/usr/share/pyshared/pandas/core/internals.py is in python-pandas 0.7.0-1.
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from numpy import nan
import numpy as np
from pandas.core.index import Index, _ensure_index
import pandas.core.common as com
import pandas._tseries 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.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()
_ref_locs = None
@property
def ref_locs(self):
if self._ref_locs is None:
indexer = self.ref_items.get_indexer(self.items)
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):
"""
Reindex using pre-computed indexer information
"""
if self.values.size > 0:
new_values = com.take_fast(self.values, indexer, mask,
needs_masking, axis=axis)
else:
shape = list(self.shape)
shape[axis] = len(indexer)
new_values = np.empty(shape)
new_values.fill(np.nan)
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):
new_values = self.values.copy()
mask = com.isnull(new_values.ravel())
new_values.flat[mask] = value
return make_block(new_values, self.items, self.ref_items)
#-------------------------------------------------------------------------------
# Is this even possible?
class FloatBlock(Block):
def should_store(self, value):
# when inserting a column should not coerce integers to floats
# unnecessarily
return issubclass(value.dtype.type, np.floating)
class IntBlock(Block):
def should_store(self, value):
return issubclass(value.dtype.type, np.integer)
class BoolBlock(Block):
def should_store(self, value):
return issubclass(value.dtype.type, np.bool_)
class ObjectBlock(Block):
def should_store(self, value):
return not issubclass(value.dtype.type,
(np.integer, np.floating, np.bool_))
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.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', 'ndim']
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()
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 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 __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]
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.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(block.values.astype(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):
num_blocks = [b for b in self.blocks
if isinstance(b, (IntBlock, FloatBlock))]
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_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])
@classmethod
def from_blocks(cls, blocks, index):
# also checks for overlap
items = _union_block_items(blocks)
return BlockManager(blocks, [items, index])
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.values
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 get(self, item):
_, block = self._find_block(item)
return block.get(item)
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._constructor(
np.delete(np.asarray(self.items), 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)
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)
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):
# Do we care about dtype at the moment?
# hm, elaborate hack?
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
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):
"""
pandas-indexer with -1's only.
"""
if axis == 0:
return self._reindex_indexer_items(new_axis, indexer)
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)
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):
# TODO: less efficient than I'd like
item_order = com.take_1d(self.items.values, indexer)
# keep track of what items aren't found anywhere
mask = np.zeros(len(item_order), dtype=bool)
new_blocks = []
for blk in self.blocks:
blk_indexer = blk.items.get_indexer(item_order)
selector = blk_indexer != -1
# update with observed items
mask |= selector
if not selector.any():
continue
new_block_items = new_items.take(selector.nonzero()[0])
new_values = com.take_fast(blk.values, blk_indexer[selector],
None, False, axis=0)
new_blocks.append(make_block(new_values, new_block_items,
new_items))
if not mask.all():
na_items = new_items[-mask]
na_block = self._make_na_block(na_items, new_items)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
def reindex_items(self, new_items, copy=True):
"""
"""
new_items = _ensure_index(new_items)
data = self
if not data.is_consolidated():
data = data.consolidate()
return data.reindex_items(new_items)
# TODO: this part could be faster (!)
new_items, indexer = self.items.reindex(new_items)
# could have some pathological (MultiIndex) issues here
new_blocks = []
if indexer is None:
for blk in self.blocks:
if copy:
new_blocks.append(blk.reindex_items_from(new_items))
else:
new_blocks.append(blk)
else:
for block in self.blocks:
newb = block.reindex_items_from(new_items, copy=copy)
if len(newb.items) > 0:
new_blocks.append(newb)
mask = indexer == -1
if mask.any():
extra_items = new_items[mask]
na_block = self._make_na_block(extra_items, new_items)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
def _make_na_block(self, items, ref_items):
block_shape = list(self.shape)
block_shape[0] = len(items)
block_values = np.empty(block_shape, dtype=np.float64)
block_values.fill(nan)
na_block = make_block(block_values, items, ref_items,
do_integrity_check=True)
return na_block
def take(self, indexer, axis=1):
if axis == 0:
raise NotImplementedError
indexer = np.asarray(indexer, dtype='i4')
n = len(self.axes[axis])
if ((indexer == -1) | (indexer >= n)).any():
raise Exception('Indices must be nonzero and less than '
'the axis length')
new_axes = list(self.axes)
new_axes[axis] = self.axes[axis].take(indexer)
new_blocks = []
for blk in self.blocks:
new_values = com.take_fast(blk.values, indexer,
None, False, axis=axis)
newb = make_block(new_values, blk.items, self.items)
new_blocks.append(newb)
return BlockManager(new_blocks, new_axes)
def merge(self, other, lsuffix=None, rsuffix=None):
assert(self._is_indexed_like(other))
this, other = self._maybe_rename_join(other, lsuffix, rsuffix)
cons_items = this.items + other.items
consolidated = _consolidate(this.blocks + other.blocks, cons_items)
new_axes = list(this.axes)
new_axes[0] = cons_items
return BlockManager(consolidated, new_axes)
def _maybe_rename_join(self, other, lsuffix, rsuffix, copydata=True):
to_rename = self.items.intersection(other.items)
if len(to_rename) > 0:
if not lsuffix and not rsuffix:
raise Exception('columns overlap: %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
this = self.rename_items(lrenamer, copydata=copydata)
other = other.rename_items(rrenamer, copydata=copydata)
else:
this = self
return this, other
def _is_indexed_like(self, other):
"""
Check all axes except items
"""
assert(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 rename_axis(self, mapper, axis=1):
new_axis = Index([mapper(x) for x in self.axes[axis]])
new_axis._verify_integrity()
new_axes = list(self.axes)
new_axes[axis] = new_axis
return BlockManager(self.blocks, new_axes)
def rename_items(self, mapper, copydata=True):
new_items = Index([mapper(x) for x in self.items])
new_items._verify_integrity()
new_blocks = []
for block in self.blocks:
newb = block.copy(deep=copydata)
newb.set_ref_items(new_items, maybe_rename=True)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes)
def add_prefix(self, prefix):
f = (('%s' % prefix) + '%s').__mod__
return self.rename_items(f)
def add_suffix(self, suffix):
f = ('%s' + ('%s' % suffix)).__mod__
return self.rename_items(f)
def fillna(self, value):
"""
"""
new_blocks = [b.fillna(value) for b in self.blocks]
return BlockManager(new_blocks, self.axes)
@property
def block_id_vector(self):
# TODO
result = np.empty(len(self.items), dtype=int)
result.fill(-1)
for i, blk in enumerate(self.blocks):
indexer = self.items.get_indexer(blk.items)
assert((indexer != -1).all())
result.put(indexer, i)
assert((result >= 0).all())
return result
@property
def item_dtypes(self):
result = np.empty(len(self.items), dtype='O')
mask = np.zeros(len(self.items), dtype=bool)
for i, blk in enumerate(self.blocks):
indexer = self.items.get_indexer(blk.items)
result.put(indexer, blk.values.dtype.name)
mask.put(indexer, 1)
assert(mask.all())
return result
def form_blocks(data, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(data) < len(items):
extra_items = items - Index(data.keys())
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_dict = {}
int_dict = {}
bool_dict = {}
object_dict = {}
for k, v in data.iteritems():
if issubclass(v.dtype.type, np.floating):
float_dict[k] = v
elif issubclass(v.dtype.type, np.integer):
int_dict[k] = v
elif v.dtype == np.bool_:
bool_dict[k] = v
else:
object_dict[k] = v
blocks = []
if len(float_dict):
float_block = _simple_blockify(float_dict, items, np.float64)
blocks.append(float_block)
if len(int_dict):
int_block = _simple_blockify(int_dict, items, np.int64)
blocks.append(int_block)
if len(bool_dict):
bool_block = _simple_blockify(bool_dict, items, np.bool_)
blocks.append(bool_block)
if len(object_dict) > 0:
object_block = _simple_blockify(object_dict, items, np.object_)
blocks.append(object_block)
if len(extra_items):
shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
block_values = np.empty(shape, dtype=float)
block_values.fill(nan)
na_block = make_block(block_values, extra_items, items,
do_integrity_check=True)
blocks.append(na_block)
blocks = _consolidate(blocks, items)
return blocks
def _simple_blockify(dct, ref_items, dtype):
block_items, values = _stack_dict(dct, ref_items, dtype)
# CHECK DTYPE?
if values.dtype != dtype: # pragma: no cover
values = values.astype(dtype)
return make_block(values, block_items, ref_items, do_integrity_check=True)
def _stack_dict(dct, ref_items, dtype):
from pandas.core.series import Series
# fml
def _asarray_compat(x):
# asarray shouldn't be called on SparseSeries
if isinstance(x, Series):
return x.values
else:
return np.asarray(x)
def _shape_compat(x):
# sparseseries
if isinstance(x, Series):
return len(x),
else:
return x.shape
items = [x for x in ref_items if x in dct]
first = dct[items[0]]
shape = (len(dct),) + _shape_compat(first)
stacked = np.empty(shape, dtype=dtype)
for i, item in enumerate(items):
stacked[i] = _asarray_compat(dct[item])
# stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
return items, stacked
def _blocks_to_series_dict(blocks, index=None):
from pandas.core.series import Series
series_dict = {}
for block in blocks:
for item, vec in zip(block.items, block.values):
series_dict[item] = Series(vec, index=index, name=item)
return series_dict
def _interleaved_dtype(blocks):
from collections import defaultdict
counts = defaultdict(lambda: 0)
for x in blocks:
counts[type(x)] += 1
have_int = counts[IntBlock] > 0
have_bool = counts[BoolBlock] > 0
have_object = counts[ObjectBlock] > 0
have_float = counts[FloatBlock] > 0
have_numeric = have_float or have_int
if have_object:
return np.object_
elif have_bool and have_numeric:
return np.object_
elif have_bool:
return np.bool_
elif have_int and not have_float:
return np.int64
else:
return np.float64
def _consolidate(blocks, items):
"""
Merge blocks having same dtype
"""
get_dtype = lambda x: x.dtype
# sort by dtype
grouper = itertools.groupby(sorted(blocks, key=get_dtype),
lambda x: x.dtype)
new_blocks = []
for dtype, group_blocks in grouper:
new_block = _merge_blocks(list(group_blocks), items)
new_blocks.append(new_block)
return new_blocks
# TODO: this could be much optimized
def _merge_blocks(blocks, items):
if len(blocks) == 1:
return blocks[0]
new_values = np.vstack([b.values for b in blocks])
new_items = blocks[0].items.append([b.items for b in blocks[1:]])
new_block = make_block(new_values, new_items, items,
do_integrity_check=True)
return new_block.reindex_items_from(items)
def _union_block_items(blocks):
tot_len = 0
all_items = []
slow = False
for b in blocks:
tot_len += len(b.items)
if type(b.items) != Index:
slow = True
all_items.append(b.items)
if slow:
the_union = _union_items_slow(all_items)
else:
the_union = Index(lib.fast_unique_multiple(all_items))
if tot_len > len(the_union):
raise Exception('item names overlap')
return the_union
def _union_items_slow(all_items):
seen = None
for items in all_items:
if seen is None:
seen = items
else:
seen = seen.union(items)
return seen
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