def get_indexer(self, target, method=None):
"""
Parameters
----------
target : Index
method :
Returns
-------
(indexer, mask)
"""
if method:
method = method.upper()
aliases = {
'FFILL' : 'PAD',
'BFILL' : 'BACKFILL'
}
target = _ensure_index(target)
method = aliases.get(method, method)
indexer, mask = _tseries.getFillVec(self, target, self.indexMap,
target.indexMap, method)
return indexer, mask
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)
"""
if method:
method = method.upper()
aliases = {
'FFILL' : 'PAD',
'BFILL' : 'BACKFILL'
}
method = aliases.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()
indexer, mask = _tseries.getFillVec(self_index, target_index,
self_index.indexMap,
target.indexMap, method)
return indexer, mask
def test_pad(self):
old = Index([1, 5, 10])
new = Index(range(12))
filler, mask = tseries.getFillVec(old, new, old.indexMap,
new.indexMap, 'PAD')
expect_filler = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2]
expect_mask = np.ones(12, dtype=bool)
expect_mask[0] = False
self.assert_(np.array_equal(filler, expect_filler))
self.assert_(np.array_equal(mask, expect_mask))
# corner case
old = Index([5, 10])
new = Index(range(5))
filler, mask = tseries.getFillVec(old, new, old.indexMap,
new.indexMap, 'PAD')
expect_filler = [-1, -1, -1, -1, -1]
expect_mask = np.zeros(5, dtype=bool)
self.assert_(np.array_equal(filler, expect_filler))
self.assert_(np.array_equal(mask, expect_mask))
def test_getMergeVec(self):
old = Index([1, 5, 10])
new = Index(range(12))
filler, mask = tseries.getFillVec(old, new, old.indexMap,
new.indexMap, None)
expect_filler = [-1, 0, -1, -1, -1, 1, -1, -1, -1, -1, 2, -1]
expect_mask = np.zeros(12, dtype=bool)
expect_mask[[1, 5, 10]] = True
self.assert_(np.array_equal(filler, expect_filler))
self.assert_(np.array_equal(mask, expect_mask))
# corner case
old = Index([1, 4])
new = Index(range(5, 10))
filler, mask = tseries.getFillVec(old, new, old.indexMap,
new.indexMap, None)
expect_filler = [-1, -1, -1, -1, -1]
expect_mask = np.zeros(5, dtype=bool)
self.assert_(np.array_equal(filler, expect_filler))
self.assert_(np.array_equal(mask, expect_mask))
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)
"""
if method:
method = method.upper()
aliases = {
'FFILL' : 'PAD',
'BFILL' : 'BACKFILL'
}
target = _ensure_index(target)
method = aliases.get(method, method)
indexer, mask = _tseries.getFillVec(self, target, self.indexMap,
target.indexMap, method)
return indexer, mask
def get_indexer(self, target, method=None):
"""
Parameters
----------
target : Index
method :
Returns
-------
(indexer, mask)
"""
if method:
method = method.upper()
aliases = {
'FFILL' : 'PAD',
'BFILL' : 'BACKFILL'
}
method = aliases.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()
indexer, mask = _tseries.getFillVec(self_index, target_index,
self_index.indexMap,
target.indexMap, method)
return indexer, mask