/pandas/core/internals.py

import copy
import itertools
import re
import operator
from datetime import datetime, timedelta, date
from collections import defaultdict

import numpy as np
from numpy import percentile as _quantile

from pandas.core.base import PandasObject

from pandas.types.dtypes import DatetimeTZDtype, CategoricalDtype
from pandas.types.common import (_TD_DTYPE, _NS_DTYPE,
                                 _ensure_int64, _ensure_platform_int,
                                 is_integer,
                                 is_dtype_equal,
                                 is_timedelta64_dtype,
                                 is_datetime64_dtype, is_datetimetz, is_sparse,
                                 is_categorical, is_categorical_dtype,
                                 is_integer_dtype,
                                 is_datetime64tz_dtype,
                                 is_object_dtype,
                                 is_datetimelike_v_numeric,
                                 is_numeric_v_string_like, is_extension_type,
                                 is_list_like,
                                 is_re,
                                 is_re_compilable,
                                 is_scalar,
                                 _get_dtype)
from pandas.types.cast import (_possibly_downcast_to_dtype,
                               _maybe_convert_string_to_object,
                               _maybe_upcast,
                               _maybe_convert_scalar, _maybe_promote,
                               _infer_dtype_from_scalar,
                               _soft_convert_objects,
                               _possibly_convert_objects,
                               _astype_nansafe,
                               _find_common_type)
from pandas.types.missing import (isnull, array_equivalent,
                                  _is_na_compat,
                                  is_null_datelike_scalar)
import pandas.types.concat as _concat

from pandas.types.generic import ABCSeries
from pandas.core.common import is_null_slice
import pandas.core.algorithms as algos

from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas.core.indexing import maybe_convert_indices, length_of_indexer
from pandas.core.categorical import Categorical, maybe_to_categorical
from pandas.tseries.index import DatetimeIndex
from pandas.formats.printing import pprint_thing

import pandas.core.missing as missing
from pandas.sparse.array import _maybe_to_sparse, SparseArray
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.computation.expressions as expressions
from pandas.util.decorators import cache_readonly

from pandas.tslib import Timedelta
from pandas import compat, _np_version_under1p9
from pandas.compat import range, map, zip, u

from pandas.lib import BlockPlacement


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_sparse = False
    _box_to_block_values = True
    _can_hold_na = False
    _downcast_dtype = None
    _can_consolidate = True
    _verify_integrity = True
    _validate_ndim = True
    _ftype = 'dense'
    _holder = None

    def __init__(self, values, placement, ndim=None, fastpath=False):
        if ndim is None:
            ndim = values.ndim
        elif values.ndim != ndim:
            raise ValueError('Wrong number of dimensions')
        self.ndim = ndim

        self.mgr_locs = placement
        self.values = values

        if ndim and len(self.mgr_locs) != len(self.values):
            raise ValueError('Wrong number of items passed %d, placement '
                             'implies %d' % (len(self.values),
                                             len(self.mgr_locs)))

    @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 is_categorical_dtype(dtype):
            if dtype == CategoricalDtype():
                return True

            # this is a pd.Categorical, but is not
            # a valid type for astypeing
            raise TypeError("invalid type {0} for astype".format(dtype))

        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 overriden to handle to_dense like operations
        """
        if is_object_dtype(dtype):
            return self.values.astype(object)
        return self.values

    def to_dense(self):
        return self.values.view()

    def to_object_block(self, mgr):
        """ return myself as an object block """
        values = self.get_values(dtype=object)
        return self.make_block(values, klass=ObjectBlock)

    @property
    def _na_value(self):
        return np.nan

    @property
    def fill_value(self):
        return np.nan

    @property
    def mgr_locs(self):
        return self._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, ndim=None, **kwargs):
        """
        Create a new block, with type inference propagate any values that are
        not specified
        """
        if placement is None:
            placement = self.mgr_locs
        if ndim is None:
            ndim = self.ndim

        return make_block(values, placement=placement, ndim=ndim, **kwargs)

    def make_block_scalar(self, values, **kwargs):
        """
        Create a ScalarBlock
        """
        return ScalarBlock(values)

    def make_block_same_class(self, values, placement=None, fastpath=True,
                              **kwargs):
        """ Wrap given values in a block of same type as self. """
        if placement is None:
            placement = self.mgr_locs
        return make_block(values, placement=placement, klass=self.__class__,
                          fastpath=fastpath, **kwargs)

    @mgr_locs.setter
    def mgr_locs(self, new_mgr_locs):
        if not isinstance(new_mgr_locs, BlockPlacement):
            new_mgr_locs = BlockPlacement(new_mgr_locs)

        self._mgr_locs = new_mgr_locs

    def __unicode__(self):

        # don't want to print out all of the items here
        name = pprint_thing(self.__class__.__name__)
        if self._is_single_block:

            result = '%s: %s dtype: %s' % (name, len(self), self.dtype)

        else:

            shape = ' x '.join([pprint_thing(s) for s in self.shape])
            result = '%s: %s, %s, dtype: %s' % (name, pprint_thing(
                self.mgr_locs.indexer), shape, self.dtype)

        return result

    def __len__(self):
        return len(self.values)

    def __getstate__(self):
        return self.mgr_locs.indexer, self.values

    def __setstate__(self, state):
        self.mgr_locs = 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 reshape_nd(self, labels, shape, ref_items, mgr=None):
        """
        Parameters
        ----------
        labels : list of new axis labels
        shape : new shape
        ref_items : new ref_items

        return a new block that is transformed to a nd block
        """

        return _block2d_to_blocknd(values=self.get_values().T,
                                   placement=self.mgr_locs, shape=shape,
                                   labels=labels, ref_items=ref_items)

    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 itemsize(self):
        return self.values.itemsize

    @property
    def dtype(self):
        return self.values.dtype

    @property
    def ftype(self):
        return "%s:%s" % (self.dtype, self._ftype)

    def merge(self, other):
        return _merge_blocks([self, other])

    def reindex_axis(self, indexer, method=None, axis=1, fill_value=None,
                     limit=None, mask_info=None):
        """
        Reindex using pre-computed indexer information
        """
        if axis < 1:
            raise AssertionError('axis must be at least 1, got %d' % axis)
        if fill_value is None:
            fill_value = self.fill_value

        new_values = algos.take_nd(self.values, indexer, axis,
                                   fill_value=fill_value, mask_info=mask_info)
        return self.make_block(new_values, fastpath=True)

    def get(self, item):
        loc = self.items.get_loc(item)
        return self.values[loc]

    def iget(self, i):
        return self.values[i]

    def set(self, locs, values, check=False):
        """
        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, mgr=None, **kwargs):
        """ apply the function to my values; return a block if we are not
        one
        """
        result = func(self.values, **kwargs)
        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,
               mgr=None):
        """ fillna on the block with the value. If we fail, then convert to
        ObjectBlock and try again
        """

        if not self._can_hold_na:
            if inplace:
                return self
            else:
                return self.copy()

        original_value = value
        mask = isnull(self.values)
        if limit is not None:
            if self.ndim > 2:
                raise NotImplementedError("number of dimensions for 'fillna' "
                                          "is currently limited to 2")
            mask[mask.cumsum(self.ndim - 1) > limit] = False

        # fillna, but if we cannot coerce, then try again as an ObjectBlock
        try:
            values, _, value, _ = self._try_coerce_args(self.values, value)
            blocks = self.putmask(mask, value, inplace=inplace)
            blocks = [b.make_block(values=self._try_coerce_result(b.values))
                      for b in blocks]
            return self._maybe_downcast(blocks, downcast)
        except (TypeError, ValueError):

            # we can't process the value, but nothing to do
            if not mask.any():
                return self if inplace else self.copy()

            # we cannot coerce the underlying object, so
            # make an ObjectBlock
            return self.to_object_block(mgr=mgr).fillna(original_value,
                                                        limit=limit,
                                                        inplace=inplace,
                                                        downcast=False)

    def _maybe_downcast(self, blocks, downcast=None):

        # no need to downcast our float
        # unless indicated
        if downcast is None and self.is_float:
            return blocks
        elif downcast is None and (self.is_timedelta or self.is_datetime):
            return blocks

        return _extend_blocks([b.downcast(downcast) for b in blocks])

    def downcast(self, dtypes=None, mgr=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 = _possibly_downcast_to_dtype(values, dtypes)
            return self.make_block(nv, fastpath=True)

        # 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")

        # item-by-item
        # this is expensive as it splits the blocks items-by-item
        blocks = []
        for i, rl in enumerate(self.mgr_locs):

            if dtypes == 'infer':
                dtype = 'infer'
            else:
                raise AssertionError("dtypes as dict is not supported yet")
                # TODO: This either should be completed or removed
                dtype = dtypes.get(item, self._downcast_dtype)  # noqa

            if dtype is None:
                nv = _block_shape(values[i], ndim=self.ndim)
            else:
                nv = _possibly_downcast_to_dtype(values[i], dtype)
                nv = _block_shape(nv, ndim=self.ndim)

            blocks.append(self.make_block(nv, fastpath=True, placement=[rl]))

        return blocks

    def astype(self, dtype, copy=False, raise_on_error=True, values=None,
               **kwargs):
        return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
                            values=values, **kwargs)

    def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
                klass=None, mgr=None, **kwargs):
        """
        Coerce to the new type (if copy=True, return a new copy)
        raise on an except if raise == True
        """

        # may need to convert to categorical
        # this is only called for non-categoricals
        if self.is_categorical_astype(dtype):
            return self.make_block(Categorical(self.values, **kwargs))

        # astype processing
        dtype = np.dtype(dtype)
        if self.dtype == dtype:
            if copy:
                return self.copy()
            return self

        if klass is None:
            if dtype == np.object_:
                klass = ObjectBlock
        try:
            # force the copy here
            if values is None:

                if issubclass(dtype.type,
                              (compat.text_type, compat.string_types)):

                    # use native type formatting for datetime/tz/timedelta
                    if self.is_datelike:
                        values = self.to_native_types()

                    # astype formatting
                    else:
                        values = self.values

                else:
                    values = self.get_values(dtype=dtype)

                # _astype_nansafe works fine with 1-d only
                values = _astype_nansafe(values.ravel(), dtype, copy=True)
                values = values.reshape(self.shape)

            newb = make_block(values, placement=self.mgr_locs, dtype=dtype,
                              klass=klass)
        except:
            if raise_on_error is True:
                raise
            newb = self.copy() if copy else self

        if newb.is_numeric and self.is_numeric:
            if newb.shape != self.shape:
                raise TypeError("cannot set astype for copy = [%s] for dtype "
                                "(%s [%s]) with smaller itemsize that current "
                                "(%s [%s])" % (copy, self.dtype.name,
                                               self.itemsize, newb.dtype.name,
                                               newb.itemsize))
        return newb

    def convert(self, copy=True, **kwargs):
        """ 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, value):
        raise NotImplementedError()

    def _try_cast(self, value):
        raise NotImplementedError()

    def _try_cast_result(self, result, dtype=None):
        """ try to cast the result to our original type, we may have
        roundtripped thru object in the mean-time
        """
        if dtype is None:
            dtype = self.dtype

        if self.is_integer or self.is_bool or self.is_datetime:
            pass
        elif self.is_float and result.dtype == self.dtype:

            # protect against a bool/object showing up here
            if isinstance(dtype, compat.string_types) and dtype == 'infer':
                return result
            if not isinstance(dtype, type):
                dtype = dtype.type
            if issubclass(dtype, (np.bool_, np.object_)):
                if issubclass(dtype, np.bool_):
                    if isnull(result).all():
                        return result.astype(np.bool_)
                    else:
                        result = result.astype(np.object_)
                        result[result == 1] = True
                        result[result == 0] = False
                        return result
                else:
                    return result.astype(np.object_)

            return result

        # may need to change the dtype here
        return _possibly_downcast_to_dtype(result, dtype)

    def _try_operate(self, values):
        """ return a version to operate on as the input """
        return values

    def _try_coerce_args(self, values, other):
        """ provide coercion to our input arguments """
        return values, False, other, False

    def _try_coerce_result(self, result):
        """ reverse of try_coerce_args """
        return result

    def _try_coerce_and_cast_result(self, result, dtype=None):
        result = self._try_coerce_result(result)
        result = self._try_cast_result(result, dtype=dtype)
        return result

    def _try_fill(self, value):
        return value

    def to_native_types(self, slicer=None, na_rep='nan', quoting=None,
                        **kwargs):
        """ convert to our native types format, slicing if desired """

        values = self.values
        if slicer is not None:
            values = values[:, slicer]
        mask = isnull(values)

        if not self.is_object and not quoting:
            values = values.astype(str)
        else:
            values = np.array(values, dtype='object')

        values[mask] = na_rep
        return values

    # block actions ####
    def copy(self, deep=True, mgr=None):
        """ copy constructor """
        values = self.values
        if deep:
            values = values.copy()
        return self.make_block_same_class(values)

    def replace(self, to_replace, value, inplace=False, filter=None,
                regex=False, convert=True, mgr=None):
        """ 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.
        """

        original_to_replace = to_replace
        mask = isnull(self.values)

        # try to replace, if we raise an error, convert to ObjectBlock and
        # retry
        try:
            values, _, to_replace, _ = self._try_coerce_args(self.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

            blocks = self.putmask(mask, value, inplace=inplace)
            if convert:
                blocks = [b.convert(by_item=True, numeric=False,
                                    copy=not inplace) for b in blocks]
            return blocks
        except (TypeError, ValueError):

            # we can't process the value, but nothing to do
            if not mask.any():
                return self if inplace else self.copy()

            return self.to_object_block(mgr=mgr).replace(
                to_replace=original_to_replace, value=value, inplace=inplace,
                filter=filter, regex=regex, convert=convert)

    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, mgr=None):
        """ set the value inplace; return a new block (of a possibly different
        dtype)

        indexer is a direct slice/positional indexer; value must be a
        compatible shape
        """

        # coerce None values, if appropriate
        if value is None:
            if self.is_numeric:
                value = np.nan

        # coerce args
        values, _, value, _ = self._try_coerce_args(self.values, value)
        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)

        transf = (lambda x: x.T) if self.ndim == 2 else (lambda x: x)
        values = transf(values)
        l = len(values)

        # length checking
        # boolean with truth values == len of the value is ok too
        if isinstance(indexer, (np.ndarray, list)):
            if is_list_like(value) and len(indexer) != len(value):
                if not (isinstance(indexer, np.ndarray) and
                        indexer.dtype == np.bool_ and
                        len(indexer[indexer]) == len(value)):
                    raise ValueError("cannot set using a list-like indexer "
                                     "with a different length than the value")

        # slice
        elif isinstance(indexer, slice):

            if is_list_like(value) and l:
                if len(value) != length_of_indexer(indexer, values):
                    raise ValueError("cannot set using a slice indexer with a "
                                     "different length than the value")

        try:

            def _is_scalar_indexer(indexer):
                # return True if we are all scalar indexers

                if arr_value.ndim == 1:
                    if not isinstance(indexer, tuple):
                        indexer = tuple([indexer])
                    return all([is_scalar(idx) for idx in indexer])
                return False

            def _is_empty_indexer(indexer):
                # return a boolean if we have an empty indexer

                if arr_value.ndim == 1:
                    if not isinstance(indexer, tuple):
                        indexer = tuple([indexer])
                    return any(isinstance(idx, np.ndarray) and len(idx) == 0
                               for idx in indexer)
                return False

            # empty indexers
            # 8669 (empty)
            if _is_empty_indexer(indexer):
                pass

            # setting a single element for each dim and with a rhs that could
            # be say a list
            # GH 6043
            elif _is_scalar_indexer(indexer):
                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
                  np.prod(arr_value.shape) == np.prod(values.shape)):
                values[indexer] = value
                values = values.astype(arr_value.dtype)

            # set
            else:
                values[indexer] = value

            # coerce and try to infer the dtypes of the result
            if hasattr(value, 'dtype') and is_dtype_equal(values.dtype,
                                                          value.dtype):
                dtype = value.dtype
            elif is_scalar(value):
                dtype, _ = _infer_dtype_from_scalar(value)
            else:
                dtype = 'infer'
            values = self._try_coerce_and_cast_result(values, dtype)
            block = self.make_block(transf(values), fastpath=True)

            # may have to soft convert_objects here
            if block.is_object and not self.is_object:
                block = block.convert(numeric=False)

            return block
        except ValueError:
            raise
        except TypeError:

            # cast to the passed dtype if possible
            # otherwise raise the original error
            try:
                # e.g. we are uint32 and our value is uint64
                # this is for compat with older numpies
                block = self.make_block(transf(values.astype(value.dtype)))
                return block.setitem(indexer=indexer, value=value, mgr=mgr)

            except:
                pass

            raise

        except Exception:
            pass

        return [self]

    def putmask(self, mask, new, align=True, inplace=False, axis=0,
                transpose=False, mgr=None):
        """ 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()

        if hasattr(new, 'reindex_axis'):
            new = new.values

        if hasattr(mask, 'reindex_axis'):
            mask = mask.values

        # if we are passed a scalar None, convert it here
        if not is_list_like(new) and isnull(new) and not self.is_object:
            new = self.fill_value

        if self._can_hold_element(new):
            if transpose:
                new_values = new_values.T

            new = self._try_cast(new)

            # If the default repeat behavior in np.putmask would go in the
            # wrong direction, then explictly 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)

            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))

            # need to go column by column
            new_blocks = []
            if self.ndim > 1:
                for i, ref_loc in enumerate(self.mgr_locs):
                    m = mask[i]
                    v = new_values[i]

                    # need a new block
                    if m.any():
                        if isinstance(new, np.ndarray):
                            n = np.squeeze(new[i % 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(v, m, n)
                    else:
                        nv = v if inplace else v.copy()

                    # Put back the dimension that was taken from it and make
                    # a block out of the result.
                    block = self.make_block(values=nv[np.newaxis],
                                            placement=[ref_loc], fastpath=True)

                    new_blocks.append(block)

            else:
                nv = _putmask_smart(new_values, mask, new)
                new_blocks.append(self.make_block(values=nv, fastpath=True))

            return new_blocks

        if inplace:
            return [self]

        if transpose:
            new_values = new_values.T

        return [self.make_block(new_values, fastpath=True)]

    def interpolate(self, method='pad', axis=0, index=None, values=None,
                    inplace=False, limit=None, limit_direction='forward',
                    fill_value=None, coerce=False, downcast=None, mgr=None,
                    **kwargs):
        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:
            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, mgr=mgr)
        # try an interp method
        try:
            m = missing.clean_interp_method(method, **kwargs)
        except:
            m = None

        if m is not None:
            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,
                                     fill_value=fill_value, inplace=inplace,
                                     downcast=downcast, mgr=mgr, **kwargs)

        raise ValueError("invalid method '{0}' to interpolate.".format(method))

    def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
                               limit=None, fill_value=None, coerce=False,
                               downcast=None, mgr=None):
        """ fillna but using the interpolate machinery """

        # 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()
        values, _, fill_value, _ = self._try_coerce_args(values, fill_value)
        values = self._try_operate(values)
        values = missing.interpolate_2d(values, method=method, axis=axis,
                                        limit=limit, fill_value=fill_value,
                                        dtype=self.dtype)
        values = self._try_coerce_result(values)

        blocks = [self.make_block(values, klass=self.__class__, fastpath=True)]
        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', inplace=False, downcast=None,
                     mgr=None, **kwargs):
        """ interpolate using scipy wrappers """

        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("{0} interpolation requires that the "
                                 "index be monotonic.".format(method))
        # 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,
                                          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(interp_values, klass=self.__class__,
                                  fastpath=True)]
        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 self.is_sparse:
            values = self.get_values()

        if fill_tuple is None:
            fill_value = self.fill_value
            new_values = algos.take_nd(values, indexer, axis=axis,
                                       allow_fill=False)
        else:
            fill_value = fill_tuple[0]
            new_values = algos.take_nd(values, indexer, axis=axis,
                                       allow_fill=True, fill_value=fill_value)

        if new_mgr_locs is None:
            if axis == 0:
                slc = lib.indexer_as_slice(indexer)
                if slc is not None:
                    new_mgr_locs = self.mgr_locs[slc]
                else:
                    new_mgr_locs = self.mgr_locs[indexer]
            else:
                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, axis=1, mgr=None):
        """ return block for the diff of the values """
        new_values = algos.diff(self.values, n, axis=axis)
        return [self.make_block(values=new_values, fastpath=True)]

    def shift(self, periods, axis=0, mgr=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)

        # 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, fastpath=True)]

    def eval(self, func, other, raise_on_error=True, try_cast=False, mgr=None):
        """
        evaluate the block; return result block from the result

        Parameters
        ----------
        func  : how to combine self, other
        other : a ndarray/object
        raise_on_error : if True, raise when I can't perform the function,
            False by default (and just return the data that we had coming in)
        try_cast : try casting the results to the input type

        Returns
        -------
        a new block, the result of the func
        """
        values = self.values

        if hasattr(other, 'reindex_axis'):
            other = other.values

        # make sure that we can broadcast
        is_transposed = False
        if hasattr(other, 'ndim') and hasattr(values, 'ndim'):
            if values.ndim != other.ndim:
                is_transposed = True
            else:
                if values.shape == other.shape[::-1]:
                    is_transposed = True
                elif values.shape[0] == other.shape[-1]:
                    is_transposed = True
                else:
                    # this is a broadcast error heree
                    raise ValueError("cannot broadcast shape [%s] with block "
                                     "values [%s]" % (values.T.shape,
                                                      other.shape))

        transf = (lambda x: x.T) if is_transposed else (lambda x: x)

        # coerce/transpose the args if needed
        values, values_mask, other, other_mask = self._try_coerce_args(
            transf(values), other)

        # get the result, may need to transpose the other
        def get_result(other):

            # avoid numpy warning of comparisons again None
            if other is None:
                result = not func.__name__ == 'eq'

            # avoid numpy warning of elementwise comparisons to object
            elif is_numeric_v_string_like(values, other):
                result = False

            else:
                result = func(values, other)

            # mask if needed
            if isinstance(values_mask, np.ndarray) and values_mask.any():
                result = result.astype('float64', copy=False)
                result[values_mask] = np.nan
            if other_mask is True:
                result = result.astype('float64', copy=False)
                result[:] = np.nan
            elif isinstance(other_mask, np.ndarray) and other_mask.any():
                result = result.astype('float64', copy=False)
                result[other_mask.ravel()] = np.nan

            return self._try_coerce_result(result)

        # error handler if we have an issue operating with the function
        def handle_error():

            if raise_on_error:
                raise TypeError('Could not operate %s with block values %s' %
                                (repr(other), str(detail)))
            else:
                # return the values
                result = np.empty(values.shape, dtype='O')
                result.fill(np.nan)
                return result

        # get the result
        try:
            result = get_result(other)

        # if we have an invalid shape/broadcast error
        # GH4576, so raise instead of allowing to pass through
        except ValueError as detail:
            raise
        except Exception as detail:
            result = handle_error()

        # technically a broadcast error in numpy can 'work' by returning a
        # boolean False
        if not isinstance(result, np.ndarray):
            if not isinstance(result, np.ndarray):

                # differentiate between an invalid ndarray-ndarray comparison
                # and an invalid type comparison
                if isinstance(values, np.ndarray) and is_list_like(other):
                    raise ValueError('Invalid broadcasting comparison [%s] '
                                     'with block values' % repr(other))

                raise TypeError('Could not compare [%s] with block values' %
                                repr(other))

        # transpose if needed
        result = transf(result)

        # try to cast if requested
        if try_cast:
            result = self._try_cast_result(result)

        return [self.make_block(result, fastpath=True, )]

    def where(self, other, cond, align=True, raise_on_error=True,
              try_cast=False, axis=0, transpose=False, mgr=None):
        """
        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
        raise_on_error : if True, raise when I can't perform the function,
            False by default (and just return the data that we had coming in)
        axis : int
        transpose : boolean
            Set to True if self is stored with axes reversed

        Returns
        -------
        a new block(s), the result of the func
        """

        values = self.values
        if transpose:
            values = values.T

        if hasattr(other, 'reindex_axis'):
            other = other.values

        if hasattr(cond, 'reindex_axis'):
            cond = cond.values

        # If the default broadcasting would go in the wrong direction, then
        # explictly 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, )))

        if not hasattr(cond, 'shape'):
            raise ValueError("where must have a condition that is ndarray "
                             "like")

        other = _maybe_convert_string_to_object(other)
        other = _maybe_convert_scalar(other)

        # our where function
        def func(cond, values, other):
            if cond.ravel().all():
                return values

            values, values_mask, other, other_mask = self._try_coerce_args(
                values, other)
            try:
                return self._try_coerce_result(expressions.where(
                    cond, values, other, raise_on_error=True))
            except Exception as detail:
                if raise_on_error:
                    raise TypeError('Could not operate [%s] with block values '
                                    '[%s]' % (repr(other), str(detail)))
                else:
                    # return the values
                    result = np.empty(values.shape, dtype='float64')
                    result.fill(np.nan)
                    return result

        # see if we can operate on the entire block, or need item-by-item
        # or if we are a single block (ndim == 1)
        result = func(cond, values, other)
        if self._can_hold_na or self.ndim == 1:

            if transpose:
                result = result.T

            # try to cast if requested
            if try_cast:
                result = self._try_cast_result(result)

            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():
                r = self._try_cast_result(result.take(m.nonzero()[0],
                                                      axis=axis))
                result_blocks.append(
                    self.make_block(r.T, placement=self.mgr_locs[m]))

        return result_blocks

    def equals(self, other):
        if self.dtype != other.dtype or self.shape != other.shape:
            return False
        return array_equivalent(self.values, other.values)

    def quantile(self, qs, interpolation='linear', axis=0, mgr=None):
        """
        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
        -------
        tuple of (axis, block)

        """
        if _np_version_under1p9:
            if interpolation != 'linear':
                raise ValueError("Interpolation methods other than linear "
                                 "are not supported in numpy < 1.9.")

        kw = {}
        if not _np_version_under1p9:
            kw.update({'interpolation': interpolation})

        values = self.get_values()
        values, _, _, _ = self._try_coerce_args(values, values)
        mask = isnull(self.values)
        if not lib.isscalar(mask) and mask.any():

            # even though this could be a 2-d mask it appears
            # as a 1-d result
            mask = mask.reshape(values.shape)
            result_shape = tuple([values.shape[0]] + [-1] * (self.ndim - 1))
            values = _block_shape(values[~mask], ndim=self.ndim)
            if self.ndim > 1:
                values = values.reshape(result_shape)

        from pandas import Float64Index
        is_empty = values.shape[axis] == 0
        if is_list_like(qs):
            ax = Float64Index(qs)

            if is_empty:
                if self.ndim == 1:
                    result = self._na_value
                else:
                    # create the array of na_values
                    # 2d len(values) * len(qs)
                    result = np.repeat(np.array([self._na_value] * len(qs)),
                                       len(values)).reshape(len(values),
                                                            len(qs))
            else:

                try:
                    result = _quantile(values, np.array(qs) * 100,
                                       axis=axis, **kw)
                except ValueError:

                    # older numpies don't handle an array for q
                    result = [_quantile(values, q * 100,
                                        axis=axis, **kw) for q in qs]

                result = np.array(result, copy=False)
                if self.ndim > 1:
                    result = result.T

        else:

            if self.ndim == 1:
                ax = Float64Index([qs])
            else:
                ax = mgr.axes[0]

            if is_empty:
                if self.ndim == 1:
                    result = self._na_value
                else:
                    result = np.array([self._na_value] * len(self))
            else:
                result = _quantile(values, qs * 100, axis=axis, **kw)

        ndim = getattr(result, 'ndim', None) or 0
        result = self._try_coerce_result(result)
        if is_scalar(result):
            return ax, self.make_block_scalar(result)
        return ax, make_block(result,
                              placement=np.arange(len(result)),
                              ndim=ndim)


class ScalarBlock(Block):
    """
    a scalar compat Block
    """
    __slots__ = ['_mgr_locs', 'values', 'ndim']

    def __init__(self, values):
        self.ndim = 0
        self.mgr_locs = [0]
        self.values = values

    @property
    def dtype(self):
        return type(self.values)

    @property
    def shape(self):
        return tuple([0])

    def __len__(self):
        return 0


class NonConsolidatableMixIn(object):
    """ hold methods for the nonconsolidatable blocks """
    _can_consolidate = False
    _verify_integrity = False
    _validate_ndim = False
    _holder = None

    def __init__(self, values, placement, ndim=None, fastpath=False, **kwargs):

        # Placement must be converted to BlockPlacement via property setter
        # before ndim logic, because placement may be a slice which doesn't
        # have a length.
        self.mgr_locs = placement

        # kludgetastic
        if ndim is None:
            if len(self.mgr_locs) != 1:
                ndim = 1
            else:
                ndim = 2
        self.ndim = ndim

        if not isinstance(values, self._holder):
            raise TypeError("values must be {0}".format(self._holder.__name__))

        self.values = values

    @property
    def shape(self):
        if self.ndim == 1:
            return (len(self.values)),
        return (len(self.mgr_locs), len(self.values))

    def get_values(self, dtype=None):
        """ need to to_dense myself (and always return a ndim sized object) """
        values = self.values.to_dense()
        if values.ndim == self.ndim - 1:
            values = values.reshape((1,) + values.shape)
        return values

    def iget(self, col):

        if self.ndim == 2 and isinstance(col, tuple):
            col, loc = col
            if not is_null_slice(col) and col != 0:
                raise IndexError("{0} only contains one item".format(self))
            return self.values[loc]
        else:
            if col != 0:
                raise IndexError("{0} only contains one item".format(self))
            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 get(self, item):
        if self.ndim == 1:
            loc = self.items.get_loc(item)
            return self.values[loc]
        else:
            return self.values

    def putmask(self, mask, new, align=True, inplace=False, axis=0,
                transpose=False, mgr=None):
        """
        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(s), the result of the putmask
        """
        new_values = self.values if inplace else self.values.copy()
        new_values, _, new, _ = self._try_coerce_args(new_values, new)

        if isinstance(new, np.ndarray) and len(new) == len(mask):
            new = new[mask]

        mask = _safe_reshape(mask, new_values.shape)
        new_values[mask] = new
        new_values = self._try_coerce_result(new_values)
        return [self.make_block(values=new_values)]

    def _slice(self, slicer):
        """ return a slice of my values (but densify first) """
        return self.get_values()[slicer]

    def _try_cast_result(self, result, dtype=None):
        return result


class NumericBlock(Block):
    __slots__ = ()
    is_numeric = True
    _can_hold_na = True


class FloatOrComplexBlock(NumericBlock):
    __slots__ = ()

    def equals(self, other):
        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
    _downcast_dtype = 'int64'

    def _can_hold_element(self, element):
        if is_list_like(element):
            element = np.array(element)
            tipo = element.dtype.type
            return (issubclass(tipo, (np.floating, np.integer)) and
                    not issubclass(tipo, (np.datetime64, np.timedelta64)))
        return (isinstance(element, (float, int, np.float_, np.int_)) and
                not isinstance(element, (bool, np.bool_, datetime, timedelta,
                                         np.datetime64, np.timedelta64)))

    def _try_cast(self, element):
        try:
            return float(element)
        except:  # pragma: no cover
            return element

    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 = isnull(values)

            if not quoting:
                values = values.astype(str)
            else:
                values = np.array(values, dtype='object')

            values[mask] = na_rep
            return values

        from pandas.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):
        if is_list_like(element):
            element = np.array(element)
            return issubclass(element.dtype.type,
                              (np.floating, np.integer, np.complexfloating))
        return (isinstance(element,
                           (float, int, complex, np.float_, np.int_)) and
                not isinstance(bool, np.bool_))

    def _try_cast(self, element):
        try:
            return complex(element)
        except:  # pragma: no cover
            return element

    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):
        if is_list_like(element):
            element = np.array(element)
            tipo = element.dtype.type
            return (issubclass(tipo, np.integer) and
                    not issubclass(tipo, (np.datetime64, np.timedelta64)))
        return is_integer(element)

    def _try_cast(self, element):
        try:
            return int(element)
        except:  # pragma: no cover
            return element

    def should_store(self, value):
        return is_integer_dtype(value) and value.dtype == self.dtype


class DatetimeLikeBlockMixin(object):

    @property
    def _na_value(self):
        return tslib.NaT

    @property
    def fill_value(self):
        return tslib.iNaT

    def _try_operate(self, values):
        """ return a version to operate on """
        return values.view('i8')

    def get_values(self, dtype=None):
        """
        return object dtype as boxed values, such as Timestamps/Timedelta
        """
        if is_object_dtype(dtype):
            return lib.map_infer(self.values.ravel(),
                                 self._box_func).reshape(self.values.shape)
        return self.values


class TimeDeltaBlock(DatetimeLikeBlockMixin, IntBlock):
    __slots__ = ()
    is_timedelta = True
    _can_hold_na = True
    is_numeric = False

    @property
    def _box_func(self):
        return lambda x: tslib.Timedelta(x, unit='ns')

    def fillna(self, value, **kwargs):

        # allow filling with integers to be
        # interpreted as seconds
        if not isinstance(value, np.timedelta64) and is_integer(value):
            value = Timedelta(value, unit='s')
        return super(TimeDeltaBlock, self).fillna(value, **kwargs)

    def _try_coerce_args(self, values, other):
        """
        Coerce values and other to int64, with null values converted to
        iNaT. values is always ndarray-like, other may not be

        Parameters
        ----------
        values : ndarray-like
        other : ndarray-like or scalar

        Returns
        -------
        base-type values, values mask, base-type other, other mask
        """

        values_mask = isnull(values)
        values = values.view('i8')
        other_mask = False

        if isinstance(other, bool):
            raise TypeError
        elif is_null_datelike_scalar(other):
            other = tslib.iNaT
            other_mask = True
        elif isinstance(other, Timedelta):
            other_mask = isnull(other)
            other = other.value
        elif isinstance(other, np.timedelta64):
            other_mask = isnull(other)
            other = other.view('i8')
        elif isinstance(other, timedelta):
            other = Timedelta(other).value
        elif isinstance(other, np.ndarray):
            other_mask = isnull(other)
            other = other.astype('i8', copy=False).view('i8')
        else:
            # scalar
            other = Timedelta(other)
            other_mask = isnull(other)
            other = other.value

        return values, values_mask, other, other_mask

    def _try_coerce_result(self, result):
        """ reverse of try_coerce_args / try_operate """
        if isinstance(result, np.ndarray):
            mask = isnull(result)
            if result.dtype.kind in ['i', 'f', 'O']:
                result = result.astype('m8[ns]')
            result[mask] = tslib.iNaT
        elif isinstance(result, (np.integer, np.float)):
            result = self._box_func(result)
        return result

    def should_store(self, value):
        return issubclass(value.dtype.type, np.timedelta64)

    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 = isnull(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


class BoolBlock(NumericBlock):
    __slots__ = ()
    is_bool = True
    _can_hold_na = False

    def _can_hold_element(self, element):
        if is_list_like(element):
            element = np.array(element)
            return issubclass(element.dtype.type, np.integer)
        return isinstance(element, (int, bool))

    def _try_cast(self, element):
        try:
            return bool(element)
        except:  # pragma: no cover
            return element

    def should_store(self, value):
        return issubclass(value.dtype.type, np.bool_)

    def replace(self, to_replace, value, inplace=False, filter=None,
                regex=False, mgr=None):
        to_replace_values = np.atleast_1d(to_replace)
        if not np.can_cast(to_replace_values, bool):
            return self
        return super(BoolBlock, self).replace(to_replace, value,
                                              inplace=inplace, filter=filter,
                                              regex=regex, mgr=mgr)


class ObjectBlock(Block):
    __slots__ = ()
    is_object = True
    _can_hold_na = True

    def __init__(self, values, ndim=2, fastpath=False, placement=None,
                 **kwargs):
        if issubclass(values.dtype.type, compat.string_types):
            values = np.array(values, dtype=object)

        super(ObjectBlock, self).__init__(values, ndim=ndim, fastpath=fastpath,
                                          placement=placement, **kwargs)

    @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())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
    def convert(self, *args, **kwargs):
        """ 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!
        """

        if args:
            raise NotImplementedError
        by_item = True if 'by_item' not in kwargs else kwargs['by_item']

        new_inputs = ['coerce', 'datetime', 'numeric', 'timedelta']
        new_style = False
        for kw in new_inputs:
            new_style |= kw in kwargs

        if new_style:
            fn = _soft_convert_objects
            fn_inputs = new_inputs
        else:
            fn = _possibly_convert_objects
            fn_inputs = ['convert_dates', 'convert_numeric',
                         'convert_timedeltas']
        fn_inputs += ['copy']

        fn_kwargs = {}
        for key in fn_inputs:
            if key in kwargs:
                fn_kwargs[key] = kwargs[key]

        # attempt to create new type blocks
        blocks = []
        if by_item and not self._is_single_block:

            for i, rl in enumerate(self.mgr_locs):
                values = self.iget(i)

                shape = values.shape
                values = fn(values.ravel(), **fn_kwargs)
                try:
                    values = values.reshape(shape)
                    values = _block_shape(values, ndim=self.ndim)
                except (AttributeError, NotImplementedError):
                    pass
                newb = make_block(values, ndim=self.ndim, placement=[rl])
                blocks.append(newb)

        else:
            values = fn(
                self.values.ravel(), **fn_kwargs).reshape(self.values.shape)
            blocks.append(make_block(values, ndim=self.ndim,
                                     placement=self.mgr_locs))

        return blocks

    def set(self, locs, values, check=False):
        """
        Modify Block in-place with new item value

        Returns
        -------
        None
        """

        # GH6026
        if check:
            try:
                if (self.values[locs] == values).all():
                    return
            except:
                pass
        try:
            self.values[locs] = values
        except (ValueError):

            # broadcasting error
            # see GH6171
            new_shape = list(values.shape)
            new_shape[0] = len(self.items)
            self.values = np.empty(tuple(new_shape), dtype=self.dtype)
            self.values.fill(np.nan)
            self.values[locs] = values

    def _maybe_downcast(self, blocks, downcast=None):

        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):
        return True

    def _try_cast(self, element):
        return element

    def should_store(self, value):
        return not (issubclass(value.dtype.type,
                               (np.integer, np.floating, np.complexfloating,
                                np.datetime64, np.bool_)) or
                    is_extension_type(value))

    def replace(self, to_replace, value, inplace=False, filter=None,
                regex=False, convert=True, mgr=None):
        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, mgr=mgr)
        elif not (either_list or regex):
            return super(ObjectBlock, self).replace(to_replace, value,
                                                    inplace=inplace,
                                                    filter=filter, regex=regex,
                                                    convert=convert, mgr=mgr)
        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, mgr=mgr)
                    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, mgr=mgr)
                    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, mgr=mgr)

    def _replace_single(self, to_replace, value, inplace=False, filter=None,
                        regex=False, convert=True, mgr=None):
        # 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
        try:
            pattern = to_replace.pattern
        except AttributeError:
            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(ObjectBlock, self).replace(to_replace, value,
                                                    inplace=inplace,
                                                    filter=filter, regex=regex,
                                                    mgr=mgr)

        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 isnull(value) or not isinstance(value, compat.string_types):

            def re_replacer(s):
                try:
                    return value if rx.search(s) is not None else s
                except TypeError:
                    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):
                try:
                    return rx.sub(value, s)
                except TypeError:
                    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]

        new_values[filt] = f(new_values[filt])

        # convert
        block = self.make_block(new_values)
        if convert:
            block = block.convert(by_item=True, numeric=False)

        return block


class CategoricalBlock(NonConsolidatableMixIn, ObjectBlock):
    __slots__ = ()
    is_categorical = True
    _verify_integrity = True
    _can_hold_na = True
    _holder = Categorical

    def __init__(self, values, placement, fastpath=False, **kwargs):

        # coerce to categorical if we can
        super(CategoricalBlock, self).__init__(maybe_to_categorical(values),
                                               fastpath=True,
                                               placement=placement, **kwargs)

    @property
    def is_view(self):
        """ I am never a view """
        return False

    def to_dense(self):
        return self.values.to_dense().view()

    def convert(self, copy=True, **kwargs):
        return self.copy() if copy else self

    @property
    def array_dtype(self):
        """ the dtype to return if I want to construct this block as an
        array
        """
        return np.object_

    def _slice(self, slicer):
        """ return a slice of my values """

        # slice the category
        # return same dims as we currently have
        return self.values._slice(slicer)

    def _try_coerce_result(self, result):
        """ reverse of try_coerce_args """

        # GH12564: CategoricalBlock is 1-dim only
        # while returned results could be any dim
        if ((not is_categorical_dtype(result)) and
                isinstance(result, np.ndarray)):
            result = _block_shape(result, ndim=self.ndim)

        return result

    def fillna(self, value, limit=None, inplace=False, downcast=None,
               mgr=None):
        # we may need to upcast our fill to match our dtype
        if limit is not None:
            raise NotImplementedError("specifying a limit for 'fillna' has "
                                      "not been implemented yet")

        values = self.values if inplace else self.values.copy()
        values = self._try_coerce_result(values.fillna(value=value,
                                                       limit=limit))
        return [self.make_block(values=values)]

    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(fill_value=fill_value, method=method,
                                 limit=limit),
            placement=self.mgr_locs)

    def shift(self, periods, axis=0, mgr=None):
        return self.make_block_same_class(values=self.values.shift(periods),
                                          placement=self.mgr_locs)

    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.bb
        """
        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_nd(indexer, fill_value=fill_value)

        # if we are a 1-dim object, then always place at 0
        if self.ndim == 1:
            new_mgr_locs = [0]
        else:
            if new_mgr_locs is None:
                new_mgr_locs = self.mgr_locs

        return self.make_block_same_class(new_values, new_mgr_locs)

    def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
                klass=None, mgr=None):
        """
        Coerce to the new type (if copy=True, return a new copy)
        raise on an except if raise == True
        """

        if self.is_categorical_astype(dtype):
            values = self.values
        else:
            values = np.asarray(self.values).astype(dtype, copy=False)

        if copy:
            values = values.copy()

        return self.make_block(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 = isnull(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))


class DatetimeBlock(DatetimeLikeBlockMixin, Block):
    __slots__ = ()
    is_datetime = True
    _can_hold_na = True

    def __init__(self, values, placement, fastpath=False, **kwargs):
        if values.dtype != _NS_DTYPE:
            values = tslib.cast_to_nanoseconds(values)

        super(DatetimeBlock, self).__init__(values, fastpath=True,
                                            placement=placement, **kwargs)

    def _astype(self, dtype, mgr=None, **kwargs):
        """
        these automatically copy, so copy=True has no effect
        raise on an except if raise == True
        """

        # if we are passed a datetime64[ns, tz]
        if is_datetime64tz_dtype(dtype):
            dtype = DatetimeTZDtype(dtype)

            values = self.values
            if getattr(values, 'tz', None) is None:
                values = DatetimeIndex(values).tz_localize('UTC')
            values = values.tz_convert(dtype.tz)
            return self.make_block(values)

        # delegate
        return super(DatetimeBlock, self)._astype(dtype=dtype, **kwargs)

    def _can_hold_element(self, element):
        if is_list_like(element):
            element = np.array(element)
            return element.dtype == _NS_DTYPE or element.dtype == np.int64
        return (is_integer(element) or isinstance(element, datetime) or
                isnull(element))

    def _try_cast(self, element):
        try:
            return int(element)
        except:
            return element

    def _try_coerce_args(self, values, other):
        """
        Coerce values and other to dtype 'i8'. NaN and NaT convert to
        the smallest i8, and will correctly round-trip to NaT if converted
        back in _try_coerce_result. values is always ndarray-like, other
        may not be

        Parameters
        ----------
        values : ndarray-like
        other : ndarray-like or scalar

        Returns
        -------
        base-type values, values mask, base-type other, other mask
        """

        values_mask = isnull(values)
        values = values.view('i8')
        other_mask = False

        if isinstance(other, bool):
            raise TypeError
        elif is_null_datelike_scalar(other):
            other = tslib.iNaT
            other_mask = True
        elif isinstance(other, (datetime, np.datetime64, date)):
            other = self._box_func(other)
            if getattr(other, 'tz') is not None:
                raise TypeError("cannot coerce a Timestamp with a tz on a "
                                "naive Block")
            other_mask = isnull(other)
            other = other.asm8.view('i8')
        elif hasattr(other, 'dtype') and is_integer_dtype(other):
            other = other.view('i8')
        else:
            try:
                other = np.asarray(other)
                other_mask = isnull(other)

                other = other.astype('i8', copy=False).view('i8')
            except ValueError:

                # coercion issues
                # let higher levels handle
                raise TypeError

        return values, values_mask, other, other_mask

    def _try_coerce_result(self, result):
        """ reverse of try_coerce_args """
        if isinstance(result, np.ndarray):
            if result.dtype.kind in ['i', 'f', 'O']:
                try:
                    result = result.astype('M8[ns]')
                except ValueError:
                    pass
        elif isinstance(result, (np.integer, np.float, np.datetime64)):
            result = self._box_func(result)
        return result

    @property
    def _box_func(self):
        return tslib.Timestamp

    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
        if slicer is not None:
            values = values[..., slicer]

        from pandas.formats.format import _get_format_datetime64_from_values
        format = _get_format_datetime64_from_values(values, date_format)

        result = tslib.format_array_from_datetime(
            values.view('i8').ravel(), tz=getattr(self.values, 'tz', None),
            format=format, na_rep=na_rep).reshape(values.shape)
        return np.atleast_2d(result)

    def should_store(self, value):
        return (issubclass(value.dtype.type, np.datetime64) and
                not is_datetimetz(value))

    def set(self, locs, values, check=False):
        """
        Modify Block in-place with new item value

        Returns
        -------
        None
        """
        if values.dtype != _NS_DTYPE:
            # Workaround for numpy 1.6 bug
            values = tslib.cast_to_nanoseconds(values)

        self.values[locs] = values


class DatetimeTZBlock(NonConsolidatableMixIn, DatetimeBlock):
    """ implement a datetime64 block with a tz attribute """
    __slots__ = ()
    _holder = DatetimeIndex
    is_datetimetz = True

    def __init__(self, values, placement, ndim=2, **kwargs):

        if not isinstance(values, self._holder):
            values = self._holder(values)

        dtype = kwargs.pop('dtype', None)

        if dtype is not None:
            if isinstance(dtype, compat.string_types):
                dtype = DatetimeTZDtype.construct_from_string(dtype)
            values = values.tz_localize('UTC').tz_convert(dtype.tz)

        if values.tz is None:
            raise ValueError("cannot create a DatetimeTZBlock without a tz")

        super(DatetimeTZBlock, self).__init__(values, placement=placement,
                                              ndim=ndim, **kwargs)

    def copy(self, deep=True, mgr=None):
        """ copy constructor """
        values = self.values
        if deep:
            values = values.copy(deep=True)
        return self.make_block_same_class(values)

    def external_values(self):
        """ we internally represent the data as a DatetimeIndex, but for
        external compat with ndarray, export as a ndarray of Timestamps
        """
        return self.values.astype('datetime64[ns]').values

    def get_values(self, dtype=None):
        # return object dtype as Timestamps with the zones
        if is_object_dtype(dtype):
            f = lambda x: lib.Timestamp(x, tz=self.values.tz)
            return lib.map_infer(
                self.values.ravel(), f).reshape(self.values.shape)
        return self.values

    def to_object_block(self, mgr):
        """
        return myself as an object block

        Since we keep the DTI as a 1-d object, this is different
        depends on BlockManager's ndim
        """
        values = self.get_values(dtype=object)
        kwargs = {}
        if mgr.ndim > 1:
            values = _block_shape(values, ndim=mgr.ndim)
            kwargs['ndim'] = mgr.ndim
            kwargs['placement'] = [0]
        return self.make_block(values, klass=ObjectBlock, **kwargs)

    def _slice(self, slicer):
        """ return a slice of my values """
        if isinstance(slicer, tuple):
            col, loc = slicer
            if not is_null_slice(col) and col != 0:
                raise IndexError("{0} only contains one item".format(self))
            return self.values[loc]
        return self.values[slicer]

    def _try_coerce_args(self, values, other):
        """
        localize and return i8 for the values

        Parameters
        ----------
        values : ndarray-like
        other : ndarray-like or scalar

        Returns
        -------
        base-type values, values mask, base-type other, other mask
        """
        values_mask = _block_shape(isnull(values), ndim=self.ndim)
        values = _block_shape(values.tz_localize(None).asi8, ndim=self.ndim)
        other_mask = False

        if isinstance(other, ABCSeries):
            other = self._holder(other)
            other_mask = isnull(other)
        if isinstance(other, bool):
            raise TypeError
        elif is_null_datelike_scalar(other):
            other = tslib.iNaT
            other_mask = True
        elif isinstance(other, self._holder):
            if other.tz != self.values.tz:
                raise ValueError("incompatible or non tz-aware value")
            other = other.tz_localize(None).asi8
            other_mask = isnull(other)
        elif isinstance(other, (np.datetime64, datetime, date)):
            other = lib.Timestamp(other)
            tz = getattr(other, 'tz', None)

            # test we can have an equal time zone
            if tz is None or str(tz) != str(self.values.tz):
                raise ValueError("incompatible or non tz-aware value")
            other_mask = isnull(other)
            other = other.tz_localize(None).value

        return values, values_mask, other, other_mask

    def _try_coerce_result(self, result):
        """ reverse of try_coerce_args """
        if isinstance(result, np.ndarray):
            if result.dtype.kind in ['i', 'f', 'O']:
                result = result.astype('M8[ns]')
        elif isinstance(result, (np.integer, np.float, np.datetime64)):
            result = lib.Timestamp(result).tz_localize(self.values.tz)
        if isinstance(result, np.ndarray):
            # allow passing of > 1dim if its trivial
            if result.ndim > 1:
                result = result.reshape(len(result))
            result = self._holder(result).tz_localize(self.values.tz)

        return result

    @property
    def _box_func(self):
        return lambda x: tslib.Timestamp(x, tz=self.dtype.tz)

    def shift(self, periods, axis=0, mgr=None):
        """ shift the block by periods """

        # think about moving this to the DatetimeIndex. This is a non-freq
        # (number of periods) shift ###

        N = len(self)
        indexer = np.zeros(N, dtype=int)
        if periods > 0:
            indexer[periods:] = np.arange(N - periods)
        else:
            indexer[:periods] = np.arange(-periods, N)

        new_values = self.values.asi8.take(indexer)

        if periods > 0:
            new_values[:periods] = tslib.iNaT
        else:
            new_values[periods:] = tslib.iNaT

        new_values = self.values._shallow_copy(new_values)
        return [self.make_block_same_class(new_values,
                                           placement=self.mgr_locs)]


class SparseBlock(NonConsolidatableMixIn, Block):
    """ implement as a list of sparse arrays of the same dtype """
    __slots__ = ()
    is_sparse = True
    is_numeric = True
    _box_to_block_values = False
    _can_hold_na = True
    _ftype = 'sparse'
    _holder = SparseArray

    @property
    def shape(self):
        return (len(self.mgr_locs), self.sp_index.length)

    @property
    def itemsize(self):
        return self.dtype.itemsize

    @property
    def fill_value(self):
        # return np.nan
        return self.values.fill_value

    @fill_value.setter
    def fill_value(self, v):
        # we may need to upcast our fill to match our dtype
        if issubclass(self.dtype.type, np.floating):
            v = float(v)
        self.values.fill_value = v

    def to_dense(self):
        return self.values.to_dense().view()

    @property
    def sp_values(self):
        return self.values.sp_values

    @sp_values.setter
    def sp_values(self, v):
        # reset the sparse values
        self.values = SparseArray(v, sparse_index=self.sp_index,
                                  kind=self.kind, dtype=v.dtype,
                                  fill_value=self.values.fill_value,
                                  copy=False)

    @property
    def sp_index(self):
        return self.values.sp_index

    @property
    def kind(self):
        return self.values.kind

    def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
                klass=None, mgr=None, **kwargs):
        if values is None:
            values = self.values
        values = values.astype(dtype, copy=copy)
        return self.make_block_same_class(values=values,
                                          placement=self.mgr_locs)

    def __len__(self):
        try:
            return self.sp_index.length
        except:
            return 0

    def copy(self, deep=True, mgr=None):
        return self.make_block_same_class(values=self.values,
                                          sparse_index=self.sp_index,
                                          kind=self.kind, copy=deep,
                                          placement=self.mgr_locs)

    def make_block_same_class(self, values, placement, sparse_index=None,
                              kind=None, dtype=None, fill_value=None,
                              copy=False, fastpath=True, **kwargs):
        """ return a new block """
        if dtype is None:
            dtype = values.dtype
        if fill_value is None and not isinstance(values, SparseArray):
            fill_value = self.values.fill_value

        # if not isinstance(values, SparseArray) and values.ndim != self.ndim:
        #     raise ValueError("ndim mismatch")

        if values.ndim == 2:
            nitems = values.shape[0]

            if nitems == 0:
                # kludgy, but SparseBlocks cannot handle slices, where the
                # output is 0-item, so let's convert it to a dense block: it
                # won't take space since there's 0 items, plus it will preserve
                # the dtype.
                return self.make_block(np.empty(values.shape, dtype=dtype),
                                       placement,
                                       fastpath=True)
            elif nitems > 1:
                raise ValueError("Only 1-item 2d sparse blocks are supported")
            else:
                values = values.reshape(values.shape[1])

        new_values = SparseArray(values, sparse_index=sparse_index,
                                 kind=kind or self.kind, dtype=dtype,
                                 fill_value=fill_value, copy=copy)
        return self.make_block(new_values, fastpath=fastpath,
                               placement=placement)

    def interpolate(self, method='pad', axis=0, inplace=False, limit=None,
                    fill_value=None, **kwargs):

        values = missing.interpolate_2d(self.values.to_dense(), method, axis,
                                        limit, fill_value)
        return self.make_block_same_class(values=values,
                                          placement=self.mgr_locs)

    def fillna(self, value, limit=None, inplace=False, downcast=None,
               mgr=None):
        # we may need to upcast our fill to match our dtype
        if limit is not None:
            raise NotImplementedError("specifying a limit for 'fillna' has "
                                      "not been implemented yet")
        values = self.values if inplace else self.values.copy()
        values = values.fillna(value, downcast=downcast)
        return [self.make_block_same_class(values=values,
                                           placement=self.mgr_locs)]

    def shift(self, periods, axis=0, mgr=None):
        """ shift the block by periods """
        N = len(self.values.T)
        indexer = np.zeros(N, dtype=int)
        if periods > 0:
            indexer[periods:] = np.arange(N - periods)
        else:
            indexer[:periods] = np.arange(-periods, N)
        new_values = self.values.to_dense().take(indexer)
        # convert integer to float if necessary. need to do a lot more than
        # that, handle boolean etc also
        new_values, fill_value = _maybe_upcast(new_values)
        if periods > 0:
            new_values[:periods] = fill_value
        else:
            new_values[periods:] = fill_value
        return [self.make_block_same_class(new_values,
                                           placement=self.mgr_locs)]

    def reindex_axis(self, indexer, method=None, axis=1, fill_value=None,
                     limit=None, mask_info=None):
        """
        Reindex using pre-computed indexer information
        """
        if axis < 1:
            raise AssertionError('axis must be at least 1, got %d' % axis)

        # taking on the 0th axis always here
        if fill_value is None:
            fill_value = self.fill_value
        return self.make_block_same_class(self.values.take(indexer),
                                          fill_value=fill_value,
                                          placement=self.mgr_locs)

    def sparse_reindex(self, new_index):
        """ sparse reindex and return a new block
            current reindex only works for float64 dtype! """
        values = self.values
        values = values.sp_index.to_int_index().reindex(
            values.sp_values.astype('float64'), values.fill_value, new_index)
        return self.make_block_same_class(values, sparse_index=new_index,
                                          placement=self.mgr_locs)


def make_block(values, placement, klass=None, ndim=None, dtype=None,
               fastpath=False):
    if klass is None:
        dtype = dtype or values.dtype
        vtype = dtype.type

        if isinstance(values, SparseArray):
            klass = SparseBlock
        elif issubclass(vtype, np.floating):
            klass = FloatBlock
        elif (issubclass(vtype, np.integer) and
              issubclass(vtype, np.timedelta64)):
            klass = TimeDeltaBlock
        elif (issubclass(vtype, np.integer) and
              not issubclass(vtype, np.datetime64)):
            klass = IntBlock
        elif dtype == np.bool_:
            klass = BoolBlock
        elif issubclass(vtype, np.datetime64):
            if hasattr(values, 'tz'):
                klass = DatetimeTZBlock
            else:
                klass = DatetimeBlock
        elif is_datetimetz(values):
            klass = DatetimeTZBlock
        elif issubclass(vtype, np.complexfloating):
            klass = ComplexBlock
        elif is_categorical(values):
            klass = CategoricalBlock
        else:
            klass = ObjectBlock

    elif klass is DatetimeTZBlock and not is_datetimetz(values):
        return klass(values, ndim=ndim, fastpath=fastpath,
                     placement=placement, dtype=dtype)

    return klass(values, ndim=ndim, fastpath=fastpath, placement=placement)

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


class BlockManager(PandasObject):
    """
    Core internal data structure to implement DataFrame, Series, Panel, 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_ftype_counts
    get_dtypes
    get_ftypes

    apply(func, axes, block_filter_fn)

    get_bool_data
    get_numeric_data

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

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

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

    Parameters
    ----------


    Notes
    -----
    This is *not* a public API class
    """
    __slots__ = ['axes', 'blocks', '_ndim', '_shape', '_known_consolidated',
                 '_is_consolidated', '_blknos', '_blklocs']

    def __init__(self, blocks, axes, do_integrity_check=True, fastpath=True):
        self.axes = [_ensure_index(ax) for ax in axes]
        self.blocks = tuple(blocks)

        for block in blocks:
            if block.is_sparse:
                if len(block.mgr_locs) != 1:
                    raise AssertionError("Sparse block refers to multiple "
                                         "items")
            else:
                if self.ndim != block.ndim:
                    raise AssertionError('Number of Block dimensions (%d) '
                                         'must equal number of axes (%d)' %
                                         (block.ndim, self.ndim))

        if do_integrity_check:
            self._verify_integrity()

        self._consolidate_check()

        self._rebuild_blknos_and_blklocs()

    def make_empty(self, axes=None):
        """ return an empty BlockManager with the items axis of len 0 """
        if axes is None:
            axes = [_ensure_index([])] + [_ensure_index(a)
                                          for a in self.axes[1:]]

        # preserve dtype if possible
        if self.ndim == 1:
            blocks = np.array([], dtype=self.array_dtype)
        else:
            blocks = []
        return self.__class__(blocks, axes)

    def __nonzero__(self):
        return True

    # Python3 compat
    __bool__ = __nonzero__

    @property
    def shape(self):
        return tuple(len(ax) for ax in self.axes)

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

    def set_axis(self, axis, new_labels):
        new_labels = _ensure_index(new_labels)
        old_len = len(self.axes[axis])
        new_len = len(new_labels)

        if new_len != old_len:
            raise ValueError('Length mismatch: Expected axis has %d elements, '
                             'new values have %d elements' %
                             (old_len, new_len))

        self.axes[axis] = new_labels

    def rename_axis(self, mapper, axis, copy=True):
        """
        Rename one of axes.

        Parameters
        ----------
        mapper : unary callable
        axis : int
        copy : boolean, default True

        """
        obj = self.copy(deep=copy)
        obj.set_axis(axis, _transform_index(self.axes[axis], mapper))
        return obj

    def add_prefix(self, prefix):
        f = (str(prefix) + '%s').__mod__
        return self.rename_axis(f, axis=0)

    def add_suffix(self, suffix):
        f = ('%s' + str(suffix)).__mod__
        return self.rename_axis(f, axis=0)

    @property
    def _is_single_block(self):
        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):
        """
        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():
            raise AssertionError("Gaps in blk ref_locs")

        self._blknos = new_blknos
        self._blklocs = new_blklocs

    # make items read only for now
    def _get_items(self):
        return self.axes[0]

    items = property(fget=_get_items)

    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_ftype_counts(self):
        return self._get_counts(lambda b: b.ftype)

    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 get_ftypes(self):
        ftypes = np.array([blk.ftype for blk in self.blocks])
        return algos.take_1d(ftypes, 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 = [ax for ax in 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):
            # numpy < 1.7 pickle compat
            if values.dtype == 'M8[us]':
                values = values.astype('M8[ns]')
            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:
            # discard anything after 3rd, support beta pickling format for a
            # little while longer
            ax_arrays, bvalues, bitems = state[:3]

            self.axes = [_ensure_index(ax) for ax in ax_arrays]

            if len(bitems) == 1 and self.axes[0].equals(bitems[0]):
                # This is a workaround for pre-0.14.1 pickles that didn't
                # support unpickling multi-block frames/panels with non-unique
                # columns/items, because given a manager with items ["a", "b",
                # "a"] there's no way of knowing which block's "a" is where.
                #
                # Single-block case can be supported under the assumption that
                # block items corresponded to manager items 1-to-1.
                all_mgr_locs = [slice(0, len(bitems[0]))]
            else:
                all_mgr_locs = [self.axes[0].get_indexer(blk_items)
                                for blk_items in bitems]

            self.blocks = tuple(
                unpickle_block(values, mgr_locs)
                for values, mgr_locs in zip(bvalues, all_mgr_locs))

        self._post_setstate()

    def _post_setstate(self):
        self._is_consolidated = False
        self._known_consolidated = False
        self._rebuild_blknos_and_blklocs()

    def __len__(self):
        return len(self.items)

    def __unicode__(self):
        output = pprint_thing(self.__class__.__name__)
        for i, ax in enumerate(self.axes):
            if i == 0:
                output += u('\nItems: %s') % ax
            else:
                output += u('\nAxis %d: %s') % (i, ax)

        for block in self.blocks:
            output += u('\n%s') % pprint_thing(block)
        return output

    def _verify_integrity(self):
        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:]:
                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 '
                                 'block items\n# manager items: {0}, # '
                                 'tot_items: {1}'.format(
                                     len(self.items), tot_items))

    def apply(self, f, axes=None, filter=None, do_integrity_check=False,
              consolidate=True, **kwargs):
        """
        iterate over the blocks, collect and create a new block manager

        Parameters
        ----------
        f : the callable or function name to operate on at the block level
        axes : optional (if not supplied, use self.axes)
        filter : list, if supplied, only call the block if the filter is in
                 the block
        do_integrity_check : boolean, default False. Do the block manager
            integrity check
        consolidate: boolean, default True. Join together blocks having same
            dtype

        Returns
        -------
        Block Manager (new object)

        """

        result_blocks = []

        # filter kwarg is used in replace-* family of methods
        if filter is not None:
            filter_locs = set(self.items.get_indexer_for(filter))
            if len(filter_locs) == len(self.items):
                # All items are included, as if there were no filtering
                filter = None
            else:
                kwargs['filter'] = filter_locs

        if consolidate:
            self._consolidate_inplace()

        if f == 'where':
            align_copy = True
            if kwargs.get('align', True):
                align_keys = ['other', 'cond']
            else:
                align_keys = ['cond']
        elif f == 'putmask':
            align_copy = False
            if kwargs.get('align', True):
                align_keys = ['new', 'mask']
            else:
                align_keys = ['mask']
        elif f == 'eval':
            align_copy = False
            align_keys = ['other']
        elif f == 'fillna':
            # fillna internally does putmask, maybe it's better to do this
            # at mgr, not block level?
            align_copy = False
            align_keys = ['value']
        else:
            align_keys = []

        aligned_args = dict((k, kwargs[k])
                            for k in align_keys
                            if hasattr(kwargs[k], 'reindex_axis'))

        for b in self.blocks:
            if filter is not None:
                if not b.mgr_locs.isin(filter_locs).any():
                    result_blocks.append(b)
                    continue

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

                for k, obj in aligned_args.items():
                    axis = getattr(obj, '_info_axis_number', 0)
                    kwargs[k] = obj.reindex_axis(b_items, axis=axis,
                                                 copy=align_copy)

            kwargs['mgr'] = self
            applied = getattr(b, f)(**kwargs)
            result_blocks = _extend_blocks(applied, result_blocks)

        if len(result_blocks) == 0:
            return self.make_empty(axes or self.axes)
        bm = self.__class__(result_blocks, axes or self.axes,
                            do_integrity_check=do_integrity_check)
        bm._consolidate_inplace()
        return bm

    def reduction(self, f, axis=0, consolidate=True, transposed=False,
                  **kwargs):
        """
        iterate over the blocks, collect and create a new block manager.
        This routine is intended for reduction type operations and
        will do inference on the generated blocks.

        Parameters
        ----------
        f: the callable or function name to operate on at the block level
        axis: reduction axis, default 0
        consolidate: boolean, default True. Join together blocks having same
            dtype
        transposed: boolean, default False
            we are holding transposed data

        Returns
        -------
        Block Manager (new object)

        """

        if consolidate:
            self._consolidate_inplace()

        axes, blocks = [], []
        for b in self.blocks:
            kwargs['mgr'] = self
            axe, block = getattr(b, f)(axis=axis, **kwargs)

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

        # note that some DatetimeTZ, Categorical are always ndim==1
        ndim = set([b.ndim for b in blocks])

        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 self.__class__(blocks, new_axes)

        # 0 ndim
        if 0 in ndim and 1 not in ndim:
            values = np.array([b.values for b in blocks])
            if len(values) == 1:
                return values.item()
            blocks = [make_block(values, ndim=1)]
            axes = Index([ax[0] for ax in axes])

        # single block
        values = _concat._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 isnull(self, **kwargs):
        return self.apply('apply', **kwargs)

    def where(self, **kwargs):
        return self.apply('where', **kwargs)

    def eval(self, **kwargs):
        return self.apply('eval', **kwargs)

    def quantile(self, **kwargs):
        return self.reduction('quantile', **kwargs)

    def setitem(self, **kwargs):
        return self.apply('setitem', **kwargs)

    def putmask(self, **kwargs):
        return self.apply('putmask', **kwargs)

    def diff(self, **kwargs):
        return self.apply('diff', **kwargs)

    def interpolate(self, **kwargs):
        return self.apply('interpolate', **kwargs)

    def shift(self, **kwargs):
        return self.apply('shift', **kwargs)

    def fillna(self, **kwargs):
        return self.apply('fillna', **kwargs)

    def downcast(self, **kwargs):
        return self.apply('downcast', **kwargs)

    def astype(self, dtype, **kwargs):
        return self.apply('astype', dtype=dtype, **kwargs)

    def convert(self, **kwargs):
        return self.apply('convert', **kwargs)

    def replace(self, **kwargs):
        return self.apply('replace', **kwargs)

    def replace_list(self, src_list, dest_list, inplace=False, regex=False,
                     mgr=None):
        """ do a list replace """

        if mgr is None:
            mgr = self

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

        def comp(s):
            if isnull(s):
                return isnull(values)
            return _possibly_compare(values, getattr(s, 'asm8', s),
                                     operator.eq)

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

        result_blocks = []
        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 = []
                for b in rb:
                    if b.dtype == np.object_:
                        result = b.replace(s, d, inplace=inplace, regex=regex,
                                           mgr=mgr)
                        new_rb = _extend_blocks(result, new_rb)
                    else:
                        # get our mask for this element, sized to this
                        # particular block
                        m = masks[i][b.mgr_locs.indexer]
                        if m.any():
                            new_rb.extend(b.putmask(m, d, inplace=True))
                        else:
                            new_rb.append(b)
                rb = new_rb
            result_blocks.extend(rb)

        bm = self.__class__(result_blocks, self.axes)
        bm._consolidate_inplace()
        return bm

    def reshape_nd(self, axes, **kwargs):
        """ a 2d-nd reshape operation on a BlockManager """
        return self.apply('reshape_nd', axes=axes, **kwargs)

    def is_consolidated(self):
        """
        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):
        ftypes = [blk.ftype for blk in self.blocks]
        self._is_consolidated = len(ftypes) == len(set(ftypes))
        self._known_consolidated = True

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

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

    @property
    def is_datelike_mixed_type(self):
        # Warning, consolidation needs to get checked upstairs
        self._consolidate_inplace()
        return any([block.is_datelike for block in self.blocks])

    @property
    def is_view(self):
        """ 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=False):
        """
        Parameters
        ----------
        copy : boolean, 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=False):
        """
        Parameters
        ----------
        copy : boolean, 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, copy=True):
        """ 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 = algos.take_1d(inv_indexer, b.mgr_locs.as_array,
                                       axis=0, allow_fill=False)
            new_blocks.append(b)

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

        return self.__class__(new_blocks, axes, do_integrity_check=False)

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

        if axis == 0:
            new_blocks = self._slice_take_blocks_ax0(slobj)
        else:
            slicer = [slice(None)] * (axis + 1)
            slicer[axis] = slobj
            slicer = tuple(slicer)
            new_blocks = [blk.getitem_block(slicer) for blk in self.blocks]

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

        bm = self.__class__(new_blocks, new_axes, do_integrity_check=False,
                            fastpath=True)
        bm._consolidate_inplace()
        return bm

    def __contains__(self, item):
        return item in self.items

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

    def copy(self, deep=True, mgr=None):
        """
        Make deep or shallow copy of BlockManager

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

        Returns
        -------
        copy : BlockManager
        """

        # this preserves the notion of view copying of axes
        if deep:
            if deep == 'all':
                copy = lambda ax: ax.copy(deep=True)
            else:
                copy = lambda ax: ax.view()
            new_axes = [copy(ax) for ax in self.axes]
        else:
            new_axes = list(self.axes)
        return self.apply('copy', axes=new_axes, deep=deep,
                          do_integrity_check=False)

    def as_matrix(self, items=None):
        if len(self.blocks) == 0:
            return np.empty(self.shape, dtype=float)

        if items is not None:
            mgr = self.reindex_axis(items, axis=0)
        else:
            mgr = self

        if self._is_single_block or not self.is_mixed_type:
            return mgr.blocks[0].get_values()
        else:
            return mgr._interleave()

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

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

        if result.shape[0] == 0:
            # Workaround for numpy 1.7 bug:
            #
            #     >>> a = np.empty((0,10))
            #     >>> a[slice(0,0)]
            #     array([], shape=(0, 10), dtype=float64)
            #     >>> a[[]]
            #     Traceback (most recent call last):
            #       File "<stdin>", line 1, in <module>
            #     IndexError: index 0 is out of bounds for axis 0 with size 0
            return result

        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 xs(self, key, axis=1, copy=True, takeable=False):
        if axis < 1:
            raise AssertionError('Can only take xs across axis >= 1, got %d' %
                                 axis)

        # take by position
        if takeable:
            loc = key
        else:
            loc = self.axes[axis].get_loc(key)

        slicer = [slice(None, None) for _ in range(self.ndim)]
        slicer[axis] = loc
        slicer = tuple(slicer)

        new_axes = list(self.axes)

        # could be an array indexer!
        if isinstance(loc, (slice, np.ndarray)):
            new_axes[axis] = new_axes[axis][loc]
        else:
            new_axes.pop(axis)

        new_blocks = []
        if len(self.blocks) > 1:
            # we must copy here as we are mixed type
            for blk in self.blocks:
                newb = make_block(values=blk.values[slicer],
                                  klass=blk.__class__, fastpath=True,
                                  placement=blk.mgr_locs)
                new_blocks.append(newb)
        elif len(self.blocks) == 1:
            block = self.blocks[0]
            vals = block.values[slicer]
            if copy:
                vals = vals.copy()
            new_blocks = [make_block(values=vals,
                                     placement=block.mgr_locs,
                                     klass=block.__class__,
                                     fastpath=True, )]

        return self.__class__(new_blocks, new_axes)

    def fast_xs(self, loc):
        """
        get a cross sectional for a given location in the
        items ; handle dups

        return the result, is *could* be a view in the case of a
        single block
        """
        if len(self.blocks) == 1:
            return self.blocks[0].iget((slice(None), loc))

        items = self.items

        # non-unique (GH4726)
        if not items.is_unique:
            result = self._interleave()
            if self.ndim == 2:
                result = result.T
            return result[loc]

        # unique
        dtype = _interleaved_dtype(self.blocks)
        n = len(items)
        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._try_coerce_result(blk.iget((i, loc)))

        return result

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

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

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

    def _consolidate_inplace(self):
        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 get(self, item, fastpath=True):
        """
        Return values for selected item (ndarray or BlockManager).
        """
        if self.items.is_unique:

            if not isnull(item):
                loc = self.items.get_loc(item)
            else:
                indexer = np.arange(len(self.items))[isnull(self.items)]

                # allow a single nan location indexer
                if not is_scalar(indexer):
                    if len(indexer) == 1:
                        loc = indexer.item()
                    else:
                        raise ValueError("cannot label index with a null key")

            return self.iget(loc, fastpath=fastpath)
        else:

            if isnull(item):
                raise TypeError("cannot label index with a null key")

            indexer = self.items.get_indexer_for([item])
            return self.reindex_indexer(new_axis=self.items[indexer],
                                        indexer=indexer, axis=0,
                                        allow_dups=True)

    def iget(self, i, fastpath=True):
        """
        Return the data as a SingleBlockManager if fastpath=True and possible

        Otherwise return as a ndarray
        """
        block = self.blocks[self._blknos[i]]
        values = block.iget(self._blklocs[i])
        if not fastpath or not block._box_to_block_values or values.ndim != 1:
            return values

        # fastpath 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, fastpath=True)],
            self.axes[1])

    def get_scalar(self, tup):
        """
        Retrieve single item
        """
        full_loc = list(ax.get_loc(x) for ax, x in zip(self.axes, tup))
        blk = self.blocks[self._blknos[full_loc[0]]]
        values = blk.values

        # FIXME: this may return non-upcasted types?
        if values.ndim == 1:
            return values[full_loc[1]]

        full_loc[0] = self._blklocs[full_loc[0]]
        return values[tuple(full_loc)]

    def delete(self, item):
        """
        Delete selected item (items if non-unique) in-place.
        """
        indexer = self.items.get_loc(item)

        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._shape = None
        self._rebuild_blknos_and_blklocs()

    def set(self, item, value, check=False):
        """
        Set new item in-place. Does not consolidate. Adds new Block if not
        contained in the current set of items
        if check, then validate that we are not setting the same data in-place
        """
        # FIXME: refactor, clearly separate broadcasting & zip-like assignment
        #        can prob also fix the various if tests for sparse/categorical

        value_is_extension_type = is_extension_type(value)

        # categorical/spares/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')

        try:
            loc = self.items.get_loc(item)
        except KeyError:
            # This item wasn't present, just insert at end
            self.insert(len(self.items), item, value)
            return

        if isinstance(loc, int):
            loc = [loc]

        blknos = self._blknos[loc]
        blklocs = self._blklocs[loc].copy()

        unfit_mgr_locs = []
        unfit_val_locs = []
        removed_blknos = []
        for blkno, val_locs in _get_blkno_placements(blknos, len(self.blocks),
                                                     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), check=check)
            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:
                    self._blklocs[blk.mgr_locs.indexer] = -1
                    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 = algos.take_1d(new_blknos, self._blknos, axis=0,
                                         allow_fill=False)
            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 = []
            if value_is_extension_type:
                # This code (ab-)uses the fact that sparse blocks contain only
                # one item.
                new_blocks.extend(
                    make_block(values=value.copy(), 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, item, value, allow_duplicates=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('cannot insert %s, already exists' % item)

        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)

        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

        if loc == self._blklocs.shape[0]:
            # np.append is a lot faster (at least in numpy 1.7.1), 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._shape = None

        self._known_consolidated = False

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

    def reindex_axis(self, new_index, axis, method=None, limit=None,
                     fill_value=None, copy=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, new_axis, indexer, axis, fill_value=None,
                        allow_dups=False, copy=True):
        """
        Parameters
        ----------
        new_axis : Index
        indexer : ndarray of int64 or None
        axis : int
        fill_value : object
        allow_dups : bool

        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_tuple=(fill_value,))
        else:
            new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(
                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 self.__class__(new_blocks, new_axes)

    def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None):
        """
        Slice/take blocks along axis=0.

        Overloaded for SingleBlock

        Returns
        -------
        new_blocks : list of Block

        """

        allow_fill = fill_tuple is not None

        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'):
                return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))]
            elif not allow_fill or self.ndim == 1:
                if allow_fill and fill_tuple[0] is None:
                    _, fill_value = _maybe_promote(blk.dtype)
                    fill_tuple = (fill_value, )

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

        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 _get_blkno_placements(blknos, len(self.blocks),
                                                     group=True):
            if blkno == -1:
                # If we've got here, fill_tuple was not None.
                fill_value = fill_tuple[0]

                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=True)
                        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,
                                              fill_tuple=None))

        return blocks

    def _make_na_block(self, placement, fill_value=None):
        # TODO: infer dtypes other than float64 from fill_value

        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=1, verify=True, convert=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 merge(self, other, lsuffix='', rsuffix=''):
        if not self._is_indexed_like(other):
            raise AssertionError('Must have same axes to merge managers')

        l, r = items_overlap_with_suffix(left=self.items, lsuffix=lsuffix,
                                         right=other.items, rsuffix=rsuffix)
        new_items = _concat_indexes([l, r])

        new_blocks = [blk.copy(deep=False) for blk in self.blocks]

        offset = self.shape[0]
        for blk in other.blocks:
            blk = blk.copy(deep=False)
            blk.mgr_locs = blk.mgr_locs.add(offset)
            new_blocks.append(blk)

        new_axes = list(self.axes)
        new_axes[0] = new_items

        return self.__class__(_consolidate(new_blocks), new_axes)

    def _is_indexed_like(self, other):
        """
        Check all axes except items
        """
        if self.ndim != other.ndim:
            raise AssertionError('Number of dimensions must agree '
                                 'got %d and %d' % (self.ndim, other.ndim))
        for ax, oax in zip(self.axes[1:], other.axes[1:]):
            if not ax.equals(oax):
                return False
        return True

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

        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))


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

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

    def __init__(self, block, axis, do_integrity_check=False, fastpath=False):

        if isinstance(axis, list):
            if len(axis) != 1:
                raise ValueError("cannot create SingleBlockManager with more "
                                 "than 1 axis")
            axis = axis[0]

        # passed from constructor, single block, single axis
        if fastpath:
            self.axes = [axis]
            if isinstance(block, list):

                # empty block
                if len(block) == 0:
                    block = [np.array([])]
                elif len(block) != 1:
                    raise ValueError('Cannot create SingleBlockManager with '
                                     'more than 1 block')
                block = block[0]
        else:
            self.axes = [_ensure_index(axis)]

            # create the block here
            if isinstance(block, list):

                # provide consolidation to the interleaved_dtype
                if len(block) > 1:
                    dtype = _interleaved_dtype(block)
                    block = [b.astype(dtype) for b in block]
                    block = _consolidate(block)

                if len(block) != 1:
                    raise ValueError('Cannot create SingleBlockManager with '
                                     'more than 1 block')
                block = block[0]

        if not isinstance(block, Block):
            block = make_block(block, placement=slice(0, len(axis)), ndim=1,
                               fastpath=True)

        self.blocks = [block]

    def _post_setstate(self):
        pass

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

    @property
    def _values(self):
        return self._block.values

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

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

    def reindex(self, new_axis, indexer=None, method=None, fill_value=None,
                limit=None, copy=True):
        # if we are the same and don't copy, just return
        if self.index.equals(new_axis):
            if copy:
                return self.copy(deep=True)
            else:
                return self

        values = self._block.get_values()

        if indexer is None:
            indexer = self.items.get_indexer_for(new_axis)

        if fill_value is None:
            fill_value = np.nan

        new_values = algos.take_1d(values, indexer, fill_value=fill_value)

        # fill if needed
        if method is not None or limit is not None:
            new_values = missing.interpolate_2d(new_values,
                                                method=method,
                                                limit=limit,
                                                fill_value=fill_value)

        if self._block.is_sparse:
            make_block = self._block.make_block_same_class

        block = make_block(new_values, copy=copy,
                           placement=slice(0, len(new_axis)))

        mgr = SingleBlockManager(block, new_axis)
        mgr._consolidate_inplace()
        return mgr

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

        return self.__class__(self._block._slice(slobj),
                              self.index[slobj], fastpath=True)

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

    def convert(self, **kwargs):
        """ convert the whole block as one """
        kwargs['by_item'] = False
        return self.apply('convert', **kwargs)

    @property
    def dtype(self):
        return self._block.dtype

    @property
    def array_dtype(self):
        return self._block.array_dtype

    @property
    def ftype(self):
        return self._block.ftype

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

    def get_ftype_counts(self):
        return {self.ftype: 1}

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

    def get_ftypes(self):
        return np.array([self._block.ftype])

    def external_values(self):
        return self._block.external_values()

    def internal_values(self):
        return self._block.internal_values()

    def get_values(self):
        """ return a dense type view """
        return np.array(self._block.to_dense(), copy=False)

    @property
    def asobject(self):
        """
        return a object dtype array. datetime/timedelta like values are boxed
        to Timestamp/Timedelta instances.
        """
        return self._block.get_values(dtype=object)

    @property
    def itemsize(self):
        return self._block.values.itemsize

    @property
    def _can_hold_na(self):
        return self._block._can_hold_na

    def is_consolidated(self):
        return True

    def _consolidate_check(self):
        pass

    def _consolidate_inplace(self):
        pass

    def delete(self, item):
        """
        Delete single item from SingleBlockManager.

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

    def fast_xs(self, loc):
        """
        fast path for getting a cross-section
        return a view of the data
        """
        return self._block.values[loc]


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)))
    implied = tuple(map(int, [len(ax) for ax in axes]))
    if passed == implied and e is not None:
        raise e
    if block_shape[0] == 0:
        raise ValueError("Empty data passed with indices specified.")
    raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
        passed, implied))


def create_block_manager_from_blocks(blocks, axes):
    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)
        construction_error(tot_items, blocks[0].shape[1:], axes, e)


def create_block_manager_from_arrays(arrays, names, axes):

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


def form_blocks(arrays, names, axes):
    # put "leftover" items in float bucket, where else?
    # generalize?
    float_items = []
    complex_items = []
    int_items = []
    bool_items = []
    object_items = []
    sparse_items = []
    datetime_items = []
    datetime_tz_items = []
    cat_items = []
    extra_locs = []

    names_idx = Index(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]

        if is_sparse(v):
            sparse_items.append((i, k, v))
        elif issubclass(v.dtype.type, np.floating):
            float_items.append((i, k, v))
        elif issubclass(v.dtype.type, np.complexfloating):
            complex_items.append((i, k, v))
        elif issubclass(v.dtype.type, np.datetime64):
            if v.dtype != _NS_DTYPE:
                v = tslib.cast_to_nanoseconds(v)

            if is_datetimetz(v):
                datetime_tz_items.append((i, k, v))
            else:
                datetime_items.append((i, k, v))
        elif is_datetimetz(v):
            datetime_tz_items.append((i, k, v))
        elif issubclass(v.dtype.type, np.integer):
            if v.dtype == np.uint64:
                # HACK #2355 definite overflow
                if (v > 2**63 - 1).any():
                    object_items.append((i, k, v))
                    continue
            int_items.append((i, k, v))
        elif v.dtype == np.bool_:
            bool_items.append((i, k, v))
        elif is_categorical(v):
            cat_items.append((i, k, v))
        else:
            object_items.append((i, k, v))

    blocks = []
    if len(float_items):
        float_blocks = _multi_blockify(float_items)
        blocks.extend(float_blocks)

    if len(complex_items):
        complex_blocks = _multi_blockify(complex_items)
        blocks.extend(complex_blocks)

    if len(int_items):
        int_blocks = _multi_blockify(int_items)
        blocks.extend(int_blocks)

    if len(datetime_items):
        datetime_blocks = _simple_blockify(datetime_items, _NS_DTYPE)
        blocks.extend(datetime_blocks)

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

    if len(bool_items):
        bool_blocks = _simple_blockify(bool_items, np.bool_)
        blocks.extend(bool_blocks)

    if len(object_items) > 0:
        object_blocks = _simple_blockify(object_items, np.object_)
        blocks.extend(object_blocks)

    if len(sparse_items) > 0:
        sparse_blocks = _sparse_blockify(sparse_items)
        blocks.extend(sparse_blocks)

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

    # 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 _sparse_blockify(tuples, dtype=None):
    """ return an array of blocks that potentially have different dtypes (and
    are sparse)
    """

    new_blocks = []
    for i, names, array in tuples:
        array = _maybe_to_sparse(array)
        block = make_block(array, klass=SparseBlock, fastpath=True,
                           placement=[i])
        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):
    if not len(blocks):
        return None

    counts = defaultdict(list)
    for x in blocks:
        counts[type(x)].append(x)

    have_int = len(counts[IntBlock]) > 0
    have_bool = len(counts[BoolBlock]) > 0
    have_object = len(counts[ObjectBlock]) > 0
    have_float = len(counts[FloatBlock]) > 0
    have_complex = len(counts[ComplexBlock]) > 0
    have_dt64 = len(counts[DatetimeBlock]) > 0
    have_dt64_tz = len(counts[DatetimeTZBlock]) > 0
    have_td64 = len(counts[TimeDeltaBlock]) > 0
    have_cat = len(counts[CategoricalBlock]) > 0
    # TODO: have_sparse is not used
    have_sparse = len(counts[SparseBlock]) > 0  # noqa
    have_numeric = have_float or have_complex or have_int
    has_non_numeric = have_dt64 or have_dt64_tz or have_td64 or have_cat

    if (have_object or
        (have_bool and
         (have_numeric or have_dt64 or have_dt64_tz or have_td64)) or
        (have_numeric and has_non_numeric) or have_cat or have_dt64 or
            have_dt64_tz or have_td64):
        return np.dtype(object)
    elif have_bool:
        return np.dtype(bool)
    elif have_int and not have_float and not have_complex:
        # if we are mixing unsigned and signed, then return
        # the next biggest int type (if we can)
        lcd = _find_common_type([b.dtype for b in counts[IntBlock]])
        kinds = set([i.dtype.kind for i in counts[IntBlock]])
        if len(kinds) == 1:
            return lcd

        if lcd == 'uint64' or lcd == 'int64':
            return np.dtype('int64')

        # return 1 bigger on the itemsize if unsinged
        if lcd.kind == 'u':
            return np.dtype('int%s' % (lcd.itemsize * 8 * 2))
        return lcd

    elif have_complex:
        return np.dtype('c16')
    else:
        introspection_blks = counts[FloatBlock] + counts[SparseBlock]
        return _find_common_type([b.dtype for b in introspection_blks])


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, dtype=None, _can_consolidate=True):

    if len(blocks) == 1:
        return blocks[0]

    if _can_consolidate:

        if dtype is None:
            if len(set([b.dtype for b in blocks])) != 1:
                raise AssertionError("_merge_blocks are invalid!")
            dtype = blocks[0].dtype

        # 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 = _vstack([b.values for b in blocks], dtype)

        argsort = np.argsort(new_mgr_locs)
        new_values = new_values[argsort]
        new_mgr_locs = new_mgr_locs[argsort]

        return make_block(new_values, fastpath=True, placement=new_mgr_locs)

    # no merge
    return blocks


def _extend_blocks(result, blocks=None):
    """ return a new extended blocks, givin the result """
    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
        values = values.reshape(tuple((1, ) + shape))
    return values


def _vstack(to_stack, dtype):

    # work around NumPy 1.6 bug
    if dtype == _NS_DTYPE or dtype == _TD_DTYPE:
        new_values = np.vstack([x.view('i8') for x in to_stack])
        return new_values.view(dtype)

    else:
        return np.vstack(to_stack)


def _possibly_compare(a, b, op):

    is_a_array = isinstance(a, np.ndarray)
    is_b_array = isinstance(b, np.ndarray)

    # numpy deprecation warning to have i8 vs integer comparisions
    if is_datetimelike_v_numeric(a, b):
        result = False

    # numpy deprecation warning if comparing numeric vs string-like
    elif is_numeric_v_string_like(a, b):
        result = False

    else:
        result = op(a, b)

    if is_scalar(result) and (is_a_array or is_b_array):
        type_names = [type(a).__name__, type(b).__name__]

        if is_a_array:
            type_names[0] = 'ndarray(dtype=%s)' % a.dtype

        if is_b_array:
            type_names[1] = 'ndarray(dtype=%s)' % b.dtype

        raise TypeError("Cannot compare types %r and %r" % tuple(type_names))
    return result


def _concat_indexes(indexes):
    return indexes[0].append(indexes[1:])


def _block2d_to_blocknd(values, placement, shape, labels, ref_items):
    """ pivot to the labels shape """
    from pandas.core.internals import make_block

    panel_shape = (len(placement),) + shape

    # TODO: lexsort depth needs to be 2!!

    # Create observation selection vector using major and minor
    # labels, for converting to panel format.
    selector = _factor_indexer(shape[1:], labels)
    mask = np.zeros(np.prod(shape), dtype=bool)
    mask.put(selector, True)

    if mask.all():
        pvalues = np.empty(panel_shape, dtype=values.dtype)
    else:
        dtype, fill_value = _maybe_promote(values.dtype)
        pvalues = np.empty(panel_shape, dtype=dtype)
        pvalues.fill(fill_value)

    values = values
    for i in range(len(placement)):
        pvalues[i].flat[mask] = values[:, i]

    return make_block(pvalues, placement=placement)


def _factor_indexer(shape, labels):
    """
    given a tuple of shape and a list of Categorical labels, return the
    expanded label indexer
    """
    mult = np.array(shape)[::-1].cumprod()[::-1]
    return _ensure_platform_int(
        np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T)


def _get_blkno_placements(blknos, blk_count, group=True):
    """

    Parameters
    ----------
    blknos : array of int64
    blk_count : int
    group : bool

    Returns
    -------
    iterator
        yield (BlockPlacement, blkno)

    """

    blknos = _ensure_int64(blknos)

    # FIXME: blk_count is unused, but it may avoid the use of dicts in cython
    for blkno, indexer in lib.get_blkno_indexers(blknos, group):
        yield blkno, BlockPlacement(indexer)


def items_overlap_with_suffix(left, lsuffix, right, rsuffix):
    """
    If two indices overlap, add suffixes to overlapping entries.

    If corresponding suffix is empty, the entry is simply converted to string.

    """
    to_rename = left.intersection(right)
    if len(to_rename) == 0:
        return left, right
    else:
        if not lsuffix and not rsuffix:
            raise ValueError('columns overlap but no suffix specified: %s' %
                             to_rename)

        def lrenamer(x):
            if x in to_rename:
                return '%s%s' % (x, lsuffix)
            return x

        def rrenamer(x):
            if x in to_rename:
                return '%s%s' % (x, rsuffix)
            return x

        return (_transform_index(left, lrenamer),
                _transform_index(right, rrenamer))


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 Categorical 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, Categorical):
        arr = arr.reshape(new_shape)
    return arr


def _transform_index(index, func):
    """
    Apply function to all values found in index.

    This includes transforming multiindex entries separately.

    """
    if isinstance(index, MultiIndex):
        items = [tuple(func(y) for y in x) for x in index]
        return MultiIndex.from_tuples(items, names=index.names)
    else:
        items = [func(x) for x in index]
        return Index(items, name=index.name)


def _putmask_smart(v, m, n):
    """
    Return a new block, try to preserve dtype if possible.

    Parameters
    ----------
    v : `values`, updated in-place (array like)
    m : `mask`, applies to both sides (array like)
    n : `new values` either scalar or an array like aligned with `values`
    """
    # n should be the length of the mask or a scalar here
    if not is_list_like(n):
        n = np.array([n] * len(m))
    elif isinstance(n, np.ndarray) and n.ndim == 0:  # numpy scalar
        n = np.repeat(np.array(n, ndmin=1), len(m))

    # see if we are only masking values that if putted
    # will work in the current dtype
    try:
        nn = n[m]

        # make sure that we have a nullable type
        # if we have nulls
        if not _is_na_compat(v, nn[0]):
            raise ValueError

        nn_at = nn.astype(v.dtype)

        # avoid invalid dtype comparisons
        if not is_numeric_v_string_like(nn, nn_at):
            comp = (nn == nn_at)
            if is_list_like(comp) and comp.all():
                nv = v.copy()
                nv[m] = nn_at
                return nv
    except (ValueError, IndexError, TypeError):
        pass

    # change the dtype
    dtype, _ = _maybe_promote(n.dtype)
    nv = v.astype(dtype)
    try:
        nv[m] = n[m]
    except ValueError:
        idx, = np.where(np.squeeze(m))
        for mask_index, new_val in zip(idx, n[m]):
            nv[mask_index] = new_val
    return nv


def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
    """
    Concatenate block managers into one.

    Parameters
    ----------
    mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
    axes : list of Index
    concat_axis : int
    copy : bool

    """
    concat_plan = combine_concat_plans(
        [get_mgr_concatenation_plan(mgr, indexers)
         for mgr, indexers in mgrs_indexers], concat_axis)

    blocks = [make_block(concatenate_join_units(join_units, concat_axis,
                                                copy=copy),
                         placement=placement)
              for placement, join_units in concat_plan]

    return BlockManager(blocks, axes)


def get_empty_dtype_and_na(join_units):
    """
    Return dtype and N/A values to use when concatenating specified units.

    Returned N/A value may be None which means there was no casting involved.

    Returns
    -------
    dtype
    na
    """

    if len(join_units) == 1:
        blk = join_units[0].block
        if blk is None:
            return np.float64, np.nan

    has_none_blocks = False
    dtypes = [None] * len(join_units)
    for i, unit in enumerate(join_units):
        if unit.block is None:
            has_none_blocks = True
        else:
            dtypes[i] = unit.dtype

    upcast_classes = defaultdict(list)
    null_upcast_classes = defaultdict(list)
    for dtype, unit in zip(dtypes, join_units):
        if dtype is None:
            continue

        if is_categorical_dtype(dtype):
            upcast_cls = 'category'
        elif is_datetimetz(dtype):
            upcast_cls = 'datetimetz'
        elif issubclass(dtype.type, np.bool_):
            upcast_cls = 'bool'
        elif issubclass(dtype.type, np.object_):
            upcast_cls = 'object'
        elif is_datetime64_dtype(dtype):
            upcast_cls = 'datetime'
        elif is_timedelta64_dtype(dtype):
            upcast_cls = 'timedelta'
        else:
            upcast_cls = 'float'

        # Null blocks should not influence upcast class selection, unless there
        # are only null blocks, when same upcasting rules must be applied to
        # null upcast classes.
        if unit.is_null:
            null_upcast_classes[upcast_cls].append(dtype)
        else:
            upcast_classes[upcast_cls].append(dtype)

    if not upcast_classes:
        upcast_classes = null_upcast_classes

    # create the result
    if 'object' in upcast_classes:
        return np.dtype(np.object_), np.nan
    elif 'bool' in upcast_classes:
        if has_none_blocks:
            return np.dtype(np.object_), np.nan
        else:
            return np.dtype(np.bool_), None
    elif 'category' in upcast_classes:
        return np.dtype(np.object_), np.nan
    elif 'float' in upcast_classes:
        return np.dtype(np.float64), np.nan
    elif 'datetimetz' in upcast_classes:
        dtype = upcast_classes['datetimetz']
        return dtype[0], tslib.iNaT
    elif 'datetime' in upcast_classes:
        return np.dtype('M8[ns]'), tslib.iNaT
    elif 'timedelta' in upcast_classes:
        return np.dtype('m8[ns]'), tslib.iNaT
    else:  # pragma
        raise AssertionError("invalid dtype determination in get_concat_dtype")


def concatenate_join_units(join_units, concat_axis, copy):
    """
    Concatenate values from several join units along selected axis.
    """
    if concat_axis == 0 and len(join_units) > 1:
        # Concatenating join units along ax0 is handled in _merge_blocks.
        raise AssertionError("Concatenating join units along axis0")

    empty_dtype, upcasted_na = get_empty_dtype_and_na(join_units)

    to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
                                         upcasted_na=upcasted_na)
                 for ju in join_units]

    if len(to_concat) == 1:
        # Only one block, nothing to concatenate.
        concat_values = to_concat[0]
        if copy and concat_values.base is not None:
            concat_values = concat_values.copy()
    else:
        concat_values = _concat._concat_compat(to_concat, axis=concat_axis)

    return concat_values


def get_mgr_concatenation_plan(mgr, indexers):
    """
    Construct concatenation plan for given block manager and indexers.

    Parameters
    ----------
    mgr : BlockManager
    indexers : dict of {axis: indexer}

    Returns
    -------
    plan : list of (BlockPlacement, JoinUnit) tuples

    """
    # Calculate post-reindex shape , save for item axis which will be separate
    # for each block anyway.
    mgr_shape = list(mgr.shape)
    for ax, indexer in indexers.items():
        mgr_shape[ax] = len(indexer)
    mgr_shape = tuple(mgr_shape)

    if 0 in indexers:
        ax0_indexer = indexers.pop(0)
        blknos = algos.take_1d(mgr._blknos, ax0_indexer, fill_value=-1)
        blklocs = algos.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1)
    else:

        if mgr._is_single_block:
            blk = mgr.blocks[0]
            return [(blk.mgr_locs, JoinUnit(blk, mgr_shape, indexers))]

        ax0_indexer = None
        blknos = mgr._blknos
        blklocs = mgr._blklocs

    plan = []
    for blkno, placements in _get_blkno_placements(blknos, len(mgr.blocks),
                                                   group=False):

        assert placements.is_slice_like

        join_unit_indexers = indexers.copy()

        shape = list(mgr_shape)
        shape[0] = len(placements)
        shape = tuple(shape)

        if blkno == -1:
            unit = JoinUnit(None, shape)
        else:
            blk = mgr.blocks[blkno]
            ax0_blk_indexer = blklocs[placements.indexer]

            unit_no_ax0_reindexing = (len(placements) == len(blk.mgr_locs) and
                                      # Fastpath detection of join unit not
                                      # needing to reindex its block: no ax0
                                      # reindexing took place and block
                                      # placement was sequential before.
                                      ((ax0_indexer is None and
                                        blk.mgr_locs.is_slice_like and
                                        blk.mgr_locs.as_slice.step == 1) or
                                       # Slow-ish detection: all indexer locs
                                       # are sequential (and length match is
                                       # checked above).
                                       (np.diff(ax0_blk_indexer) == 1).all()))

            # Omit indexer if no item reindexing is required.
            if unit_no_ax0_reindexing:
                join_unit_indexers.pop(0, None)
            else:
                join_unit_indexers[0] = ax0_blk_indexer

            unit = JoinUnit(blk, shape, join_unit_indexers)

        plan.append((placements, unit))

    return plan


def combine_concat_plans(plans, concat_axis):
    """
    Combine multiple concatenation plans into one.

    existing_plan is updated in-place.
    """
    if len(plans) == 1:
        for p in plans[0]:
            yield p[0], [p[1]]

    elif concat_axis == 0:
        offset = 0
        for plan in plans:
            last_plc = None

            for plc, unit in plan:
                yield plc.add(offset), [unit]
                last_plc = plc

            if last_plc is not None:
                offset += last_plc.as_slice.stop

    else:
        num_ended = [0]

        def _next_or_none(seq):
            retval = next(seq, None)
            if retval is None:
                num_ended[0] += 1
            return retval

        plans = list(map(iter, plans))
        next_items = list(map(_next_or_none, plans))

        while num_ended[0] != len(next_items):
            if num_ended[0] > 0:
                raise ValueError("Plan shapes are not aligned")

            placements, units = zip(*next_items)

            lengths = list(map(len, placements))
            min_len, max_len = min(lengths), max(lengths)

            if min_len == max_len:
                yield placements[0], units
                next_items[:] = map(_next_or_none, plans)
            else:
                yielded_placement = None
                yielded_units = [None] * len(next_items)
                for i, (plc, unit) in enumerate(next_items):
                    yielded_units[i] = unit
                    if len(plc) > min_len:
                        # trim_join_unit updates unit in place, so only
                        # placement needs to be sliced to skip min_len.
                        next_items[i] = (plc[min_len:],
                                         trim_join_unit(unit, min_len))
                    else:
                        yielded_placement = plc
                        next_items[i] = _next_or_none(plans[i])

                yield yielded_placement, yielded_units


def trim_join_unit(join_unit, length):
    """
    Reduce join_unit's shape along item axis to length.

    Extra items that didn't fit are returned as a separate block.
    """

    if 0 not in join_unit.indexers:
        extra_indexers = join_unit.indexers

        if join_unit.block is None:
            extra_block = None
        else:
            extra_block = join_unit.block.getitem_block(slice(length, None))
            join_unit.block = join_unit.block.getitem_block(slice(length))
    else:
        extra_block = join_unit.block

        extra_indexers = copy.copy(join_unit.indexers)
        extra_indexers[0] = extra_indexers[0][length:]
        join_unit.indexers[0] = join_unit.indexers[0][:length]

    extra_shape = (join_unit.shape[0] - length,) + join_unit.shape[1:]
    join_unit.shape = (length,) + join_unit.shape[1:]

    return JoinUnit(block=extra_block, indexers=extra_indexers,
                    shape=extra_shape)


class JoinUnit(object):
    def __init__(self, block, shape, indexers=None):
        # Passing shape explicitly is required for cases when block is None.
        if indexers is None:
            indexers = {}
        self.block = block
        self.indexers = indexers
        self.shape = shape

    def __repr__(self):
        return '%s(%r, %s)' % (self.__class__.__name__, self.block,
                               self.indexers)

    @cache_readonly
    def needs_filling(self):
        for indexer in self.indexers.values():
            # FIXME: cache results of indexer == -1 checks.
            if (indexer == -1).any():
                return True

        return False

    @cache_readonly
    def dtype(self):
        if self.block is None:
            raise AssertionError("Block is None, no dtype")

        if not self.needs_filling:
            return self.block.dtype
        else:
            return _get_dtype(_maybe_promote(self.block.dtype,
                                             self.block.fill_value)[0])

        return self._dtype

    @cache_readonly
    def is_null(self):
        if self.block is None:
            return True

        if not self.block._can_hold_na:
            return False

        # Usually it's enough to check but a small fraction of values to see if
        # a block is NOT null, chunks should help in such cases.  1000 value
        # was chosen rather arbitrarily.
        values = self.block.values
        if self.block.is_categorical:
            values_flat = values.categories
        elif self.block.is_sparse:
            # fill_value is not NaN and have holes
            if not values._null_fill_value and values.sp_index.ngaps > 0:
                return False
            values_flat = values.ravel(order='K')
        else:
            values_flat = values.ravel(order='K')
        total_len = values_flat.shape[0]
        chunk_len = max(total_len // 40, 1000)
        for i in range(0, total_len, chunk_len):
            if not isnull(values_flat[i:i + chunk_len]).all():
                return False

        return True

    def get_reindexed_values(self, empty_dtype, upcasted_na):

        if upcasted_na is None:
            # No upcasting is necessary
            fill_value = self.block.fill_value
            values = self.block.get_values()
        else:
            fill_value = upcasted_na

            if self.is_null:
                if getattr(self.block, 'is_object', False):
                    # we want to avoid filling with np.nan if we are
                    # using None; we already know that we are all
                    # nulls
                    values = self.block.values.ravel(order='K')
                    if len(values) and values[0] is None:
                        fill_value = None

                if getattr(self.block, 'is_datetimetz', False):
                    pass
                elif getattr(self.block, 'is_categorical', False):
                    pass
                elif getattr(self.block, 'is_sparse', False):
                    pass
                else:
                    missing_arr = np.empty(self.shape, dtype=empty_dtype)
                    missing_arr.fill(fill_value)
                    return missing_arr

            if not self.indexers:
                if not self.block._can_consolidate:
                    # preserve these for validation in _concat_compat
                    return self.block.values

            if self.block.is_bool:
                # External code requested filling/upcasting, bool values must
                # be upcasted to object to avoid being upcasted to numeric.
                values = self.block.astype(np.object_).values
            else:
                # No dtype upcasting is done here, it will be performed during
                # concatenation itself.
                values = self.block.get_values()

        if not self.indexers:
            # If there's no indexing to be done, we want to signal outside
            # code that this array must be copied explicitly.  This is done
            # by returning a view and checking `retval.base`.
            values = values.view()

        else:
            for ax, indexer in self.indexers.items():
                values = algos.take_nd(values, indexer, axis=ax,
                                       fill_value=fill_value)

        return values


def _fast_count_smallints(arr):
    """Faster version of set(arr) for sequences of small numbers."""
    if len(arr) == 0:
        # Handle empty arr case separately: numpy 1.6 chokes on that.
        return np.empty((0, 2), dtype=arr.dtype)
    else:
        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, allow_fill):
    if isinstance(slice_or_indexer, slice):
        return 'slice', slice_or_indexer, lib.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)