notoecd 0.1.4

Unofficial library for interacting with the OECD Data Explorer through Python.

notoecd

⚠️ Unofficial package, not endorsed by the OECD.

A lightweight Python interface for exploring OECD SDMX structures and downloading OECD datasets.

The package provides utilities for:

• Discovering dataset metadata
• Searching for relevant datasets using keyword matching
• Exploring the structure and code lists of a dataset
• Fetching filtered SDMX data directly into a pandas DataFrame

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Installation

You can install the package by running:

pip install notoecd

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Quick Start

import notoecd

The main functions in this module are:

search_keywords(keywords) -> pd.DataFrame
get_structure(agencyID, dataflowID) -> Structure
get_df(agencyID, dataflowID, filters) -> pd.DataFrame

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Searching for datasets

search_keywords performs:

• Normalized text matching
• Accent-insensitive search
• Multi-keyword OR matching
• Ranking by number of matched keywords

Example:

hits = notoecd.search_keywords('gross domestic product', 'tl2', 'tl3')

This returns datasets that mention GDP and regional levels (TL2/TL3). It gives their name, description, and identifiers (agencyID and dataflowID), which we will need for the next step.

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Inspecting dataset structure

Once a dataset is identified, load its SDMX structure:

dataset = 'Gross domestic product - Regions'
agencyID = 'OECD.CFE.EDS'
dataflowID = 'DSD_REG_ECO@DF_GDP'

s = notoecd.get_structure(agencyID, dataflowID)

Table of contents

s.toc

This shows all filters and their available values.

Exploring code values

s.explain_vals('MEASURE')
s.explain_vals('UNIT_MEASURE')

This shows the available measures and units used in the dataset.

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Filtering and downloading data

To download data, build a dictionary of filters.
Keys correspond to SDMX dimensions, values are strings or lists (for multiple values):

filters = {
    'territorial_level': ['tl2', 'tl3'],
    'measure': 'gdp',
    'prices': 'Q',
    'unit_measure': 'USD_PPP_PS'
}

Fetch the filtered dataset:

df = notoecd.get_df(agency, dataflow, filters)
df.head()

The returned object is a pandas DataFrame containing the requested subset of OECD SDMX data.

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Examples

You can see this full example as a notebook called example.ipynb.