How to Plug-in Various SDMX Data Formats into .Stat Suite
George VerouchisAlexandros Polichronopoulos
Introduction
Integrating diverse data sources can feel like wrangling a wild beast, especially when it comes to SDMX formats. We faced this challenge head-on at a Central Bank in Europe, wrestling with over 150 datasets from five different SDMX data providers. Using text mining techniques and the powerful .Stat Suite tool, we turned this data chaos into streamlined clarity. Our efforts boosted the decision-making process and proved that with the right tools, even the toughest data challenges can be conquered. This article is addressed to readers already familiar with SDMX terms and have an essential understanding of Python.
The Challenge
The primary challenge encountered is the inconsistency in SDMX data formats across different sources. This discrepancy hampers the seamless integration and compatibility of these datasets with the .Stat Suite, a tool primarily designed for handling OECD data. The Test Compliance Kit (TCK) was employed to assess how well various SDMX data formats align with the requirements of the .Stat Suite. Our findings highlight the following issues:
These results highlighted significant inconsistencies across different vendors. For instance, while the ECB’s service showed relatively high compliance and coverage, ESTAT and IMF services exhibited much lower compatibility.
Methodology
To integrate multiple SDMX data sources into the .Stat Suite, we used a combination of regular expressions (regex) and the ElementTree (etree) library in Python. Here’s a step-by-step breakdown of our approach:
1. Data Extraction and Cleaning with Regex
We started with regex in Python for initial data extraction and cleaning. Regex is powerful for pattern matching and text manipulation, which helped us identify and extract specific elements from SDMX datasets.
Key Steps:
- Initial Step: We began with regex in Python for data extraction and cleaning.
- Pattern Matching: Utilized regular expressions for powerful pattern matching and text manipulation.
- Element Identification: Defined precise regex patterns to identify and extract specific elements from SDMX datasets.
- XML Parsing: Parsed XML structures of SDMX files, focusing on key metadata and data elements required for further processing.
- Handling Diversity: This step was crucial for managing the heterogeneous formats and terminologies used by different data providers.
Sample Code:
import re
import xmltodict
def pretty_print_xml(xml_string):
had_xml_declaration = xml_string.startswith('<?xml')
xml_dict = xmltodict.parse(xml_string.strip())
formatted_xml = xmltodict.unparse(xml_dict, pretty=True)
if not had_xml_declaration:
formatted_xml = formatted_xml.replace('<?xml version="1.0" encoding="utf-8"?>\n', '')
return formatted_xml
def find_namespace(xml_string, element_name='Header', namespace=NAMESPACE_MESSAGE):
pattern_text = f'<({namespace})?{element_name}>'
pattern = re.compile(pattern_text, re.DOTALL)
match = re.search(pattern, xml_string)
if match:
return match.group(1).replace(':', '')
else:
return None
def extract_header(xml_string):
headers = extract_elements(xml_string, element_name='Header', namespace=NAMESPACE_MESSAGE)
if len(headers) >= 1:
return headers[0]
else:
return None
def extract_elements(xml_string, element_name='Header', namespace=NAMESPACE_MESSAGE):
pattern_text = f'<(?:{namespace})?{element_name}>.*?</(?:{namespace})?{element_name}>'
pattern = re.compile(pattern_text, re.DOTALL)
elements = re.findall(pattern, xml_string)
return elements
def add_element(xml_string, new_element, element_name='Structure', namespace='message'):
namespace = f'{namespace}:'.replace('::', ':') if namespace else ''
modified_xml = xml_string.replace(f'</{namespace}{element_name}>', f'{new_element}</{namespace}{element_name}>')
return modified_xml
def delete_element(xml_string, element_name='Structure', namespace=NAMESPACE_COMMON):
pattern_text = f'<(?:{namespace})?{element_name}>.*?</(?:{namespace})?{element_name}>'
pattern = re.compile(pattern_text, re.DOTALL)
modified_xml = pattern.sub('', xml_string)
return modified_xml
2. Parsing and Manipulating XML with ElementTree
a) Remove Embargo Time
Description: In the context of Statistical Data and Metadata eXchange (SDMX), “embargo time” is a period during which the release of data is temporarily restricted to ensure all users receive it simultaneously. This helps agencies manage the dissemination process and prepare precise reports. Eliminating embargo times allows data to be accessed and used immediately, improving efficiency and speed in data processing. However, adjusting embargo times can be challenging as it requires coordination among various stakeholders and ensuring that all users are prepared for immediate access. Importantly, this decision has no legal impact and is left to the discretion of the data user.
Example:
from lxml import etree as ET
def remove_embargo_time(xml_data):
root = ET.fromstring(xml_data)
namespaces = {
'mes': 'http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message',
'str': 'http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure'
}
# Find and remove the specific attribute element
for parent in root.findall(".//str:AttributeList", namespaces):
for attr in parent.findall(".//str:Attribute[@id='EMBARGO_TIME']", namespaces):
parent.remove(attr)
# Save the modified XML tree to a string
return ET.tostring(root, encoding='utf-8').decode()
Why It’s Useful: — Immediate access to data allows for faster analysis and reporting. — Ensures that time-sensitive decisions are made without unnecessary delays.
b) Auto-Generate Externally Defined Dataflows
Description: Generating externally defined dataflows involves creating data structures dynamically based on external definitions. This automation allows seamless integration with the .Stat Suite, ensuring all necessary dataflows are correctly formatted and available.
Example:
import requests
from lxml import etree as ET
def generate_dataflows(dataset_id, provider):
response = requests.get(provider.get_structure_url(dataset_id))
xml_data = response.text
# Parse the XML
tree = ET.fromstring(xml_data)
# Extract the version value from the <structure:Dataflow> element
dataflow_elem = tree.find('.//{http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure}Dataflow')
if dataflow_elem is not None:
version_value = dataflow_elem.get('version')
else:
version_value = "1.0"
# Find all parent elements of <ns0:Structure> elements
parent_elements = tree.findall('.//{http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure}Structure/..')
# Remove <ns0:Structure> elements along with their parent elements
for parent_elem in parent_elements:
parent_elem.remove(parent_elem.find('.//{http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure}Structure'))
# Add attributes to the Dataflow element
for dataflow_elem in tree.iter('{http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure}Dataflow'):
dataflow_elem.set('isExternalReference', 'true')
link = provider.get_structure_url(dataset_id) + '/' + str(version_value)
dataflow_elem.set('structureURL', link)
# Convert the modified XML back to string
return ET.tostring(tree, encoding='utf-8').decode()
Why It’s Useful:
- Seamless Integration: Automatically generating dataflows ensures that all necessary data structures are available and correctly formatted for the .Stat Suite.
- Efficiency: Reduces manual effort and potential errors in creating dataflows, making the integration process more efficient and reliable.
- Scalability: Facilitates the addition of new dataflows as needed without extensive manual reconfiguration.
Conclusions
By leveraging regex and the etree library in Python, we standardized over 150 datasets, improving their quality, consistency, and accessibility. Removing embargo times allowed for immediate data processing, and auto-generating dataflows ensured seamless integration with the .Stat Suite. These enhancements led to faster data analysis and more informed decision-making processes.