def read_jsdmx(path_or_buf):
"""
Convert a SDMX-JSON string to pandas object
Parameters
----------
filepath_or_buffer : a valid SDMX-JSON string or file-like
http://sdmx.org/wp-content/uploads/2014/07/sdmx-json-data-message.pdf
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
jdata = _read_content(path_or_buf)
import json
if isinstance(jdata, dict):
data = jdata
else:
data = json.loads(jdata, object_pairs_hook=compat.OrderedDict)
structure = data['structure']
index = _parse_dimensions(structure['dimensions']['observation'])
columns = _parse_dimensions(structure['dimensions']['series'])
dataset = data['dataSets']
if len(dataset) != 1:
raise ValueError("length of 'dataSets' must be 1")
dataset = dataset[0]
values = _parse_values(dataset, index=index, columns=columns)
df = pd.DataFrame(values, columns=columns, index=index)
return df
def read_jsdmx(path_or_buf):
"""
Convert a SDMX-JSON string to pandas object
Parameters
----------
filepath_or_buffer : a valid SDMX-JSON string or file-like
http://sdmx.org/wp-content/uploads/2014/07/sdmx-json-data-message.pdf
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
jdata = _read_content(path_or_buf)
import json
if isinstance(jdata, dict):
data = jdata
else:
data = json.loads(jdata, object_pairs_hook=compat.OrderedDict)
structure = data['structure']
index = _parse_dimensions(structure['dimensions']['observation'])
columns = _parse_dimensions(structure['dimensions']['series'])
dataset = data['dataSets']
if len(dataset) != 1:
raise ValueError("length of 'dataSets' must be 1")
dataset = dataset[0]
values = _parse_values(dataset, index=index, columns=columns)
df = pd.DataFrame(values, columns=columns, index=index)
return df
def read_jstat(path_or_buf, typ='frame', squeeze=True):
"""
Convert a JSON-Stat string to pandas object
Parameters
----------
filepath_or_buffer : a valid JSON-Stat string or file-like
http://json-stat.org/
typ : {'frame', 'series'}
Type of object to recover (series or frame), default 'frame'
squeeze : bool, default True
If True, return DataFrame or Series when the input has only one dataset.
When the input has multiple dataset, returns dictionary of results.
If False, always return a dictionary.
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
jdata = _read_content(path_or_buf)
import json
if isinstance(jdata, dict):
datasets = jdata
else:
datasets = json.loads(jdata, object_pairs_hook=compat.OrderedDict)
results = {}
for dataname, dataset in compat.iteritems(datasets):
values = dataset['value'] # mandatory
dimensions = dataset['dimension'] # mandatory
# Not supported, as the reis no specific meaning
# in current format specification
# status = dataset.get('status', None) # optional
midx = _parse_dimensions(dimensions)
values = _parse_values(values, size=len(midx))
result = pd.Series(values, index=midx)
if typ == 'frame':
if result.index.nlevels > 1:
result = result.unstack()
else:
result = result.to_frame()
elif typ == 'series':
pass
else:
raise ValueError("'typ' must be either 'frame' or 'series'")
if len(datasets) == 1 and squeeze:
return result
results[dataname] = result
return results
def read_jstat(path_or_buf, typ='frame', squeeze=True):
"""
Convert a JSON-Stat string to pandas object
Parameters
----------
filepath_or_buffer : a valid JSON-Stat string or file-like
http://json-stat.org/
typ : {'frame', 'series'}
Type of object to recover (series or frame), default 'frame'
squeeze : bool, default True
If True, return DataFrame or Series when the input has only one dataset.
When the input has multiple dataset, returns dictionary of results.
If False, always return a dictionary.
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
jdata = _read_content(path_or_buf)
import json
if isinstance(jdata, dict):
datasets = jdata
else:
datasets = json.loads(jdata, object_pairs_hook=compat.OrderedDict)
results = {}
for dataname, dataset in compat.iteritems(datasets):
values = dataset['value'] # mandatory
dimensions = dataset['dimension'] # mandatory
# Not supported, as the reis no specific meaning
# in current format specification
# status = dataset.get('status', None) # optional
midx = _parse_dimensions(dimensions)
values = _parse_values(values, size=len(midx))
result = pd.Series(values, index=midx)
if typ == 'frame':
if result.index.nlevels > 1:
result = result.unstack()
else:
result = result.to_frame()
elif typ == 'series':
pass
else:
raise ValueError("'typ' must be either 'frame' or 'series'")
if len(datasets) == 1 and squeeze:
return result
results[dataname] = result
return results
def read_sdmx(path_or_buf, dtype='float64', dsd=None):
"""
Convert a SDMX-XML string to pandas object
Parameters
----------
filepath_or_buffer : a valid SDMX-XML string or file-like
https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php/Main_Page
dtype : str
dtype to coerce values
dsd : dict
parsed DSD dict corresponding to the SDMX-XML data
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
xdata = _read_content(path_or_buf)
import xml.etree.ElementTree as ET
root = ET.fromstring(xdata)
structure = _get_child(root, _MESSAGE + 'Structure')
idx_name = structure.get('dimensionAtObservation')
dataset = _get_child(root, _DATASET)
keys = []
obss = []
for series in dataset.iter(_SERIES):
key = _parse_series_key(series)
obs = _parse_observations(series.iter(_OBSERVATION))
keys.append(key)
obss.append(obs)
mcols = _construct_index(keys, dsd=dsd)
mseries = _construct_series(obss, name=idx_name, dsd=dsd)
df = pd.DataFrame(mseries, dtype=dtype)
df = df.T
df.columns = mcols
return df
def read_sdmx(path_or_buf, dtype='float64', dsd=None):
"""
Convert a SDMX-XML string to pandas object
Parameters
----------
filepath_or_buffer : a valid SDMX-XML string or file-like
https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php/Main_Page
dtype : str
dtype to coerce values
dsd : dict
parsed DSD dict corresponding to the SDMX-XML data
Returns
-------
results : Series, DataFrame, or dictionaly of Series or DataFrame.
"""
xdata = _read_content(path_or_buf)
import xml.etree.ElementTree as ET
root = ET.fromstring(xdata)
structure = _get_child(root, _MESSAGE + 'Structure')
idx_name = structure.get('dimensionAtObservation')
dataset = _get_child(root, _DATASET)
keys = []
obss = []
for series in dataset.iter(_SERIES):
key = _parse_series_key(series)
obs = _parse_observations(series.iter(_OBSERVATION))
keys.append(key)
obss.append(obs)
mcols = _construct_index(keys, dsd=dsd)
mseries = _construct_series(obss, name=idx_name, dsd=dsd)
df = pd.DataFrame(mseries, dtype=dtype)
df = df.T
df.columns = mcols
return df
def _read_sdmx_dsd(path_or_buf):
"""
Convert a SDMX-XML DSD string to mapping dictionary
Parameters
----------
filepath_or_buffer : a valid SDMX-XML DSD string or file-like
https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php/Main_Page
Returns
-------
results : namedtuple (SDMXCode)
"""
xdata = _read_content(path_or_buf)
import xml.etree.cElementTree as ET
root = ET.fromstring(xdata)
structure = _get_child(root, _MESSAGE + 'Structures')
codes = _get_child(structure, _STRUCTURE + 'Codelists')
# concepts = _get_child(structure, _STRUCTURE + 'Concepts')
datastructures = _get_child(structure, _STRUCTURE + 'DataStructures')
code_results = {}
for codelist in codes:
# codelist_id = codelist.get('id')
codelist_name = _get_english_name(codelist)
mapper = {}
for code in codelist.iter(_CODE):
code_id = code.get('id')
name = _get_english_name(code)
mapper[code_id] = name
# codeobj = SDMXCode(id=codelist_id, name=codelist_name, mapper=mapper)
# code_results[codelist_id] = codeobj
code_results[codelist_name] = mapper
times = list(datastructures.iter(_TIMEDIMENSION))
times = [t.get('id') for t in times]
result = SDMXCode(codes=code_results, ts=times)
return result
def _read_sdmx_dsd(path_or_buf):
"""
Convert a SDMX-XML DSD string to mapping dictionary
Parameters
----------
filepath_or_buffer : a valid SDMX-XML DSD string or file-like
https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php/Main_Page
Returns
-------
results : namedtuple (SDMXCode)
"""
xdata = _read_content(path_or_buf)
import xml.etree.cElementTree as ET
root = ET.fromstring(xdata)
structure = _get_child(root, _MESSAGE + 'Structures')
codes = _get_child(structure, _STRUCTURE + 'Codelists')
# concepts = _get_child(structure, _STRUCTURE + 'Concepts')
datastructures = _get_child(structure, _STRUCTURE + 'DataStructures')
code_results = {}
for codelist in codes:
# codelist_id = codelist.get('id')
codelist_name = _get_english_name(codelist)
mapper = {}
for code in codelist.iter(_CODE):
code_id = code.get('id')
name = _get_english_name(code)
mapper[code_id] = name
# codeobj = SDMXCode(id=codelist_id, name=codelist_name, mapper=mapper)
# code_results[codelist_id] = codeobj
code_results[codelist_name] = mapper
times = list(datastructures.iter(_TIMEDIMENSION))
times = [t.get('id') for t in times]
result = SDMXCode(codes=code_results, ts=times)
return result
