EuStatsPy

A Python wrapper for the Eurostat APIs, providing easy access to European statistical data.

Note: This is an independent project and is not associated with Eurostat.

Installation

pip install eustatspy

Requirements

EuStatsPy requires Python 3.7+ and the following dependencies:

• requests (≥2.25.0) - HTTP library for API communication
• pandas (≥1.0.0) - Data manipulation and analysis

These dependencies are automatically installed when you install EuStatsPy.

Quick Start

import eustatspy as est

# Initialize the client with caching enabled
client = est.EurostatClient(cache_enabled=True)

# Pre-load metabase for optimal performance (one-time cost)
client.preload_metabase()

Core Functionality

EuStatsPy provides four essential functions for working with Eurostat data:

1. Browse Database Structure

Navigate through the Eurostat database hierarchy to discover datasets:

# Start at the root to see main themes
client.browse_database()

# Explore specific themes
client.browse_database('general')  # General statistics
client.browse_database('euroind')  # European indicators

2. Search Datasets

Find datasets by keyword, with optional date filtering:

# Basic search
results = client.search_datasets("GDP")

# Search with date filter (datasets updated since specific date)
recent_data = client.search_datasets(
    query="unemployment", 
    updated_since="2025-06-01",
    max_results=20
)

# Search for today's updates
today_updates = client.search_datasets(
    query="",  # Empty query = all datasets
    updated_since="2025-07-09",
    max_results=100
)

3. Explore Dataset Details

Understand dataset dimensions and available filters:

# Get comprehensive dataset information
client.describe_dataset("lfst_r_lfsd2pop")

# See all values for a specific dimension
client.describe_dataset("nama_10_gdp", show_all_for_dimension='geo')

# Limit displayed values per dimension
client.describe_dataset("ei_isbr_m", max_values_per_dimension=5)

4. Get Data as DataFrame

Retrieve data with flexible filtering options:

Geographic Filtering

# Single country
df = client.get_data_as_dataframe('nama_10_gdp', geo='SE')

# Multiple countries
df = client.get_data_as_dataframe(
    'lfst_r_lfsd2pop',
    geo=['SE11', 'DK01']
)

# By geographic level
df = client.get_data_as_dataframe(
    'tour_occ_nin2m',
    geoLevel='country',
    lastTimePeriod=1
)

Time Filtering

# Specific years
df = client.get_data_as_dataframe(
    'lfst_r_lfsd2pop',
    geo='SE',
    time=['2022', '2023']
)

# Latest periods
df = client.get_data_as_dataframe(
    'nama_10_gdp',
    geo='SE',
    lastTimePeriod=5
)

# Time ranges
df = client.get_data_as_dataframe(
    'ei_isbr_m',
    geo='SE',
    sinceTimePeriod='2024-01',
    untilTimePeriod='2024-12'
)

# Data from specific time
df = client.get_data_as_dataframe(
    'nama_10_gdp',
    geo='SE',
    sinceTimePeriod='2020'
    )

Multi-dimensional Filtering

# Complex filtering with multiple dimensions
df = client.get_data_as_dataframe(
    'lfst_r_lfsd2pop',
    geo=['SE11', 'SE12'],
    age='Y25-64',
    isced11=['ED0-2', 'ED3_4', 'ED5-8'],
    sex=['M', 'F'],
    lastTimePeriod=3
)

Common Filter Parameters

• geo: Geographic areas - 'SE', ['SE', 'DK'], or 'all'
• time: Time periods - '2020', ['2020', '2021'], '2020-Q1'
• geoLevel: Geographic level - 'country', 'nuts1', 'nuts2', 'nuts3', 'city', 'aggregate'
• lastTimePeriod: Number of latest periods - 1, 5, 10
• sinceTimePeriod: Start period - '2020', '2020-Q1', '2020-01'
• untilTimePeriod: End period - '2023', '2023-Q4', '2023-12'

Plus dataset-specific dimensions like unit, na_item, sex, age etc.

Performance Tips

1. Enable caching for faster repeated queries:

   client = est.EurostatClient(cache_enabled=True)

2. Pre-load metabase for instant dataset exploration:

   client.preload_metabase()  # One-time cost
   # Now all describe_dataset() calls are instant!

3. Use specific filters to reduce data size:

   # Instead of getting all data
   df = client.get_data_as_dataframe('nama_10_gdp')
   
   # Filter to what you need
   df = client.get_data_as_dataframe(
       'nama_10_gdp',
       geo='SE',
       unit='CP_MEUR',
       lastTimePeriod=5
   )

Data Frequency Examples

• Annual data: nama_10_gdp (GDP), lfst_r_lfsd2pop (Population)
• Quarterly data: tipsbp53 (Balance of payments), namq_10_gdp (GDP quarterly)
• Monthly data: ei_isbr_m (Industrial production), tour_occ_nin2m (Tourism)

Error Handling

try:
    df = client.get_data_as_dataframe('invalid_dataset')
except est.DatasetNotFoundError:
    print("Dataset not found")
except est.InvalidParameterError as e:
    print(f"Invalid parameters: {e}")
except est.EurostatAPIError as e:
    print(f"API error: {e}")

Cache Management

# Clear cache when needed
client.clear_cache()

# Check if metabase is loaded
if not client.is_metabase_loaded():
    client.preload_metabase()

Example Workflow

import eustatspy as est

# 1. Initialize and setup
client = est.EurostatClient(cache_enabled=True)
client.preload_metabase()

# 2. Discover data
client.browse_database()  # Explore themes
results = client.search_datasets("employment")  # Find datasets

# 3. Understand dataset
client.describe_dataset("lfst_r_lfsd2pop")  # Explore dimensions

# 4. Get data
df = client.get_data_as_dataframe(
    'lfst_r_lfsd2pop',
    geo=['SE11', 'DK01'],
    age='Y25-64',
    lastTimePeriod=5
)

print(df.head())

Language Support

This package returns all data and metadata in English only for consistency and simplicity.

Contributing

Contributions are welcome! Please see our contributing guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Dependency Licenses

EuStatsPy includes the following dependencies:

Runtime Dependencies:

• requests
• pandas

Development/Testing Dependencies (not distributed):

• pytest
• pytest-cov
• pytest-mock

All dependency licenses are available in the LICENSES/ directory.

Acknowledgments

• Data provided by Eurostat
• Built using the official Eurostat APIs