def _init_dict(self, data, index, columns, dtype=None):
# pre-filter out columns if we passed it
if columns is not None:
columns = ensure_index(columns)
data = {k: v for k, v in data.items() if k in columns}
else:
keys = com.dict_keys_to_ordered_list(data)
columns = Index(keys)
if index is None:
index = extract_index(list(data.values()))
def sp_maker(x):
return SparseArray(
x,
kind=self._default_kind,
fill_value=self._default_fill_value,
copy=True,
dtype=dtype,
)
sdict = {}
for k, v in data.items():
if isinstance(v, Series):
# Force alignment, no copy necessary
if not v.index.equals(index):
v = v.reindex(index)
if not isinstance(v, SparseSeries):
v = sp_maker(v.values)
elif isinstance(v, SparseArray):
v = v.copy()
else:
if isinstance(v, dict):
v = [v.get(i, np.nan) for i in index]
v = sp_maker(v)
if index is not None and len(v) != len(index):
msg = "Length of passed values is {}, index implies {}"
raise ValueError(msg.format(len(v), len(index)))
sdict[k] = v
if len(columns.difference(sdict)):
# TODO: figure out how to handle this case, all nan's?
# add in any other columns we want to have (completeness)
nan_arr = np.empty(len(index), dtype="float64")
nan_arr.fill(np.nan)
nan_arr = SparseArray(
nan_arr,
kind=self._default_kind,
fill_value=self._default_fill_value,
copy=False,
)
sdict.update((c, nan_arr) for c in columns if c not in sdict)
return to_manager(sdict, columns, index)
def _init_dict(self, data, index, columns, dtype=None):
# pre-filter out columns if we passed it
if columns is not None:
columns = ensure_index(columns)
data = {k: v for k, v in compat.iteritems(data) if k in columns}
else:
keys = com.dict_keys_to_ordered_list(data)
columns = Index(keys)
if index is None:
index = extract_index(list(data.values()))
def sp_maker(x):
return SparseArray(x, kind=self._default_kind,
fill_value=self._default_fill_value,
copy=True, dtype=dtype)
sdict = {}
for k, v in compat.iteritems(data):
if isinstance(v, Series):
# Force alignment, no copy necessary
if not v.index.equals(index):
v = v.reindex(index)
if not isinstance(v, SparseSeries):
v = sp_maker(v.values)
elif isinstance(v, SparseArray):
v = v.copy()
else:
if isinstance(v, dict):
v = [v.get(i, np.nan) for i in index]
v = sp_maker(v)
if index is not None and len(v) != len(index):
msg = "Length of passed values is {}, index implies {}"
raise ValueError(msg.format(len(v), len(index)))
sdict[k] = v
if len(columns.difference(sdict)):
# TODO: figure out how to handle this case, all nan's?
# add in any other columns we want to have (completeness)
nan_arr = np.empty(len(index), dtype='float64')
nan_arr.fill(np.nan)
nan_arr = SparseArray(nan_arr, kind=self._default_kind,
fill_value=self._default_fill_value,
copy=False)
sdict.update((c, nan_arr) for c in columns if c not in sdict)
return to_manager(sdict, columns, index)
