def test_merge_indexer(self):
old = Index([1, 5, 10])
new = Index(range(12))
filler = lib.merge_indexer_object(new, old.indexMap)
expect_filler = [-1, 0, -1, -1, -1, 1, -1, -1, -1, -1, 2, -1]
self.assert_(np.array_equal(filler, expect_filler))
# corner case
old = Index([1, 4])
new = Index(range(5, 10))
filler = lib.merge_indexer_object(new, old.indexMap)
expect_filler = [-1, -1, -1, -1, -1]
self.assert_(np.array_equal(filler, expect_filler))
def get_indexer(self, target, method=None):
"""
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index. The mask determines whether labels are
found or not in the current index
Parameters
----------
target : MultiIndex or Index (of tuples)
method : {'pad', 'ffill', 'backfill', 'bfill'}
pad / ffill: propagate LAST valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
Notes
-----
This is a low-level method and probably should be used at your own risk
Examples
--------
>>> indexer, mask = index.get_indexer(new_index)
>>> new_values = cur_values.take(indexer)
>>> new_values[-mask] = np.nan
Returns
-------
(indexer, mask) : (ndarray, ndarray)
"""
method = self._get_method(method)
if isinstance(target, MultiIndex):
target_index = target.get_tuple_index()
else:
if len(target) > 0:
val = target[0]
if not isinstance(val, tuple) or len(val) != self.nlevels:
raise ValueError("can only pass MultiIndex or " "array of tuples")
target_index = target
self_index = self.get_tuple_index()
if method == "pad":
indexer = lib.pad_object(self_index, target_index, self_index.indexMap, target.indexMap)
elif method == "backfill":
indexer = lib.backfill_object(self_index, target_index, self_index.indexMap, target.indexMap)
else:
indexer = lib.merge_indexer_object(target_index, self_index.indexMap)
return indexer
def _read_panel_table(self, group, where=None):
from pandas.core.common import _asarray_tuplesafe
table = getattr(group, 'table')
# create the selection
sel = Selection(table, where)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'],
table._v_attrs.index_kind)
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = LongPanel(sel.values['values'], index=long_index,
columns=fields)
if lp.consistent:
lp = lp.sortlevel(level=0)
wp = lp.to_wide()
else:
if not self._quiet: # pragma: no cover
print ('Duplicate entries in table, taking most recently '
'appended')
# need a better algorithm
tuple_index = long_index.get_tuple_index()
index_map = lib.map_indices_object(tuple_index)
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = lib.merge_indexer_object(unique_tuples, index_map)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = LongPanel(new_values, index=new_index, columns=lp.columns)
wp = lp.to_wide()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
def get_indexer(self, target, method=None):
"""
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index. The mask determines whether labels are
found or not in the current index
Parameters
----------
target : Index
method : {'pad', 'ffill', 'backfill', 'bfill'}
pad / ffill: propagate LAST valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
Notes
-----
This is a low-level method and probably should be used at your own risk
Examples
--------
>>> indexer, mask = index.get_indexer(new_index)
>>> new_values = cur_values.take(indexer)
>>> new_values[-mask] = np.nan
Returns
-------
(indexer, mask) : (ndarray, ndarray)
"""
method = self._get_method(method)
target = _ensure_index(target)
if self.dtype != target.dtype:
target = Index(target, dtype=object)
if method == 'pad':
indexer = lib.pad_object(self, target, self.indexMap,
target.indexMap)
elif method == 'backfill':
indexer = lib.backfill_object(self, target, self.indexMap,
target.indexMap)
elif method is None:
indexer = lib.merge_indexer_object(target, self.indexMap)
else:
raise ValueError('unrecognized method: %s' % method)
return indexer
def _read_panel_table(self, group, where=None):
from pandas.core.common import _asarray_tuplesafe
table = getattr(group, 'table')
# create the selection
sel = Selection(table, where)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'], table._v_attrs.index_kind)
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = LongPanel(sel.values['values'], index=long_index, columns=fields)
if lp.consistent:
lp = lp.sortlevel(level=0)
wp = lp.to_wide()
else:
if not self._quiet: # pragma: no cover
print(
'Duplicate entries in table, taking most recently '
'appended')
# need a better algorithm
tuple_index = long_index.get_tuple_index()
index_map = lib.map_indices_object(tuple_index)
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = lib.merge_indexer_object(unique_tuples, index_map)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = LongPanel(new_values, index=new_index, columns=lp.columns)
wp = lp.to_wide()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
def lookup_python(values):
table = lib.map_indices_object(values)
return _timeit(lambda: lib.merge_indexer_object(values, table))
def _read_panel_table(self, group, where=None):
from pandas.core.index import unique_int64, Factor
from pandas.core.common import _asarray_tuplesafe
from pandas.core.internals import BlockManager
from pandas.core.reshape import block2d_to_block3d
table = getattr(group, 'table')
# create the selection
sel = Selection(table, where)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'],
table._v_attrs.index_kind)
values = sel.values['values']
major = Factor(index)
minor = Factor(columns)
J, K = len(major.levels), len(minor.levels)
key = major.labels * K + minor.labels
if len(unique_int64(key)) == len(key):
sorter, _ = lib.groupsort_indexer(key, J * K)
# the data need to be sorted
sorted_values = values.take(sorter, axis=0)
major_labels = major.labels.take(sorter)
minor_labels = minor.labels.take(sorter)
block = block2d_to_block3d(sorted_values, fields, (J, K),
major_labels, minor_labels)
mgr = BlockManager([block], [block.items,
major.levels, minor.levels])
wp = Panel(mgr)
else:
if not self._quiet: # pragma: no cover
print ('Duplicate entries in table, taking most recently '
'appended')
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = DataFrame(values, index=long_index, columns=fields)
# need a better algorithm
tuple_index = long_index.get_tuple_index()
index_map = lib.map_indices_object(tuple_index)
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = lib.merge_indexer_object(unique_tuples, index_map)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = DataFrame(new_values, index=new_index, columns=lp.columns)
wp = lp.to_panel()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
def lookup_python(values):
table = lib.map_indices_object(values)
return _timeit(lambda: lib.merge_indexer_object(values, table))
def _read_panel_table(self, group, where=None):
from pandas.core.index import unique_int64, Factor
from pandas.core.common import _asarray_tuplesafe
from pandas.core.internals import BlockManager
from pandas.core.reshape import block2d_to_block3d
table = getattr(group, 'table')
# create the selection
sel = Selection(table, where)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'], table._v_attrs.index_kind)
values = sel.values['values']
major = Factor(index)
minor = Factor(columns)
J, K = len(major.levels), len(minor.levels)
key = major.labels * K + minor.labels
if len(unique_int64(key)) == len(key):
sorter, _ = lib.groupsort_indexer(key, J * K)
# the data need to be sorted
sorted_values = values.take(sorter, axis=0)
major_labels = major.labels.take(sorter)
minor_labels = minor.labels.take(sorter)
block = block2d_to_block3d(sorted_values, fields, (J, K),
major_labels, minor_labels)
mgr = BlockManager([block],
[block.items, major.levels, minor.levels])
wp = Panel(mgr)
else:
if not self._quiet: # pragma: no cover
print(
'Duplicate entries in table, taking most recently '
'appended')
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = DataFrame(values, index=long_index, columns=fields)
# need a better algorithm
tuple_index = long_index.get_tuple_index()
index_map = lib.map_indices_object(tuple_index)
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = lib.merge_indexer_object(unique_tuples, index_map)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = DataFrame(new_values, index=new_index, columns=lp.columns)
wp = lp.to_panel()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
