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internals.py
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internals.py
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import itertools
from datetime import datetime
from numpy import nan
import numpy as np
from pandas.core.index import Index, _ensure_index, _handle_legacy_indexes
import pandas.core.common as com
import pandas.lib as lib
class Block(object):
"""
Canonical n-dimensional unit of homogeneous dtype contained in a pandas data
structure
Index-ignorant; let the container take care of that
"""
__slots__ = ['items', 'ref_items', '_ref_locs', 'values', 'ndim']
def __init__(self, values, items, ref_items, ndim=2,
do_integrity_check=False):
if issubclass(values.dtype.type, basestring):
values = np.array(values, dtype=object)
assert(values.ndim == ndim)
assert(len(items) == len(values))
self._ref_locs = None
self.values = values
self.ndim = ndim
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
if do_integrity_check:
self._check_integrity()
def _check_integrity(self):
if len(self.items) < 2:
return
# monotonicity
return (self.ref_locs[1:] > self.ref_locs[:-1]).all()
@property
def ref_locs(self):
if self._ref_locs is None:
indexer = self.ref_items.get_indexer(self.items)
indexer = com._ensure_platform_int(indexer)
assert((indexer != -1).all())
self._ref_locs = indexer
return self._ref_locs
def set_ref_items(self, ref_items, maybe_rename=True):
"""
If maybe_rename=True, need to set the items for this guy
"""
assert(isinstance(ref_items, Index))
if maybe_rename:
self.items = ref_items.take(self.ref_locs)
self.ref_items = ref_items
def __repr__(self):
shape = ' x '.join([str(s) for s in self.shape])
name = type(self).__name__
return '%s: %s, %s, dtype %s' % (name, self.items, shape, self.dtype)
def __contains__(self, item):
return item in self.items
def __len__(self):
return len(self.values)
def __getstate__(self):
# should not pickle generally (want to share ref_items), but here for
# completeness
return (self.items, self.ref_items, self.values)
def __setstate__(self, state):
items, ref_items, values = state
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
self.values = values
self.ndim = values.ndim
@property
def shape(self):
return self.values.shape
@property
def dtype(self):
return self.values.dtype
def copy(self, deep=True):
values = self.values
if deep:
values = values.copy()
return make_block(values, self.items, self.ref_items)
def merge(self, other):
assert(self.ref_items.equals(other.ref_items))
# Not sure whether to allow this or not
# if not union_ref.equals(other.ref_items):
# union_ref = self.ref_items + other.ref_items
return _merge_blocks([self, other], self.ref_items)
def reindex_axis(self, indexer, mask, needs_masking, axis=0,
fill_value=np.nan):
"""
Reindex using pre-computed indexer information
"""
if self.values.size > 0:
new_values = com.take_fast(self.values, indexer, mask,
needs_masking, axis=axis,
fill_value=fill_value)
else:
shape = list(self.shape)
shape[axis] = len(indexer)
new_values = np.empty(shape)
new_values.fill(fill_value)
return make_block(new_values, self.items, self.ref_items)
def reindex_items_from(self, new_ref_items, copy=True):
"""
Reindex to only those items contained in the input set of items
E.g. if you have ['a', 'b'], and the input items is ['b', 'c', 'd'],
then the resulting items will be ['b']
Returns
-------
reindexed : Block
"""
new_ref_items, indexer = self.items.reindex(new_ref_items)
if indexer is None:
new_items = new_ref_items
new_values = self.values.copy() if copy else self.values
else:
mask = indexer != -1
masked_idx = indexer[mask]
if self.values.ndim == 2:
new_values = com.take_2d(self.values, masked_idx, axis=0,
needs_masking=False)
else:
new_values = self.values.take(masked_idx, axis=0)
new_items = self.items.take(masked_idx)
return make_block(new_values, new_items, new_ref_items)
def get(self, item):
loc = self.items.get_loc(item)
return self.values[loc]
def set(self, item, value):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
self.values[loc] = value
def delete(self, item):
"""
Returns
-------
y : Block (new object)
"""
loc = self.items.get_loc(item)
new_items = self.items.delete(loc)
new_values = np.delete(self.values, loc, 0)
return make_block(new_values, new_items, self.ref_items)
def split_block_at(self, item):
"""
Split block around given column, for "deleting" a column without
having to copy data by returning views on the original array
Returns
-------
leftb, rightb : (Block or None, Block or None)
"""
loc = self.items.get_loc(item)
if len(self.items) == 1:
# no blocks left
return None, None
if loc == 0:
# at front
left_block = None
right_block = make_block(self.values[1:], self.items[1:].copy(),
self.ref_items)
elif loc == len(self.values) - 1:
# at back
left_block = make_block(self.values[:-1], self.items[:-1].copy(),
self.ref_items)
right_block = None
else:
# in the middle
left_block = make_block(self.values[:loc],
self.items[:loc].copy(), self.ref_items)
right_block = make_block(self.values[loc + 1:],
self.items[loc + 1:].copy(),
self.ref_items)
return left_block, right_block
def fillna(self, value, inplace=False):
new_values = self.values if inplace else self.values.copy()
mask = com.isnull(new_values)
np.putmask(new_values, mask, value)
if inplace:
return self
else:
return make_block(new_values, self.items, self.ref_items)
def _can_hold_element(self, value):
raise NotImplementedError()
def _try_cast(self, value):
raise NotImplementedError()
def replace(self, to_replace, value, inplace=False):
new_values = self.values if inplace else self.values.copy()
if self._can_hold_element(value):
value = self._try_cast(value)
if not isinstance(to_replace, (list, np.ndarray)):
if self._can_hold_element(to_replace):
to_replace = self._try_cast(to_replace)
np.putmask(new_values, com.mask_missing(new_values, to_replace),
value)
else:
try:
to_replace = np.array(to_replace, dtype=self.dtype)
np.putmask(new_values, com.mask_missing(new_values, to_replace),
value)
except:
to_replace = np.array(to_replace, dtype=object)
for r in to_replace:
if self._can_hold_element(r):
r = self._try_cast(r)
np.putmask(new_values, com.mask_missing(new_values, to_replace),
value)
if inplace:
return self
else:
return make_block(new_values, self.items, self.ref_items)
def putmask(self, mask, new, inplace=False):
new_values = self.values if inplace else self.values.copy()
if self._can_hold_element(new):
new = self._try_cast(new)
np.putmask(new_values, mask, new)
if inplace:
return self
else:
return make_block(new_values, self.items, self.ref_items)
def interpolate(self, method='pad', axis=0, inplace=False,
limit=None, missing=None):
values = self.values if inplace else self.values.copy()
if values.ndim != 2:
raise NotImplementedError
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
if missing is None:
mask = None
else: # todo create faster fill func without masking
mask = _mask_missing(transf(values), missing)
if method == 'pad':
com.pad_2d(transf(values), limit=limit, mask=mask)
else:
com.backfill_2d(transf(values), limit=limit, mask=mask)
return make_block(values, self.items, self.ref_items)
def take(self, indexer, axis=1, fill_value=np.nan):
assert(axis >= 1)
new_values = com.take_fast(self.values, indexer, None,
None, axis=axis,
fill_value=fill_value)
return make_block(new_values, self.items, self.ref_items)
def get_values(self, dtype):
return self.values
def _mask_missing(array, missing_values):
if not isinstance(missing_values, (list, np.ndarray)):
missing_values = [missing_values]
mask = None
missing_values = np.array(missing_values, dtype=object)
if com.isnull(missing_values).any():
mask = com.isnull(array)
missing_values = missing_values[com.notnull(missing_values)]
for v in missing_values:
if mask is None:
mask = array == missing_values
else:
mask |= array == missing_values
return mask
#-------------------------------------------------------------------------------
# Is this even possible?
class FloatBlock(Block):
_can_hold_na = True
def _can_hold_element(self, element):
return isinstance(element, (float, int))
def _try_cast(self, element):
try:
return float(element)
except: # pragma: no cover
return element
def should_store(self, value):
# when inserting a column should not coerce integers to floats
# unnecessarily
return issubclass(value.dtype.type, np.floating)
class ComplexBlock(Block):
_can_hold_na = True
def _can_hold_element(self, element):
return isinstance(element, complex)
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(Block):
_can_hold_na = False
def _can_hold_element(self, element):
return com.is_integer(element)
def _try_cast(self, element):
try:
return int(element)
except: # pragma: no cover
return element
def should_store(self, value):
return issubclass(value.dtype.type, np.integer)
class BoolBlock(Block):
_can_hold_na = False
def _can_hold_element(self, element):
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_)
class ObjectBlock(Block):
_can_hold_na = True
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_))
_NS_DTYPE = np.dtype('M8[ns]')
class DatetimeBlock(Block):
_can_hold_na = True
def __init__(self, values, items, ref_items, ndim=2,
do_integrity_check=False):
if values.dtype != _NS_DTYPE:
values = lib.cast_to_nanoseconds(values)
Block.__init__(self, values, items, ref_items, ndim=ndim,
do_integrity_check=do_integrity_check)
def _can_hold_element(self, element):
return com.is_integer(element) or isinstance(element, datetime)
def _try_cast(self, element):
try:
return int(element)
except:
return element
def should_store(self, value):
return issubclass(value.dtype.type, np.datetime64)
def set(self, item, value):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
if value.dtype != _NS_DTYPE:
value = lib.cast_to_nanoseconds(value)
self.values[loc] = value
def get_values(self, dtype):
if dtype == object:
flat_i8 = self.values.ravel().view(np.int64)
res = lib.ints_to_pydatetime(flat_i8)
return res.reshape(self.values.shape)
return self.values
def make_block(values, items, ref_items, do_integrity_check=False):
dtype = values.dtype
vtype = dtype.type
if issubclass(vtype, np.floating):
klass = FloatBlock
elif issubclass(vtype, np.complexfloating):
klass = ComplexBlock
elif issubclass(vtype, np.datetime64):
klass = DatetimeBlock
elif issubclass(vtype, np.integer):
if vtype != np.int64:
values = values.astype('i8')
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
else:
klass = ObjectBlock
return klass(values, items, ref_items, ndim=values.ndim,
do_integrity_check=do_integrity_check)
# TODO: flexible with index=None and/or items=None
class BlockManager(object):
"""
Core internal data structure to implement DataFrame
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
Parameters
----------
Notes
-----
This is *not* a public API class
"""
__slots__ = ['axes', 'blocks']
def __init__(self, blocks, axes, do_integrity_check=True):
self.axes = [_ensure_index(ax) for ax in axes]
self.blocks = blocks
ndim = len(axes)
for block in blocks:
assert(ndim == block.values.ndim)
if do_integrity_check:
self._verify_integrity()
@classmethod
def make_empty(self):
return BlockManager([], [[], []])
def __nonzero__(self):
return True
@property
def ndim(self):
return len(self.axes)
def is_mixed_dtype(self):
counts = set()
for block in self.blocks:
counts.add(block.dtype)
if len(counts) > 1:
return True
return False
def set_axis(self, axis, value):
cur_axis = self.axes[axis]
if len(value) != len(cur_axis):
raise Exception('Length mismatch (%d vs %d)'
% (len(value), len(cur_axis)))
self.axes[axis] = _ensure_index(value)
if axis == 0:
for block in self.blocks:
block.set_ref_items(self.items, maybe_rename=True)
# make items read only for now
def _get_items(self):
return self.axes[0]
items = property(fget=_get_items)
def __getstate__(self):
block_values = [b.values for b in self.blocks]
block_items = [b.items for b in self.blocks]
axes_array = [ax for ax in self.axes]
return axes_array, block_values, block_items
def __setstate__(self, state):
# 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]
self.axes = _handle_legacy_indexes(self.axes)
blocks = []
for values, items in zip(bvalues, bitems):
blk = make_block(values, items, self.axes[0],
do_integrity_check=True)
blocks.append(blk)
self.blocks = blocks
def __len__(self):
return len(self.items)
def __repr__(self):
output = 'BlockManager'
for i, ax in enumerate(self.axes):
if i == 0:
output += '\nItems: %s' % ax
else:
output += '\nAxis %d: %s' % (i, ax)
for block in self.blocks:
output += '\n%s' % repr(block)
return output
@property
def shape(self):
return tuple(len(ax) for ax in self.axes)
def _verify_integrity(self):
# _union_block_items(self.blocks)
mgr_shape = self.shape
for block in self.blocks:
assert(block.ref_items is self.items)
assert(block.values.shape[1:] == mgr_shape[1:])
tot_items = sum(len(x.items) for x in self.blocks)
assert(len(self.items) == tot_items)
def astype(self, dtype):
new_blocks = []
for block in self.blocks:
newb = make_block(com._astype_nansafe(block.values, dtype),
block.items, block.ref_items)
new_blocks.append(newb)
new_mgr = BlockManager(new_blocks, self.axes)
return new_mgr.consolidate()
def is_consolidated(self):
"""
Return True if more than one block with the same dtype
"""
dtypes = [blk.dtype.type for blk in self.blocks]
return len(dtypes) == len(set(dtypes))
def get_numeric_data(self, copy=False, type_list=None):
"""
Parameters
----------
copy : boolean, default False
Whether to copy the blocks
type_list : tuple of type, default None
Numeric types by default (Float/Complex/Int but not Datetime)
"""
if type_list is None:
def filter_blocks(block):
return (isinstance(block, (IntBlock, FloatBlock, ComplexBlock))
and not isinstance(block, DatetimeBlock))
else:
type_list = self._get_clean_block_types(type_list)
filter_blocks = lambda block: isinstance(block, type_list)
maybe_copy = lambda b: b.copy() if copy else b
num_blocks = [maybe_copy(b) for b in self.blocks if filter_blocks(b)]
if len(num_blocks) == 0:
return BlockManager.make_empty()
indexer = np.sort(np.concatenate([b.ref_locs for b in num_blocks]))
new_items = self.items.take(indexer)
new_blocks = []
for b in num_blocks:
b = b.copy(deep=False)
b.ref_items = new_items
new_blocks.append(b)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes, do_integrity_check=False)
def _get_clean_block_types(self, type_list):
if not isinstance(type_list, tuple):
try:
type_list = tuple(type_list)
except TypeError:
type_list = (type_list,)
type_map = {int : IntBlock, float : FloatBlock,
complex : ComplexBlock,
np.datetime64 : DatetimeBlock,
datetime : DatetimeBlock,
bool : BoolBlock,
object : ObjectBlock}
type_list = tuple([type_map.get(t, t) for t in type_list])
return type_list
def get_bool_data(self, copy=False):
return self.get_numeric_data(copy=copy, type_list=(BoolBlock,))
def get_slice(self, slobj, axis=0):
new_axes = list(self.axes)
new_axes[axis] = new_axes[axis][slobj]
if axis == 0:
new_items = new_axes[0]
if len(self.blocks) == 1:
blk = self.blocks[0]
newb = make_block(blk.values[slobj], new_items,
new_items)
new_blocks = [newb]
else:
return self.reindex_items(new_items)
else:
new_blocks = self._slice_blocks(slobj, axis)
return BlockManager(new_blocks, new_axes, do_integrity_check=False)
def _slice_blocks(self, slobj, axis):
new_blocks = []
slicer = [slice(None, None) for _ in range(self.ndim)]
slicer[axis] = slobj
slicer = tuple(slicer)
for block in self.blocks:
newb = make_block(block.values[slicer], block.items,
block.ref_items)
new_blocks.append(newb)
return new_blocks
def get_series_dict(self):
# For DataFrame
return _blocks_to_series_dict(self.blocks, self.axes[1])
def __contains__(self, item):
return item in self.items
@property
def nblocks(self):
return len(self.blocks)
def copy(self, deep=True):
"""
Make deep or shallow copy of BlockManager
Parameters
----------
deep : boolean, default True
If False, return shallow copy (do not copy data)
Returns
-------
copy : BlockManager
"""
copy_blocks = [block.copy(deep=deep) for block in self.blocks]
# copy_axes = [ax.copy() for ax in self.axes]
copy_axes = list(self.axes)
return BlockManager(copy_blocks, copy_axes, do_integrity_check=False)
def as_matrix(self, items=None):
if len(self.blocks) == 0:
mat = np.empty(self.shape, dtype=float)
elif len(self.blocks) == 1:
blk = self.blocks[0]
if items is None or blk.items.equals(items):
# if not, then just call interleave per below
mat = blk.values
else:
mat = self.reindex_items(items).as_matrix()
else:
if items is None:
mat = self._interleave(self.items)
else:
mat = self.reindex_items(items).as_matrix()
return mat
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert((indexer != -1).all())
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
assert(itemmask.all())
return result
def xs(self, key, axis=1, copy=True):
assert(axis >= 1)
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:
if not copy:
raise Exception('cannot get view of mixed-type or '
'non-consolidated DataFrame')
for blk in self.blocks:
newb = make_block(blk.values[slicer], blk.items, blk.ref_items)
new_blocks.append(newb)
elif len(self.blocks) == 1:
vals = self.blocks[0].values[slicer]
if copy:
vals = vals.copy()
new_blocks = [make_block(vals, self.items, self.items)]
return BlockManager(new_blocks, new_axes)
def fast_2d_xs(self, loc, copy=False):
"""
"""
if len(self.blocks) == 1:
result = self.blocks[0].values[:, loc]
if copy:
result = result.copy()
return result
if not copy:
raise Exception('cannot get view of mixed-type or '
'non-consolidated DataFrame')
dtype = _interleaved_dtype(self.blocks)
items = self.items
n = len(items)
result = np.empty(n, dtype=dtype)
for blk in self.blocks:
values = blk.values
for j, item in enumerate(blk.items):
i = items.get_loc(item)
result[i] = values[j, loc]
return result
def consolidate(self):
"""
Join together blocks having same dtype
Returns
-------
y : BlockManager
"""
if self.is_consolidated():
return self
new_blocks = _consolidate(self.blocks, self.items)
return BlockManager(new_blocks, self.axes)
def _consolidate_inplace(self):
self.blocks = _consolidate(self.blocks, self.items)
def get(self, item):
_, block = self._find_block(item)
return block.get(item)
def iget(self, i):
item = self.items[i]
if self.items.is_unique:
return self.get(item)
else:
# ugh
try:
inds, = (self.items == item).nonzero()
except AttributeError: #MultiIndex
inds, = self.items.map(lambda x: x == item).nonzero()
_, block = self._find_block(item)
try:
binds, = (block.items == item).nonzero()
except AttributeError: #MultiIndex
binds, = block.items.map(lambda x: x == item).nonzero()
for j, (k, b) in enumerate(zip(inds, binds)):
if i == k:
return block.values[b]
raise Exception('Cannot have duplicate column names '
'split across dtypes')
def get_scalar(self, tup):
"""
Retrieve single item
"""
item = tup[0]
_, blk = self._find_block(item)
# this could obviously be seriously sped up in cython
item_loc = blk.items.get_loc(item),
full_loc = item_loc + tuple(ax.get_loc(x)
for ax, x in zip(self.axes[1:], tup[1:]))
return blk.values[full_loc]
def delete(self, item):
i, _ = self._find_block(item)
loc = self.items.get_loc(item)
new_items = self.items.delete(loc)
self._delete_from_block(i, item)
self.set_items_norename(new_items)
def set(self, item, value):
"""
Set new item in-place. Does not consolidate. Adds new Block if not
contained in the current set of items
"""
if value.ndim == self.ndim - 1:
value = value.reshape((1,) + value.shape)
assert(value.shape[1:] == self.shape[1:])
if item in self.items:
i, block = self._find_block(item)
if not block.should_store(value):
# delete from block, create and append new block
self._delete_from_block(i, item)
self._add_new_block(item, value, loc=None)
else:
block.set(item, value)
else:
# insert at end
self.insert(len(self.items), item, value)
def insert(self, loc, item, value):
if item in self.items:
raise Exception('cannot insert %s, already exists' % item)
new_items = self.items.insert(loc, item)
self.set_items_norename(new_items)
# new block
self._add_new_block(item, value, loc=loc)
if len(self.blocks) > 100:
self._consolidate_inplace()
def set_items_norename(self, value):
value = _ensure_index(value)
self.axes[0] = value
for block in self.blocks:
block.set_ref_items(value, maybe_rename=False)
def _delete_from_block(self, i, item):
"""
Delete and maybe remove the whole block
"""
block = self.blocks.pop(i)
new_left, new_right = block.split_block_at(item)
if new_left is not None:
self.blocks.append(new_left)
if new_right is not None:
self.blocks.append(new_right)
def _add_new_block(self, item, value, loc=None):
# Do we care about dtype at the moment?
# hm, elaborate hack?
if loc is None:
loc = self.items.get_loc(item)
new_block = make_block(value, self.items[loc:loc+1].copy(),
self.items)
self.blocks.append(new_block)
def _find_block(self, item):
self._check_have(item)
for i, block in enumerate(self.blocks):
if item in block:
return i, block
def _check_have(self, item):
if item not in self.items:
raise KeyError('no item named %s' % str(item))
def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
new_axis = _ensure_index(new_axis)
cur_axis = self.axes[axis]
if new_axis.equals(cur_axis):
if copy:
result = self.copy(deep=True)
result.axes[axis] = new_axis
if axis == 0:
# patch ref_items, #1823
for blk in result.blocks:
blk.ref_items = new_axis
return result
else:
return self
if axis == 0:
assert(method is None)
return self.reindex_items(new_axis)
new_axis, indexer = cur_axis.reindex(new_axis, method)
return self.reindex_indexer(new_axis, indexer, axis=axis)
def reindex_indexer(self, new_axis, indexer, axis=1, fill_value=np.nan):
"""
pandas-indexer with -1's only.
"""
if axis == 0:
return self._reindex_indexer_items(new_axis, indexer, fill_value)
mask = indexer == -1
# TODO: deal with length-0 case? or does it fall out?
needs_masking = len(new_axis) > 0 and mask.any()
new_blocks = []
for block in self.blocks:
newb = block.reindex_axis(indexer, mask, needs_masking,
axis=axis, fill_value=fill_value)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[axis] = new_axis
return BlockManager(new_blocks, new_axes)
def _reindex_indexer_items(self, new_items, indexer, fill_value):
# TODO: less efficient than I'd like
item_order = com.take_1d(self.items.values, indexer)