Skip to content

Download data

Data can be downloaded from DBnomics website, from several applications or from programming languages.

DBnomics website

There is a "Download" button available on each dataset and series pages, allowing to download data in CSV, Excel or JSON formats.

From a dataset page, you can download many series with a limit of 1000 series per file. You are encouraged to add search filters to narrow your search. Otherwise, to bypass this limit, you can download data from a programming language.

From a series page, you can download the displayed series.

Applications

LibreOffice Calc

You can download one or many times series from DBnomics in LibreOffice Calc, by importing a CSV or an Excel file.

To do so:

  • As explained in the DBnomics website section, go to the page of a dataset to download many series, or to the page of a specific series.
  • Click on the "Download" button
  • In the menu, click on the "Copy URL" icon of the Excel or CSV item
  • In LibreOffice Calc, link the data following this documentation (this can be tricky due to the user interface)
  • Select the menu "Sheet" then "Link to External Data"
  • Paste the URL you just copied
  • Press Enter on your keyboard
  • Click OK to proceed

EViews

EViews offers a custom interface to DBnomics data.

Programming languages

Data can be accessed from programming languages.

DBnomics core team provide packages for Python and R.

DBnomics Community provide packages for Julia, Matlab and Stata. Also, there is a plugin for Gretl, Gnu Regression, Econometrics and Time-series Library.

Internally those modules call DBnomics Web API and transform data to idiomatic data types like dataframes.

Python

A tutorial is available as a Jupyter notebook.

Quick start (using Python 3 venv):

python3 -m venv test-dbnomics-python
source test-dbnomics-python/bin/activate
pip install dbnomics ipython
ipython

# In ipython:

In [1]: import dbnomics
In [2]: df = dbnomics.fetch_series('OECD', 'MEI', 'USA.B6BLTT01.CXCUSA.Q')
In [3]: df
Out[3]:
    @frequency provider_code dataset_code  ...                                            Subject                                           Measure  Frequency
0    quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
1    quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
2    quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
3    quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
4    quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
..         ...           ...          ...  ...                                                ...                                               ...        ...
235  quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
236  quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
237  quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
238  quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly
239  quarterly          OECD          MEI  ...  Balance of payments BPM6 > Current account Bal...  US Dollars, sum over component sub-periods, s.a.  Quarterly

[240 rows x 18 columns]

From here you get the time series OECD/MEI/USA.B6BLTT01.CXCUSA.Q in the DataFrame df.

Contributing

If you're using a programming language or an application that is not already supported, please tell us about it!

To download DBnomics data, you can always call the Web API directly, but manipulating a dedicated client should feel easier.

You can contribute by telling us how you're accessing DBnomics data from your favorite application.

You can also contribute by writing a new package for a programming language, allowing others to retrieve data from DBnomics from that language, and helping DBnomics Community to grow!

Contact information is in the FAQ.

More ways to contribute are described on the contributing page.