raise/warn. But this is a bit
        # complicated

        return False

    def get_bool_data(self, copy: bool = False) -> "BlockManager":
        """
        Parameters
        ----------
        copy : bool, default False
            Whether to copy the blocks
        """
        self._consolidate_inplace()
        return self._combine([b for b in self.blocks if b.is_bool], copy)

    def get_numeric_data(self, copy: bool = False) -> "BlockManager":
        """
        Parameters
        ----------
        copy : bool, default False
            Whether to copy the blocks
        """
        self._consolidate_inplace()
        return self._combine([b for b in self.blocks if b.is_numeric], copy)

    def _combine(self, blocks: List[Block], copy: bool = True) -> "BlockManager":
        """ return a new manager with the blocks """
        if len(blocks) == 0:
            return self.make_empty()

        # FIXME: optimization potential
        indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))
        inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])

        new_blocks = []
        for b in blocks:
            b = b.copy(deep=copy)
            b.mgr_locs = inv_indexer[b.mgr_locs.indexer]
            new_blocks.append(b)

        axes = list(self.axes)
        axes[0] = self.items.take(indexer)

        return type(self).from_blocks(new_blocks, axes)

    def get_slice(self, slobj: slice, axis: int = 0) -> "BlockManager":

        if axis == 0:
            new_blocks = self._slice_take_blocks_ax0(slobj)
        elif axis == 1:
            slicer = (slice(None), slobj)
            new_blocks = [blk.getitem_block(slicer) for blk in self.blocks]
        else:
            raise IndexError("Requested axis not found in manager")

        new_axes = list(self.axes)
        new_axes[axis] = new_axes[axis][slobj]

        bm = type(self)(new_blocks, new_axes, do_integrity_check=False)
        return bm

    @property
    def nblocks(self) -> int:
        return len(self.blocks)

    def copy(self: T, deep=True) -> T:
        """
        Make deep or shallow copy of BlockManager

        Parameters
        ----------
        deep : bool or string, default True
            If False, return shallow copy (do not copy data)
            If 'all', copy data and a deep copy of the index

        Returns
        -------
        BlockManager
        """
        # this preserves the notion of view copying of axes
        if deep:
            # hit in e.g. tests.io.json.test_pandas

            def copy_func(ax):
                return ax.copy(deep=True) if deep == "all" else ax.view()

            new_axes = [copy_func(ax) for ax in self.axes]
        else:
            new_axes = list(self.axes)

        res = self.apply("copy", deep=deep)
        res.axes = new_axes
        return res

    def as_array(self, transpose: bool = False) -> np.ndarray:
        """
        Convert the blockmanager data into an numpy array.

        Parameters
        ----------
        transpose : bool, default False
            If True, transpose the return array,

        Returns
        -------
        arr : ndarray
        """
        if len(self.blocks) == 0:
            arr = np.empty(self.shape, dtype=float)
            return arr.transpose() if transpose else arr

        if self._is_single_block and self.blocks[0].is_datetimetz:
            # TODO(Block.get_values): Make DatetimeTZBlock.get_values
            # always be object dtype. Some callers seem to want the
            # DatetimeArray (previously DTI)
            arr = self.blocks[0].get_values(dtype=object)
        elif self._is_single_block or not self.is_mixed_type:
            arr = np.asarray(self.blocks[0].get_values())
        else:
            arr = self._interleave()

        return arr.transpose() if transpose else arr

    def _interleave(self) -> np.ndarray:
        """
        Return ndarray from blocks with specified item order
        Items must be contained in the blocks
        """
        dtype = _interleaved_dtype(self.blocks)

        # TODO: https://github.com/pandas-dev/pandas/issues/22791
        # Give EAs some input on what happens here. Sparse needs this.
        if isinstance(dtype, SparseDtype):
            dtype = dtype.subtype
        elif is_extension_array_dtype(dtype):
            dtype = "object"

        result = np.empty(self.shape, dtype=dtype)

        itemmask = np.zeros(self.shape[0])

        for blk in self.blocks:
            rl = blk.mgr_locs
            result[rl.indexer] = blk.get_values(dtype)
            itemmask[rl.indexer] = 1

        if not itemmask.all():
            raise AssertionError("Some items were not contained in blocks")

        return result

    def to_dict(self, copy: bool = True):
        """
        Return a dict of str(dtype) -> BlockManager

        Parameters
        ----------
        copy : bool, default True

        Returns
        -------
        values : a dict of dtype -> BlockManager

        Notes
        -----
        This consolidates based on str(dtype)
        """
        self._consolidate_inplace()

        bd: Dict[str, List[Block]] = {}
        for b in self.blocks:
            bd.setdefault(str(b.dtype), []).append(b)

        # TODO(EA2D): the combine will be unnecessary with 2D EAs
        return {dtype: self._combine(blocks, copy=copy) for dtype, blocks in bd.items()}

    def fast_xs(self, loc: int) -> ArrayLike:
        """
        Return the array corresponding to `frame.iloc[loc]`.

        Parameters
        ----------
        loc : int

        Returns
        -------
        np.ndarray or ExtensionArray
        """
        if len(self.blocks) == 1:
            return self.blocks[0].iget((slice(None), loc))

        dtype = _interleaved_dtype(self.blocks)

        n = len(self)
        if is_extension_array_dtype(dtype):
            # we'll eventually construct an ExtensionArray.
            result = np.empty(n, dtype=object)
        else:
            result = np.empty(n, dtype=dtype)

        for blk in self.blocks:
            # Such assignment may incorrectly coerce NaT to None
            # result[blk.mgr_locs] = blk._slice((slice(None), loc))
            for i, rl in enumerate(blk.mgr_locs):
                result[rl] = blk.iget((i, loc))

        if isinstance(dtype, ExtensionDtype):
            result = dtype.construct_array_type()._from_sequence(result, dtype=dtype)

        return result

    def consolidate(self) -> "BlockManager":
        """
        Join together blocks having same dtype

        Returns
        -------
        y : BlockManager
        """
        if self.is_consolidated():
            return self

        bm = type(self)(self.blocks, self.axes)
        bm._is_consolidated = False
        bm._consolidate_inplace()
        return bm

    def _consolidate_inplace(self) -> None:
        if not self.is_consolidated():
            self.blocks = tuple(_consolidate(self.blocks))
            self._is_consolidated = True
            self._known_consolidated = True
            self._rebuild_blknos_and_blklocs()

    def iget(self, i: int) -> "SingleBlockManager":
        """
        Return the data as a SingleBlockManager.
        """
        block = self.blocks[self.blknos[i]]
        values = block.iget(self.blklocs[i])

        # shortcut for select a single-dim from a 2-dim BM
        return SingleBlockManager(
            block.make_block_same_class(
                values, placement=slice(0, len(values)), ndim=1
            ),
            self.axes[1],
        )

    def iget_values(self, i: int) -> ArrayLike:
        """
        Return the data for column i as the values (ndarray or ExtensionArray).
        """
        block = self.blocks[self.blknos[i]]
        values = block.iget(self.blklocs[i])
        return values

    def idelete(self, indexer):
        """
        Delete selected locations in-place (new block and array, same BlockManager)
        """
        is_deleted = np.zeros(self.shape[0], dtype=np.bool_)
        is_deleted[indexer] = True
        ref_loc_offset = -is_deleted.cumsum()

        is_blk_deleted = [False] * len(self.blocks)

        if isinstance(indexer, int):
            affected_start = indexer
        else:
            affected_start = is_deleted.nonzero()[0][0]

        for blkno, _ in _fast_count_smallints(self.blknos[affected_start:]):
            blk = self.blocks[blkno]
            bml = blk.mgr_locs
            blk_del = is_deleted[bml.indexer].nonzero()[0]

            if len(blk_del) == len(bml):
                is_blk_deleted[blkno] = True
                continue
            elif len(blk_del) != 0:
                blk.delete(blk_del)
                bml = blk.mgr_locs

            blk.mgr_locs = bml.add(ref_loc_offset[bml.indexer])

        # FIXME: use Index.delete as soon as it uses fastpath=True
        self.axes[0] = self.items[~is_deleted]
        self.blocks = tuple(
            b for blkno, b in enumerate(self.blocks) if not is_blk_deleted[blkno]
        )
        self._rebuild_blknos_and_blklocs()

    def iset(self, loc: Union[int, slice, np.ndarray], value):
        """
        Set new item in-place. Does not consolidate. Adds new Block if not
        contained in the current set of items
        """
        # FIXME: refactor, clearly separate broadcasting & zip-like assignment
        #        can prob also fix the various if tests for sparse/categorical
        if self._blklocs is None and self.ndim > 1:
            self._rebuild_blknos_and_blklocs()

        value_is_extension_type = is_extension_array_dtype(value)

        # categorical/sparse/datetimetz
        if value_is_extension_type:

            def value_getitem(placement):
                return value

        else:
            if value.ndim == self.ndim - 1:
                value = _safe_reshape(value, (1,) + value.shape)

                def value_getitem(placement):
                    return value

            else:

                def value_getitem(placement):
                    return value[placement.indexer]

            if value.shape[1:] != self.shape[1:]:
                raise AssertionError(
                    "Shape of new values must be compatible with manager shape"
                )

        if lib.is_integer(loc):
            # We have 6 tests where loc is _not_ an int.
            # In this case, get_blkno_placements will yield only one tuple,
            #  containing (self._blknos[loc], BlockPlacement(slice(0, 1, 1)))
            loc = [loc]

        # Accessing public blknos ensures the public versions are initialized
        blknos = self.blknos[loc]
        blklocs = self.blklocs[loc].copy()

        unfit_mgr_locs = []
        unfit_val_locs = []
        removed_blknos = []
        for blkno, val_locs in libinternals.get_blkno_placements(blknos, group=True):
            blk = self.blocks[blkno]
            blk_locs = blklocs[val_locs.indexer]
            if blk.should_store(value):
                blk.set(blk_locs, value_getitem(val_locs))
            else:
                unfit_mgr_locs.append(blk.mgr_locs.as_array[blk_locs])
                unfit_val_locs.append(val_locs)

                # If all block items are unfit, schedule the block for removal.
                if len(val_locs) == len(blk.mgr_locs):
                    removed_blknos.append(blkno)
                else:
                    blk.delete(blk_locs)
                    self._blklocs[blk.mgr_locs.indexer] = np.arange(len(blk))

        if len(removed_blknos):
            # Remove blocks & update blknos accordingly
            is_deleted = np.zeros(self.nblocks, dtype=np.bool_)
            is_deleted[removed_blknos] = True

            new_blknos = np.empty(self.nblocks, dtype=np.int64)
            new_blknos.fill(-1)
            new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos))
            self._blknos = new_blknos[self._blknos]
            self.blocks = tuple(
                blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos)
            )

        if unfit_val_locs:
            unfit_mgr_locs = np.concatenate(unfit_mgr_locs)
            unfit_count = len(unfit_mgr_locs)

            new_blocks: List[Block] = []
            if value_is_extension_type:
                # This code (ab-)uses the fact that EA blocks contain only
                # one item.
                # TODO(EA2D): special casing unnecessary with 2D EAs
                new_blocks.extend(
                    make_block(
                        values=value,
                        ndim=self.ndim,
                        placement=slice(mgr_loc, mgr_loc + 1),
                    )
                    for mgr_loc in unfit_mgr_locs
                )

                self._blknos[unfit_mgr_locs] = np.arange(unfit_count) + len(self.blocks)
                self._blklocs[unfit_mgr_locs] = 0

            else:
                # unfit_val_locs contains BlockPlacement objects
                unfit_val_items = unfit_val_locs[0].append(unfit_val_locs[1:])

                new_blocks.append(
                    make_block(
                        values=value_getitem(unfit_val_items),
                        ndim=self.ndim,
                        placement=unfit_mgr_locs,
                    )
                )

                self._blknos[unfit_mgr_locs] = len(self.blocks)
                self._blklocs[unfit_mgr_locs] = np.arange(unfit_count)

            self.blocks += tuple(new_blocks)

            # Newly created block's dtype may already be present.
            self._known_consolidated = False

    def insert(self, loc: int, item: Label, value, allow_duplicates: bool = False):
        """
        Insert item at selected position.

        Parameters
        ----------
        loc : int
        item : hashable
        value : array_like
        allow_duplicates: bool
            If False, trying to insert non-unique item will raise

        """
        if not allow_duplicates and item in self.items:
            # Should this be a different kind of error??
            raise ValueError(f"cannot insert {item}, already exists")

        if not isinstance(loc, int):
            raise TypeError("loc must be int")

        # insert to the axis; this could possibly raise a TypeError
        new_axis = self.items.insert(loc, item)

        if value.ndim == self.ndim - 1 and not is_extension_array_dtype(value.dtype):
            value = _safe_reshape(value, (1,) + value.shape)

        block = make_block(values=value, ndim=self.ndim, placement=slice(loc, loc + 1))

        for blkno, count in _fast_count_smallints(self.blknos[loc:]):
            blk = self.blocks[blkno]
            if count == len(blk.mgr_locs):
                blk.mgr_locs = blk.mgr_locs.add(1)
            else:
                new_mgr_locs = blk.mgr_locs.as_array.copy()
                new_mgr_locs[new_mgr_locs >= loc] += 1
                blk.mgr_locs = new_mgr_locs

        # Accessing public blklocs ensures the public versions are initialized
        if loc == self.blklocs.shape[0]:
            # np.append is a lot faster, let's use it if we can.
            self._blklocs = np.append(self._blklocs, 0)
            self._blknos = np.append(self._blknos, len(self.blocks))
        else:
            self._blklocs = np.insert(self._blklocs, loc, 0)
            self._blknos = np.insert(self._blknos, loc, len(self.blocks))

        self.axes[0] = new_axis
        self.blocks += (block,)

        self._known_consolidated = False

        if len(self.blocks) > 100:
            self._consolidate_inplace()

    def reindex_axis(
        self,
        new_index,
        axis: int,
        method=None,
        limit=None,
        fill_value=None,
        copy: bool = True,
    ):
        """
        Conform block manager to new index.
        """
        new_index = ensure_index(new_index)
        new_index, indexer = self.axes[axis].reindex(
            new_index, method=method, limit=limit
        )

        return self.reindex_indexer(
            new_index, indexer, axis=axis, fill_value=fill_value, copy=copy
        )

    def reindex_indexer(
        self: T,
        new_axis,
        indexer,
        axis: int,
        fill_value=None,
        allow_dups: bool = False,
        copy: bool = True,
    ) -> T:
        """
        Parameters
        ----------
        new_axis : Index
        indexer : ndarray of int64 or None
        axis : int
        fill_value : object, default None
        allow_dups : bool, default False
        copy : bool, default True


        pandas-indexer with -1's only.
        """
        if indexer is None:
            if new_axis is self.axes[axis] and not copy:
                return self

            result = self.copy(deep=copy)
            result.axes = list(self.axes)
            result.axes[axis] = new_axis
            return result

        self._consolidate_inplace()

        # some axes don't allow reindexing with dups
        if not allow_dups:
            self.axes[axis]._can_reindex(indexer)

        if axis >= self.ndim:
            raise IndexError("Requested axis not found in manager")

        if axis == 0:
            new_blocks = self._slice_take_blocks_ax0(indexer, fill_value=fill_value)
        else:
            new_blocks = [
                blk.take_nd(
                    indexer,
                    axis=axis,
                    fill_value=(
                        fill_value if fill_value is not None else blk.fill_value
                    ),
                )
                for blk in self.blocks
            ]

        new_axes = list(self.axes)
        new_axes[axis] = new_axis

        return type(self).from_blocks(new_blocks, new_axes)

    def _slice_take_blocks_ax0(self, slice_or_indexer, fill_value=lib.no_default):
        """
        Slice/take blocks along axis=0.

        Overloaded for SingleBlock

        Returns
        -------
        new_blocks : list of Block
        """
        allow_fill = fill_value is not lib.no_default

        sl_type, slobj, sllen = _preprocess_slice_or_indexer(
            slice_or_indexer, self.shape[0], allow_fill=allow_fill
        )

        if self._is_single_block:
            blk = self.blocks[0]

            if sl_type in ("slice", "mask"):
                # GH#32959 EABlock would fail since we cant make 0-width
                # TODO(EA2D): special casing unnecessary with 2D EAs
                if sllen == 0:
                    return []
                return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))]
            elif not allow_fill or self.ndim == 1:
                if allow_fill and fill_value is None:
                    _, fill_value = maybe_promote(blk.dtype)

                return [
                    blk.take_nd(
                        slobj,
                        axis=0,
                        new_mgr_locs=slice(0, sllen),
                        fill_value=fill_value,
                    )
                ]

        if sl_type in ("slice", "mask"):
            blknos = self.blknos[slobj]
            blklocs = self.blklocs[slobj]
        else:
            blknos = algos.take_1d(
                self.blknos, slobj, fill_value=-1, allow_fill=allow_fill
            )
            blklocs = algos.take_1d(
                self.blklocs, slobj, fill_value=-1, allow_fill=allow_fill
            )

        # When filling blknos, make sure blknos is updated before appending to
        # blocks list, that way new blkno is exactly len(blocks).
        #
        # FIXME: mgr_groupby_blknos must return mgr_locs in ascending order,
        # pytables serialization will break otherwise.
        blocks = []
        for blkno, mgr_locs in libinternals.get_blkno_placements(blknos, group=True):
            if blkno == -1:
                # If we've got here, fill_value was not lib.no_default

                blocks.append(
                    self._make_na_block(placement=mgr_locs, fill_value=fill_value)
                )
            else:
                blk = self.blocks[blkno]

                # Otherwise, slicing along items axis is necessary.
                if not blk._can_consolidate:
                    # A non-consolidatable block, it's easy, because there's
                    # only one item and each mgr loc is a copy of that single
                    # item.
                    for mgr_loc in mgr_locs:
                        newblk = blk.copy(deep=False)
                        newblk.mgr_locs = slice(mgr_loc, mgr_loc + 1)
                        blocks.append(newblk)

                else:
                    blocks.append(
                        blk.take_nd(
                            blklocs[mgr_locs.indexer], axis=0, new_mgr_locs=mgr_locs,
                        )
                    )

        return blocks

    def _make_na_block(self, placement, fill_value=None):

        if fill_value is None:
            fill_value = np.nan
        block_shape = list(self.shape)
        block_shape[0] = len(placement)

        dtype, fill_value = infer_dtype_from_scalar(fill_value)
        block_values = np.empty(block_shape, dtype=dtype)
        block_values.fill(fill_value)
        return make_block(block_values, placement=placement)

    def take(self, indexer, axis: int = 1, verify: bool = True, convert: bool = True):
        """
        Take items along any axis.
        """
        self._consolidate_inplace()
        indexer = (
            np.arange(indexer.start, indexer.stop, indexer.step, dtype="int64")
            if isinstance(indexer, slice)
            else np.asanyarray(indexer, dtype="int64")
        )

        n = self.shape[axis]
        if convert:
            indexer = maybe_convert_indices(indexer, n)

        if verify:
            if ((indexer == -1) | (indexer >= n)).any():
                raise Exception("Indices must be nonzero and less than the axis length")

        new_labels = self.axes[axis].take(indexer)
        return self.reindex_indexer(
            new_axis=new_labels, indexer=indexer, axis=axis, allow_dups=True
        )

    def equals(self, other) -> bool:
        self_axes, other_axes = self.axes, other.axes
        if len(self_axes) != len(other_axes):
            return False
        if not all(ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
            return False
        self._consolidate_inplace()
        other._consolidate_inplace()
        if len(self.blocks) != len(other.blocks):
            return False

        # canonicalize block order, using a tuple combining the mgr_locs
        # then type name because there might be unconsolidated
        # blocks (say, Categorical) which can only be distinguished by
        # the iteration order
        def canonicalize(block):
            return (block.mgr_locs.as_array.tolist(), block.dtype.name)

        self_blocks = sorted(self.blocks, key=canonicalize)
        other_blocks = sorted(other.blocks, key=canonicalize)
        return all(
            block.equals(oblock) for block, oblock in zip(self_blocks, other_blocks)
        )

    def unstack(self, unstacker, fill_value) -> "BlockManager":
        """
        Return a BlockManager with all blocks unstacked..

        Parameters
        ----------
        unstacker : reshape._Unstacker
        fill_value : Any
            fill_value for newly introduced missing values.

        Returns
        -------
        unstacked : BlockManager
        """
        new_columns = unstacker.get_new_columns(self.items)
        new_index = unstacker.new_index

        new_blocks: List[Block] = []
        columns_mask: List[np.ndarray] = []

        for blk in self.blocks:
            blk_cols = self.items[blk.mgr_locs.indexer]
            new_items = unstacker.get_new_columns(blk_cols)
            new_placement = new_columns.get_indexer(new_items)

            blocks, mask = blk._unstack(
                unstacker, fill_value, new_placement=new_placement
            )

            new_blocks.extend(blocks)
            columns_mask.extend(mask)

        new_columns = new_columns[columns_mask]

        bm = BlockManager(new_blocks, [new_columns, new_index])
        return bm


class SingleBlockManager(BlockManager):
    """ manage a single block with """

    ndim = 1
    _is_consolidated = True
    _known_consolidated = True
    __slots__ = ()

    def __init__(
        self,
        block: Block,
        axis: Index,
        do_integrity_check: bool = False,
        fastpath=lib.no_default,
    ):
        assert isinstance(block, Block), type(block)
        assert isinstance(axis, Index), type(axis)

        if fastpath is not lib.no_default:
            warnings.warn(
                "The `fastpath` keyword is deprecated and will be removed "
                "in a future version.",
                FutureWarning,
                stacklevel=2,
            )

        self.axes = [axis]
        self.blocks = tuple([block])

    @classmethod
    def from_blocks(
        cls, blocks: List[Block], axes: List[Index]
    ) -> "SingleBlockManager":
        """
        Constructor for BlockManager and SingleBlockManager with same signature.
        """
        assert len(blocks) == 1
        assert len(axes) == 1
        return cls(blocks[0], axes[0], do_integrity_check=False)

    @classmethod
    def from_array(cls, array: ArrayLike, index: Index) -> "SingleBlockManager":
        """
        Constructor for if we have an array that is not yet a Block.
        """
        block = make_block(array, placement=slice(0, len(index)), ndim=1)
        return cls(block, index)

    def _post_setstate(self):
        pass

    @property
    def _block(self) -> Block:
        return self.blocks[0]

    @property
    def _blknos(self):
        """ compat with BlockManager """
        return None

    @property
    def _blklocs(self):
        """ compat with BlockManager """
        return None

    def get_slice(self, slobj: slice, axis: int = 0) -> "SingleBlockManager":
        if axis >= self.ndim:
            raise IndexError("Requested axis not found in manager")

        blk = self._block
        array = blk._slice(slobj)
        block = blk.make_block_same_class(array, placement=slice(0, len(array)))
        return type(self)(block, self.index[slobj])

    @property
    def index(self) -> Index:
        return self.axes[0]

    @property
    def dtype(self) -> DtypeObj:
        return self._block.dtype

    def get_dtype_counts(self):
        return {self.dtype.name: 1}

    def get_dtypes(self) -> np.ndarray:
        return np.array([self._block.dtype])

    def external_values(self):
        """The array that Series.values returns"""
        return self._block.external_values()

    def internal_values(self):
        """The array that Series._values returns"""
        return self._block.internal_values()

    @property
    def _can_hold_na(self) -> bool:
        return self._block._can_hold_na

    def is_consolidated(self) -> bool:
        return True

    def _consolidate_check(self):
        pass

    def _consolidate_inplace(self):
        pass

    def idelete(self, indexer):
        """
        Delete single location from SingleBlockManager.

        Ensures that self.blocks doesn't become empty.
        """
        self._block.delete(indexer)
        self.axes[0] = self.axes[0].delete(indexer)

    def fast_xs(self, loc):
        """
        fast path for getting a cross-section
        return a view of the data
        """
        raise NotImplementedError("Use series._values[loc] instead")


# --------------------------------------------------------------------
# Constructor Helpers


def create_block_manager_from_blocks(blocks, axes: List[Index]) -> BlockManager:
    try:
        if len(blocks) == 1 and not isinstance(blocks[0], Block):
            # if blocks[0] is of length 0, return empty blocks
            if not len(blocks[0]):
                blocks = []
            else:
                # It's OK if a single block is passed as values, its placement
                # is basically "all items", but if there're many, don't bother
                # converting, it's an error anyway.
                blocks = [
                    make_block(values=blocks[0], placement=slice(0, len(axes[0])))
                ]

        mgr = BlockManager(blocks, axes)
        mgr._consolidate_inplace()
        return mgr

    except ValueError as e:
        blocks = [getattr(b, "values", b) for b in blocks]
        tot_items = sum(b.shape[0] for b in blocks)
        raise construction_error(tot_items, blocks[0].shape[1:], axes, e)


def create_block_manager_from_arrays(
    arrays, names: Index, axes: List[Index]
) -> BlockManager:
    assert isinstance(names, Index)
    assert isinstance(axes, list)
    assert all(isinstance(x, Index) for x in axes)

    try:
        blocks = form_blocks(arrays, names, axes)
        mgr = BlockManager(blocks, axes)
        mgr._consolidate_inplace()
        return mgr
    except ValueError as e:
        raise construction_error(len(arrays), arrays[0].shape, axes, e)


def construction_error(tot_items, block_shape, axes, e=None):
    """ raise a helpful message about our construction """
    passed = tuple(map(int, [tot_items] + list(block_shape)))
    # Correcting the user facing error message during dataframe construction
    if len(passed) <= 2:
        passed = passed[::-1]

    implied = tuple(len(ax) for ax in axes)
    # Correcting the user facing error message during dataframe construction
    if len(implied) <= 2:
        implied = implied[::-1]

    # We return the exception object instead of raising it so that we
    #  can raise it in the caller; mypy plays better with that
    if passed == implied and e is not None:
        return e
    if block_shape[0] == 0:
        return ValueError("Empty data passed with indices specified.")
    return ValueError(f"Shape of passed values is {passed}, indices imply {implied}")


# -----------------------------------------------------------------------


def form_blocks(arrays, names: Index, axes) -> List[Block]:
    # put "leftover" items in float bucket, where else?
    # generalize?
    items_dict: DefaultDict[str, List] = defaultdict(list)
    extra_locs = []

    names_idx = names
    if names_idx.equals(axes[0]):
        names_indexer = np.arange(len(names_idx))
    else:
        assert names_idx.intersection(axes[0]).is_unique
        names_indexer = names_idx.get_indexer_for(axes[0])

    for i, name_idx in enumerate(names_indexer):
        if name_idx == -1:
            extra_locs.append(i)
            continue

        k = names[name_idx]
        v = arrays[name_idx]

        block_type = get_block_type(v)
        items_dict[block_type.__name__].append((i, k, v))

    blocks: List[Block] = []
    if len(items_dict["FloatBlock"]):
        float_blocks = _multi_blockify(items_dict["FloatBlock"])
        blocks.extend(float_blocks)

    if len(items_dict["ComplexBlock"]):
        complex_blocks = _multi_blockify(items_dict["ComplexBlock"])
        blocks.extend(complex_blocks)

    if len(items_dict["TimeDeltaBlock"]):
        timedelta_blocks = _multi_blockify(items_dict["TimeDeltaBlock"])
        blocks.extend(timedelta_blocks)

    if len(items_dict["IntBlock"]):
        int_blocks = _multi_blockify(items_dict["IntBlock"])
        blocks.extend(int_blocks)

    if len(items_dict["DatetimeBlock"]):
        datetime_blocks = _simple_blockify(items_dict["DatetimeBlock"], DT64NS_DTYPE)
        blocks.extend(datetime_blocks)

    if len(items_dict["DatetimeTZBlock"]):
        dttz_blocks = [
            make_block(array, klass=DatetimeTZBlock, placement=i)
            for i, _, array in items_dict["DatetimeTZBlock"]
        ]
        blocks.extend(dttz_blocks)

    if len(items_dict["BoolBlock"]):
        bool_blocks = _simple_blockify(items_dict["BoolBlock"], np.bool_)
        blocks.extend(bool_blocks)

    if len(items_dict["ObjectBlock"]) > 0:
        object_blocks = _simple_blockify(items_dict["ObjectBlock"], np.object_)
        blocks.extend(object_blocks)

    if len(items_dict["CategoricalBlock"]) > 0:
        cat_blocks = [
            make_block(array, klass=CategoricalBlock, placement=i)
            for i, _, array in items_dict["CategoricalBlock"]
        ]
        blocks.extend(cat_blocks)

    if len(items_dict["ExtensionBlock"]):

        external_blocks = [
            make_block(array, klass=ExtensionBlock, placement=i)
            for i, _, array in items_dict["ExtensionBlock"]
        ]

        blocks.extend(external_blocks)

    if len(items_dict["ObjectValuesExtensionBlock"]):
        external_blocks = [
            make_block(array, klass=ObjectValuesExtensionBlock, placement=i)
            for i, _, array in items_dict["ObjectValuesExtensionBlock"]
        ]

        blocks.extend(external_blocks)

    if len(extra_locs):
        shape = (len(extra_locs),) + tuple(len(x) for x in axes[1:])

        # empty items -> dtype object
        block_values = np.empty(shape, dtype=object)
        block_values.fill(np.nan)

        na_block = make_block(block_values, placement=extra_locs)
        blocks.append(na_block)

    return blocks


def _simple_blockify(tuples, dtype):
    """
    return a single array of a block that has a single dtype; if dtype is
    not None, coerce to this dtype
    """
    values, placement = _stack_arrays(tuples, dtype)

    # TODO: CHECK DTYPE?
    if dtype is not None and values.dtype != dtype:  # pragma: no cover
        values = values.astype(dtype)

    block = make_block(values, placement=placement)
    return [block]


def _multi_blockify(tuples, dtype=None):
    """ return an array of blocks that potentially have different dtypes """
    # group by dtype
    grouper = itertools.groupby(tuples, lambda x: x[2].dtype)

    new_blocks = []
    for dtype, tup_block in grouper:

        values, placement = _stack_arrays(list(tup_block), dtype)

        block = make_block(values, placement=placement)
        new_blocks.append(block)

    return new_blocks


def _stack_arrays(tuples, dtype):

    # fml
    def _asarray_compat(x):
        if isinstance(x, ABCSeries):
            return x._values
        else:
            return np.asarray(x)

    def _shape_compat(x):
        if isinstance(x, ABCSeries):
            return (len(x),)
        else:
            return x.shape

    placement, names, arrays = zip(*tuples)

    first = arrays[0]
    shape = (len(arrays),) + _shape_compat(first)

    stacked = np.empty(shape, dtype=dtype)
    for i, arr in enumerate(arrays):
        stacked[i] = _asarray_compat(arr)

    return stacked, placement


def _interleaved_dtype(blocks: Sequence[Block]) -> Optional[DtypeObj]:
    """
    Find the common dtype for `blocks`.

    Parameters
    ----------
    blocks : List[Block]

    Returns
    -------
    dtype : np.dtype, ExtensionDtype, or None
        None is returned when `blocks` is empty.
    """
    if not len(blocks):
        return None

    return find_common_type([b.dtype for b in blocks])


def _consolidate(blocks):
    """
    Merge blocks having same dtype, exclude non-consolidating blocks
    """
    # sort by _can_consolidate, dtype
    gkey = lambda x: x._consolidate_key
    grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)

    new_blocks = []
    for (_can_consolidate, dtype), group_blocks in grouper:
        merged_blocks = _merge_blocks(
            list(group_blocks), dtype=dtype, can_consolidate=_can_consolidate
        )
        new_blocks = _extend_blocks(merged_blocks, new_blocks)
    return new_blocks


def _merge_blocks(
    blocks: List[Block], dtype: DtypeObj, can_consolidate: bool
) -> List[Block]:

    if len(blocks) == 1:
        return blocks

    if can_consolidate:

        if dtype is None:
            if len({b.dtype for b in blocks}) != 1:
                raise AssertionError("_merge_blocks are invalid!")

        # TODO: 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)]

    # can't consolidate --> no merge
    return blocks


def _compare_or_regex_search(
    a: Union[ArrayLike, Scalar], b: Union[ArrayLike, Scalar], regex: bool = False
) -> Union[ArrayLike, bool]:
    """
    Compare two array_like inputs of the same shape or two scalar values

    Calls operator.eq or re.search, depending on regex argument. If regex is
    True, perform an element-wise regex matching.

    Parameters
    ----------
    a : array_like or scalar
    b : array_like or scalar
    regex : bool, default False

    Returns
    -------
    mask : array_like of bool
    """

    def _check_comparison_types(
        result: Union[ArrayLike, bool],
        a: Union[ArrayLike, Scalar],
        b: Union[ArrayLike, Scalar],
    ) -> Union[ArrayLike, bool]:
        """
        Raises an error if the two arrays (a,b) cannot be compared.
        Otherwise, returns the comparison result as expected.
        """
        if is_scalar(result) and (
            isinstance(a, np.ndarray) or isinstance(b, np.ndarray)
        ):
            type_names = [type(a).__name__, type(b).__name__]

            if isinstance(a, np.ndarray):
                type_names[0] = f"ndarray(dtype={a.dtype})"

            if isinstance(b, np.ndarray):
                type_names[1] = f"ndarray(dtype={b.dtype})"

            raise TypeError(
                f"Cannot compare types {repr(type_names[0])} and {repr(type_names[1])}"
            )
        return result

    if not regex:
        op = lambda x: operator.eq(x, b)
    else:
        op = np.vectorize(
            lambda x: bool(re.search(b, x))
            if isinstance(x, str) and isinstance(b, str)
            else False
        )

    # GH#32621 use mask to avoid comparing to NAs
    if isinstance(a, np.ndarray) and not isinstance(b, np.ndarray):
        mask = np.reshape(~(isna(a)), a.shape)
    elif isinstance(b, np.ndarray) and not isinstance(a, np.ndarray):
        mask = np.reshape(~(isna(b)), b.shape)
    elif isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
        mask = ~(isna(a) | isna(b))
    if isinstance(a, np.ndarray):
        a = a[mask]
    if isinstance(b, np.ndarray):
        b = b[mask]

    if is_datetimelike_v_numeric(a, b) or is_numeric_v_string_like(a, b):
        # GH#29553 avoid deprecation warnings from numpy
        return _check_comparison_types(False, a, b)

    result = op(a)

    if isinstance(result, np.ndarray):
        # The shape of the mask can differ to that of the result
        # since we may compare only a subset of a's or b's elements
        tmp = np.zeros(mask.shape, dtype=np.bool)
        tmp[mask] = result
        result = tmp

    return _check_comparison_types(result, a, b)


def _fast_count_smallints(arr: np.ndarray) -> np.ndarray:
    """Faster version of set(arr) for sequences of small numbers."""
    counts = np.bincount(arr.astype(np.int_))
    nz = counts.nonzero()[0]
    return np.c_[nz, counts[nz]]


def _preprocess_slice_or_indexer(slice_or_indexer, length: int, allow_fill: bool):
    if isinstance(slice_or_indexer, slice):
        return (
            "slice",
            slice_or_indexer,
            libinternals.slice_len(slice_or_indexer, length),
        )
    elif (
        isinstance(slice_or_indexer, np.ndarray) and slice_or_indexer.dtype == np.bool_
    ):
        return "mask", slice_or_indexer, slice_or_indexer.sum()
    else:
        indexer = np.asanyarray(slice_or_indexer, dtype=np.int64)
        if not allow_fill:
            indexer = maybe_convert_indices(indexer, length)
        return "fancy", indexer, len(indexer)