def from_dict(cls, data, intersect=False, dtype=float):
"""
Construct WidePanel from dict of DataFrame objects
Parameters
----------
data : dict
{field : DataFrame}
intersect : boolean
Returns
-------
WidePanel
"""
data, index, columns = _homogenize(data, intersect=intersect, dtype=dtype)
items = Index(sorted(data.keys()))
axes = [items, index, columns]
reshaped_data = {}
for k, v in data.iteritems():
values = v.values
shape = values.shape
reshaped_data[k] = values.reshape((1,) + shape)
blocks = form_blocks(reshaped_data, axes)
mgr = BlockManager(blocks, axes).consolidate()
return WidePanel(mgr, items, index, columns)
def _init_dict(self, data, axes, dtype=None):
items = axes[0]
# prefilter if items passed
if items is not None:
items = _ensure_index(items)
data = dict((k, v) for k, v in data.iteritems() if k in items)
else:
items = Index(_try_sort(data.keys()))
# figure out the index, if necessary
if index is None:
index = extract_index(data)
# don't force copy because getting jammed in an ndarray anyway
# homogenized = _homogenize(data, index, columns, dtype)
data, index, columns = _homogenize(data, intersect=intersect)
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(homogenized, index, columns)
# consolidate for now
mgr = BlockManager(blocks, [columns, index])
return mgr.consolidate()
def _init_dict(self, data, axes, dtype=None):
items, major, minor = axes
# prefilter if items passed
if items is not None:
items = _ensure_index(items)
data = dict((k, v) for k, v in data.iteritems() if k in items)
else:
items = Index(_try_sort(data.keys()))
for k, v in data.iteritems():
if not isinstance(v, DataFrame):
data[k] = DataFrame(v)
if major is None:
indexes = [v.index for v in data.values()]
major = _union_indexes(indexes)
if minor is None:
indexes = [v.columns for v in data.values()]
minor = _union_indexes(indexes)
axes = [items, major, minor]
reshaped_data = data.copy() # shallow
# homogenize
item_shape = (1, len(major), len(minor))
for k in items:
if k not in data:
values = np.empty(item_shape, dtype=dtype)
values.fill(np.nan)
reshaped_data[k] = values
else:
v = data[k]
v = v.reindex(index=major, columns=minor, copy=False)
if dtype is not None:
v = v.astype(dtype)
values = v.values
shape = values.shape
reshaped_data[k] = values.reshape((1,) + shape)
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(reshaped_data, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr
def _init_dict(self, data, axes, dtype=None):
items, major, minor = axes
# prefilter if items passed
if items is not None:
items = _ensure_index(items)
data = dict((k, v) for k, v in data.iteritems() if k in items)
else:
items = Index(_try_sort(data.keys()))
for k, v in data.iteritems():
if isinstance(v, dict):
data[k] = DataFrame(v)
if major is None:
major = _extract_axis(data, axis=0)
if minor is None:
minor = _extract_axis(data, axis=1)
axes = [items, major, minor]
reshaped_data = data.copy() # shallow
item_shape = len(major), len(minor)
for item in items:
v = values = data.get(item)
if v is None:
values = np.empty(item_shape, dtype=dtype)
values.fill(np.nan)
elif isinstance(v, DataFrame):
v = v.reindex(index=major, columns=minor, copy=False)
if dtype is not None:
v = v.astype(dtype)
values = v.values
reshaped_data[item] = values
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(reshaped_data, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr
def _init_dict(self, data, axes, dtype=None):
items, major, minor = axes
# prefilter if items passed
if items is not None:
items = _ensure_index(items)
data = dict((k, v) for k, v in data.iteritems() if k in items)
else:
items = Index(_try_sort(data.keys()))
for k, v in data.iteritems():
if isinstance(v, dict):
data[k] = DataFrame(v)
if major is None:
major = _extract_axis(data, axis=0)
if minor is None:
minor = _extract_axis(data, axis=1)
axes = [items, major, minor]
reshaped_data = data.copy() # shallow
item_shape = len(major), len(minor)
for item in items:
v = values = data.get(item)
if v is None:
values = np.empty(item_shape, dtype=dtype)
values.fill(np.nan)
elif isinstance(v, DataFrame):
v = v.reindex(index=major, columns=minor, copy=False)
if dtype is not None:
v = v.astype(dtype)
values = v.values
reshaped_data[item] = values
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(reshaped_data, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr
def _init_arrays(self, arrays, arr_names, axes):
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(arrays, arr_names, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr
def _init_arrays(self, arrays, arr_names, axes):
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(arrays, arr_names, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr