Eurostat Python Package
Project description
Eurostat Python Package
Tools to read data from Eurostat API.
Important!
From version 1.0.0, this package implements MAJOR CHANGES.
The previous official Eurostat API, used by the older releases of this Python package, is supposed to be decommissioned in January 2023. It has been replaced with a new dissemination API. This forced to rewrite this Python package, almost completely.
I have done my best to make the new Eurostat Python package compatible with the old releases. Nevertheless, you may see some differences, also in the output format.
The main differences are in the SDMX functions. In particular, OBS_STATUS is not more provided by the API. It is replaced by a flag, that has different symbols and meanings. From version 1.0.0, the SDMX functions of the Eurostat Python package are deprecated, but temporarily kept available. These functions will be removed from version 2.0.0 of the Eurostat Python package. They always print an alert message, that can be "muted" by setting the argument noalert=True.
Features
- Read Eurostat data and metadata as list of tuples or as pandas dataframe.
- Use the new SDMX 2.1 Eurostat web services.
- Download data from Eurostat, COMEXT, DG COMP, DG ENV, DG GROW.
- Available from both pip and conda.
- MIT license.
Documentation
Getting started:
Requires Python 3.5+
pip install eurostat
Read the table of contents of the Eurostat database:
As a list of tuples:
eurostat.get_toc()
Read the table of contents and return a list of tuples. The first element of the list contains the header line. Dates are represented as strings.
Example:
>>> import eurostat
>>> toc = eurostat.get_toc()
>>> toc[0]
('title', 'code', 'type', 'last update of data', 'last table structure change', 'data start', 'data end')
>>> toc[12:15]
[('Employment by NACE Rev. 2 activity and metropolitan typology', 'MET_10R_3EMP', 'dataset', '2022-05-06T23:00:00+0200', '2022-05-06T23:00:00+0200', '1995', '2020'),
('Gross domestic product (GDP) at current market prices by metropolitan regions', 'MET_10R_3GDP', 'dataset', '2022-04-21T23:00:00+0200', '2022-04-21T23:00:00+0200', '2000', '2020'),
('Gross value added at basic prices by metropolitan regions', 'MET_10R_3GVA', 'dataset', '2022-04-21T23:00:00+0200', '2022-04-21T23:00:00+0200', '1995', '2020')]
As a pandas dataframe:
eurostat.get_toc_df()
Read the table of contents of the main database and return a dataframe.
Example:
>>> import eurostat
>>> toc_df = eurostat.get_toc_df()
>>> toc_df
title ... data end
0 Road equipment: number of road vehicles by cat... ... 2018
1 Road equipment: number of road vehicles by age ... 2018
2 Road equipment: load capacity of lorries ... 2015
3 Road equipment: new registrations by categories ... 2015
4 Road traffic: road freight transport in volume ... 2018
... ... ...
7225 Tropical wood imports to the EU from chapter 4... ... 2020-12
7226 Candidate countries and potential candidates: ... ... 2019
7227 Candidate countries and potential candidates: ... ... 2014
7228 Candidate countries and potential candidates: ... ... 2015
7229 Candidate countries and potential candidates: ... ... 2014
You may also want to extract the datasets that pertain a topic. In that case, you can use:
eurostat.subset_toc_df(toc_df, keyword)
Extract from toc_df the rows where the column title contains keyword (case-insensitive).
Example:
>>> f = eurostat.subset_toc_df(toc_df, 'fleet')
>>> f
title ... data end
4873 Fishing fleet, total tonnage ... 2021
4895 Fishing Fleet, Number of Vessels ... 2021
6169 Commercial aircraft fleet by age of aircraft a... ... 2020
6172 Commercial aircraft fleet by age of aircraft a... ... 2020
6175 Commercial aircraft fleet by aircraft category... ... 2020
6178 Commercial aircraft fleet by aircraft category... ... 2020
6576 Commercial aircraft fleet by type of aircraft ... 2020
7120 Fishing fleet by age, length and gross tonnage ... 2021
7121 Fishing fleet by type of gear and engine power ... 2021
Get the filter parameters to download a subset of a dataset:
eurostat.get_pars(code)
Read the parameter names that can be filtered for a given dataset code and return them as a list.
Example:
>>> import eurostat
>>> pars = eurostat.get_pars('demo_r_d2jan')
>>> pars
['freq', 'unit', 'sex', 'age', 'geo']
From the example, you can note that code is generally case-insensitive.
To get the parameter values for filtering, you can use:
eurostat.get_par_values(code, par)
Read the values of a given parameter par that can be found in a given dataset code.
Example:
>>> import eurostat
>>> par_values = eurostat.get_par_values('demo_r_d2jan', 'sex')
>>> par_values
['T', 'M', 'F']
Get an Eurostat dictionary:
eurostat.get_dic(code, par, [full=True], [frmt="list"], [lang="en"])
Read the dictionary of a given parameter par as a list of tuples or as a dictionary. You must provide also the dataset code.
If you want the full list of possible values of par, set full=True, while full=False returns only the values that are in the dataset, output from get_par_values(). Default is full=True.
frmt="list" makes the function return a list of tuples, where the first element of each tuple is the code value and the second one is its description. If frmt=dict it returns a dictionary. Default is frmt="list".
lang allows to download the dictionary in one of the following languages: "en"=English, "fr"=French, "de"=German, when provided by Eurostat. Default is English.
Example:
>>> import eurostat
>>> dic = eurostat.get_dic('demo_r_d2jan', 'sex')
>>> dic
[('T', 'Total'),
('M', 'Males'),
('F', 'Females'),
('DIFF', 'Absolute difference between males and females'),
('NAP', 'Not applicable'),
('NRP', 'No response'),
('UNK', 'Unknown')]
Read a dataset:
As a list of tuples:
eurostat.get_data(code, [flags=False], [filter_pars=dict()], [verbose=False], [reverse_time=False])
Read an Eurostat dataset and returns it as a list of tuples. The first element of the list ("the first row") is the data header.
To get a subset, set filter_pars (a dictionary where keys are parameter names, values are the wanted items).
To see a rough progress status, set verbose=True.
flag=True downloads also the flags associated to the data. Pay attention: the data format changes if flags is True or not. Flag meanings can be found here.
reverse_time=True reverses the order of the time columns. For compatibility with 0.x.x versions.
Example: Full download a dataset without flags as list of tuples
>>> import eurostat
>>> data = eurostat.get_data('GOV_10DD_SLGD')
>>> data[0]
('freq', 'na_item', 'sector', 'maturity', 'unit', 'geo\\TIME_PERIOD', 2018, 2019, 2020, 2021)
>>> data[90:95]
[('A', 'F3', 'S11', 'TOTAL', 'MIO_EUR', 'BE', None, None, 23.8, 39.3),
('A', 'F3', 'S11', 'TOTAL', 'MIO_EUR', 'ES', None, None, 130.0, 122.2),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'AT', None, None, 0.0, 0.0),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'BE', None, None, 23.8, 39.3),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'ES', None, None, 130.0, 122.2)]
Example: Full download a dataset with flags as list of tuples
>>> import eurostat
>>> data = eurostat.get_data('GOV_10DD_SLGD', True)
>>> data[0]
('freq', 'na_item', 'sector', 'maturity', 'unit', 'geo\\TIME_PERIOD', '2018_value', '2018_flag', '2019_value', '2019_flag', '2020_value', '2020_flag', '2021_value', '2021_flag')
>>> data[90:95]
[('A', 'F3', 'S11', 'TOTAL', 'MIO_EUR', 'BE', None, ':', None, ':', 23.8, '', 39.3, ''),
('A', 'F3', 'S11', 'TOTAL', 'MIO_EUR', 'ES', None, ':', None, ':', 130.0, '', 122.2, ''),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'AT', None, ':', None, ':', 0.0, '', 0.0, ''),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'BE', None, ':', None, ':', 23.8, '', 39.3, ''),
('A', 'F3', 'S11', 'TOTAL', 'MIO_NAC', 'ES', None, ':', None, ':', 130.0, '', 122.2, '')]
Example: Download a subset of data from a dataset without flags as list of tuples
To download a subset, you need to set the filter_pars dictionary. Its keys can be: 'startPeriod', 'endPeriod' and any parameter you get with eurostat.get_pars(). Values can be number, string or list and generally are derived by eurostat.get_par_values().
>>> import eurostat
>>> code = 'GOV_10DD_SLGD'
>>> pars = eurostat.get_pars(code)
>>> pars
['freq', 'na_item', 'sector', 'maturity', 'unit', 'geo']
>>> par_values = eurostat.get_par_values(code, 'geo')
>>> par_values
['BE', 'DE', 'ES', 'AT']
>>> my_filter_pars = {'startPeriod': 2019, 'geo': ['AT','BE']}
>>> data = eurostat.get_data(code, filter_pars=my_filter_pars)
>>> data[0]
('freq', 'na_item', 'sector', 'maturity', 'unit', 'geo\\TIME_PERIOD', 2019, 2020, 2021)
>>> data[445:447]
[('A', 'GD', 'S1_S2', 'Y_LT1', 'PC_TOT', 'AT', None, 1.0, 0.9),
('A', 'F22', 'S1_S2', 'TOTAL', 'MIO_EUR', 'BE', None, 0.0, 0.0)]
Example: Download a subset of data from a dataset with flags as list of tuples
>>> import eurostat
>>> code = 'GOV_10DD_SLGD'
>>> pars = eurostat.get_pars(code)
>>> pars
['freq', 'na_item', 'sector', 'maturity', 'unit', 'geo']
>>> par_values = eurostat.get_par_values(code, 'geo')
>>> par_values
['BE', 'DE', 'ES', 'AT']
>>> my_filter_pars = {'startPeriod': 2019, 'geo': ['AT','BE']}
>>> data = eurostat.get_data(code, True, filter_pars=my_filter_pars)
>>> data[0]
('freq', 'na_item', 'sector', 'maturity', 'unit', 'geo\\TIME_PERIOD', '2019_value', '2019_flag', '2020_value', '2020_flag', '2021_value', '2021_flag')
>>> data[446:448]
[('A', 'GD', 'S1_S2', 'Y_LT1', 'PC_TOT', 'AT', None, ':', 1.0, '', 0.9, ''),
('A', 'F22', 'S1_S2', 'TOTAL', 'MIO_EUR', 'BE', None, ':', 0.0, '', 0.0, '')]
As a pandas dataframe:
eurostat.get_data_df(code, [flags=False], [filter_pars=None], [verbose=False], [reverse_time=False])
Read an Eurostat dataset and returns it as pandas dataframe. The first element of the list ("the first row") is the data header.
To get a subset, set filter_pars (a dictionary where keys are parameter names, values are the wanted items).
To see a rough progress status, set verbose=True.
flag=True downloads also the flags associated to the data. Pay attention: the data format changes if flags is True or not. Flag meanings can be found here.
reverse_time=True reverses the order of the time columns. For compatibility with 0.x.x versions.
Example: Full download a dataset without flags as pandas dataframe
>>> import eurostat
>>> data = eurostat.get_data_df('GOV_10DD_SLGD')
>>> data
freq na_item sector maturity ... 2018 2019 2020 2021
0 A F22 S1_S2 TOTAL ... NaN NaN 0.0 0.0
1 A F22 S1_S2 TOTAL ... NaN NaN 0.0 0.0
2 A F22 S1_S2 TOTAL ... 0.0 0.0 0.0 0.0
3 A F22 S1_S2 TOTAL ... NaN NaN 0.0 0.0
4 A F22 S1_S2 TOTAL ... NaN NaN 0.0 0.0
... ... ... ... ... ... ... ... ...
1181 A GD S1_S2 Y_LT1 ... NaN NaN 2849.0 2408.2
1182 A GD S1_S2 Y_LT1 ... NaN NaN 1.0 0.9
1183 A GD S1_S2 Y_LT1 ... NaN NaN 6.9 5.4
1184 A GD S1_S2 Y_LT1 ... 4.9 6.0 5.9 5.5
1185 A GD S1_S2 Y_LT1 ... NaN NaN 0.9 0.8
Example: Full download a dataset with flags as pandas dataframe
>>> import eurostat
>>> data = eurostat.get_data_df('GOV_10DD_SLGD', True)
>>> data
freq na_item sector maturity ... 2020_value 2020_flag 2021_value 2021_flag
0 A F22 S1_S2 TOTAL ... 0.0 : 0.0
1 A F22 S1_S2 TOTAL ... 0.0 0.0
2 A F22 S1_S2 TOTAL ... 0.0 : 0.0
3 A F22 S1_S2 TOTAL ... 0.0 0.0
4 A F22 S1_S2 TOTAL ... 0.0 0.0
... ... ... ... ... ... ... ... ...
1182 A GD S1_S2 Y_LT1 ... 1.0 0.9
1183 A GD S1_S2 Y_LT1 ... 6.9 5.4
1184 A GD S1_S2 Y_LT1 ... 5.9 5.5
1185 A GD S1_S2 Y_LT1 ... 0.9 : 0.8
1186 A GD S1_S2 Y_LT1 ... 0.9 0.8
Example: Download a subset of data from a dataset without flags as pandas dataframe
To download a subset, you need to set the filter_pars dictionary. Its keys can be: 'startPeriod', 'endPeriod' and any parameter you get with eurostat.get_pars(). Values can be number, string or list and generally are derived by eurostat.get_par_values().
>>> import eurostat
>>> code = 'GOV_10DD_SLGD'
>>> pars = eurostat.get_pars(code)
>>> pars
['freq', 'na_item', 'sector', 'maturity', 'unit', 'geo']
>>> par_values = eurostat.get_par_values(code, 'geo')
>>> par_values
['BE', 'DE', 'ES', 'AT']
>>> my_filter_pars = {'endPeriod': 2020, 'geo': ['AT','BE']}
>>> data = eurostat.get_data_df(code, filter_pars=my_filter_pars)
>>> data
freq na_item sector maturity unit geo\TIME_PERIOD 2018 2019 2020
0 A F22 S1_S2 TOTAL MIO_EUR AT NaN NaN 0.0
1 A F22 S1_S2 TOTAL MIO_NAC AT NaN NaN 0.0
2 A F22 S1_S2 Y_LT1 MIO_EUR AT NaN NaN 0.0
3 A F22 S1_S2 Y_LT1 MIO_NAC AT NaN NaN 0.0
4 A F29 S1_S2 TOTAL MIO_EUR AT NaN NaN 0.0
.. ... ... ... ... ... ... ... ... ...
633 A GD S1_S2 Y_GT1 MIO_NAC BE NaN NaN 72660.0
634 A GD S1_S2 Y_GT1 PC_TOT BE NaN NaN 93.1
635 A GD S1_S2 Y_LT1 MIO_EUR BE NaN NaN 5387.0
636 A GD S1_S2 Y_LT1 MIO_NAC BE NaN NaN 5387.0
637 A GD S1_S2 Y_LT1 PC_TOT BE NaN NaN 6.9
Example: Download a subset of data from a dataset with flags as pandas dataframe
>>> import eurostat
>>> code = 'GOV_10DD_SLGD'
>>> pars = eurostat.get_pars(code)
>>> pars
['freq', 'na_item', 'sector', 'maturity', 'unit', 'geo']
>>> par_values = eurostat.get_par_values(code, 'geo')
>>> par_values
['BE', 'DE', 'ES', 'AT']
>>> my_filter_pars = {'endPeriod': 2020, 'geo': ['AT','BE']}
>>> data = eurostat.get_data_df(code, True, filter_pars=my_filter_pars)
>>> data
freq na_item sector maturity ... 2019_value 2019_flag 2020_value 2020_flag
0 A F22 S1_S2 TOTAL ... NaN : 0.0
1 A F22 S1_S2 TOTAL ... NaN : 0.0
2 A F22 S1_S2 Y_LT1 ... NaN : 0.0
3 A F22 S1_S2 Y_LT1 ... NaN : 0.0
4 A F29 S1_S2 TOTAL ... NaN : 0.0
.. ... ... ... ... ... ... ... ... ...
635 A GD S1_S2 Y_GT1 ... NaN : 93.1
636 A GD S1_S2 Y_LT1 ... NaN : 5387.0
637 A GD S1_S2 Y_LT1 ... NaN : 5387.0
638 A GD S1_S2 Y_LT1 ... NaN : 6.9
639 A GD S1_S2 Y_LT1 ... NaN : 6.9
In case you need to use a proxy:
Before doing anything else, you must configure the https proxy.
eurostat.setproxy(proxyinfo)
It requires in input proxyinfo, a dictionary with one key ('https') and value containing the connection parameters in a list.
If authentication is not needed, set username and password to None.
Example:
>>> import eurostat
>>> proxyinfo = {'https': ['myuser', 'mypassword', 'url:port']}
>>> eurostat.setproxy(proxyinfo)
It always returns None.
Bug reports and feature requests:
Please open an issue or send a message to noemi.cazzaniga [at] polimi.it .
Disclaimer:
Download and usage of Eurostat data is subject to Eurostat's general copyright notice and licence policy (see Policies). Please also be aware of the European Commission's general conditions.
Data sources:
- Eurostat database: online catalog.
- Eurostat nomenclatures: RAMON metadata.
- Eurostat Interactive Data Browser: Data Browser.
- Eurostat Interactive Tool for Comext Data: Easy Comext.
- Eurostat PRODCOM website: PRODCOM.
- Eurostat acronyms: Symbols and abbreviations.
- Eurostat web services description: Web services.
References:
- R package eurostat: R Tools for Eurostat Open Data.
- Python package pandas: Python Data Analysis Library.
History:
version 1.0.0 (08 Oct 2022):
- Adapted to the new Eurostat API (SDMX 2.1).
- pandasdmx not required anymore.
- Error messages improved.
- Compiled also for conda install.
- Multilingual dictionary.
version 0.2.3 (06 Apr 2021):
- Internal bug fix.
version 0.2.2 (31 Mar 2021):
- Bug fix (sdmx non-annual data). Deprecated.
version 0.2.1 (10 Nov 2020):
- Bug fix (pandasdmx 0.9).
version 0.2.0 (22 May 2020):
- Improved SDMX download capability in case of slow internet connections.
version 0.1.5 (08 Jan 2020):
- Bug fix (proxy info).
- get_avail_sdmx, get_avail_sdmx_df, subset_avail_sdmx_df added.
version 0.1.4 (20 Dec 2019):
- Added support to proxy.
version 0.1.3 (17 Dec 2019):
- Bug fix (non-annual data headers).
version 0.1.2 (25 Nov 2019):
- Added possibility to download flags.
- get_toc_df, subset_toc_df added.
version 0.1.1 (21 Nov 2019):
- First official release.
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