xbrl-core 0.1.0

Lightweight XBRL 2.1 / iXBRL 1.1 parser and structured data extraction library

xbrl-core — Lightweight XBRL 2.1 / iXBRL 1.1 Parser for Python

xbrl-core is a pure-Python parser and structured data extraction library for XBRL 2.1 instance documents and iXBRL (Inline XBRL) documents. It supports fact extraction, context/unit structuring, all five linkbase types (presentation, calculation, definition, label, reference), XSD schema parsing, calculation validation, text block extraction, pandas/DataFrame conversion, and Rich/HTML rendering. The only required dependency is lxml.

GitHub Repository

Installation

pip install xbrl-core

Optional dependencies:

# pandas + pyarrow (DataFrame conversion, Parquet export)
pip install 'xbrl-core[analysis]'

# Rich terminal display
pip install 'xbrl-core[display]'

# Excel export (pandas + openpyxl)
pip install 'xbrl-core[excel]'

# Everything
pip install 'xbrl-core[all]'

Quick Start

from xbrl_core import parse_xbrl_facts, structure_contexts, build_line_items

# 1. Parse an XBRL instance document
with open("instance.xbrl", "rb") as f:
    parsed = parse_xbrl_facts(f.read(), source_path="instance.xbrl")

print(f"Facts: {parsed.fact_count}")

# 2. Structure contexts and build typed LineItems
ctx_map = structure_contexts(parsed.contexts)
items = build_line_items(parsed.facts, ctx_map)

for item in items[:5]:
    print(item.local_name, item.value, item.period)

Parsing

XBRL Instance

parse_xbrl_facts() takes raw bytes and returns a ParsedXBRL containing facts, contexts, units, schema refs, footnote links, and ignored elements.

from xbrl_core import parse_xbrl_facts

parsed = parse_xbrl_facts(xbrl_bytes, source_path="example.xbrl")

# Extracted data
parsed.facts             # tuple[RawFact, ...]
parsed.contexts          # tuple[RawContext, ...]
parsed.units             # tuple[RawUnit, ...]
parsed.schema_refs       # tuple[RawSchemaRef, ...]
parsed.footnote_links    # tuple[RawFootnoteLink, ...]
parsed.ignored_elements  # tuple[IgnoredElement, ...]
parsed.fact_count        # int

iXBRL (Inline XBRL)

parse_ixbrl_facts() parses iXBRL (XHTML-embedded XBRL) documents. The output is the same ParsedXBRL type, so downstream pipelines work identically.

from xbrl_core import parse_ixbrl_facts

parsed = parse_ixbrl_facts(ixbrl_bytes, source_path="report.htm")

for fact in parsed.facts[:5]:
    print(fact.local_name, fact.value_raw)

iXBRL format attributes (ixt:numdotdecimal, ixt:numcommadecimal, etc.) and scale/sign attributes are automatically applied. Custom formats can be registered:

from xbrl_core import FormatRegistry, parse_ixbrl_facts

registry = FormatRegistry()
registry.register("dateyearmonthdaycjk", my_cjk_date_func)

parsed = parse_ixbrl_facts(ixbrl_bytes, format_registry=registry)

IXDS (Inline XBRL Document Set)

Multiple iXBRL files from a single filing can be merged:

from xbrl_core import parse_ixbrl_facts, merge_ixbrl_results

results = [parse_ixbrl_facts(f) for f in ixbrl_files]
merged = merge_ixbrl_results(results)

Strict / Lenient Mode

Both parsers accept a strict parameter. When strict=True (default), spec violations raise XbrlParseError. When strict=False, violations emit warnings and are recorded in ignored_elements.

parsed = parse_xbrl_facts(xbrl_bytes, strict=False)
for elem in parsed.ignored_elements:
    print(elem.reason, elem.source_line)

Context Structuring

structure_contexts() converts raw context XML fragments into typed StructuredContext objects with period, entity, and dimension information.

from xbrl_core import structure_contexts, ContextCollection

ctx_map = structure_contexts(parsed.contexts)

# Direct dict access
ctx = ctx_map["CurrentYearInstant"]
print(ctx.period)       # InstantPeriod(instant=datetime.date(2024, 3, 31))
print(ctx.entity_id)    # "E00001"
print(ctx.dimensions)   # tuple[DimensionMember, ...]

# ContextCollection for filtering
coll = ContextCollection(ctx_map)
coll.filter_instant()                   # instant contexts only
coll.filter_duration()                  # duration contexts only
coll.filter_no_dimensions()             # no dimension members
coll.filter_by_dimension(axis="{ns}ProductAxis", member="{ns}SegmentA")

coll.latest_instant_period              # most recent InstantPeriod
coll.unique_duration_periods            # unique DurationPeriods, sorted

Unit Structuring

structure_units() converts raw unit XML fragments into typed StructuredUnit objects.

from xbrl_core import structure_units

unit_map = structure_units(parsed.units)

unit = unit_map["JPY"]
print(unit.is_monetary)     # True
print(unit.currency_code)   # "JPY"

unit = unit_map["pure"]
print(unit.is_pure)         # True

unit = unit_map["JPYPerShare"]
print(unit.is_per_share)    # True

Building LineItems

build_line_items() merges RawFact + StructuredContext + optional LabelResolver into fully typed LineItem objects.

from xbrl_core import build_line_items

items = build_line_items(parsed.facts, ctx_map, langs=("en", "ja"))

for item in items:
    print(item.local_name)    # "NetSales"
    print(item.value)         # Decimal('1234567890')
    print(item.period)        # InstantPeriod / DurationPeriod
    print(item.entity_id)     # "E00001"
    print(item.dimensions)    # tuple[DimensionMember, ...]
    print(item.label("en"))   # "Net sales"
    print(item.label("ja"))   # "売上高"

Linkbase Parsing

Presentation Linkbase

from xbrl_core import parse_presentation_linkbase, merge_presentation_trees

trees = parse_presentation_linkbase(pre_xml_bytes)

for role_uri, tree in trees.items():
    # Flatten the tree (depth-first)
    for node in tree.flatten(skip_abstract=True, skip_dimension=True):
        print("  " * node.depth + node.concept)

    # Get only the line-items subtree
    for node in tree.line_items_roots():
        print(node.concept, node.order)

# Merge multiple presentation linkbases
merged = merge_presentation_trees(trees_a, trees_b)

Calculation Linkbase

from xbrl_core import parse_calculation_linkbase

calc_lb = parse_calculation_linkbase(cal_xml_bytes)

for role_uri in calc_lb.role_uris:
    tree = calc_lb.get_tree(role_uri)
    for arc in tree.arcs:
        sign = "+" if arc.weight == 1 else "-"
        print(f"  {arc.parent} {sign}-> {arc.child}")

# Query relationships
calc_lb.children_of("GrossProfit")        # child arcs
calc_lb.parent_of("NetSales")             # parent arcs
calc_lb.ancestors_of("NetSales", role_uri=role)  # root-ward chain

Definition Linkbase

from xbrl_core import parse_definition_linkbase

def_trees = parse_definition_linkbase(def_xml_bytes)

for role_uri, tree in def_trees.items():
    for hc in tree.hypercubes:
        print(f"Table: {hc.table_concept}")
        for axis in hc.axes:
            print(f"  Axis: {axis.axis_concept}")
            if axis.domain:
                print(f"  Domain: {axis.domain.concept}")

Label Linkbase

from xbrl_core import parse_label_linkbase

labels = parse_label_linkbase(lab_xml_bytes)

for lab in labels:
    print(f"{lab.concept_name} [{lab.lang}] = {lab.text}")

Reference Linkbase

from xbrl_core import parse_reference_linkbase

refs = parse_reference_linkbase(ref_xml_bytes)

for ref in refs:
    print(f"{ref.concept_name}: {ref.role}")
    for part in ref.parts:
        print(f"  {part.local_name} = {part.value}")

Footnotes

from xbrl_core import parse_footnote_links

footnote_map = parse_footnote_links(parsed.footnote_links)

notes = footnote_map.get("IdFact1234")
for n in notes:
    print(n.text, n.lang)

print(footnote_map.fact_ids)       # Fact IDs with footnotes
print(len(footnote_map))           # number of Facts with footnotes

Schema Parsing

from xbrl_core import parse_xsd_elements

elements = parse_xsd_elements(xsd_bytes)

elem = elements["NetSales"]
print(elem.period_type)         # "duration"
print(elem.balance)             # "credit"
print(elem.abstract)            # False
print(elem.type_name)           # "xbrli:monetaryItemType"
print(elem.substitution_group)  # "xbrli:item"

Calculation Validation

Validates summation-item relationships per XBRL 2.1 section 5.2.5.2, with decimals-based rounding tolerance.

from xbrl_core import validate_calculations, parse_calculation_linkbase

calc_lb = parse_calculation_linkbase(cal_xml_bytes)
result = validate_calculations(items, calc_lb)

print(result)           # "Calculation validation: PASS (checked=42, passed=42, errors=0, skipped=3)"
print(result.is_valid)  # True

for issue in result.issues:
    print(issue.parent_concept, issue.expected, issue.actual, issue.severity)

Text Block Extraction

Extracts textBlockItemType facts (e.g. MD&A, risk factors, notes) from filings.

from xbrl_core import extract_text_blocks, clean_html

blocks = extract_text_blocks(parsed.facts, ctx_map)

for block in blocks:
    print(block.concept)         # "BusinessRisksTextBlock"
    print(block.period)          # DurationPeriod(...)
    plain = clean_html(block.html)
    print(plain[:200])

clean_html() converts HTML fragments to plain text, preserving table structure with tabs and newlines — useful as preprocessing for LLM / RAG pipelines.

DataFrame Conversion

Requires pip install 'xbrl-core[analysis]'.

from xbrl_core import line_items_to_dataframe, to_csv, to_parquet

df = line_items_to_dataframe(items, label_lang="en")
print(df[["local_name", "label", "value", "period_end"]].head())

# Export
to_csv(df, "output.csv")
to_parquet(df, "output.parquet")

Requires pip install 'xbrl-core[excel]':

from xbrl_core import to_excel

to_excel(df, "output.xlsx", sheet_name="BalanceSheet")

Display

Rich (Terminal)

Requires pip install 'xbrl-core[display]'.

from rich.console import Console
from xbrl_core import render_statement

table = render_statement(items, title="Balance Sheet", label_lang="en")
Console().print(table)

Hierarchical Display

Use DisplayHint with presentation tree data for indented financial statements:

from xbrl_core import (
    build_display_rows,
    render_hierarchical_statement,
    DisplayHint,
)

hints = [
    DisplayHint(concept="AssetsAbstract", depth=0, is_abstract=True, label="Assets"),
    DisplayHint(concept="CashAndDeposits", depth=1),
    DisplayHint(concept="TotalAssets", depth=0, is_total=True),
]

# Rich Table
table = render_hierarchical_statement(items, hints=hints, title="BS")

# Or get raw DisplayRow objects
rows = build_display_rows(items, hints=hints)

HTML (Jupyter)

from xbrl_core import to_html

html = to_html(items, hints=hints, title="Balance Sheet")

Label Resolution

LabelResolver is a Protocol — implement it to inject taxonomy labels into build_line_items().

from xbrl_core import LabelResolver, LabelInfo, LabelSource

class MyResolver:
    def resolve(self, concept_qname, lang, role):
        # Look up label from your taxonomy data
        return LabelInfo(text="Net sales", role=role, lang=lang, source=LabelSource.STANDARD)

    def resolve_batch(self, concept_qnames, lang, role):
        return {qn: self.resolve(qn, lang, role) for qn in concept_qnames}

items = build_line_items(parsed.facts, ctx_map, resolver=MyResolver(), langs=("en",))

Error Handling

All errors inherit from XbrlError and carry a structured error code and context.

from xbrl_core import XbrlError, XbrlParseError, XbrlValidationError

try:
    parsed = parse_xbrl_facts(bad_bytes)
except XbrlParseError as e:
    print(e.code)     # "XBRL_PARSE_001"
    print(e.context)  # {"source_path": "..."}

Error code prefix	Exception class	Description
XBRL_PARSE_xxx	XbrlParseError	XML/XBRL parse errors
XBRL_CTX_xxx	XbrlParseError	Context structuring errors
XBRL_UNIT_xxx	XbrlParseError	Unit structuring errors
XBRL_LINK_xxx	XbrlParseError	Linkbase parse errors
XBRL_IXBRL_xxx	XbrlParseError	iXBRL parse errors
XBRL_VAL_xxx	XbrlValidationError	Validation errors

XbrlWarning (a UserWarning subclass) is emitted for non-fatal issues.

Requirements

Python 3.12+. The only required dependency is lxml >= 5.0.