Fetching Data with ukpyn¶

This tutorial covers advanced data retrieval techniques:

1. Understanding the OpenDataSoft Query Language (ODSQL)
2. Filtering records with WHERE clauses
3. Sorting and ordering results
4. Pagination for large datasets
5. Field selection and projection
6. Working with facets and refinements

Prerequisites: Complete 01-getting-started.ipynb first.

Note: These tutorials require additional dependencies. Install them with pip install "ukpyn[all]" — see Tutorial 01 for full setup instructions.

In [ ]:

import ukpyn

ukpyn.check_api_key()
print("API key configured!")

import ukpyn ukpyn.check_api_key() print("API key configured!")

1. Understanding ODSQL¶

OpenDataSoft Query Language (ODSQL) is a SQL-like language for filtering and querying data.

Key Operators¶

Operator	Description	Example
=	Equal	field = 'value'
!=	Not equal	field != 'value'
>, >=	Greater than	count > 100
<, <=	Less than	count <= 50
IN	In list	status IN ('active', 'pending')
LIKE	Pattern match	name LIKE '%London%'
AND, OR	Logical	a = 1 AND b = 2
NOT	Negation	NOT status = 'deleted'

Date Functions¶

Function	Description	Example
date(field)	Extract date	date(timestamp) = '2024-01-01'
year(field)	Extract year	year(timestamp) = 2024
month(field)	Extract month	month(timestamp) >= 6

2. Filtering Records with WHERE Clauses¶

The where parameter accepts ODSQL expressions to filter records.

In [ ]:

# First, let's find a dataset to work with
from ukpyn import UKPNClient

async with UKPNClient() as client:
    # List available datasets
    datasets = await client.list_datasets(limit=20)

    print(f"Available datasets ({datasets.total_count} total):\n")

    for item in datasets.datasets:
        ds = item.dataset
        title = ""
        if ds.metas and ds.metas.default:
            title = ds.metas.default.get("title", ds.dataset_id)
        print(f"- {ds.dataset_id}")
        if title and title != ds.dataset_id:
            print(f"  Title: {title}")

# First, let's find a dataset to work with from ukpyn import UKPNClient async with UKPNClient() as client: # List available datasets datasets = await client.list_datasets(limit=20) print(f"Available datasets ({datasets.total_count} total):\n") for item in datasets.datasets: ds = item.dataset title = "" if ds.metas and ds.metas.default: title = ds.metas.default.get("title", ds.dataset_id) print(f"- {ds.dataset_id}") if title and title != ds.dataset_id: print(f" Title: {title}")

In [ ]:

# Let's examine a dataset's fields to understand what we can filter on
# Replace with a dataset ID from the list above
DATASET_ID = "ukpn-smart-meter-installation-volumes"  # Example - adjust as needed

async with UKPNClient() as client:
    try:
        dataset = await client.get_dataset(DATASET_ID)

        print(f"Dataset: {dataset.dataset_id}")
        print("\nAvailable fields for filtering:")
        print("-" * 50)

        if dataset.fields:
            for field in dataset.fields:
                print(f"  {field.name:<30} ({field.type})")
                if field.description:
                    print(f"    └─ {field.description[:60]}...")
    except Exception as e:
        print(f"Error: {e}")
        print("\nTip: Replace DATASET_ID with a valid dataset from the list above.")

# Let's examine a dataset's fields to understand what we can filter on # Replace with a dataset ID from the list above DATASET_ID = "ukpn-smart-meter-installation-volumes" # Example - adjust as needed async with UKPNClient() as client: try: dataset = await client.get_dataset(DATASET_ID) print(f"Dataset: {dataset.dataset_id}") print("\nAvailable fields for filtering:") print("-" * 50) if dataset.fields: for field in dataset.fields: print(f" {field.name:<30} ({field.type})") if field.description: print(f" └─ {field.description[:60]}...") except Exception as e: print(f"Error: {e}") print("\nTip: Replace DATASET_ID with a valid dataset from the list above.")

In [ ]:

# Example: Basic filtering with WHERE
# Adjust the field names based on your chosen dataset

async with UKPNClient() as client:
    try:
        # Filter with a simple condition
        # Example: filter by a text field
        records = await client.get_records(
            DATASET_ID,
            limit=5,
            # where="local_authority = 'Surrey'",  # Uncomment and adjust
        )

        print(f"Found {records.total_count} records")
        print(f"\nFirst {len(records.records)} records:")

        for i, record in enumerate(records.records, 1):
            print(f"\n[{i}] ID: {record.id}")
            if record.fields:
                for key, value in list(record.fields.items())[:5]:
                    print(f"    {key}: {value}")

    except Exception as e:
        print(f"Error: {e}")

# Example: Basic filtering with WHERE # Adjust the field names based on your chosen dataset async with UKPNClient() as client: try: # Filter with a simple condition # Example: filter by a text field records = await client.get_records( DATASET_ID, limit=5, # where="local_authority = 'Surrey'", # Uncomment and adjust ) print(f"Found {records.total_count} records") print(f"\nFirst {len(records.records)} records:") for i, record in enumerate(records.records, 1): print(f"\n[{i}] ID: {record.id}") if record.fields: for key, value in list(record.fields.items())[:5]: print(f" {key}: {value}") except Exception as e: print(f"Error: {e}")

In [ ]:

# Example: Combining multiple conditions with AND/OR
TABLE_3A_ID = "ltds-table-3a-load-data-observed-transposed"
async with UKPNClient() as client:
    try:
        # Multiple conditions
        # Example: combining filters
        records = await client.get_records(
            TABLE_3A_ID,
            limit=5,
            where="year = '25-26' AND season = 'Winter'",  # Adjust to your dataset
        )

        print(f"Filtered results: {records.total_count} records")

    except Exception as e:
        print(f"Error: {e}")
        print("\nTip: Adjust the WHERE clause to match your dataset's fields.")

# Example: Combining multiple conditions with AND/OR TABLE_3A_ID = "ltds-table-3a-load-data-observed-transposed" async with UKPNClient() as client: try: # Multiple conditions # Example: combining filters records = await client.get_records( TABLE_3A_ID, limit=5, where="year = '25-26' AND season = 'Winter'", # Adjust to your dataset ) print(f"Filtered results: {records.total_count} records") except Exception as e: print(f"Error: {e}") print("\nTip: Adjust the WHERE clause to match your dataset's fields.")

3. Sorting and Ordering Results¶

Use order_by to sort records:

• field_name - Ascending order (A-Z, 0-9)
• -field_name - Descending order (Z-A, 9-0)

In [ ]:

# Sorting examples
CONSTRAINTS_DATA_ID = "ukpn-constraints-real-time-meter-readings"
async with UKPNClient() as client:
    try:
        # Sort by record timestamp (newest first)
        records = await client.get_records(
            CONSTRAINTS_DATA_ID,
            limit=5,
            # order_by="-timestamp",  # Descending (newest first)
        )

        print("Records sorted by timestamp (newest first):")
        print("-" * 50)
        print(f"Total records: {records.total_count}")
        print(records.records[0].fields if records.records else "No records found")
        for record in records.records:
            # Access timestamp from fields dict (it's a data field, not metadata)
            timestamp = record.fields.get("timestamp") if record.fields else "N/A"
            constraint_id = (
                record.fields.get("constraint_id") if record.fields else "N/A"
            )
            print(f"  {constraint_id}: {timestamp}")

    except Exception as e:
        print(f"Error: {e}")

# Sorting examples CONSTRAINTS_DATA_ID = "ukpn-constraints-real-time-meter-readings" async with UKPNClient() as client: try: # Sort by record timestamp (newest first) records = await client.get_records( CONSTRAINTS_DATA_ID, limit=5, # order_by="-timestamp", # Descending (newest first) ) print("Records sorted by timestamp (newest first):") print("-" * 50) print(f"Total records: {records.total_count}") print(records.records[0].fields if records.records else "No records found") for record in records.records: # Access timestamp from fields dict (it's a data field, not metadata) timestamp = record.fields.get("timestamp") if record.fields else "N/A" constraint_id = ( record.fields.get("constraint_id") if record.fields else "N/A" ) print(f" {constraint_id}: {timestamp}") except Exception as e: print(f"Error: {e}")

In [ ]:

# Sort by a specific field
# Adjust the field name to match your dataset
POLES_DATASET_ID = "ukpn-132kv-poles-towers"
async with UKPNClient() as client:
    try:
        # Sort ascending
        records_asc = await client.get_records(
            POLES_DATASET_ID,
            limit=3,
            # order_by="field_name",  # Ascending
        )

        # Sort descending
        records_desc = await client.get_records(
            POLES_DATASET_ID,
            limit=3,
            # order_by="-field_name",  # Descending
        )

        print("Ascending order:")
        for r in records_asc.records:
            print(f"  {r.id}")

        print("\nDescending order:")
        for r in records_desc.records:
            print(f"  {r.id}")

    except Exception as e:
        print(f"Error: {e}")

# Sort by a specific field # Adjust the field name to match your dataset POLES_DATASET_ID = "ukpn-132kv-poles-towers" async with UKPNClient() as client: try: # Sort ascending records_asc = await client.get_records( POLES_DATASET_ID, limit=3, # order_by="field_name", # Ascending ) # Sort descending records_desc = await client.get_records( POLES_DATASET_ID, limit=3, # order_by="-field_name", # Descending ) print("Ascending order:") for r in records_asc.records: print(f" {r.id}") print("\nDescending order:") for r in records_desc.records: print(f" {r.id}") except Exception as e: print(f"Error: {e}")

4. Pagination for Large Datasets¶

Large datasets require pagination. Use limit and offset to page through results.

Page 1: offset=0,  limit=10  -> records 1-10
Page 2: offset=10, limit=10  -> records 11-20
Page 3: offset=20, limit=10  -> records 21-30

In [ ]:

# Basic pagination example

async with UKPNClient() as client:
    page_size = 10

    # Get first page
    page1 = await client.get_records(
        DATASET_ID,
        limit=page_size,
        offset=0,
    )

    total_records = page1.total_count
    total_pages = (total_records + page_size - 1) // page_size

    print(f"Dataset: {DATASET_ID}")
    print(f"Total records: {total_records}")
    print(f"Page size: {page_size}")
    print(f"Total pages: {total_pages}")
    print(f"\nPage 1 has {len(page1.records)} records")

# Basic pagination example async with UKPNClient() as client: page_size = 10 # Get first page page1 = await client.get_records( DATASET_ID, limit=page_size, offset=0, ) total_records = page1.total_count total_pages = (total_records + page_size - 1) // page_size print(f"Dataset: {DATASET_ID}") print(f"Total records: {total_records}") print(f"Page size: {page_size}") print(f"Total pages: {total_pages}") print(f"\nPage 1 has {len(page1.records)} records")

In [ ]:

# Iterate through multiple pages

async with UKPNClient() as client:
    page_size = 10
    max_pages = 3  # Limit for this example

    all_records = []

    for page_num in range(max_pages):
        offset = page_num * page_size

        response = await client.get_records(
            DATASET_ID,
            limit=page_size,
            offset=offset,
        )

        records = response.records
        all_records.extend(records)

        print(
            f"Page {page_num + 1}: Retrieved {len(records)} records (offset={offset})"
        )

        # Stop if we've retrieved all records
        if len(records) < page_size:
            break

    print(f"\nTotal records collected: {len(all_records)}")

# Iterate through multiple pages async with UKPNClient() as client: page_size = 10 max_pages = 3 # Limit for this example all_records = [] for page_num in range(max_pages): offset = page_num * page_size response = await client.get_records( DATASET_ID, limit=page_size, offset=offset, ) records = response.records all_records.extend(records) print( f"Page {page_num + 1}: Retrieved {len(records)} records (offset={offset})" ) # Stop if we've retrieved all records if len(records) < page_size: break print(f"\nTotal records collected: {len(all_records)}")

In [ ]:

# Helper function for paginated retrieval


async def fetch_all_records(
    client: UKPNClient,
    dataset_id: str,
    page_size: int = 100,
    max_records: int = 1000,
    where: str = None,
):
    """
    Fetch multiple pages of records from a dataset.

    Args:
        client: UKPNClient instance
        dataset_id: Dataset to query
        page_size: Records per page (max 100)
        max_records: Maximum total records to fetch
        where: Optional filter expression

    Returns:
        List of all fetched records
    """
    all_records = []
    offset = 0

    while len(all_records) < max_records:
        response = await client.get_records(
            dataset_id,
            limit=min(page_size, max_records - len(all_records)),
            offset=offset,
            where=where,
        )

        records = response.records
        if not records:
            break

        all_records.extend(records)
        offset += len(records)

        # Stop if we've reached the end
        if len(records) < page_size or len(all_records) >= response.total_count:
            break

    return all_records


# Example usage
async with UKPNClient() as client:
    records = await fetch_all_records(
        client,
        DATASET_ID,
        page_size=50,
        max_records=200,
    )
    print(f"Fetched {len(records)} records total")

# Helper function for paginated retrieval async def fetch_all_records( client: UKPNClient, dataset_id: str, page_size: int = 100, max_records: int = 1000, where: str = None, ): """ Fetch multiple pages of records from a dataset. Args: client: UKPNClient instance dataset_id: Dataset to query page_size: Records per page (max 100) max_records: Maximum total records to fetch where: Optional filter expression Returns: List of all fetched records """ all_records = [] offset = 0 while len(all_records) < max_records: response = await client.get_records( dataset_id, limit=min(page_size, max_records - len(all_records)), offset=offset, where=where, ) records = response.records if not records: break all_records.extend(records) offset += len(records) # Stop if we've reached the end if len(records) < page_size or len(all_records) >= response.total_count: break return all_records # Example usage async with UKPNClient() as client: records = await fetch_all_records( client, DATASET_ID, page_size=50, max_records=200, ) print(f"Fetched {len(records)} records total")

5. Field Selection and Projection¶

Use select to retrieve only specific fields. This reduces response size and improves performance.

In [ ]:

# Select specific fields only

async with UKPNClient() as client:
    # First, get all fields to see what's available
    dataset = await client.get_dataset(DATASET_ID)

    if dataset.fields:
        field_names = [f.name for f in dataset.fields[:5]]  # First 5 fields
        print(f"Selecting fields: {field_names}")

        # Query with field selection
        records = await client.get_records(
            DATASET_ID,
            limit=3,
            select=", ".join(field_names),  # Comma-separated field names
        )

        print("\nRecords (with selected fields only):")
        for record in records.records:
            print(f"\n  {record.id}:")
            if record.fields:
                for key, value in record.fields.items():
                    print(f"    {key}: {value}")

# Select specific fields only async with UKPNClient() as client: # First, get all fields to see what's available dataset = await client.get_dataset(DATASET_ID) if dataset.fields: field_names = [f.name for f in dataset.fields[:5]] # First 5 fields print(f"Selecting fields: {field_names}") # Query with field selection records = await client.get_records( DATASET_ID, limit=3, select=", ".join(field_names), # Comma-separated field names ) print("\nRecords (with selected fields only):") for record in records.records: print(f"\n {record.id}:") if record.fields: for key, value in record.fields.items(): print(f" {key}: {value}")

In [ ]:

# Compare response sizes with and without field selection
import json

async with UKPNClient() as client:
    # Fetch without selection (all fields)
    full_records = await client.get_records(DATASET_ID, limit=10)

    # Fetch with selection (fewer fields)
    dataset = await client.get_dataset(DATASET_ID)
    if dataset.fields and len(dataset.fields) >= 2:
        two_fields = f"{dataset.fields[0].name}, {dataset.fields[1].name}"
        selected_records = await client.get_records(
            DATASET_ID,
            limit=10,
            select=two_fields,
        )

        # Estimate size difference
        full_size = sum(
            len(json.dumps(r.fields)) for r in full_records.records if r.fields
        )
        selected_size = sum(
            len(json.dumps(r.fields)) for r in selected_records.records if r.fields
        )

        print(f"All fields: ~{full_size} bytes")
        print(f"Selected fields ({two_fields}): ~{selected_size} bytes")
        print(f"Reduction: {100 - (selected_size / full_size * 100):.1f}%")

# Compare response sizes with and without field selection import json async with UKPNClient() as client: # Fetch without selection (all fields) full_records = await client.get_records(DATASET_ID, limit=10) # Fetch with selection (fewer fields) dataset = await client.get_dataset(DATASET_ID) if dataset.fields and len(dataset.fields) >= 2: two_fields = f"{dataset.fields[0].name}, {dataset.fields[1].name}" selected_records = await client.get_records( DATASET_ID, limit=10, select=two_fields, ) # Estimate size difference full_size = sum( len(json.dumps(r.fields)) for r in full_records.records if r.fields ) selected_size = sum( len(json.dumps(r.fields)) for r in selected_records.records if r.fields ) print(f"All fields: ~{full_size} bytes") print(f"Selected fields ({two_fields}): ~{selected_size} bytes") print(f"Reduction: {100 - (selected_size / full_size * 100):.1f}%")

6. Working with Facets and Refinements¶

Facets allow you to filter by categorical values. Use refine and exclude parameters.

In [ ]:

# Refine results by a facet value

async with UKPNClient() as client:
    try:
        # Refine by a specific value
        # Adjust the facet name and value for your dataset
        records = await client.get_records(
            DATASET_ID,
            limit=5,
            # refine={"region": "London"},  # Uncomment and adjust
        )

        print(f"Found {records.total_count} records")

    except Exception as e:
        print(f"Error: {e}")

# Refine results by a facet value async with UKPNClient() as client: try: # Refine by a specific value # Adjust the facet name and value for your dataset records = await client.get_records( DATASET_ID, limit=5, # refine={"region": "London"}, # Uncomment and adjust ) print(f"Found {records.total_count} records") except Exception as e: print(f"Error: {e}")

In [ ]:

# Exclude specific facet values

async with UKPNClient() as client:
    try:
        # Exclude records with specific value
        records = await client.get_records(
            DATASET_ID,
            limit=5,
            # exclude={"status": "inactive"},  # Uncomment and adjust
        )

        print(f"Found {records.total_count} records (excluding specified values)")

    except Exception as e:
        print(f"Error: {e}")

# Exclude specific facet values async with UKPNClient() as client: try: # Exclude records with specific value records = await client.get_records( DATASET_ID, limit=5, # exclude={"status": "inactive"}, # Uncomment and adjust ) print(f"Found {records.total_count} records (excluding specified values)") except Exception as e: print(f"Error: {e}")

7. Combining Techniques¶

You can combine filtering, sorting, selection, and pagination for powerful queries.

In [ ]:

# Combined query example

async with UKPNClient() as client:
    try:
        # Get dataset fields
        dataset = await client.get_dataset(DATASET_ID)

        if dataset.fields:
            # Select first 3 fields
            fields_to_select = [f.name for f in dataset.fields[:3]]

            # Combined query
            records = await client.get_records(
                DATASET_ID,
                limit=10,
                offset=0,
                select=", ".join(fields_to_select),
                order_by="-record_timestamp",
                # where="year >= 2023",  # Uncomment and adjust
            )

            print(f"Query: SELECT {', '.join(fields_to_select)}")
            print("       ORDER BY -record_timestamp")
            print("       LIMIT 10 OFFSET 0")
            print(
                f"\nResults: {records.total_count} total, showing {len(records.records)}"
            )
            print("-" * 60)

            for record in records.records[:5]:
                print(f"\n{record.id}:")
                if record.fields:
                    for key, value in record.fields.items():
                        print(f"  {key}: {value}")

    except Exception as e:
        print(f"Error: {e}")

# Combined query example async with UKPNClient() as client: try: # Get dataset fields dataset = await client.get_dataset(DATASET_ID) if dataset.fields: # Select first 3 fields fields_to_select = [f.name for f in dataset.fields[:3]] # Combined query records = await client.get_records( DATASET_ID, limit=10, offset=0, select=", ".join(fields_to_select), order_by="-record_timestamp", # where="year >= 2023", # Uncomment and adjust ) print(f"Query: SELECT {', '.join(fields_to_select)}") print(" ORDER BY -record_timestamp") print(" LIMIT 10 OFFSET 0") print( f"\nResults: {records.total_count} total, showing {len(records.records)}" ) print("-" * 60) for record in records.records[:5]: print(f"\n{record.id}:") if record.fields: for key, value in record.fields.items(): print(f" {key}: {value}") except Exception as e: print(f"Error: {e}")

Summary¶

You've learned how to:

• Use ODSQL for filtering with where clauses
• Sort results with order_by (ascending/descending)
• Paginate through large datasets with limit and offset
• Select specific fields with select to improve performance
• Use facet refinement with refine and exclude

Next Steps¶

• Check out 03-analysis-patterns.ipynb for data analysis workflows with pandas
• Explore the ODSQL documentation) for advanced queries
• Try building your own queries on the UK Power Networks datasets!