class DataSet(AnnotableArtefact):
# SDMX-IM features
action: ActionType = None
attrib: DictLike[str, AttributeValue] = DictLike()
valid_from: str = None
structured_by: DataStructureDefinition = None
obs: List[Observation] = []
#: Map of series key → list of observations.
#: :mod:`pandaSDMX` extension not in the IM.
series: DictLike[SeriesKey, List[Observation]] = DictLike()
#: Map of group key → list of observations.
#: :mod:`pandaSDMX` extension not in the IM.
group: DictLike[GroupKey, List[Observation]] = DictLike()
def _add_group_refs(self, target):
"""Associate *target* with groups in this dataset.
*target* may be an instance of SeriesKey or Observation.
"""
for group_key in self.group:
if group_key in (target
if isinstance(target, SeriesKey) else target.key):
target.group_keys.add(group_key)
if isinstance(target, Observation):
self.group[group_key].append(target)
def add_obs(self, observations, series_key=None):
"""Add *observations* to a series with *series_key*.
Checks consistency and adds group associations."""
if series_key:
# Associate series_key with any GroupKeys that apply to it
self._add_group_refs(series_key)
if series_key not in self.series:
# Initialize empty series
self.series[series_key] = []
for obs in observations:
# Associate the observation with any GroupKeys that contain it
self._add_group_refs(obs)
# Store a reference to the observation
self.obs.append(obs)
if series_key:
# Check that the Observation is not associated with a different
# SeriesKey
assert obs.series_key is series_key, \
(obs.series_key, id(obs.series_key), series_key,
id(series_key))
# Store a reference to the observation
self.series[series_key].append(obs)
@validator('action')
def _validate_action(cls, value):
if value in ActionType:
return value
else:
return ActionType[value]
def group_attrib(self):
"""Return a view of combined group attributes."""
# Needed to pass existing tests
view = DictLike()
for gk in self.group_keys:
view.update(gk.attrib)
return view
class StructureMessage(Message):
#: Collection of :class:`.Categorisation`.
categorisation: DictLike[str, model.Categorisation] = DictLike()
#: Collection of :class:`.CategoryScheme`.
category_scheme: DictLike[str, model.CategoryScheme] = DictLike()
#: Collection of :class:`.Codelist`.
codelist: DictLike[str, model.Codelist] = DictLike()
#: Collection of :class:`.ConceptScheme`.
concept_scheme: DictLike[str, model.ConceptScheme] = DictLike()
#: Collection of :class:`.ContentConstraint`.
constraint: DictLike[str, model.ContentConstraint] = DictLike()
#: Collection of :class:`.DataflowDefinition`.
dataflow: DictLike[str, model.DataflowDefinition] = DictLike()
#: Collection of :class:`.DataStructureDefinition`.
structure: DictLike[str, model.DataStructureDefinition] = DictLike()
#: Collection of :class:`.AgencyScheme`.
organisation_scheme: DictLike[str, model.AgencyScheme] = DictLike()
#: Collection of :class:`.ProvisionAgreement`.
provisionagreement: DictLike[str, model.ProvisionAgreement] = DictLike()
def compare(self, other, strict=True):
"""Return :obj:`True` if `self` is the same as `other`.
Two StructureMessages compare equal if :meth:`.DictLike.compare` is :obj:`True`
for each of the object collection attributes.
Parameters
----------
strict : bool, optional
Passed to :meth:`.DictLike.compare`.
"""
return all(
getattr(self, attr).compare(getattr(other, attr), strict)
for attr in (
"categorisation",
"category_scheme",
"codelist",
"concept_scheme",
"constraint",
"dataflow",
"structure",
"organisation_scheme",
"provisionagreement",
)
)
def __repr__(self):
"""String representation."""
lines = [super().__repr__()]
# StructureMessage contents
for attr in self.__dict__.values():
if isinstance(attr, DictLike) and attr:
lines.append(summarize_dictlike(attr))
return "\n ".join(lines)
class StructureMessage(Message):
#: Collection of :class:`.CategoryScheme`.
category_scheme: DictLike[str, CategoryScheme] = DictLike()
#: Collection of :class:`.Codelist`.
codelist: DictLike[str, Codelist] = DictLike()
#: Collection of :class:`.ConceptScheme`.
concept_scheme: DictLike[str, ConceptScheme] = DictLike()
#: Collection of :class:`.ContentConstraint`.
constraint: DictLike[str, ContentConstraint] = DictLike()
#: Collection of :class:`.DataflowDefinition`.
dataflow: DictLike[str, DataflowDefinition] = DictLike()
#: Collection of :class:`.DataStructureDefinition`.
structure: DictLike[str, DataStructureDefinition] = DictLike()
#: Collection of :class:`.AgencyScheme`.
organisation_scheme: DictLike[str, AgencyScheme] = DictLike()
#: Collection of :class:`.ProvisionAgreement`.
provisionagreement: DictLike[str, ProvisionAgreement] = DictLike()
def __repr__(self):
"""String representation."""
lines = [super().__repr__()]
# StructureMessage contents
for attr in self.__dict__.values():
if isinstance(attr, DictLike) and attr:
lines.append(summarize_dictlike(attr))
return '\n '.join(lines)
def write_cuberegion(obj, **kwargs):
"""Convert :class:`.CubeRegion`."""
result = DictLike()
for dim, memberselection in obj.member.items():
result[dim] = pd.Series([mv.value for mv in memberselection.values],
name=dim.id)
return result
def write_dict(obj, *args, **kwargs):
"""Convert mappings.
The values of the mapping are write()'d individually. If the resulting
values are :class:`str` or :class:`pd.Series` *with indexes that share the
same name*, then they are converted to a pd.Series, possibly with a
pd.MultiIndex. Otherwise, a DictLike is returned.
"""
result = {k: write(v, *args, **kwargs) for k, v in obj.items()}
result_type = set(type(v) for v in result.values())
if result_type <= {pd.Series, pd.DataFrame}:
if (len(set(map(lambda s: s.index.name, result.values()))) == 1
and len(result) > 1):
# Can safely concatenate these to a pd.MultiIndex'd Series.
return pd.concat(result)
else:
# The individual pd.Series are indexed by different dimensions; do
# not concatenate.
return DictLike(result)
elif result_type == {str}:
return pd.Series(result)
elif result_type == set():
return pd.Series()
else:
raise ValueError(result_type)
def _cr(obj: model.CubeRegion, **kwargs):
"""Convert :class:`.CubeRegion`."""
result: DictLike[str, pd.Series] = DictLike()
for dim, memberselection in obj.member.items():
result[dim.id] = pd.Series([mv.value for mv in memberselection.values],
name=dim.id)
return result
def write_structuremessage(obj: message.StructureMessage,
include=None,
**kwargs):
"""Convert :class:`.StructureMessage`.
Parameters
----------
obj : .StructureMessage
include : iterable of str or str, optional
One or more of the attributes of the StructureMessage (
'category_scheme', 'codelist', etc.) to transform.
kwargs :
Passed to :meth:`write` for each attribute.
Returns
-------
.DictLike
Keys are StructureMessage attributes; values are pandas objects.
"""
all_contents = {
"category_scheme",
"codelist",
"concept_scheme",
"constraint",
"dataflow",
"structure",
"organisation_scheme",
}
# Handle arguments
if include is None:
attr_set = all_contents
else:
attr_set = set([include] if isinstance(include, str) else include)
# Silently discard invalid names
attr_set &= all_contents
attrs = sorted(attr_set)
result: DictLike[str, Union[pd.Series, pd.DataFrame]] = DictLike()
for a in attrs:
dl = writer.recurse(getattr(obj, a), **kwargs)
if len(dl):
# Only add non-empty elements
result[a] = dl
return result
def write_structuremessage(obj, include=None, **kwargs):
"""Convert :class:`StructureMessage <pandasdmx.message.StructureMessage>`.
Parameters
----------
obj : pandasdmx.message.StructureMessage
include : iterable of str or str, optional
One or more of the attributes of the StructureMessage (
'category_scheme', 'codelist', etc.) to transform.
kwargs :
Passed to :meth:`write` for each attribute.
Returns
-------
:class:`pandasdmx.util.DictLike`
Keys are StructureMessage attributes; values are pandas objects.
"""
all_contents = {
'category_scheme',
'codelist',
'concept_scheme',
'constraint',
'dataflow',
'structure',
'organisation_scheme',
}
# Handle arguments
if include is None:
attrs = all_contents
else:
attrs = set([include] if isinstance(include, str) else include)
# Silently discard invalid names
attrs &= all_contents
attrs = sorted(attrs)
result = DictLike()
for a in attrs:
dl = write(getattr(obj, a), **kwargs)
if len(dl):
# Only add non-empty elements
result[a] = dl
return result
class Observation(BaseModel):
"""SDMX-IM Observation.
This class also implements the spec classes ObservationValue,
UncodedObservationValue, and CodedObservation.
"""
#:
attached_attribute: DictLike[str, AttributeValue] = DictLike()
#:
series_key: SeriesKey = None
#: Key for dimension(s) varying at the observation level.
dimension: Key = None
#: Data value.
value: Union[Any, Code] = None
#:
value_for: PrimaryMeasure = None
#: :mod:`pandaSDMX` extension not in the IM.
group_keys: Set[GroupKey] = set()
@property
def attrib(self):
"""Return a view of combined observation, series & group attributes."""
view = self.attached_attribute.copy()
view.update(getattr(self.series_key, 'attrib', {}))
for gk in self.group_keys:
view.update(gk.attrib)
return view
@property
def dim(self):
return self.dimension
@property
def key(self):
"""Return the entire key, including KeyValues at the series level."""
return self.series_key + self.dimension
def __len__(self):
# FIXME this is unintuitive; maybe deprecate/remove?
return len(self.key)
def __str__(self):
return '{0.key}: {0.value}'.format(self)
class StructureMessage(Message):
category_scheme: DictLike[str, CategoryScheme] = DictLike()
codelist: DictLike[str, Codelist] = DictLike()
concept_scheme: DictLike[str, ConceptScheme] = DictLike()
constraint: DictLike[str, ContentConstraint] = DictLike()
dataflow: DictLike[str, DataflowDefinition] = DictLike()
structure: DictLike[str, DataStructureDefinition] = DictLike()
organisation_scheme: DictLike[str, AgencyScheme] = DictLike()
provisionagreement: DictLike[str, ProvisionAgreement] = DictLike()
def __repr__(self):
"""String representation."""
lines = [super().__repr__()]
# StructureMessage contents
for name in dir(self):
attr = getattr(self, name)
if not isinstance(attr, DictLike) or len(attr) == 0:
continue
lines.append(summarize_dictlike(attr))
return '\n '.join(lines)
def test_dictlike_anno():
@validate_dictlike("items")
class Foo(BaseModel):
items: DictLike[StrictStr, int] = DictLike()
f = Foo()
assert type(f.items) == DictLike
# Can be set with DictLike
f.items = DictLike(a=1, b=2)
assert type(f.items) == DictLike
# Can be set with dict()
f.items = {"a": 1, "b": 2}
assert type(f.items) == DictLike
# Type checking on creation
with pytest.raises(pydantic.ValidationError):
f = Foo(items={1: "a"})
# Type checking on assignment
f = Foo()
with pytest.raises(pydantic.ValidationError):
f.items = {1: "a"}
# Type checking on setting elements
f = Foo(items={"a": 1})
with pytest.raises(pydantic.ValidationError):
f.items[123] = 456
# commented: this does not work, since validate_dictlike does not operate
# until initial values are assigned to the field
# f = Foo()
# with pytest.raises(pydantic.ValidationError):
# f.items[123] = 456
# Use validate_dictlike() twice
@validate_dictlike("elems")
class Bar(BaseModel):
elems: DictLike[StrictStr, float] = DictLike()
def test_dictlike():
dl = DictLike()
# Set by item name
dl['TIME_PERIOD'] = 3
dl['CURRENCY'] = 'USD'
# Access by attribute name
assert dl.TIME_PERIOD == 3
# Access by item index
assert dl[1] == 'USD'
# Access beyond index
with pytest.raises(KeyError):
dl['FOO']
with pytest.raises(IndexError):
dl[2]
with pytest.raises(AttributeError):
dl.FOO
def test_dictlike():
dl = DictLike()
# Set by item name
dl["TIME_PERIOD"] = 3
dl["CURRENCY"] = "USD"
# Access by attribute name
assert dl.TIME_PERIOD == 3
# Access by item index
assert dl[1] == "USD"
# Access beyond index
with pytest.raises(KeyError):
dl["FOO"]
with pytest.raises(IndexError):
dl[2]
with pytest.raises(AttributeError):
dl.FOO
def _dict(obj: dict, *args, **kwargs):
"""Convert mappings."""
result = {k: writer.recurse(v, *args, **kwargs) for k, v in obj.items()}
result_type = set(type(v) for v in result.values())
if result_type <= {pd.Series, pd.DataFrame}:
if (len(set(map(lambda s: s.index.name, result.values()))) == 1
and len(result) > 1):
# Can safely concatenate these to a pd.MultiIndex'd Series.
return pd.concat(result)
else:
# The individual pd.Series are indexed by different dimensions; do
# not concatenate.
return DictLike(result)
elif result_type == {str}:
return pd.Series(result)
elif result_type == {DictLike}:
return result
elif result_type == set():
# No results
return pd.Series()
else:
raise ValueError(result_type)
class Foo(BaseModel):
items: DictLike[StrictStr, int] = DictLike()
class Key(BaseModel):
"""SDMX Key class.
The constructor takes an optional list of keyword arguments; the keywords
are used as Dimension or Attribute IDs, and the values as KeyValues.
For convience, the values of the key may be accessed directly:
>>> k = Key(foo=1, bar=2)
>>> k.values['foo']
1
>>> k['foo']
1
Parameters
----------
dsd : DataStructureDefinition
If supplied, the :attr:`~.DataStructureDefinition.dimensions` and
:attr:`~.DataStructureDefinition.attributes` are used to separate the
*kwargs* into :class:`KeyValues <.KeyValue>` and
:class:`AttributeValues <.AttributeValue>`. The *kwarg* for
:attr:`described_by`, if any, must be
:attr:`~.DataStructureDefinition.dimensions` or appear in
:attr:`~.DataStructureDefinition.group_dimensions`.
kwargs
Dimension and Attribute IDs, and/or the class properties.
"""
#:
attrib: DictLike[str, AttributeValue] = DictLike()
#:
described_by: DimensionDescriptor = None
#: Individual KeyValues that describe the key.
values: DictLike[str, KeyValue] = DictLike()
def __init__(self, arg=None, **kwargs):
super().__init__()
if arg:
if len(kwargs):
raise ValueError("Key() accepts either a single argument, or "
"keyword arguments; not both.")
kwargs.update(arg)
# DSD argument
dsd = kwargs.pop('dsd', None)
# DimensionDescriptor
dd = kwargs.pop('described_by', None)
if dsd:
if not dd:
dd = dsd.dimensions
# DD must appear in the DSD if both are supplied
if (dd is not dsd.dimensions
and dd not in dsd.group_dimensions.values()):
raise ValueError(f'described_by={dd} is not a [Group]'
f'DimensionDescriptor of dsd={dsd}')
try:
self.described_by = dd
except Exception:
dd = None
# Convert keyword arguments to either KeyValue or AttributeValue
values = []
for order, (id, value) in enumerate(kwargs.items()):
args = dict(id=id, value=value)
if dsd and id in dsd.attributes:
# Reference a DataAttribute from the AttributeDescriptor
da = dsd.attributes.get(id)
# Store the attribute value
self.attrib[da.id] = AttributeValue(**args, value_for=da)
continue
if dd:
# Reference a Dimension from the DimensionDescriptor
args['value_for'] = dd.get(id)
# Retrieve the order
order = args['value_for'].order
# Store a KeyValue, to be sorted later
values.append((order, KeyValue(**args)))
# Sort the values according to *order*
self.values.update({kv.id: kv for _, kv in sorted(values)})
def __len__(self):
"""The length of the Key is the number of KeyValues it contains."""
return len(self.values)
def __contains__(self, other):
"""A Key contains another if it is a superset."""
try:
return all([self.values[k] == v for k, v in other.values.items()])
except KeyError:
# 'k' in other does not appear in this Key()
return False
def __iter__(self):
yield from self.values.values()
# Convenience access to values by name
def __getitem__(self, name):
return self.values[name]
def __setitem__(self, name, value):
# Convert a bare string or other Python object to a KeyValue instance
if not isinstance(value, KeyValue):
value = KeyValue(id=name, value=value)
self.values[name] = value
# Convenience access to values by attribute
def __getattr__(self, name):
try:
return self.__getitem__(name)
except KeyError as e:
raise e
# Copying
def __copy__(self):
result = Key()
if self.described_by:
result.described_by = self.described_by
for kv in self.values.values():
result[kv.id] = kv
return result
def copy(self, arg=None, **kwargs):
result = copy(self)
for id, value in kwargs.items():
result[id] = value
return result
def __add__(self, other):
if not isinstance(other, Key):
raise NotImplementedError
result = copy(self)
for id, value in other.values.items():
result[id] = value
return result
def __radd__(self, other):
if other is None:
return copy(self)
else:
raise NotImplementedError
def __eq__(self, other):
if hasattr(other, 'values'):
return all([a == b for a, b in zip(self.values, other.values)])
elif isinstance(other, str) and len(self.values) == 1:
return self.values[0] == other
else:
raise ValueError(other)
def __hash__(self):
# Hash of the individual KeyValues, in order
return hash(tuple(hash(kv) for kv in self.values.values()))
# Representations
def __str__(self):
return '({})'.format(', '.join(map(str, self.values.values())))
def __repr__(self):
return '<{}: {}>'.format(self.__class__.__name__,
', '.join(map(str, self.values.values())))
def order(self, value=None):
if value is None:
value = self
try:
return self.described_by.order_key(value)
except AttributeError:
return value
def get_values(self):
return tuple([kv.value for kv in self.values.values()])
class DataStructureDefinition(Structure, ConstrainableArtefact):
"""Defines a data structure. Referred to as “DSD”."""
#: A :class:`AttributeDescriptor` that describes the attributes of the
#: data structure.
attributes: AttributeDescriptor = AttributeDescriptor()
#: A :class:`DimensionDescriptor` that describes the dimensions of the
#: data structure.
dimensions: DimensionDescriptor = DimensionDescriptor()
#: A :class:`.MeasureDescriptor`.
measures: MeasureDescriptor = None
#: A :class:`.GroupDimensionDescriptor`.
group_dimensions: DictLike[str, GroupDimensionDescriptor] = DictLike()
# Convenience methods
def attribute(self, id, **kwargs):
"""Call :meth:`ComponentList.get` on :attr:`attributes`."""
return self.attributes.get(id, **kwargs)
def dimension(self, id, **kwargs):
"""Call :meth:`ComponentList.get` on :attr:`dimensions`."""
return self.dimensions.get(id, **kwargs)
def make_constraint(self, key):
"""Return a constraint for *key*.
*key* is a :class:`dict` wherein:
- keys are :class:`str` ids of Dimensions appearing in this
DSD's :attr:`dimensions`, and
- values are '+'-delimited :class:`str` containing allowable values,
_or_ iterables of :class:`str`, each an allowable value.
For example::
cc2 = dsd.make_constraint({'foo': 'bar+baz', 'qux': 'q1+q2+q3'})
``cc2`` includes any key where the 'foo' dimension is 'bar' *or* 'baz',
*and* the 'qux' dimension is one of 'q1', 'q2', or 'q3'.
Returns
-------
ContentConstraint
A constraint with one :class:`CubeRegion` in its
:attr:`data_content_region <ContentConstraint.data_content_region>`
, including only the values appearing in *keys*.
Raises
------
ValueError
if *key* contains a dimension IDs not appearing in
:attr:`dimensions`.
"""
# Make a copy to avoid pop()'ing off the object in the calling scope
key = key.copy()
cr = CubeRegion()
for dim in self.dimensions:
mvs = set()
try:
values = key.pop(dim.id)
except KeyError:
continue
values = values.split('+') if isinstance(values, str) else values
for value in values:
# TODO validate values
mvs.add(MemberValue(value=value))
cr.member[dim] = MemberSelection(included=True,
values_for=dim,
values=mvs)
if len(key):
raise ValueError('Dimensions {!r} not in {!r}'.format(
list(key.keys()), self.dimensions))
return ContentConstraint(
data_content_region=[cr],
role=ConstraintRole(role=ConstraintRoleType.allowable))
@classmethod
def from_keys(cls, keys):
"""Return a new DSD given some *keys*.
The DSD's :attr:`dimensions` refers to a set of new :class:`Concepts
<Concept>` and :class:`Codelists <Codelist>`, created to represent all
the values observed across *keys* for each dimension.
Parameters
----------
keys : iterable of :class:`Key`
or of subclasses such as :class:`SeriesKey` or :class:`GroupKey`.
"""
iter_keys = iter(keys)
dd = DimensionDescriptor.from_key(next(iter_keys))
for k in iter_keys:
for i, (id, kv) in enumerate(k.values.items()):
dd[i].local_representation.enumerated.append(Code(id=kv.value))
return cls(dimensions=dd)
class Key(BaseModel):
"""SDMX Key class.
The constructor takes an optional list of keyword arguments; the keywords
are used as Dimension IDs, and the values as KeyValues.
For convience, the values of the key may be accessed directly:
>>> k = Key(foo=1, bar=2)
>>> k.values['foo']
1
>>> k['foo']
1
"""
attrib: DictLike[str, AttributeValue] = DictLike()
values: DictLike[str, KeyValue] = DictLike()
described_by: DimensionDescriptor = None
def __init__(self, arg=None, **kwargs):
super().__init__()
if arg:
if len(kwargs):
raise ValueError("Key() accepts either a single argument, or "
"keyword arguments; not both.")
kwargs.update(arg)
# DimensionDescriptor
self.described_by = kwargs.pop('described_by', None)
# Convert keyword arguments to KeyValues
for id, value in kwargs.items():
kv = KeyValue(id=id, value=value)
try:
# Reference a Dimension from the DimensionDescriptor
kv.value_for = self.described_by.get(id)
except AttributeError:
pass
self.values[id] = kv
def __len__(self):
"""The length of the Key is the number of KeyValues it contains."""
return len(self.values)
def __contains__(self, other):
"""A Key contains another if it is a superset."""
try:
return all([self.values[k] == v for k, v in other.values.items()])
except KeyError:
# 'k' in other does not appear in this Key()
return False
def __iter__(self):
yield from self.values.values()
# Convenience access to values by name
def __getitem__(self, name):
return self.values[name]
def __setitem__(self, name, value):
# Convert a bare string or other Python object to a KeyValue instance
if not isinstance(value, KeyValue):
value = KeyValue(id=name, value=value)
self.values[name] = value
# Convenience access to values by attribute
def __getattr__(self, name):
try:
return self.__getitem__(name)
except KeyError as e:
raise e
# Copying
def __copy__(self):
result = Key()
if self.described_by:
result.described_by = self.described_by
for kv in self.values.values():
result[kv.id] = kv
return result
def copy(self, arg=None, **kwargs):
result = copy(self)
for id, value in kwargs.items():
result[id] = value
return result
def __add__(self, other):
if not isinstance(other, Key):
raise NotImplementedError
result = copy(self)
for id, value in other.values.items():
result[id] = value
return result
def __radd__(self, other):
if other is None:
return copy(self)
else:
raise NotImplementedError
def __eq__(self, other):
if hasattr(other, 'values'):
return all([a == b for a, b in zip(self.values, other.values)])
elif isinstance(other, str) and len(self.values) == 1:
return self.values[0] == other
else:
raise ValueError(other)
def __hash__(self):
# Hash of the individual KeyValues, in order
return hash(tuple(hash(kv) for kv in self.values.values()))
# Representations
def __str__(self):
return '({})'.format(', '.join(map(str, self.values.values())))
def __repr__(self):
return '<{}: {}>'.format(self.__class__.__name__,
', '.join(map(str, self.values.values())))
def order(self, value=None):
if value is None:
value = self
try:
return self.described_by.order_key(value)
except AttributeError:
return value
def get_values(self):
return tuple([kv.value for kv in self.values.values()])
class DataSet(AnnotableArtefact):
# SDMX-IM features
action: ActionType = None
attrib: DictLike[str, AttributeValue] = DictLike()
valid_from: Text = None
structured_by: DataStructureDefinition = None
# Internal storage: a pd.DataFrame with columns:
# - 'value': the Observation value.
# - ('attr_obs', *id*): value for Observation.attached_attribute[id].
# - TODO 'series_key': integer index of the SeriesKey associated with the
# Observation.
# - TODO 'group_keys': integer indices of the GroupKey(s) associated with
# the Observation.
_data = None
def add_obs(self, observations, series_key=None):
"""Add *observations* to a series with *series_key*."""
if series_key:
raise NotImplementedError
# dict of dicts: key → {value, attributes, etc. }
obs_dict = {}
for obs in observations:
# DataFrame row for this Observation
row = {'value': obs.value}
# Store attributes
for attr_id, av in obs.attached_attribute.items():
row[('attr_obs', attr_id)] = av.value
# Store the row
obs_dict[obs.key.order().get_values()] = row
# Convert to pd.DataFrame. Note similarity to pandasdmx.writer
self._data = pd.DataFrame.from_dict(obs_dict, orient='index')
if len(obs_dict):
self._data.index.names = obs.key.order().values.keys()
@property
def obs(self):
# In pandasdmx.model.DataSet, .obs is typed as List[Observation].
# Here, the Observations are generated on request.
for key, data in self._data.iterrows():
yield self._make_obs(key, data)
def _make_obs(self, key, data):
"""Create an Observation from tuple *key* and pd.Series *data."""
# Create the Key
key = Key(
{dim: value
for dim, value in zip(self._data.index.names, key)})
attrs = {}
# Handle columns of ._data
for col, value in data.items():
try:
# A tuple column label is ('attr_obs', attr_id)
group, attr_id = col
except ValueError:
# Not a tuple → the 'value' column, handled below
continue
if group == 'attr_obs':
# Create a DataAttribute
attrs[attr_id] = AttributeValue(
value_for=DataAttribute(id=attr_id), value=value)
return Observation(dimension=key,
value=data['value'],
attached_attribute=attrs)
class Bar(BaseModel):
elems: DictLike[StrictStr, float] = DictLike()
