/usr/share/pyshared/pandas/tools/merge.py is in python-pandas 0.7.0-1.
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SQL-style merge routines
"""
import numpy as np
from pandas.core.frame import DataFrame, _merge_doc
from pandas.core.generic import NDFrame
from pandas.core.groupby import get_group_index
from pandas.core.series import Series
from pandas.core.index import (Factor, Index, MultiIndex, _get_combined_index,
_ensure_index)
from pandas.core.internals import (IntBlock, BoolBlock, BlockManager,
make_block, _consolidate)
from pandas.util.decorators import cache_readonly, Appender, Substitution
from pandas.sparse.frame import SparseDataFrame
import pandas.core.common as com
import pandas._tseries as lib
@Substitution('\nleft : DataFrame')
@Appender(_merge_doc, indents=0)
def merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('.x', '.y'), copy=True):
op = _MergeOperation(left, right, how=how, on=on, left_on=left_on,
right_on=right_on, left_index=left_index,
right_index=right_index, sort=sort, suffixes=suffixes,
copy=copy)
return op.get_result()
if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'
# TODO: NA group handling
# TODO: transformations??
# TODO: only copy DataFrames when modification necessary
class _MergeOperation(object):
"""
Perform a database (SQL) merge operation between two DataFrame objects
using either columns as keys or their row indexes
"""
def __init__(self, left, right, how='inner', on=None,
left_on=None, right_on=None, axis=1,
left_index=False, right_index=False, sort=True,
suffixes=('.x', '.y'), copy=True):
self.left = self.orig_left = left
self.right = self.orig_right = right
self.how = how
self.axis = axis
self.on = com._maybe_make_list(on)
self.left_on = com._maybe_make_list(left_on)
self.right_on = com._maybe_make_list(right_on)
self.copy = copy
self.suffixes = suffixes
self.sort = sort
self.left_index = left_index
self.right_index = right_index
# note this function has side effects
(self.left_join_keys,
self.right_join_keys,
self.join_names) = self._get_merge_keys()
def get_result(self):
join_index, left_indexer, right_indexer = self._get_join_info()
# this is a bit kludgy
ldata, rdata = self._get_merge_data()
# TODO: more efficiently handle group keys to avoid extra consolidation!
join_op = _BlockJoinOperation([ldata, rdata], join_index,
[left_indexer, right_indexer], axis=1,
copy=self.copy)
result_data = join_op.get_result()
result = DataFrame(result_data)
self._maybe_add_join_keys(result, left_indexer, right_indexer)
return result
def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
# insert group keys
keys = zip(self.join_names, self.left_on, self.right_on)
for i, (name, lname, rname) in enumerate(keys):
if not _should_fill(lname, rname):
continue
if name in result:
key_col = result[name]
if name in self.left and left_indexer is not None:
na_indexer = (left_indexer == -1).nonzero()[0]
if len(na_indexer) == 0:
continue
right_na_indexer = right_indexer.take(na_indexer)
key_col.put(na_indexer, com.take_1d(self.right_join_keys[i],
right_na_indexer))
elif name in self.right and right_indexer is not None:
na_indexer = (right_indexer == -1).nonzero()[0]
if len(na_indexer) == 0:
continue
left_na_indexer = left_indexer.take(na_indexer)
key_col.put(na_indexer, com.take_1d(self.left_join_keys[i],
left_na_indexer))
elif left_indexer is not None:
if name is None:
name = 'key_%d' % i
# a faster way?
key_col = com.take_1d(self.left_join_keys[i], left_indexer)
na_indexer = (left_indexer == -1).nonzero()[0]
right_na_indexer = right_indexer.take(na_indexer)
key_col.put(na_indexer, com.take_1d(self.right_join_keys[i],
right_na_indexer))
result.insert(i, name, key_col)
def _get_join_info(self):
left_ax = self.left._data.axes[self.axis]
right_ax = self.right._data.axes[self.axis]
if self.left_index and self.right_index:
join_index, left_indexer, right_indexer = \
left_ax.join(right_ax, how=self.how, return_indexers=True)
elif self.right_index and self.how == 'left':
join_index, left_indexer, right_indexer = \
_left_join_on_index(left_ax, right_ax, self.left_join_keys,
sort=self.sort)
elif self.left_index and self.how == 'right':
join_index, right_indexer, left_indexer = \
_left_join_on_index(right_ax, left_ax, self.right_join_keys,
sort=self.sort)
else:
# max groups = largest possible number of distinct groups
left_key, right_key, max_groups = self._get_group_keys()
join_func = _join_functions[self.how]
left_indexer, right_indexer = join_func(left_key.astype('i4'),
right_key.astype('i4'),
max_groups)
if self.right_index:
join_index = self.left.index.take(left_indexer)
elif self.left_index:
join_index = self.right.index.take(right_indexer)
else:
join_index = Index(np.arange(len(left_indexer)))
return join_index, left_indexer, right_indexer
def _get_merge_data(self):
"""
Handles overlapping column names etc.
"""
ldata, rdata = self.left._data, self.right._data
lsuf, rsuf = self.suffixes
ldata, rdata = ldata._maybe_rename_join(rdata, lsuf, rsuf,
copydata=False)
return ldata, rdata
def _get_merge_keys(self):
"""
Note: has side effects (copy/delete key columns)
Parameters
----------
left
right
on
Returns
-------
left_keys, right_keys
"""
self._validate_specification()
left_keys = []
right_keys = []
join_names = []
right_drop = []
left, right = self.left, self.right
is_lkey = lambda x: isinstance(x, np.ndarray) and len(x) == len(left)
is_rkey = lambda x: isinstance(x, np.ndarray) and len(x) == len(right)
# ugh, spaghetti re #733
if _any(self.left_on) and _any(self.right_on):
for lk, rk in zip(self.left_on, self.right_on):
if is_lkey(lk):
left_keys.append(lk)
if is_rkey(rk):
right_keys.append(rk)
join_names.append(None) # what to do?
else:
right_keys.append(right[rk].values)
join_names.append(rk)
else:
if not is_rkey(rk):
right_keys.append(right[rk].values)
if lk == rk:
right_drop.append(rk)
else:
right_keys.append(rk)
left_keys.append(left[lk].values)
join_names.append(lk)
elif _any(self.left_on):
for k in self.left_on:
if is_lkey(k):
left_keys.append(k)
join_names.append(None)
else:
left_keys.append(left[k].values)
join_names.append(k)
if isinstance(self.right.index, MultiIndex):
right_keys = [lev.values.take(lab)
for lev, lab in zip(self.right.index.levels,
self.right.index.labels)]
else:
right_keys = [self.right.index.values]
elif _any(self.right_on):
for k in self.right_on:
if is_rkey(k):
right_keys.append(k)
join_names.append(None)
else:
right_keys.append(right[k].values)
join_names.append(k)
if isinstance(self.left.index, MultiIndex):
left_keys = [lev.values.take(lab)
for lev, lab in zip(self.left.index.levels,
self.left.index.labels)]
else:
left_keys = [self.left.index.values]
if right_drop:
self.right = self.right.drop(right_drop, axis=1)
return left_keys, right_keys, join_names
def _validate_specification(self):
# Hm, any way to make this logic less complicated??
if (self.on is None and self.left_on is None
and self.right_on is None):
if self.left_index and self.right_index:
self.left_on, self.right_on = (), ()
elif self.left_index:
if self.right_on is None:
raise Exception('Must pass right_on or right_index=True')
elif self.right_index:
if self.left_on is None:
raise Exception('Must pass left_on or left_index=True')
else:
# use the common columns
common_cols = self.left.columns.intersection(self.right.columns)
self.left_on = self.right_on = common_cols
elif self.on is not None:
if self.left_on is not None or self.right_on is not None:
raise Exception('Can only pass on OR left_on and '
'right_on')
self.left_on = self.right_on = self.on
elif self.left_on is not None:
n = len(self.left_on)
if self.right_index:
assert(len(self.left_on) == self.right.index.nlevels)
self.right_on = [None] * n
elif self.right_on is not None:
n = len(self.right_on)
if self.left_index:
assert(len(self.right_on) == self.left.index.nlevels)
self.left_on = [None] * n
assert(len(self.right_on) == len(self.left_on))
def _get_group_keys(self):
"""
Parameters
----------
Returns
-------
"""
left_keys = self.left_join_keys
right_keys = self.right_join_keys
assert(len(left_keys) == len(right_keys))
left_labels = []
right_labels = []
group_sizes = []
for lk, rk in zip(left_keys, right_keys):
llab, rlab, count = _factorize_objects(lk, rk, sort=self.sort)
left_labels.append(llab)
right_labels.append(rlab)
group_sizes.append(count)
left_group_key = get_group_index(left_labels, group_sizes)
right_group_key = get_group_index(right_labels, group_sizes)
max_groups = 1L
for x in group_sizes:
max_groups *= long(x)
if max_groups > 2**63: # pragma: no cover
raise Exception('Combinatorial explosion! (boom)')
left_group_key, right_group_key, max_groups = \
_factorize_int64(left_group_key, right_group_key,
sort=self.sort)
return left_group_key, right_group_key, max_groups
def _get_multiindex_indexer(join_keys, index, sort=False):
shape = []
labels = []
for level, key in zip(index.levels, join_keys):
llab, rlab, count = _factorize_objects(level, key, sort=False)
labels.append(rlab)
shape.append(count)
left_group_key = get_group_index(labels, shape)
right_group_key = get_group_index(index.labels, shape)
left_group_key, right_group_key, max_groups = \
_factorize_int64(left_group_key, right_group_key,
sort=False)
left_indexer, right_indexer = \
lib.left_outer_join(left_group_key.astype('i4'),
right_group_key.astype('i4'),
max_groups, sort=False)
return left_indexer, right_indexer
def _get_single_indexer(join_key, index, sort=False):
left_key, right_key, count = _factorize_objects(join_key, index, sort=sort)
left_indexer, right_indexer = \
lib.left_outer_join(left_key.astype('i4'), right_key.astype('i4'),
count, sort=sort)
return left_indexer, right_indexer
def _right_outer_join(x, y, max_groups):
right_indexer, left_indexer = lib.left_outer_join(y, x, max_groups)
return left_indexer, right_indexer
def _left_join_on_index(left_ax, right_ax, join_keys, sort=False):
join_index = left_ax
left_indexer = None
if len(join_keys) > 1:
assert(isinstance(right_ax, MultiIndex) and
len(join_keys) == right_ax.nlevels)
left_tmp, right_indexer = \
_get_multiindex_indexer(join_keys, right_ax,
sort=sort)
if sort:
left_indexer = left_tmp
join_index = left_ax.take(left_indexer)
else:
jkey = join_keys[0]
if sort:
left_indexer, right_indexer = \
_get_single_indexer(jkey, right_ax, sort=sort)
join_index = left_ax.take(left_indexer)
else:
right_indexer = right_ax.get_indexer(jkey)
return join_index, left_indexer, right_indexer
_join_functions = {
'inner' : lib.inner_join,
'left' : lib.left_outer_join,
'right' : _right_outer_join,
'outer' : lib.full_outer_join,
}
def _factorize_int64(left_index, right_index, sort=True):
rizer = lib.Int64Factorizer(max(len(left_index), len(right_index)))
# 32-bit compatibility
if left_index.dtype != np.int64: # pragma: no cover
left_index = left_index.astype('i8')
if right_index.dtype != np.int64: # pragma: no cover
right_index = right_index.astype('i8')
llab, _ = rizer.factorize(left_index)
rlab, _ = rizer.factorize(right_index)
if sort:
llab, rlab = _sort_labels(np.array(rizer.uniques), llab, rlab)
return llab, rlab, rizer.get_count()
def _factorize_objects(left_index, right_index, sort=True):
rizer = lib.Factorizer(max(len(left_index), len(right_index)))
llab, _ = rizer.factorize(left_index.astype('O'))
rlab, _ = rizer.factorize(right_index.astype('O'))
count = rizer.get_count()
if sort:
llab, rlab = _sort_labels(rizer.uniques, llab, rlab)
# TODO: na handling
return llab, rlab, count
def _sort_labels(uniques, left, right):
if not isinstance(uniques, np.ndarray):
# tuplesafe
uniques = Index(uniques).values
sorter = uniques.argsort()
reverse_indexer = np.empty(len(sorter), dtype=np.int32)
reverse_indexer.put(sorter, np.arange(len(sorter)))
new_left = reverse_indexer.take(left)
np.putmask(new_left, left == -1, -1)
new_right = reverse_indexer.take(right)
np.putmask(new_right, right == -1, -1)
return new_left, new_right
class _BlockJoinOperation(object):
"""
BlockJoinOperation made generic for N DataFrames
Object responsible for orchestrating efficient join operation between two
BlockManager data structures
"""
def __init__(self, data_list, join_index, indexers, axis=1, copy=True):
if axis <= 0: # pragma: no cover
raise Exception('Only axis >= 1 supported for this operation')
assert(len(data_list) == len(indexers))
self.units = []
for data, indexer in zip(data_list, indexers):
if not data.is_consolidated():
data = data.consolidate()
self.units.append(_JoinUnit(data.blocks, indexer))
self.join_index = join_index
self.axis = axis
self.copy = copy
# do NOT sort
self.result_items = _concat_indexes([d.items for d in data_list])
self.result_axes = list(data_list[0].axes)
self.result_axes[0] = self.result_items
self.result_axes[axis] = self.join_index
def _prepare_blocks(self):
blockmaps = []
for unit in self.units:
join_blocks = unit.get_upcasted_blocks()
type_map = dict((type(blk), blk) for blk in join_blocks)
blockmaps.append(type_map)
return blockmaps
def get_result(self):
"""
Returns
-------
merged : BlockManager
"""
blockmaps = self._prepare_blocks()
kinds = _get_all_block_kinds(blockmaps)
result_blocks = []
# maybe want to enable flexible copying <-- what did I mean?
for klass in kinds:
klass_blocks = [mapping.get(klass) for mapping in blockmaps]
res_blk = self._get_merged_block(klass_blocks)
result_blocks.append(res_blk)
return BlockManager(result_blocks, self.result_axes)
def _get_merged_block(self, blocks):
to_merge = []
for unit, block in zip(self.units, blocks):
if block is not None:
to_merge.append((unit, block))
if len(to_merge) > 1:
return self._merge_blocks(to_merge)
else:
unit, block = to_merge[0]
return unit.reindex_block(block, self.axis,
self.result_items, copy=self.copy)
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='i4'),
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)
class _JoinUnit(object):
"""
Blocks plus indexer
"""
def __init__(self, blocks, indexer):
self.blocks = blocks
self.indexer = indexer
@cache_readonly
def mask_info(self):
if self.indexer is None or not _may_need_upcasting(self.blocks):
mask = None
need_masking = False
else:
mask = self.indexer == -1
need_masking = mask.any()
return mask, need_masking
@property
def need_masking(self):
return self.mask_info[1]
def get_upcasted_blocks(self):
# will short-circuit and not compute lneed_masking if indexer is None
if self.need_masking:
return _upcast_blocks(self.blocks)
return self.blocks
def reindex_block(self, block, axis, ref_items, copy=True):
# still some inefficiency here for bool/int64 because in the case where
# no masking is needed, take_fast will recompute the mask
mask, need_masking = self.mask_info
if self.indexer is None:
if copy:
result = block.copy()
else:
result = block
else:
result = block.reindex_axis(self.indexer, mask, need_masking,
axis=axis)
result.ref_items = ref_items
return result
def _may_need_upcasting(blocks):
for block in blocks:
if isinstance(block, (IntBlock, BoolBlock)):
return True
return False
def _upcast_blocks(blocks):
"""
Upcast and consolidate if necessary
"""
new_blocks = []
for block in blocks:
if isinstance(block, IntBlock):
newb = make_block(block.values.astype(float), block.items,
block.ref_items)
elif isinstance(block, BoolBlock):
newb = make_block(block.values.astype(object), block.items,
block.ref_items)
else:
newb = block
new_blocks.append(newb)
# use any ref_items
return _consolidate(new_blocks, newb.ref_items)
def _get_all_block_kinds(blockmaps):
kinds = set()
for mapping in blockmaps:
kinds |= set(mapping)
return kinds
#----------------------------------------------------------------------
# Concatenate DataFrame objects
def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False):
"""
Concatenate pandas objects along a particular axis with optional set logic
along the other axes. Can also add a layer of hierarchical indexing on the
concatenation axis, which may be useful if the labels are the same (or
overlapping) on the passed axis number
Parameters
----------
objs : list or dict of Series, DataFrame, or Panel objects
If a dict is passed, the sorted keys will be used as the `keys`
argument, unless it is passed, in which case the values will be
selected (see below). Any None objects will be dropped silently unless
they are all None in which case an Exception will be raised
axis : {0, 1, ...}, default 0
The axis to concatenate along
join : {'inner', 'outer'}, default 'outer'
How to handle indexes on other axis(es)
join_axes : list of Index objects
Specific indexes to use for the other n - 1 axes instead of performing
inner/outer set logic
verify_integrity : boolean, default False
Check whether the new concatenated axis contains duplicates. This can
be very expensive relative to the actual data concatenation
keys : sequence, default None
If multiple levels passed, should contain tuples. Construct
hierarchical index using the passed keys as the outermost level
levels : list of sequences, default None
Specific levels (unique values) to use for constructing a
MultiIndex. Otherwise they will be inferred from the keys
names : list, default None
Names for the levels in the resulting hierarchical index
ignore_index : boolean, default False
If True, do not use the index values on the concatenation axis. The
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
concatenating objects where the concatenation axis does not have
meaningful indexing information.
Notes
-----
The keys, levels, and names arguments are all optional
Returns
-------
concatenated : type of objects
"""
op = _Concatenator(objs, axis=axis, join_axes=join_axes,
ignore_index=ignore_index, join=join,
keys=keys, levels=levels, names=names,
verify_integrity=verify_integrity)
return op.get_result()
class _Concatenator(object):
"""
Orchestrates a concatenation operation for BlockManagers, with little hacks
to support sparse data structures, etc.
"""
def __init__(self, objs, axis=0, join='outer', join_axes=None,
keys=None, levels=None, names=None,
ignore_index=False, verify_integrity=False):
if join == 'outer':
self.intersect = False
elif join == 'inner':
self.intersect = True
else: # pragma: no cover
raise ValueError('Only can inner (intersect) or outer (union) join '
'the other axis')
if isinstance(objs, dict):
if keys is None:
keys = sorted(objs)
objs = [objs[k] for k in keys]
# filter Nones
objs = [obj for obj in objs if obj is not None]
if len(objs) == 0:
raise Exception('All objects passed were None')
# consolidate data
for obj in objs:
if isinstance(obj, NDFrame):
obj.consolidate(inplace=True)
self.objs = objs
sample = objs[0]
# Need to flip BlockManager axis in the DataFrame special case
if isinstance(sample, DataFrame):
axis = 1 if axis == 0 else 0
self._is_series = isinstance(sample, Series)
assert(0 <= axis <= sample.ndim)
# note: this is the BlockManager axis (since DataFrame is transposed)
self.axis = axis
self.join_axes = join_axes
self.keys = keys
self.names = names
self.levels = levels
self.ignore_index = ignore_index
self.verify_integrity = verify_integrity
self.new_axes = self._get_new_axes()
def get_result(self):
if self._is_series:
new_data = np.concatenate([x.values for x in self.objs])
name = _consensus_name_attr(self.objs)
return Series(new_data, index=self.new_axes[0], name=name)
else:
new_data = self._get_concatenated_data()
return self.objs[0]._from_axes(new_data, self.new_axes)
def _get_concatenated_data(self):
try:
# need to conform to same other (joined) axes for block join
reindexed_data = self._get_reindexed_data()
blockmaps = []
for data in reindexed_data:
type_map = dict((type(blk), blk) for blk in data.blocks)
blockmaps.append(type_map)
kinds = _get_all_block_kinds(blockmaps)
new_blocks = []
for kind in kinds:
klass_blocks = [mapping.get(kind) for mapping in blockmaps]
stacked_block = self._concat_blocks(klass_blocks)
new_blocks.append(stacked_block)
new_data = BlockManager(new_blocks, self.new_axes)
except Exception: # EAFP
# should not be possible to fail here for the expected reason with
# axis = 0
if self.axis == 0: # pragma: no cover
raise
new_data = {}
for item in self.new_axes[0]:
new_data[item] = self._concat_single_item(item)
return new_data
def _get_reindexed_data(self):
# HACK: ugh
reindexed_data = []
if isinstance(self.objs[0], SparseDataFrame):
pass
else:
axes_to_reindex = list(enumerate(self.new_axes))
axes_to_reindex.pop(self.axis)
for obj in self.objs:
data = obj._data
for i, ax in axes_to_reindex:
data = data.reindex_axis(ax, axis=i, copy=False)
reindexed_data.append(data)
return reindexed_data
def _concat_blocks(self, blocks):
concat_values = np.concatenate([b.values for b in blocks
if b is not None],
axis=self.axis)
if self.axis > 0:
# Not safe to remove this check, need to profile
if not _all_indexes_same([b.items for b in blocks]):
raise Exception('dtypes are not consistent throughout '
'DataFrames')
return make_block(concat_values, blocks[0].items, self.new_axes[0])
else:
all_items = [b.items for b in blocks if b is not None]
if self.axis == 0 and self.keys is not None:
offsets = np.r_[0, np.cumsum([len(x._data.axes[self.axis]) for
x in self.objs])]
indexer = np.concatenate([offsets[i] + b.ref_locs
for i, b in enumerate(blocks)
if b is not None])
concat_items = self.new_axes[0].take(indexer)
else:
concat_items = _concat_indexes(all_items)
return make_block(concat_values, concat_items, self.new_axes[0])
def _concat_single_item(self, item):
all_values = []
dtypes = set()
for obj in self.objs:
try:
values = obj._data.get(item)
dtypes.add(values.dtype)
all_values.append(values)
except KeyError:
all_values.append(None)
# this stinks
have_object = False
for dtype in dtypes:
if issubclass(dtype.type, (np.object_, np.bool_)):
have_object = True
if have_object:
empty_dtype = np.object_
else:
empty_dtype = np.float64
to_concat = []
for obj, item_values in zip(self.objs, all_values):
if item_values is None:
shape = obj._data.shape[1:]
missing_arr = np.empty(shape, dtype=empty_dtype)
missing_arr.fill(np.nan)
to_concat.append(missing_arr)
else:
to_concat.append(item_values)
# this method only gets called with axis >= 1
assert(self.axis >= 1)
return np.concatenate(to_concat, axis=self.axis - 1)
def _get_new_axes(self):
ndim = self.objs[0].ndim
new_axes = [None] * ndim
if self.ignore_index:
concat_axis = None
else:
concat_axis = self._get_concat_axis()
new_axes[self.axis] = concat_axis
if self.join_axes is None:
for i in range(ndim):
if i == self.axis:
continue
all_indexes = [x._data.axes[i] for x in self.objs]
comb_axis = _get_combined_index(all_indexes,
intersect=self.intersect)
new_axes[i] = comb_axis
else:
assert(len(self.join_axes) == ndim - 1)
# ufff...
indices = range(ndim)
indices.remove(self.axis)
for i, ax in zip(indices, self.join_axes):
new_axes[i] = ax
return new_axes
def _get_concat_axis(self):
if self._is_series:
indexes = [x.index for x in self.objs]
else:
indexes = [x._data.axes[self.axis] for x in self.objs]
if self.keys is None:
concat_axis = _concat_indexes(indexes)
else:
concat_axis = _make_concat_multiindex(indexes, self.keys,
self.levels, self.names)
self._maybe_check_integrity(concat_axis)
return concat_axis
def _maybe_check_integrity(self, concat_index):
if self.verify_integrity:
if not concat_index._verify_integrity():
overlap = concat_index.get_duplicates()
raise Exception('Indexes have overlapping values: %s'
% str(overlap))
def _concat_indexes(indexes):
return indexes[0].append(indexes[1:])
def _make_concat_multiindex(indexes, keys, levels=None, names=None):
if ((levels is None and isinstance(keys[0], tuple)) or
(levels is not None and len(levels) > 1)):
zipped = zip(*keys)
if names is None:
names = [None] * len(zipped)
if levels is None:
levels = [Factor(zp).levels for zp in zipped]
else:
levels = [_ensure_index(x) for x in levels]
else:
zipped = [keys]
if names is None:
names = [None]
if levels is None:
levels = [_ensure_index(keys)]
else:
levels = [_ensure_index(x) for x in levels]
if not _all_indexes_same(indexes):
label_list = []
# things are potentially different sizes, so compute the exact labels
# for each level and pass those to MultiIndex.from_arrays
for hlevel, level in zip(zipped, levels):
to_concat = []
for key, index in zip(hlevel, indexes):
i = level.get_loc(key)
to_concat.append(np.repeat(i, len(index)))
label_list.append(np.concatenate(to_concat))
concat_index = _concat_indexes(indexes)
# these go at the end
if isinstance(concat_index, MultiIndex):
levels.extend(concat_index.levels)
label_list.extend(concat_index.labels)
else:
factor = Factor(concat_index)
levels.append(factor.levels)
label_list.append(factor.labels)
# also copies
names = names + _get_consensus_names(indexes)
return MultiIndex(levels=levels, labels=label_list, names=names)
new_index = indexes[0]
n = len(new_index)
kpieces = len(indexes)
# also copies
new_names = list(names)
new_levels = list(levels)
# construct labels
new_labels = []
# do something a bit more speedy
for hlevel, level in zip(zipped, levels):
mapped = level.get_indexer(hlevel)
new_labels.append(np.repeat(mapped, n))
if isinstance(new_index, MultiIndex):
new_levels.extend(new_index.levels)
new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
new_names.extend(new_index.names)
else:
new_levels.append(new_index)
new_names.append(new_index.name)
new_labels.append(np.tile(np.arange(n), kpieces))
return MultiIndex(levels=new_levels, labels=new_labels, names=new_names)
def _get_consensus_names(indexes):
consensus_name = indexes[0].names
for index in indexes[1:]:
if index.names != consensus_name:
consensus_name = [None] * index.nlevels
break
return consensus_name
def _consensus_name_attr(objs):
name = objs[0].name
for obj in objs[1:]:
if obj.name != name:
return None
return name
def _should_fill(lname, rname):
if not isinstance(lname, basestring) or not isinstance(rname, basestring):
return True
return lname == rname
def _all_indexes_same(indexes):
first = indexes[0]
for index in indexes[1:]:
if not first.equals(index):
return False
return True
def _any(x):
return x is not None and len(x) > 0 and any([y is not None for y in x])
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