10 minutes to VTL Engine

The VTL Engine is a Python library that runs VTL 2.1 scripts against tabular data. You bring three things — a script, a description of your data, and the data itself — and the engine parses, type-checks, and executes the whole thing.

There are two main ways to call it, depending on where your data comes from:

run — for plain tabular workflows: pandas DataFrames or CSV files, with structures described in VTL JSON.
run_sdmx — the SDMX path: load your SDMX files through pysdmx and execute a VTL script against them in a single call. This is the recommended path if you’re working with SDMX data.

Tip

Already working with SDMX data? Skip to the Run SDMX section below for a complete “load an SDMX file → run a VTL transformation” example. For additional SDMX patterns (file inputs, dataflow mappings, TransformationScheme as the script), see SDMX Inputs.

Both methods accept exactly the same script. They differ in how the data structures and datapoints are supplied — plus a couple of related arguments you can read about under Optional below.

If you just want to validate a script without running it, see Semantic Analysis later on this page. The rest of the API — generate_sdmx(), validate_dataset(), validate_value_domain(), and validate_external_routine() — is documented in API.

What you need

Every run takes the same three required inputs, in the same conceptual roles, regardless of which method you use:

The VTL script — the transformations you want to apply. A Python str, a Path to a .vtl file, or a pysdmx TransformationScheme. See VTL Scripts.
The data structures — the description of every input dataset (what columns it has, of what types, in what role). The format depends on the method. See Data Structures.
The datapoints — the actual rows. Again, the format depends on the method. See Datapoints.

A few optional inputs let you extend or tune the run:

value_domains — codelists referenced by the in operator. See Extra Inputs.
external_routines — SQL snippets invoked from inside eval. See Extra Inputs.
scalar_values — scalar inputs to the script (run only). See Example 2 below.
output_folder — write results to disk instead of returning them in memory.
sdmx_mappings — alias an SDMX short-URN to whatever name the script uses (exposed as mappings in run_sdmx). See SDMX Inputs.

Run

Use vtlengine.run() when your data is in pandas DataFrames or CSV files.

Note

If you’re working with SDMX data, prefer run_sdmx — it’s the idiomatic path. run can also read SDMX inputs directly (file paths and pysdmx structure objects), but you have to pass structure and data as two separate arguments and manage the mapping yourself. See SDMX Inputs if you need to go that route.

For run you pass the structure and the data separately:

Data structures — a VTL JSON dict or a Path to a .json file. If you have SDMX structure files or pysdmx objects instead, the engine accepts those too — see SDMX Inputs.
Datapoints — a dict keyed by dataset name, where each value is a pandas.DataFrame or a Path to a plain CSV file. For SDMX data files (SDMX-ML, SDMX-CSV), see SDMX Inputs.

The method returns a dictionary of all generated datasets, keyed by their VTL output name. If you’d rather have the results written to disk than held in memory, set output_folder.

Before executing, run performs two validations — either can raise an error and stop the run:

Semantic analysis — the same check vtlengine.semantic_analysis() performs, applied to your script against the declared structures.
Data load analysis — a basic check that the data’s column names and types line up with the structures.

Example 1: Simple run

from vtlengine import run
import pandas as pd

script = """
    DS_A <- DS_1 * 10;
"""

data_structures = {
    'datasets': [
        {'name': 'DS_1',
         'DataStructure': [
             {'name': 'Id_1',
              'type':
                  'Integer',
              'role': 'Identifier',
              'nullable': False},
             {'name': 'Me_1',
              'type': 'Number',
              'role': 'Measure',
              'nullable': True}
         ]
         }
    ]
}

data_df = pd.DataFrame(
    {"Id_1": [1, 2, 3],
     "Me_1": [10, 20, 30]})

datapoints = {"DS_1": data_df}

run_result = run(script=script, data_structures=data_structures,
                 datapoints=datapoints)

print(run_result["DS_A"].data)

Returns:
Id_1	Me_1
1	100.0
2	200.0
3	300.0

Note

run returns a dict keyed by output name. Each value is a Dataset (with .data as a pandas.DataFrame) or a Scalar (with .value). The example above accesses run_result["DS_A"].data to get the DataFrame.

Example 2: Run with Scalar Values

run supports scalar inputs to the script via the scalar_values parameter. When an output_folder is provided, the engine generates CSV files containing the results of the script execution. Scalar results are saved as a CSV file containing all resulting scalar values.

from vtlengine import run
import pandas as pd

script = """
    DS_r <- DS_1[filter Me_1 = Sc_1];
    Sc_r <- Sc_1 + 10;
    Sc_r2 <- Sc_r * 2;
    Sc_r3 <- null;
"""

data_structures = {
    "datasets": [
        {
            "name": "DS_1",
            "DataStructure": [
                {"name": "Id_1", "type": "Integer", "role": "Identifier", "nullable": False},
                {"name": "Me_1", "type": "Number", "role": "Measure", "nullable": True},
            ],
        }
    ],
    "scalars": [
        {
            "name": "Sc_1",
            "type": "Number",
        }
    ],
}

data_df = pd.DataFrame({"Id_1": [1, 2, 3], "Me_1": [10, 20, 30]})
datapoints = {"DS_1": data_df}
scalars = {"Sc_1": 20}

run_result = run(
    script=script,
    data_structures=data_structures,
    datapoints=datapoints,
    scalar_values=scalars,
    return_only_persistent=True
)

print(run_result)

Returns:

Id_1	Me_1
2	20.0

Name	Value
Sc_r	30
Sc_r2	60
Sc_r3

Run SDMX

Use vtlengine.run_sdmx() when you’re working with SDMX data. In SDMX, data and structure are inseparable — every dataset arrives with its structure attached. pysdmx mirrors this by representing each dataset as a single object (the class is called PandasDataset in its model) that carries the rows and the structure together. You hand those objects directly to run_sdmx, which unpacks the bundles internally and delegates execution to vtlengine.run().

If you’re curious about the internals: each dataset exposes its rows as .data (a pandas.DataFrame) and its structure as .structure (a pysdmx Schema). When the Schema is present, the engine auto-casts the DataFrame columns to the declared component types, so you don’t have to.

For the common case — your input list contains a single dataset and your script references a single input — the engine auto-matches them. Whatever name your script uses for the input becomes the alias for that dataset; no mappings argument is required.

If your script references multiple inputs, or your input list contains datasets the script doesn’t all use, you must pass a mappings argument: a dict mapping each SDMX short-URN to a VTL alias, or a pysdmx VtlDataflowMapping object.

Note

Multiple datasets in one source. The 1-to-1 auto-match only works when both the input list and the script’s inputs have exactly one entry each. As soon as either side has more than one, you must pass an explicit mappings argument: every mapped dataset must appear in the script (extras error out), and every script input must be covered by the mapping.

For details on reading and writing SDMX datasets, see the pysdmx documentation.

Important

The short-urn is the meaningful part of the URN. The format is: SDMX_type=Agency:ID(Version).

Example:

Dataflow=MD:TEST_DF(1.0) is the short-urn for urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=MD:TEST_DF(1.0)

Example 3: Run from SDMX Dataset

The example below loads a small SDMX-ML structure file and a matching data file, then applies a single VTL transformation that adds a new measure Me_4 equal to the existing OBS_VALUE column.

The structure file defines one Dataflow=MD:TEST_DF(1.0) with components DIM_1, DIM_2, and OBS_VALUE; the data file contains six observations. The script references the dataset as DS_1. Because there’s only one dataset in the list and the script references only one input, the engine auto-matches them — no mappings argument needed.

from pathlib import Path

from pysdmx.io import get_datasets

from vtlengine import run_sdmx

data = Path("docs/_static/data.xml")
structure = Path("docs/_static/metadata.xml")
datasets = get_datasets(data, structure)

# Add a new measure Me_4 with the same value as OBS_VALUE, and
# call the resulting dataset DS_r.
script = "DS_r <- DS_1 [calc Me_4 := OBS_VALUE];"
print(run_sdmx(script, datasets)['DS_r'].data)

Returns:
DIM_1	DIM_2	OBS_VALUE	Me_4
A	A	100	100
A	B	102	102
B	B	14206.490766	14206.490766
C	B	10130	10130
C	C
C	D	29929.036014	29929.036014

For VTL syntax reference, see the VTL 2.1 manual.

Files used in the example:

Semantic Analysis

The vtlengine.semantic_analysis() method validates the correctness of a VTL script and computes the data structures of the datasets generated by the script itself (a prerequisite for semantic analysis).

If the VTL script is correct, the method returns a dictionary containing the data structures of all datasets generated by the script.
If the VTL script is incorrect, a SemanticError is raised.

The data_structures parameter accepts multiple formats:

VTL JSON format: Dictionaries or paths to .json files
SDMX structure files: Paths to SDMX-ML (.xml) or SDMX-JSON (.json) files
pysdmx objects: Schema, DataStructureDefinition, or Dataflow objects
Mixed lists: Any combination of the above formats

Example 4: Correct VTL

from vtlengine import semantic_analysis

script = """
    DS_A <- DS_1 * 10;
"""

data_structures = {
    'datasets': [
        {'name': 'DS_1',
         'DataStructure': [
             {'name': 'Id_1',
              'type':
                  'Integer',
              'role': 'Identifier',
              'nullable': False},
             {'name': 'Me_1',
              'type': 'Number',
              'role': 'Measure',
              'nullable': True}
         ]
         }
    ]
}

sa_result = semantic_analysis(script=script, data_structures=data_structures)

print(sa_result)

Returns:

{'DS_A': Dataset(name='DS_A', components={'Id_1': Component(name='Id_1', data_type="Integer", role="Identifier", nullable=False), 'Me_1': Component(name='Me_1', data_type="Number", role="Measure", nullable=True)}, data=None)}

Example 5: Incorrect VTL

Compared to Example 4, the only difference is that Me_1 uses a String data type instead of Number.

from vtlengine import semantic_analysis

script = """
    DS_A <- DS_1 * 10;
"""

data_structures = {
    'datasets': [
        {'name': 'DS_1',
         'DataStructure': [
             {'name': 'Id_1',
              'type':
                  'Integer',
              'role': 'Identifier',
              'nullable': False},
             {'name': 'Me_1',
              'type': 'String',
              'role': 'Measure',
              'nullable': True}
         ]
         }
    ]
}

sa_result = semantic_analysis(script=script, data_structures=data_structures)

print(sa_result)

Raises the following Error:

raise SemanticError(code="1-1-1-2",
vtlengine.Exceptions.SemanticError: ('Invalid implicit cast from String and Integer to Number.', '1-1-1-2')

Example 6: Using SDMX Structures

The semantic_analysis function can also accept SDMX structure files or pysdmx objects:

from pathlib import Path

from vtlengine import semantic_analysis

script = """
    DS_A <- DS_1 * 10;
"""

# Using an SDMX-ML structure file
sdmx_structure = Path("path/to/structure.xml")

sa_result = semantic_analysis(script=script, data_structures=sdmx_structure)

print(sa_result)

Using pysdmx objects directly:

from pathlib import Path

from pysdmx.io import read_sdmx

from vtlengine import semantic_analysis

script = """
    DS_A <- DS_1 * 10;
"""

# Load structure using pysdmx
msg = read_sdmx(Path("path/to/structure.xml"))
dsds = msg.get_data_structure_definitions()

sa_result = semantic_analysis(script=script, data_structures=dsds)

print(sa_result)

Prettify

The vtlengine.prettify() method formats a VTL script to improve readability.

from vtlengine import prettify
script = """
    define hierarchical ruleset accountingEntry (variable rule ACCOUNTING_ENTRY) is
                    B = C - D errorcode "Balance (credit-debit)" errorlevel 4;
                    N = A - L errorcode "Net (assets-liabilities)" errorlevel 4
                end hierarchical ruleset;

    DS_r <- check_hierarchy(BOP, accountingEntry rule ACCOUNTING_ENTRY dataset);
    """
prettified_script = prettify(script)
print(prettified_script)

Returns:

define hierarchical ruleset accountingEntry(variable rule ACCOUNTING_ENTRY) is
    B = C - D
    errorcode "Balance (credit-debit)"
    errorlevel 4;

    N = A - L
    errorcode "Net (assets-liabilities)"
    errorlevel 4
end hierarchical ruleset;

DS_r <-
    check_hierarchy(
        BOP,
        accountingEntry,
        rule ACCOUNTING_ENTRY);

For more information on usage, please refer to the API documentation