Pre-release

This release is a pre-release and may not be stable for production use.

About:

  • Parses CIM RDF/XML data to pandas dataframe with 4 columns [ID, KEY, VALUE, INSTANCE_ID] (triplestore like)
  • The solution does not care about CIM version nor namespaces
  • Input files can be xml or zip files (containing one or mutiple xml files)
  • All files are parsed into one and same Pandas DataFrame, thus if you want single file or single data model, you need to filter on INSTANCE_ID column

Documentation:

https://haigutus.github.io/triplets

Upgrading from 0.0.x? See docs/migration_0.0_to_0.1.md.

To get started:

# Core (python_lxml_pandas engine, no extra deps)
pip install triplets

# With pyarrow (enables python_lxml_arrow + cython_pugixml_arrow engines, ~12x faster)
pip install triplets[arrow]

Install extras by feature:

Extra Enables
arrow compiled Arrow parser engines (~12x faster parsing)
polars polars DataFrames (polars.read_rdf, .triplets namespace)
duckdb DuckDB connections (con.read_rdf, SQL over triplets)
sparql SPARQL queries (rdflib reference engine)
oxigraph recommended pip performance path — embedded Rust SPARQL engine (auto-preferred over rdflib)
validation SHACL validation (pyshacl reference engine)
excel / networkx / visualization Excel export / graph export / drawing

The embedded qlever SPARQL engine (fastest) ships in no wheel — it is a local source build, see docs/building.md.

import pandas
import triplets

path = "CGMES_v2.4.15_RealGridTestConfiguration_v2.zip"
data = pandas.read_RDF([path])

Result:

image

You can then query a dataframe of all same type elements and its parameters across all [EQ, SSH, TP, SV etc.] instance files, where parameters are columns and index is object ID-s

data.tableview_by_type("ACLineSegment")

image

Export:

data.export_to_cimxml(
    rdf_map=schemas.ENTSOE_CGMES_2_4_15_552_ED1,
    export_type=ExportType.XML_PER_INSTANCE_ZIP_PER_XML,
)

Look into examples folders for more

Parser engines

Three parser engines with automatic fallback (fastest available):

Engine Install Speed
python_lxml_pandas pip install triplets 1x baseline, always works
python_lxml_arrow pip install triplets[arrow] ~1x, better interop
cython_pugixml_arrow pip install triplets[arrow] (included in wheels) 12x faster

The cython_pugixml_arrow engine is a compiled C++ extension included in published wheels. It requires pyarrow at runtime, so install with triplets[arrow] to enable it.

The cython engine is pre-built in published wheels — no compilation needed.

Polars

import polars
import triplets

data = polars.read_rdf(["grid_EQ.xml", "data.zip"])   # returns polars DataFrame

data.triplets.get_types_count()
data.triplets.tableview_by_type("ACLineSegment")
data.triplets.filter_triplets(KEY="Type", VALUE=".*Generator.*", regex=True)
data.triplets.export_to_csv(export_to_memory=True)
data.triplets.export_to_nquads("/tmp/output.nq")

DuckDB

import duckdb
import triplets

data = duckdb.connect()                              # in-memory
data = duckdb.connect("grid.duckdb")                 # persistent (no re-parsing next session)

data.read_rdf(["grid_EQ.xml", "data.zip"])           # parse via Arrow (zero-copy into DuckDB)
data.get_types_count()                                     # → dict
data.tableview_by_type("ACLineSegment").df()             # → pandas DataFrame
data.tableview_by_type("ACLineSegment").pl()             # → polars DataFrame
data.filter_triplets(KEY="Type", VALUE=".*Sub.*", regex=True).df()
data.filter_triplets_by_type("Terminal").df()
data.references_to("some-uuid").df()
data.export_to_nquads("/tmp/output.nq")

# Direct SQL (full DuckDB SQL on the triplets table)
data.sql("SELECT VALUE, COUNT(*) FROM triplets WHERE KEY = 'Type' GROUP BY VALUE").df()

# The same tools are also on the `.triplets` namespace (parity with pandas/polars)
data.triplets.tableview_by_type("ACLineSegment").df()
data.triplets.get_types_count()

SPARQL queries

SPARQL 1.1 over the loaded data — SELECT → DataFrame, ASK → bool, CONSTRUCT → triplet DataFrame. Works on pandas, polars and DuckDB inputs:

PREFIXES = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX cim: <http://iec.ch/TC57/CIM100#>
"""
names = data.sparql.query(PREFIXES + "SELECT ?s ?name WHERE { ?s cim:IdentifiedObject.name ?name }")

Three engines behind one API (auto picks the fastest available):

Engine Install Role
qlever local source build (docs/building.md) fastest — embedded C++, persistent on-disk index
oxigraph pip install triplets[oxigraph] embedded Rust — ~3x faster import, 2–5x faster queries than rdflib
rdflib pip install triplets[sparql] pure-Python reference

Details and measured numbers: docs/sparql.md.

SHACL validation

Validate against SHACL shape files; the result is a violations DataFrame (empty = conforms) with the same shape across all engines:

violations = data.shacl.validate("shapes.ttl", rdf_map=schemas.ENTSOE_CGMES_3_0_0_552_ED1)

# slower optional context pass: source file, object type/name,
# shape sh:name/sh:description, schema attribute/class definitions
violations = data.shacl.validate(shapes, context=True)

# SARIF 2.1.0 for GitHub / SonarQube / any SARIF viewer — grouped by default
# (one result per rule with occurrenceCount + sample instances)
violations.shacl.to_sarif(path="report.sarif")

Engines: polars (auto, real profiles in ~2 s) → pandas → pyshacl (reference); duckdb for larger-than-memory data. sh:sparql constraints ride the SPARQL engine above (minutes → milliseconds with oxigraph/qlever). Details: docs/validation.md.

Accessor namespace

pandas and polars DataFrames use df.triplets.*; a DuckDB connection uses con.triplets.*. The same method names are available on both (DuckDB returns relations — add .df() or .pl() when needed):

# pandas / polars
df.triplets.tableview_by_type("ACLineSegment")
df.triplets.export_to_nquads("/tmp/output.nq")

# DuckDB
con.triplets.tableview_by_type("ACLineSegment").df()
con.triplets.get_types_count()

Root-level methods (df.type_tableview(...), con.filter_triplets(...)) still work for backwards compatibility.

CLI tools

cim-spreadsheet -i model.xml -o output.xlsx
cim-diff original.xml modified.xml

Performance (RealGrid, 1.14M rows)

Operation pandas polars DuckDB
Parse (cython engine) 128ms 156ms 283ms
tableview_by_type 72ms 21ms 53ms
filter_triplets_by_type 103ms 9ms 50ms
get_types_count 21ms 11ms 18ms

The old rdf_parser.py functions still work but emit deprecation warnings. See docs/migration_0.0_to_0.1.md for renames and breaking changes.

Project links

Data verified by PyPI on Jul 16, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.2.0)

1 maintainer

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Avatar for Haigutus from gravatar.com Haigutus

Credits

Author: Kristjan Vilgo

GitHub Statistics

Data verified by PyPI on Jul 16, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.11

Provides Extra

arrow polars excel networkx sparql oxigraph validation dev visualization docs duckdb

Tags

CIM CGMES RDF XML power-grid triplets pandas polars duckdb

Classifiers

Report project as malware

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