def _convert_frames(frames, index, columns, fill_value=np.nan, kind='block'):
from pandas.core.panel import _get_combined_index
output = {}
for item, df in compat.iteritems(frames):
if not isinstance(df, SparseDataFrame):
df = SparseDataFrame(df, default_kind=kind,
default_fill_value=fill_value)
output[item] = df
if index is None:
all_indexes = [df.index for df in output.values()]
index = _get_combined_index(all_indexes)
if columns is None:
all_columns = [df.columns for df in output.values()]
columns = _get_combined_index(all_columns)
index = _ensure_index(index)
columns = _ensure_index(columns)
for item, df in compat.iteritems(output):
if not (df.index.equals(index) and df.columns.equals(columns)):
output[item] = df.reindex(index=index, columns=columns)
return output, index, columns
def _combineFrame(self, other, func, axis=0):
index, columns = self._get_plane_axes(axis)
axis = self._get_axis_number(axis)
other = other.reindex(index=index, columns=columns)
if axis == 0:
new_values = func(self.values, other.values)
elif axis == 1:
new_values = func(self.values.swapaxes(0, 1), other.values.T)
new_values = new_values.swapaxes(0, 1)
elif axis == 2:
new_values = func(self.values.swapaxes(0, 2), other.values)
new_values = new_values.swapaxes(0, 2)
# TODO: make faster!
new_frames = {}
for item, item_slice in zip(self.items, new_values):
old_frame = self[item]
ofv = old_frame.default_fill_value
ok = old_frame.default_kind
new_frames[item] = SparseDataFrame(item_slice,
index=self.major_axis,
columns=self.minor_axis,
default_fill_value=ofv,
default_kind=ok)
return self._new_like(new_frames)
def __init__(self,
frames=None,
items=None,
major_axis=None,
minor_axis=None,
default_fill_value=np.nan,
default_kind='block',
copy=False):
# deprecation #11157
warnings.warn(
"SparsePanel is deprecated and will be removed in a "
"future version",
FutureWarning,
stacklevel=2)
if frames is None:
frames = {}
if isinstance(frames, np.ndarray):
new_frames = {}
for item, vals in zip(items, frames):
new_frames[item] = SparseDataFrame(
vals,
index=major_axis,
columns=minor_axis,
default_fill_value=default_fill_value,
default_kind=default_kind)
frames = new_frames
if not isinstance(frames, dict):
raise TypeError('input must be a dict, a %r was passed' %
type(frames).__name__)
self.default_fill_value = fill_value = default_fill_value
self.default_kind = kind = default_kind
# pre-filter, if necessary
if items is None:
items = Index(sorted(frames.keys()))
items = _ensure_index(items)
(clean_frames, major_axis,
minor_axis) = _convert_frames(frames,
major_axis,
minor_axis,
kind=kind,
fill_value=fill_value)
self._frames = clean_frames
# do we want to fill missing ones?
for item in items:
if item not in clean_frames:
raise ValueError('column %r not found in data' % item)
self._items = items
self.major_axis = major_axis
self.minor_axis = minor_axis
def __init__(self,
frames,
items=None,
major_axis=None,
minor_axis=None,
default_fill_value=np.nan,
default_kind='block',
copy=False):
if isinstance(frames, np.ndarray):
new_frames = {}
for item, vals in zip(items, frames):
new_frames[item] = \
SparseDataFrame(vals, index=major_axis,
columns=minor_axis,
default_fill_value=default_fill_value,
default_kind=default_kind)
frames = new_frames
if not (isinstance(frames, dict)):
raise AssertionError()
self.default_fill_value = fill_value = default_fill_value
self.default_kind = kind = default_kind
# pre-filter, if necessary
if items is None:
items = Index(sorted(frames.keys()))
items = _ensure_index(items)
(clean_frames, major_axis,
minor_axis) = _convert_frames(frames,
major_axis,
minor_axis,
kind=kind,
fill_value=fill_value)
self._frames = clean_frames
# do we want to fill missing ones?
for item in items:
if item not in clean_frames:
raise Exception('column %s not found in data' % item)
self._items = items
self.major_axis = major_axis
self.minor_axis = minor_axis
def minor_xs(self, key):
"""
Return slice of panel along minor axis
Parameters
----------
key : object
Minor axis label
Returns
-------
y : SparseDataFrame
index -> major axis, columns -> items
"""
slices = dict((k, v[key]) for k, v in compat.iteritems(self))
return SparseDataFrame(slices, index=self.major_axis,
columns=self.items,
default_fill_value=self.default_fill_value,
default_kind=self.default_kind)
