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Python library for information extraction of quantities, measurements and their units from unstructured text.


Try it here.


First, install sklearn. Quantulum would still work without it, but it wouldn't be able to disambiguate between units with the same name (e.g. pound as currency or as unit of mass).


$ pip install quantulum


>>> from quantulum import parser
>>> quants = parser.parse('I want 2 liters of wine')
>>> quants
[Quantity(2, 'litre')]

The Quantity class stores the surface of the original text it was extracted from, as well as the (start, end) positions of the match:

>>> quants[0].surface
u'2 liters'
>>> quants[0].span
(7, 15)

An inline parser that embeds the parsed quantities in the text is also available (especially useful for debugging):

>>> print parser.inline_parse('I want 2 liters of wine')
I want 2 liters {Quantity(2, "litre")} of wine

Units and entities

All units (e.g. litre) and the entities they are associated to (e.g. volume) are reconciled against WikiPedia:

>>> quants[0].unit
Unit(name="litre", entity=Entity("volume"), uri=

>>> quants[0].unit.entity
Entity(name="volume", uri=

This library includes more than 290 units and 75 entities. It also parses spelled-out numbers, ranges and uncertainties:

>>> parser.parse('I want a gallon of beer')
[Quantity(1, 'gallon')]

>>> parser.parse('The LHC smashes proton beams at 12.813.0 TeV')
[Quantity(12.8, "teraelectronvolt"), Quantity(13, "teraelectronvolt")]

>>> quant = parser.parse('The LHC smashes proton beams at 12.90.1 TeV')
>>> quant[0].uncertainty

Non-standard units usually don't have a WikiPedia page. The parser will still try to guess their underlying entity based on their dimensionality:

>>> parser.parse('Sound travels at 0.34 km/s')[0].unit
Unit(name="kilometre per second", entity=Entity("speed"), uri=None)


If the parser detects an ambiguity, a classifier based on the WikiPedia pages of the ambiguous units or entities tries to guess the right one:

>>> parser.parse('I spent 20 pounds on this!')
[Quantity(20, "pound sterling")]

>>> parser.parse('It weighs no more than 20 pounds')
[Quantity(20, "pound-mass")]


>>> text = 'The average density of the Earth is about 5.5x10-3 kg/cm'
>>> parser.parse(text)[0].unit.entity
Entity(name="density", uri=

>>> text = 'The amount of O is 2.98e-4 kg per liter of atmosphere'
>>> parser.parse(text)[0].unit.entity
Entity(name="concentration", uri=


While quantities cannot be manipulated within this library, there are many great options out there:


See units.json for the complete list of units and entities.json for the complete list of entities. The criteria for adding units have been:

It's easy to extend these two files to the units/entities of interest. Here is an example of an entry in entities.json:

    "name": "speed",
    "dimensions": [{"base": "length", "power": 1}, {"base": "time", "power": -1}],
    "URI": ""
  • name and URI are self explanatory.
  • dimensions is the dimensionality, a list of dictionaries each having a base (the name of another entity) and a power (an integer, can be negative).

Here is an example of an entry in units.json:

    "name": "metre per second",
    "surfaces": ["metre per second", "meter per second"],
    "entity": "speed",
    "URI": "",
    "dimensions": [{"base": "metre", "power": 1}, {"base": "second", "power": -1}],
    "symbols": ["mps"]
  • name and URI are self explanatory.
  • surfaces is a list of strings that refer to that unit. The library takes care of plurals, no need to specify them.
  • entity is the name of an entity in entities.json
  • dimensions follows the same schema as in entities.json, but the base is the name of another unit, not of another entity.
  • symbols is a list of possible symbols and abbreviations for that unit.

All fields are case sensitive.

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