def _init_spmatrix(self,
data,
index,
columns,
dtype=None,
fill_value=None):
""" Init self from scipy.sparse matrix """
index, columns = self._prep_index(data, index, columns)
data = data.tocoo()
N = len(index)
# Construct a dict of SparseSeries
sdict = {}
values = Series(data.data, index=data.row, copy=False)
for col, rowvals in values.groupby(data.col):
# get_blocks expects int32 row indices in sorted order
rowvals = rowvals.sort_index()
rows = rowvals.index.values.astype(np.int32)
blocs, blens = get_blocks(rows)
sdict[columns[col]] = SparseSeries(rowvals.values,
index=index,
fill_value=fill_value,
sparse_index=BlockIndex(
N, blocs, blens))
# Add any columns that were empty and thus not grouped on above
sdict.update({
column: SparseSeries(index=index,
fill_value=fill_value,
sparse_index=BlockIndex(N, [], []))
for column in columns if column not in sdict
})
return self._init_dict(sdict, index, columns, dtype)
def _init_spmatrix(self, data, index, columns, dtype=None,
fill_value=None):
""" Init self from scipy.sparse matrix """
index, columns = self._prep_index(data, index, columns)
data = data.tocoo()
N = len(index)
# Construct a dict of SparseSeries
sdict = {}
values = Series(data.data, index=data.row, copy=False)
for col, rowvals in values.groupby(data.col):
# get_blocks expects int32 row indices in sorted order
rowvals = rowvals.sort_index()
rows = rowvals.index.values.astype(np.int32)
blocs, blens = get_blocks(rows)
sdict[columns[col]] = SparseSeries(
rowvals.values, index=index,
fill_value=fill_value,
sparse_index=BlockIndex(N, blocs, blens))
# Add any columns that were empty and thus not grouped on above
sdict.update({column: SparseSeries(index=index,
fill_value=fill_value,
sparse_index=BlockIndex(N, [], []))
for column in columns
if column not in sdict})
return self._init_dict(sdict, index, columns, dtype)
def _make_index(length, indices, kind):
if kind == 'block' or isinstance(kind, BlockIndex):
locs, lens = splib.get_blocks(indices)
index = BlockIndex(length, locs, lens)
elif kind == 'integer' or isinstance(kind, IntIndex):
index = IntIndex(length, indices)
else: # pragma: no cover
raise ValueError('must be block or integer type')
return index
def _make_index(length, indices, kind):
if kind == 'block' or isinstance(kind, BlockIndex):
locs, lens = splib.get_blocks(indices)
index = BlockIndex(length, locs, lens)
elif kind == 'integer' or isinstance(kind, IntIndex):
index = IntIndex(length, indices)
else: # pragma: no cover
raise ValueError('must be block or integer type')
return index
def make_sparse_index(length, indices, kind):
if kind == "block" or isinstance(kind, BlockIndex):
locs, lens = splib.get_blocks(indices)
index = BlockIndex(length, locs, lens)
elif kind == "integer" or isinstance(kind, IntIndex):
index = IntIndex(length, indices)
else: # pragma: no cover
raise ValueError("must be block or integer type")
return index
