def _merge_blocks(self, merge_chunks):
"""
merge_chunks -> [(_JoinUnit, Block)]
"""
funit, fblock = merge_chunks[0]
fidx = funit.indexer
out_shape = list(fblock.values.shape)
n = len(fidx) if fidx is not None else out_shape[self.axis]
out_shape[0] = sum(len(blk) for unit, blk in merge_chunks)
out_shape[self.axis] = n
# Should use Fortran order??
block_dtype = _get_block_dtype([x[1] for x in merge_chunks])
out = np.empty(out_shape, dtype=block_dtype)
sofar = 0
for unit, blk in merge_chunks:
out_chunk = out[sofar: sofar + len(blk)]
com.take_fast(blk.values, unit.indexer,
None, False, axis=self.axis,
out=out_chunk)
sofar += len(blk)
# does not sort
new_block_items = _concat_indexes([b.items for _, b in merge_chunks])
return make_block(out, new_block_items, self.result_items)
def _merge_blocks(self, merge_chunks):
"""
merge_chunks -> [(_JoinUnit, Block)]
"""
funit, fblock = merge_chunks[0]
fidx = funit.indexer
out_shape = list(fblock.values.shape)
n = len(fidx) if fidx is not None else out_shape[self.axis]
out_shape[0] = sum(len(blk) for unit, blk in merge_chunks)
out_shape[self.axis] = n
# Should use Fortran order??
out = np.empty(out_shape, dtype=fblock.values.dtype)
sofar = 0
for unit, blk in merge_chunks:
out_chunk = out[sofar : sofar + len(blk)]
if unit.indexer is None:
# is this really faster than assigning to arr.flat?
com.take_fast(blk.values, np.arange(n, dtype=np.int64), None, False, axis=self.axis, out=out_chunk)
else:
# write out the values to the result array
com.take_fast(blk.values, unit.indexer, None, False, axis=self.axis, out=out_chunk)
sofar += len(blk)
# does not sort
new_block_items = _concat_indexes([b.items for _, b in merge_chunks])
return make_block(out, new_block_items, self.result_items)
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)
# 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,
fill_value=fill_value)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
def take(self, indexer, axis=1, fill_value=np.nan):
if axis < 1:
raise AssertionError('axis must be at least 1, got %d' % axis)
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 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_indexer(self, new_axis, indexer, axis=1):
"""
pandas-indexer with -1's only
"""
if axis == 0:
raise NotImplementedError
new_axes = list(self.axes)
new_axes[axis] = new_axis
new_blocks = []
for blk in self.blocks:
new_values = common.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 take(self, indexer, axis=1, pandas_indexer=False):
if axis == 0:
raise NotImplementedError
if pandas_indexer:
take_f = lambda arr: common.take_fast(arr, indexer,
None, False, axis=axis)
else:
take_f = lambda arr: arr.take(indexer, axis=axis)
new_axes = list(self.axes)
new_axes[axis] = self.axes[axis].take(indexer)
new_blocks = []
for blk in self.blocks:
newb = make_block(take_f(blk.values), blk.items, self.items)
new_blocks.append(newb)
return BlockManager(new_blocks, new_axes)
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_blocks(self, lblk, rblk):
lidx = self.lindexer
ridx = self.rindexer
n = lblk.values.shape[self.axis] if lidx is None else len(lidx)
lk = len(lblk.items)
rk = len(rblk.items)
out_shape = list(lblk.shape)
out_shape[0] = lk + rk
out_shape[self.axis] = n
out = np.empty(out_shape, dtype=lblk.values.dtype)
# is this really faster than assigning to arr.flat?
if lidx is None:
# out[:lk] = lblk.values
common.take_fast(lblk.values, np.arange(n, dtype='i4'),
None, False,
axis=self.axis, out=out[:lk])
else:
# write out the values to the result array
common.take_fast(lblk.values, lidx, None, False,
axis=self.axis, out=out[:lk])
if ridx is None:
# out[lk:] = lblk.values
common.take_fast(rblk.values, np.arange(n, dtype='i4'),
None, False,
axis=self.axis, out=out[lk:])
else:
common.take_fast(rblk.values, ridx, None, False,
axis=self.axis, out=out[lk:])
# does not sort
new_items = lblk.items.append(rblk.items)
return make_block(out, new_items, self.result_items)
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 _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)
# 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,
fill_value=fill_value)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
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 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)
