Eurostat Statistics Scraper

Eurostat Statistics Scraper allows you to collect structured EU statistical datasets and convert them into clean formats like JSON, CSV, or Excel. Simply provide a dataset code and your desired dimension filters to retrieve and store the exact metrics you need.

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Key Features & Use Cases

• National Accounts: Pull GDP metrics across different countries by applying unit and indicator filters (e.g., using nama_10_gdp).
• Labour Market Trends: Track unemployment statistics over time with custom start periods (e.g., using une_rt_a).
• Automated Archiving: Periodically rerun your saved filters to capture updated figures and timestamps automatically.
• Dataset Structure Lookup: Use catalogue mode to explore all available dimensions inside a dataset before refining your filters.
• Flexible Data Export: Easily plug extracted data directly into downstream workflows, spreadsheets, or automation pipelines.

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Input Parameters

Parameter	Type	Default	Description
mode	String	values	values extracts standard dataset rows. catalogue saves one row per dimension contained in the dataset.
datasetCode	String	nama_10_gdp	The code identifying the dataset to fetch.
filters	Object	{"geo": ["DE"], "unit": ["CP_MEUR"], "na_item": ["B1GQ"]}	Specific dimension filters to apply. Lists are passed as repeating filter parameters.
sinceTimePeriod	String	""	Optional starting period (e.g., 2021).
lang	String	EN	Language for labels (EN, FR, or DE).
maxItems	Integer	100	Maximum number of rows to save (capped at 1000).
requestDelaySecs	Number	0.5	Pause duration between consecutive requests (minimum 0.5s).
timeoutSecs	Integer	300	Total run time limit in seconds before stopping new requests.
requestTimeoutSecs	Integer	30	Timeout duration for individual HTTP requests.
proxyConfiguration	Object	{"useApifyProxy": false}	Custom proxy settings if required.

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Example Input Configuration

{
    "mode": "values",
    "datasetCode": "nama_10_gdp",
    "filters": {
        "geo": ["DE", "FR"],
        "unit": ["CP_MEUR"],
        "na_item": ["B1GQ"],
        "time": ["2023"]
    },
    "sinceTimePeriod": "",
    "lang": "EN",
    "maxItems": 100,
    "requestDelaySecs": 0.5,
    "timeoutSecs": 300,
    "requestTimeoutSecs": 30,
    "proxyConfiguration": { "useApifyProxy": false }
}

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Sample Extracted Data

Below is an example output record collected in values mode:

{
    "version": "2.0",
    "class": "dataset",
    "label": "Gross domestic product (GDP) and main components (output, expenditure and income) - annual data",
    "source": "ESTAT",
    "updated": "2026-10-09T23:00:00+0200",
    "value": { "0": 4254930.0, "1": 2833826.4 },
    "status": { "0": "p" },
    "id": ["freq", "unit", "na_item", "geo", "time"],
    "size": [1, 1, 1, 2, 1],
    "dimension": {},
    "extension": { "id": "NAMA_10_GDP", "agencyId": "ESTAT" },
    "mode": "values",
    "scraped_at": "2026-10-12T00:00:00Z"
}

Output Fields Explanation

• version: Payload format version.
• class: Dataset categorization flag.
• label: Human-readable dataset title.
• source: Data source provider.
• updated: Timestamp showing when the source last modified the data.
• value: Map of numerical data observations keyed by index.
• status: Observation flags or status indicators.
• id: Array defining dimension order.
• size: Array indicating size per dimension.
• dimension: Category indices and label definitions.
• extension: Dataset metadata details.
• mode: Extraction mode used (values or catalogue).
• scraped_at: UTC timestamp when the row was extracted.

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How It Works

1. Parameters: Reads dataset codes, filters, language preferences, and optional time periods.
2. Execution: Issues structured retrieval requests while respecting your configured delays.
3. Resilience: Features automatic retry handling with exponential backoff for rate limits or server blips.
4. Error Handling: Gracefully logs missing datasets or structural errors into clear error rows without crashing your entire run.
5. Batch Processing: Outputs clean, deduplicated rows in manageable batches until reaching your set item limits.

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Frequently Asked Questions

How up-to-date are the numbers?

Every record contains an updated timestamp directly from the origin source so you always know how fresh your data is.

What happens if I enter an invalid dataset code?

The actor captures the response status into a single structured error row and completes cleanly instead of failing the job.

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Legal & Compliance

• Attribution: Always provide proper credit when publishing or sharing figures collected using this scraper.
• Terms & Fair Use: Respect established rate limits and applicable licensing or usage policies when reusing collected metrics.
• Disclaimer: This actor is an independent utility and is not affiliated with, endorsed by, or officially associated with Eurostat or any related entity.

Trademark Notice

Eurostat Statistics and related names, logos, and trademarks belong to their respective owners. Their use in this documentation is solely to identify the source of the data and does not imply affiliation, sponsorship, endorsement, or partnership.

Disclaimer

Disclaimer: This Actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or otherwise officially associated with Eurostat Statistics or its owners. All data is collected from publicly available sources. Users are responsible for complying with the source's terms of use, rate limits, and attribution or licensing requirements.