from itertools import chain
import numpy as np
import pandas as pd
from pandasdmx import model
from pandasdmx.model import (
DEFAULT_LOCALE,
AgencyScheme,
AllDimensions,
DataAttribute,
DataflowDefinition,
DataStructureDefinition,
DataSet,
Dimension,
DimensionComponent,
# DimensionDescriptor,
CategoryScheme,
Codelist,
Component,
ConceptScheme,
ItemScheme,
NameableArtefact,
Observation,
SeriesKey,
TimeDimension,
)
from pandasdmx.util import DictLike
#: Default return type for :func:`write_dataset` and similar methods. Either
#: 'compat' or 'rows'. See the ref:`HOWTO <howto-rtype>`.
DEFAULT_RTYPE = 'compat'
# Class → common write_*() methods
_ALIAS = {
DictLike: dict,
AgencyScheme: ItemScheme,
CategoryScheme: ItemScheme,
ConceptScheme: ItemScheme,
Codelist: ItemScheme,
DataflowDefinition: NameableArtefact,
DataStructureDefinition: NameableArtefact,
Dimension: Component,
TimeDimension: Component,
model.GenericDataSet: DataSet,
model.GenericTimeSeriesDataSet: DataSet,
model.StructureSpecificDataSet: DataSet,
model.StructureSpecificTimeSeriesDataSet: DataSet,
}
def write(obj, *args, **kwargs):
"""Convert an SDMX *obj* to :mod:`pandas` object(s).
Implements a dispatch pattern according to the type of *obj*. For instance,
a :class:`.DataSet` object is converted using :func:`.write_dataset`. See
individual ``write_*`` methods named for more information on their
behaviour, including accepted *args* and *kwargs*.
"""
cls = obj.__class__
function = 'write_' + _ALIAS.get(cls, cls).__name__.lower()
return globals()[function](obj, *args, **kwargs)
# Functions for Python containers
[docs]def write_list(obj, *args, **kwargs):
"""Convert a :class:`list` of SDMX objects.
For the following *obj*, :meth:`write_list` returns :class:`pandas.Series`
instead of a :class:`list`:
- a list of :class:`Observation <pandasdmx.model.Observation>`:
the Observations are written using :meth:`write_dataset`.
- a list with only 1 :class:`DataSet <pandasdmx.model.DataSet>` (e.g. the
:attr:`data <pandasdmx.message.DataMessage.data>` attribute of
:class:`DataMessage <pandasdmx.message.DataMessage>`): the Series for
the single element is returned.
- a list of :class:`SeriesKey`: the key values (but no data) are returned.
"""
if isinstance(obj[0], Observation):
return write_dataset(obj, *args, **kwargs)
elif isinstance(obj[0], DataSet) and len(obj) == 1:
return write(obj[0], *args, **kwargs)
elif isinstance(obj[0], SeriesKey):
return write_serieskeys(obj, *args, **kwargs)
else:
return [write(item, *args, **kwargs) for item in obj]
[docs]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 == {DictLike}:
return result
elif result_type == set():
# No results
return pd.Series()
else:
raise ValueError(result_type)
[docs]def write_set(obj, *args, **kwargs):
"""Convert :class:`set`."""
result = {write(o, *args, **kwargs) for o in obj}
return result
# Functions for message classes
[docs]def write_datamessage(obj, *args, rtype=None, **kwargs):
"""Convert :class:`.DataMessage`.
The data set(s) within the message are converted to pandas objects.
Parameters
----------
rtype : 'compat' or 'rows', optional
Data type to return; default :data:`.DEFAULT_RTYPE`. See the
:ref:`HOWTO <howto-rtype>`.
kwargs :
Passed to :meth:`write_dataset` for each data set.
Returns
-------
:class:`pandas.Series` or :class:`pandas.DataFrame`
if `obj` has only one data set.
list of (:class:`pandas.Series` or :class:`pandas.DataFrame`)
if `obj` has more than one data set.
"""
# Pass the message's DSD to assist datetime handling
kwargs.setdefault('dsd', obj.dataflow.structure)
# Pass the return type and associated information
kwargs['_rtype'] = rtype or DEFAULT_RTYPE
if kwargs['_rtype'] == 'compat':
kwargs['_message_class'] = obj.__class__
kwargs['_observation_dimension'] = obj.observation_dimension
if len(obj.data) == 1:
return write(obj.data[0], *args, **kwargs)
else:
return [write(ds, *args, **kwargs) for ds in obj.data]
[docs]def write_structuremessage(obj, 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:
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
# Functions for model classes
[docs]def write_component(obj):
"""Convert :class:`.Component`.
The :attr:`~.Concept.id` attribute of the
:attr:`~.Component.concept_identity` is returned.
"""
return str(obj.concept_identity.id)
[docs]def write_contentconstraint(obj, **kwargs):
"""Convert :class:`.ContentConstraint`."""
if len(obj.data_content_region) != 1:
raise NotImplementedError
return write(obj.data_content_region[0], **kwargs)
[docs]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
[docs]def write_dataset(obj, attributes='', dtype=np.float64, constraint=None,
datetime=False, **kwargs):
"""Convert :class:`~.DataSet`.
See the :ref:`walkthrough <datetime>` for examples of using the `datetime`
argument.
Parameters
----------
obj : :class:`~.DataSet` or iterable of :class:`~.Observation`
attributes : str
Types of attributes to return with the data. A string containing
zero or more of:
- ``'o'``: attributes attached to each :class:`~.Observation` .
- ``'s'``: attributes attached to any (0 or 1) :class:`~.SeriesKey`
associated with each Observation.
- ``'g'``: attributes attached to any (0 or more) :class:`~.GroupKey`
associated with each Observation.
- ``'d'``: attributes attached to the :class:`~.DataSet` containing the
Observations.
dtype : str or :class:`numpy.dtype` or None
Datatype for values. If None, do not return the values of a series.
In this case, `attributes` must not be an empty string so that some
attribute is returned.
constraint : .ContentConstraint, optional
If given, only Observations included by the *constraint* are returned.
datetime : bool or str or or .Dimension or dict, optional
If given, return a DataFrame with a :class:`~pandas.DatetimeIndex`
or :class:`~pandas.PeriodIndex` as the index and all other dimensions
as columns. Valid `datetime` values include:
- :class:`bool`: if :obj:`True`, determine the time dimension
automatically by detecting a :class:`~.TimeDimension`.
- :class:`str`: ID of the time dimension.
- :class:`~.Dimension`: the matching Dimension is the time dimension.
- :class:`dict`: advanced behaviour. Keys may include:
- **dim** (:class:`~.Dimension` or :class:`str`): the time dimension
or its ID.
- **axis** (`{0 or 'index', 1 or 'columns'}`): axis on which to place
the time dimension (default: 0).
- **freq** (:obj:`True` or :class:`str` or :class:`~.Dimension`):
produce :class:`pandas.PeriodIndex`. If :class:`str`, the ID of a
Dimension containing a frequency specification. If a Dimension, the
specified dimension is used for the frequency specification.
Any Dimension used for the frequency specification is does not
appear in the returned DataFrame.
Returns
-------
:class:`pandas.DataFrame`
- if `attributes` is not ``''``, a data frame with one row per
Observation, ``value`` as the first column, and additional columns
for each attribute;
- if `datetime` is given, various layouts as described above; or
- if `_rtype` (passed from :func:`write_datamessage`) is 'compat',
various layouts as described in the :ref:`HOWTO <howto-rtype>`.
:class:`pandas.Series` with :class:`pandas.MultiIndex`
Otherwise.
"""
# If called directly on a DataSet (rather than a parent DataMessage),
# cannot determine the "dimension at observation level"
rtype = kwargs.setdefault('_rtype', 'rows')
# Validate attributes argument
attributes = attributes or ''
try:
attributes = attributes.lower()
except AttributeError:
raise TypeError("'attributes' argument must be str")
if rtype == 'compat' and \
kwargs['_observation_dimension'] is not AllDimensions:
# Cannot return attributes in this case
attributes = ''
elif set(attributes) - {'o', 's', 'g', 'd'}:
raise ValueError(f"attributes must be in 'osgd'; got {attributes}")
# Iterate on observations
result = {}
for observation in getattr(obj, 'obs', obj):
# Check that the Observation is within the constraint, if any
key = observation.key.order()
if constraint and key not in constraint:
continue
# Add value and attributes
row = {}
if dtype:
row['value'] = observation.value
if attributes:
row.update(observation.attrib)
result[tuple(map(str, key.get_values()))] = row
result = pd.DataFrame.from_dict(result, orient='index')
if len(result):
result.index.names = observation.key.order().values.keys()
if dtype:
result['value'] = result['value'].astype(dtype)
if not attributes:
result = result['value']
# Reshape for compatibility with v0.9
result, datetime, kwargs = _dataset_compat(result, datetime, kwargs)
# Handle the datetime argument, if any
return _maybe_convert_datetime(result, datetime, obj=obj, **kwargs)
def _dataset_compat(df, datetime, kwargs):
"""Helper for :meth:`.write_dataset` 0.9 compatibility."""
rtype = kwargs.pop('_rtype')
if rtype != 'compat':
return df, datetime, kwargs # Do nothing
# Remove compatibility arguments from kwargs
kwargs.pop('_message_class')
obs_dim = kwargs.pop('_observation_dimension')
if isinstance(obs_dim, list) and len(obs_dim) == 1:
# Unwrap a length-1 list
obs_dim = obs_dim[0]
if obs_dim in (AllDimensions, None):
pass # Do nothing
elif isinstance(obs_dim, TimeDimension):
# Don't modify *df*; only change arguments so that
# _maybe_convert_datetime performs the desired changes
if datetime is False or datetime is True:
# Either datetime is not given, or True without specifying a
# dimension; overwrite
datetime = obs_dim
elif isinstance(datetime, dict):
# Dict argument; ensure the 'dim' key is the same as obs_dim
if datetime.setdefault('dim', obs_dim) != obs_dim:
msg = (f"datetime={datetime} conflicts with rtype='compat' and"
f" {obs_dim} at observation level")
raise ValueError(msg)
else:
assert datetime == obs_dim, (datetime, obs_dim)
elif isinstance(obs_dim, DimensionComponent):
# Pivot all levels except the observation dimension
df = df.unstack([n for n in df.index.names if n != obs_dim.id])
else:
# E.g. some JSON messages have two dimensions at the observation level;
# behaviour is unspecified here, so do nothing.
pass
return df, datetime, kwargs
def _maybe_convert_datetime(df, arg, obj, dsd=None):
"""Helper for :meth:`.write_dataset` to handle datetime indices.
Parameters
----------
df : pandas.DataFrame
arg : dict
From the `datetime` argument to :meth:`write_dataset`.
obj :
From the `obj` argument to :meth:`write_dataset`.
dsd: ~.DataStructureDefinition, optional
"""
if not arg:
# False, None, empty dict: no datetime conversion
return df
# Check argument values
param = dict(dim=None, axis=0, freq=False)
if isinstance(arg, str):
param['dim'] = arg
elif isinstance(arg, DimensionComponent):
param['dim'] = arg.id
elif isinstance(arg, dict):
extra_keys = set(arg.keys()) - set(param.keys())
if extra_keys:
raise ValueError(extra_keys)
param.update(arg)
elif isinstance(arg, bool):
pass # True
else:
raise ValueError(arg)
def _get_dims():
"""Return an appropriate list of dimensions."""
if len(obj.structured_by.dimensions.components):
return obj.structured_by.dimensions.components
elif dsd:
return dsd.dimensions.components
else:
return []
def _get_attrs():
"""Return an appropriate list of attributes."""
if len(obj.structured_by.attributes.components):
return obj.structured_by.attributes.components
elif dsd:
return dsd.attributes.components
else:
return []
if not param['dim']:
# Determine time dimension
dims = _get_dims()
for dim in dims:
if isinstance(dim, TimeDimension):
param['dim'] = dim
break
if not param['dim']:
raise ValueError(f'no TimeDimension in {dims}')
# Unstack all but the time dimension and convert
other_dims = list(filter(lambda d: d != param['dim'], df.index.names))
df = df.unstack(other_dims)
df.index = pd.to_datetime(df.index)
if param['freq']:
# Determine frequency string, Dimension, or Attribute
freq = param['freq']
if isinstance(freq, str) and freq not in pd.offsets.prefix_mapping:
# ID of a Dimension or Attribute
for component in chain(_get_dims(), _get_attrs()):
if component.id == freq:
freq = component
break
# No named dimension in the DSD; but perhaps on the df
if isinstance(freq, str):
if freq in df.columns.names:
freq = Dimension(id=freq)
else:
raise ValueError(freq)
if isinstance(freq, Dimension):
# Retrieve Dimension values from pd.MultiIndex level
level = freq.id
i = df.columns.names.index(level)
values = set(df.columns.levels[i])
if len(values) > 1:
values = sorted(values)
raise ValueError('cannot convert to PeriodIndex with '
f'non-unique freq={values}')
# Store the unique value
freq = values.pop()
# Remove the index level
df.columns = df.columns.droplevel(i)
elif isinstance(freq, DataAttribute): # pragma: no cover
raise NotImplementedError
df.index = df.index.to_period(freq=freq)
if param['axis'] in {1, 'columns'}:
# Change axis
df = df.transpose()
return df
[docs]def write_dimensiondescriptor(obj):
"""Convert :class:`.DimensionDescriptor`.
The :attr:`~.DimensionDescriptor.components` of the DimensionDescriptor
are written.
"""
return write(obj.components)
[docs]def write_itemscheme(obj, locale=DEFAULT_LOCALE):
"""Convert :class:`.ItemScheme`.
Names from *locale* are serialized.
Returns
-------
pandas.Series
"""
items = {}
seen = set()
def add_item(item):
"""Recursive helper for adding items."""
# Track seen items
if item in seen:
return
else:
seen.add(item)
# Localized name
row = {'name': item.name.localized_default(locale)}
try:
# Parent ID
row['parent'] = item.parent.id
except AttributeError:
row['parent'] = ''
items[item.id] = row
# Add this item's children, recursively
for child in item.child:
add_item(child)
for item in obj:
add_item(item)
# Convert to DataFrame
result = pd.DataFrame.from_dict(items, orient='index', dtype=object) \
.rename_axis(obj.id, axis='index')
if len(result) and not result['parent'].str.len().any():
# 'parent' column is empty; convert to pd.Series and rename
result = result['name'].rename(obj.name.localized_default(locale))
return result
[docs]def write_membervalue(obj):
"""Convert :class:`.MemberValue`."""
return obj.value
[docs]def write_nameableartefact(obj):
"""Convert :class:`.NameableArtefact`.
The :attr:`~.NameableArtefact.name` attribute of *obj* is returned.
"""
return str(obj.name)
[docs]def write_serieskeys(obj):
"""Convert a list of :class:`.SeriesKey`."""
result = []
for sk in obj:
result.append({dim: kv.value for dim, kv in sk.order().values.items()})
# TODO perhaps return as a pd.MultiIndex if that is more useful
return pd.DataFrame(result)