ixbrlparse 0.4

A python module for getting useful data out of ixbrl files.

ixbrl-parse

A python module for getting useful data out of ixbrl files. The library is at an early stage - feedback and improvements are very welcome.

New in version 0.4: I've added initial support for pure XBRL files as well as tagged HTML iXBRL files. Feedback on this feature is welcome - particularly around getting values out of numeric items.

Requirements

The module requires BeautifulSoup and lxml to parse the documents.

word2number is used to process the numeric items with the numsenwords format.

How to install

You can install from pypi using pip:

pip install ixbrlparse

How to use

Run the python module

You can run the module directly to extract data from an IXBRL file.

python -m ixbrlparse example_file.html

The various options for using this can be found through:

python -m ixbrlparse -h
# optional arguments:
#   -h, --help            show this help message and exit
#   --outfile OUTFILE     Where to output the file
#   --format {csv,json,jsonlines,jsonl}
#                         format of the output
#   --fields {numeric,nonnumeric,all}
#                         Which fields to output

Use as a python module

An example of usage is shown in test.py.

Import the IXBRL class which parses the file.

from ixbrlparse import IXBRL

Initialise an object and parse the file

You need to pass a file handle or other object with a .read() method.

with open('sample_ixbrl.html', encoding="utf8") as a:
  x = IXBRL(a)

If your IXBRL data comes as a string then use a io.StringIO wrapper to pass it to the class:

import io
from ixbrlparse import IXBRL

content = '''<some ixbrl content>'''
x = IXBRL(io.StringIO(content))

Get the contexts and units used in the data

These are held in the object. The contexts are stored as a dictionary with the context id as the key, and a ixbrlContext object as the value.

print(x.contexts)
# {
#    "cfwd_2018_03_31": ixbrlContext(
#       id="cfwd_2018_03_31",
#       entity="0123456", # company number
#       segments=[], # used for hypercubes
#       instant="2018-03-31",
#       startdate=None, # used for periods
#       enddate=None, # used for periods
#    ),
#    ....
# }

The units are stored as key:value dictionary entries

print(x.units)
# {
#    "GBP": "ISO4107:GBP"
#    "shares": "shares"
# }

Get financial facts

Numeric facts are stored in x.numeric as a list of ixbrlNumeric objects. The ixbrlNumeric.value object contains the value as a parsed python number (after the sign and scale formatting values have been applied).

ixbrlNumeric.context holds the context object relating to this value. The .name and .schema values give the key of this value, according to the applied schema.

Non-numeric facts are stored in x.nonnumeric as a list of ixbrlNonnumeric objects, with similar .value, .context, .name and .schema values. The value of .value will be a string for non-numeric facts.

Check for any parsing errors

By default, the parser will throw an exception if it encounters an error when processing the document.

You can parse raise_on_error=False to the initial object to suppress these exceptions. You can then access a list of the errors (and the element) that created them through the .errors attribute. For example:

with open('sample_ixbrl.html', encoding="utf8") as a:
  x = IXBRL(a, raise_on_error=False)
  print(x.errors) # populated with any exceptions found
  # [ eg...
  #   {
  #     "error": <NotImplementedError>,
  #     "element": <BeautifulSoupElement>
  #   }
  # ]

Note that the error catching is only available for parsing of .nonnumeric and numeric items in the document. Any other errors with parsing will be thrown as normal no matter what raise_on_error is set to.

Run tests

Tests can be run with pytest:

pip install -e . # install the package
pytest tests

Linting

Black and isort should be run before committing any changes.

isort ixbrlparse tests
black ixbrlparse tests

Install development version

The development requirements are installed using pip install -r dev-requirements.txt.

Any additional requirements for the module itself must be added to install_requires in setup.py. You should then generate a new requirements.txt using using pip-tools (pip-compile). You can then run pip-sync to install the requirement.

Any additional development requirements must be added to dev-requirements.in and then the dev-requirements.txt should be generated using pip-compile dev-requirements.in. You can then install the development requirements using pip-sync dev-requirements.txt.

Acknowledgements

Originally developed for a project with Power to Change looking at how to extract data from financial documents of community businesses.