What’s new?

v1.10.0 (2023-02-25)

  • update ESTAT config to support new API Asynchronous endpoints are not yet supported though.

  • added data sources by the European Commission: EC_COMP (DG Competition), EC_EMPL (DG Employment) and EC_DG_GROW (Growth). Asynchronous endpoints are not yet supported

  • Add __repr__ method to pandasdmx.api.Request,

  • to_pandas: Add the option to derive the pandas dtype from the datastructure definition’s facet value types when exporting to a pandas DataFrame or series. The default behavior (i.e. try to convert to float64) remains unchanged.

  • clean up sources: remove unnecessary adaptors for INSEE and ISTAT

  • require Python 3.9+ and pydantic 1.9.2+

v1.9.0 (2022-02-23)

  • new feature: validate SDMXML messages against the XML schemas included in the SDMX 2.1 standard. Simply install the schemas locally using the convenience function pandasdmx.api.install_schemas(), then call pandasdmx.api.Request.validate() passing any pandasdmx.message.Message instance or a file-like containing the XML data.

v1.8.1 (2022-02-01)

  • make default locale configurable via pandasdmx.api.Request.default_locale property. This controls output of multi-lingual strings when generating pandas objects.

  • add documentation on configuring the default locale. See walkthrough.

  • default_validation_level is now a property of pandasdmx.api.Request

  • Bug fix: make timeout a kwarg of pandasdmx.api.Request constructor and store it as an attribute. A property is no longer needed. propagate timeout param to each HTTP request. Befor, it was erroneously attached to the Session instance, thus it had no effect.

v1.8.0 (2022-01-28)

  • model: make validation level configurable. Default is sloppy. This is currently used to omit URN matching as it fails for DSDs from BBK. And this feature is hardly needed anyway.

  • add pandasdmx.api.Request.set_validation_level() and corresponding pandasdmx.api.Request.get_validation_level() static convenience methods

  • New source added: ABS_XML for the new XML-based API of Australian Bureau of Stats

  • add àpi_id` item for sources. Can be different from id.

v1.7.0 (2022-01-27)

  • fix regression regarding model.InternationLString.__str__

  • add ABS_XML as new agency, but does not seem to work, yet. ABS API is in beta.

v1.7.0 (2022-01-13)

  • update some internals from khaeru/sdmx

  • Python 3.10 and pydantic 1.9 compat

  • remove obsolete and unstable tests that depend on external data. TODO: improve coverage

  • remove deprecated Message.write method

v1.6.0 (2021-05-25)

  • add support for two new data sources:

  • new feature: Source: allow resource-level URLs overriding the source-level URL

  • new feature: Source: optionally specify a default_version. may be ‘’. defaults to ‘latest’ as before

  • Bug fix: str representation of model.InternationalString: return first non-empty string if value for LOCALE is None or not given

v1.5.0 (2021-04-11)

  • Added Bank for International Settlements (BIS) as new data source

v1.4.2 (2021-03-26)

  • dependencies: set range for pydantic to >=1.7.1,<1.8 for now

v1.4.1 (2021-02-25)

  • Fix reader discovery for footer messages from ESTAT when querying large datasets which ESTAT delivers through a link to a zip file

v1.4.0 (2021-02-04)

  • update end point link for ECB data source

  • update data source config for ILO enabling access to its new API. Remove obsolete adapter. Please report any issues.

  • Add three new data sources (all of which support the sdmxjson format, hence only data requests are allowed: - National Bank of Belgium (NBB) - Statistics Lithuania (LSB) - Statistics Estonia

v1.3.0 (2021-01-03)

  • Add new data source Pacific Data Hub

  • add support for Python 3.9

  • properly validate URLs in source.Source

  • sources.json: add URLs to documentation on data sources

  • api.Request: add method view_doc to view doc website in a new browser tab

  • bug fix: propagate all relevant kwargs to remote.Session

  • Bug fix in source.ESTAT: honor get_footer_url arg for interval and frequency to download zip file

  • CI: move from travis-ci to GitHub Actions

v1.2.0 (2020-11-XX)

  • Add new data source: World Bank - World Development Indicators

  • Pass files for reading and writing in a with-context. See the documentation for pandasdmx.reader.read_sdmx() and pandasdmx.api.Request.get() for details

  • Allow FSSPEC files. Hence, all major cloud storage providers can be leveraged directly, alonside many other features of FSSPEC.

  • merge improvements from khaeru/sdmx1 fork: write DataMessages to SDMXML

v1.1.0 (2020-08-02)

Overview

This is a major feature release. The SDMXML reader has been refactored. It now uses an event-driven XML parser. In future releases, this may allow parsing of large XML files which do not fit into memory.

Changes

  • add support for new data source UNICEF

  • Remove data source UNESCO as their SDMX web API has been discontinued. Bulk downloads should still be available though.

  • Ported code-base from v1.2.0 of recent fork. New features: * event-driven SDMXML reader * new sdmxml writer to serialize a programmatically generated model representation as SDMXML file (in case anyone needs this)

  • Fix crash when passing str typed filepath to pandasdmx.reader.read_sdmx()

  • Add support for pandasdmx.message.DataMessage attributes reporting_begin, reporting_en and extracted.

  • remove test suite from source distribution and wheels as the test suite has become rather unwieldy (E.g., on first run, 300MB of data are downloaded.)

  • Do not raise XMLParseError and terminate when an unsupported tag is found in SDMXML message. A gentle warning is logged instead.

Migrating from v1.0.x

No code-breaking changes are known so far. However, due to the new SDMX-ML reader design, performance of reading XML messages is further reduced by a factor of about six. Compared to v0.9, reading a large SDMX-ML message may take about 150 to 200 times longer. However, the new reader benefits from cleaner code and flexibility as regards memory footprint.

v1.0.1 (2020-05-28)

This release fixes a number of bugs and update resources.

  • IMF no longer accepts data queries. Update source and docs to reflect this.

  • Fix crash when making data requests to JSON-based data sources

v1.0.0 (2020-05-15)

Overview

  • pandasdmx.model has been reimplemented from the ground up. Fundamental concepts have not changed though.

    • Python typing and pydantic are used to enforce compliance with the SDMX Information Model (IM). Users familiar with the IM can use pandaSDMX without the need to understand implementation-specific details.

    • IM classes are no longer tied to pandasdmx.reader instances and can be created and manipulated outside of a read operation.

  • pandasdmx.api and pandasdmx.remote are reimplemented to (1) match the semantics of the requests package and (2) be much thinner.

  • Data sources are modularized in Source.

    • Idiosyncrasies of particular data sources (e.g. ESTAT’s process for large requests) are handled by source-specific subclasses. As a result, pandasdmx.api is leaner.

  • Test coverage has been significantly expanded.

    • There are tests for each data source (tests/test_sources.py`) to ensure the package can handle idiosyncratic behaviour.

    • The pytest-remotedata pytest plugin allows developers and users to run or skip network tests with –remote-data.

Breaking changes

  • Python 3.6 and earlier (including Python 2) are not supported.

  • various API changes. E.g., pandasdmx.message.Message.write() is deprecated. Use to_pandas() or pandasdmx.message.Message.to_pandas() instead. - The layout of generated pandas objects may differ from that in v0.9.

Migrating from v0.9

v1.0 include many code-breaking changes. Most notably, the default layout of pandas objects generated by pandaSDMX differs from v0.9 (see be.ow). Moreover, core modules including the SDMX information model were rewritten almost from scratch. The main benefit of this overhaul is the complete separation of file readers and the model-level representation of a received SDMX message. The main drawback is a severe performance hit. While up to v0.9, the model representation was built lazily, and some SDMX features were not supported due to certain pragmatic design choices, v1.x strives to translate the information contained in a given SDMX-ML or SDMX-JSON file entirely as instances of model classes. As a result, reading a largeSDMX message may take about 30 times longer than with v0.9. On the other hand, the pandas writer is considerably faster in v1.x than in v0.9 as it generates Series objects only, while delegating further conversions to DataFrames to the highly optimized pandaslayers. Further changes include:

  • Writer.write(…, reverse_obs=True): use the standard pandas indexing approach to reverse a pd.Series: s.iloc[::-1]

  • odo support is no longer built-in; however, users can still register a pandaSDMX resource with odo. See the HOWTO.

  • write_dataset(): the parse_time and fromfreq arguments are replaced by datetime; see the method documentation and the walkthrough section for examples.

v0.9 (2018-04)

This version is the last tested on Python 2.x. Future versions will be tested on Python 3.5+ only

New features

  • four new data providers INEGI (Mexico), Norges Bank (Norway), International Labour Organization (ILO) and and Italian statistics office (ISTAT)

  • model: make Ref instances callable for resolving them, i.e. getting the referenced object by making a remote request if needed

  • improve loading of structure-specific messages when DSD is not passed / must be requested on the fly

  • process multiple and cascading content constraints as described in the Technical Guide (Chap. 6 of the SDMX 2.1 standard)

  • StructureMessages and DataMessages now have properties to compute the constrained and unconstrained codelists as dicts of frozensets of codes. For DataMessage this is useful when series_keys was set to True when making the request. This prompts the data provider to generate a dataset without data, but with the complete set of series keys. This is the most accurate representation of the available series. Agencies such as IMF and ECB support this feature.

v0.8.2 (2017-12-21)

  • fix reading of structure-specific data sets when DSD_ID is present in the data set

v0.8.1 (2017-12-20)

  • fix broken package preventing pip installs of the wheel

v0.8 (2017-12-12)

  • add support for an alternative data set format defined for SDMXML messages. These so-called structure-specific data sets lend themselves for large data queries. File sizes are typically about 60 % smaller than with equivalent generic data sets. To make use of structure-specific data sets, instantiate Request objects with agency IDs such as ‘ECB_S’, ‘INSEE_S’ or ‘ESTAT_S’ instead of ‘ECB’ etc. These alternative agency profiles prompt pandaSDMX to execute data queries for structure-specific data sets. For all other queries they behave exactly as their siblings. See a code example in chapter 5 of the docs.

  • raise ValueError when user attempts to request a resource other than data from an agency delivering data in SCMX-JSON format only (OECD and ABS).

  • Update INSEE profile

  • handle empty series properly

  • data2pd writer: the code for Series index generation was rewritten from scratch to make better use of pandas’ time series functionality. However, some data sets, in particular from INSEE, which come with bimonthly or semestrial frequencies cannot be rendered as PeriodIndex. Pass parse_time=False to the .write method to prevent errors.

v0.9 (2018-04)

This version is the last tested on Python 2.x. Future versions will be tested on Python 3.5+ only

New features

  • four new data providers INEGI (Mexico), Norges Bank (Norway), International Labour Organization (ILO) and and Italian statistics office (ISTAT)

  • model: make Ref instances callable for resolving them, i.e. getting the referenced object by making a remote request if needed

  • improve loading of structure-specific messages when DSD is not passed / must be requested on the fly

  • process multiple and cascading content constraints as described in the Technical Guide (Chap. 6 of the SDMX 2.1 standard)

  • StructureMessages and DataMessages now have properties to compute the constrained and unconstrained codelists as dicts of frozensets of codes. For DataMessage this is useful when series_keys was set to True when making the request. This prompts the data provider to generate a dataset without data, but with the complete set of series keys. This is the most accurate representation of the available series. Agencies such as IMF and ECB support this feature.

v0.8.2 (2017-12-21)

  • fix reading of structure-specific data sets when DSD_ID is present in the data set

v0.8.1 (2017-12-20)

  • fix broken package preventing pip installs of the wheel

v0.8 (2017-12-12)

  • add support for an alternative data set format defined for SDMXML messages. These so-called structure-specific data sets lend themselves for large data queries. File sizes are typically about 60 % smaller than with equivalent generic data sets. To make use of structure-specific data sets, instantiate Request objects with agency IDs such as ‘ECB_S’, ‘INSEE_S’ or ‘ESTAT_S’ instead of ‘ECB’ etc. These alternative agency profiles prompt pandaSDMX to execute data queries for structure-specific data sets. For all other queries they behave exactly as their siblings. See a code example in chapter 5 of the docs.

  • raise ValueError when user attempts to request a resource other than data from an agency delivering data in SCMX-JSON format only (OECD and ABS).

  • Update INSEE profile

  • handle empty series properly

  • data2pd writer: the code for Series index generation was rewritten from scratch to make better use of pandas’ time series functionality. However, some data sets, in particular from INSEE, which come with bimonthly or semestrial frequencies cannot be rendered as PeriodIndex. Pass parse_time=False to the .write method to prevent errors.

v0.7.0 (2017-06-10)

  • add new data providers:

    • Australian Bureau of Statistics

    • International Monetary Fund - SDMXCentral only

    • United Nations Division of Statistics

    • UNESCO (free registration required)

    • World Bank - World Integrated Trade Solution (WITS)

  • new feature: load metadata on data providers from json file; allow the user to add new agencies on the fly by specifying an appropriate JSON file using the pandasdmx.api.Request.load_agency_profile().

  • new pandasdmx.api.Request.preview_data() providing a powerful fine-grain key validation algorithm by downloading all series-keys of a dataset and exposing them as a pandas DataFrame which is then mapped to the cartesian product of the given dimension values. Works only with data providers such as ECB and UNSD which support “series-keys-only” requests. This feature could be wrapped by a browser-based UI for building queries.

  • sdjxjson reader: add support for flat and cross-sectional datasets, preserve dimension order where possible

  • structure2pd writer: in codelists, output Concept rather than Code attributes in the first line of each code-list. This may provide more information.

v0.6.1 (2017-02-03)

  • fix 2to3 issue which caused crashes on Python 2.7

v0.6 (2017-01-07)

This release contains some important stability improvements.

Bug fixes

  • JSON data from OECD is now properly downloaded

  • The data writer tries to gleen a frequency value for a time series from its attributes. This is helpful when exporting data sets, e.g., from INSEE (Issue 41).

Known issues

A data set which lacks a FREQ dimension or attribute can be exported as pandas DataFrame only when parse_time=False?, i.e. no DateTime index is generated. The resulting DataFrame has a string index. Use pandas magic to create a DateTimeIndex from there.

v0.5 (2016-10-30)

New features

  • new reader module for SDMX JSON data messages

  • add OECD as data provider (data messages only)

  • pandasdmx.model.Category is now an iterator over categorised objects. This greatly simplifies category usage. Besides, categories with the same ID while belonging to multiple category schemes are no longer conflated.

API changes

  • Request constructor: make agency ID case-insensitive

  • As Category is now an iterator over categorised objects, Categorisations is no longer considered part of the public API.

Bug fixes

  • sdmxml reader: fix AttributeError in write_source method, thanks to Topas

  • correctly distinguish between categories with same ID while belonging to different category schemes

v0.4 (2016-04-11)

New features

  • add new provider INSEE, the French statistics office (thanks to Stéphan Rault)

  • register ‘.sdmx’ files with Odo if available

  • logging of http requests and file operations.

  • new structure2pd writer to export codelists, dataflow-definitions and other structural metadata from structure messages as multi-indexed pandas DataFrames. Desired attributes can be specified and are represented by columns.

API changes

  • pandasdmx.api.Request constructor accepts a log_level keyword argument which can be set to a log-level for the pandasdmx logger and its children (currently only pandasdmx.api)

  • pandasdmx.api.Request now has a timeout property to set the timeout for http requests

  • extend api.Request._agencies configuration to specify agency- and resource-specific settings such as headers. Future versions may exploit this to provide reader selection information.

  • api.Request.get: specify http_headers per request. Defaults are set according to agency configuration

  • Response instances expose Message attributes to make application code more succinct

  • rename pandasdmx.api.Message attributes to singular form Old names are deprecated and will be removed in the future.

  • pandasdmx.api.Request exposes resource names such as data, datastructure, dataflow etc. as descriptors calling ‘get’ without specifying the resource type as string. In interactive environments, this saves typing and enables code completion.

  • data2pd writer: return attributes as namedtuples rather than dict

  • use patched version of namedtuple that accepts non-identifier strings as field names and makes all fields accessible through dict syntax.

  • remove GenericDataSet and GenericDataMessage. Use DataSet and DataMessage instead

  • sdmxml reader: return strings or unicode strings instead of LXML smart strings

  • sdmxml reader: remove most of the specialized read methods. Adapt model to use generalized methods. This makes code more maintainable.

  • pandasdmx.model.Representation for DSD attributes and dimensions now supports text not just codelists.

Other changes and enhancements

  • documentation has been overhauled. Code examples are now much simpler thanks to the new structure2pd writer

  • testing: switch from nose to py.test

  • improve packaging. Include tests in sdist only

  • numerous bug fixes

v0.3.1 (2015-10-04)

This release fixes a few bugs which caused crashes in some situations.

v0.3.0 (2015-09-22)

  • support for requests-cache allowing to cache SDMX messages in memory, MongoDB, Redis or SQLite

  • pythonic selection of series when requesting a dataset: Request.get allows the key keyword argument in a data request to be a dict mapping dimension names to values. In this case, the dataflow definition and datastructure definition, and content-constraint are downloaded on the fly, cached in memory and used to validate the keys. The dotted key string needed to construct the URL will be generated automatically.

  • The Response.write method takes a parse_time keyword arg. Set it to False to avoid parsing of dates, times and time periods as exotic formats may cause crashes.

  • The Request.get method takes a memcache keyward argument. If set to a string, the received Response instance will be stored in the dict Request.cache for later use. This is useful when, e.g., a DSD is needed multiple times to validate keys.

  • fixed base URL for Eurostat

  • major refactorings to enhance code maintainability

v0.2.2

  • Make HTTP connections configurable by exposing the requests.get API through the pandasdmx.api.Request constructor. Hence, proxy servers, authorisation information and other HTTP-related parameters consumed by requests.get can be specified for each Request instance and used in subsequent requests. The configuration is exposed as a dict through a new Request.client.config attribute.

  • Responses have a new http_headers attribute containing the HTTP headers returned by the SDMX server

v0.2.1

  • Request.get: allow fromfile to be a file-like object

  • extract SDMX messages from zip archives if given. Important for large datasets from Eurostat

  • automatically get a resource at an URL given in the footer of the received message. This allows to automatically get large datasets from Eurostat that have been made available at the given URL. The number of attempts and the time to wait before each request are configurable via the get_footer_url argument.

v0.2.0 (2015-04-13)

This version is a quantum leap. The whole project has been redesigned and rewritten from scratch to provide robust support for many SDMX features. The new architecture is centered around a pythonic representation of the SDMX information model. It is extensible through readers and writers for alternative input and output formats. Export to pandas has been dramatically improved. Sphinx documentation has been added.

v0.1.2 (2014-09-17)

  • fix xml encoding. This brings dramatic speedups when downloading and parsing data

  • extend description.rst

v0.1 (2014-09)

  • Initial release