Functions

Some Wrangles require authentication to the WrangleWorks servers to function. See the installation instructions

Wrangles can be used as functions, directly incorporated into python code.

>>> import wrangles
>>> wrangles.extract.attributes('it is 15mm long')

{'length': ['15mm']}

Wrangles broadly accept a single input string, or a list of strings. If a list is provided, the results will be returned in an equivalent list in the same order and length as the original.

Classify

Predict which categories items belong to. A classification wrangle must be created to be able to use this.

Tabset

Samples

>>> wrangles.classify('ball bearing', '<model id>')
MechPT

>>> wrangles.classify(['ball bearing', 'spanner'], '<model id>')
['MechPT', 'Tools']

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be classified.
model_id	✓	str	ID of the model to run.

Extract

Address

Tabset

Samples

Extract features from addresses such as streets or countries.

>>> wrangles.extract.address('1100 Congress Ave, Austin, TX 78701, USA', 'streets')
['1100 Congress Ave']

>>> wrangles.extract.address(['1100 Congress Ave, Austin, TX 78701, USA'], 'streets')
[['1100 Congress Ave']]

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for attributes.
dataType	✓	streets / cities / regions / countries	The type of information to return.

Ai

Tabset

Samples

Use the power of AI (OpenAI's chatGPT in particular) to extract meaningful data

>>> wrangles.extract.ai('Yellow Submarine', api_key='<api key>', output='The names of any colors found in the input)
'Yellow'

>>> wrangles.extract.ai('Yellow Submarine', api_key='<api key>', output={'type': 'string', 'description': 'The names of any colors found in the input'})
'Yellow'

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	A single value or list of values to extract information from. If a list is provided, each element will be analyzed individually and a list of equal length will be returned.
api_key	✓	str	An OpenAI API key.
output		str, dict	This can be a string prompting the output, a JSON schema definition of the output requested or a dict of JSON schema definitions.
model_id		str	An extract.ai model ID containing a saved definition. Use this or output. If both are provided, output that precedence over the definition from the model_id.
model		str	The model to use for the extraction. Current default is set to gpt-4o-mini.
threads		int	Number of threads to use for parallel processing.
timeout		int	Timeout in seconds for each API call.
retries		int	Number of retries to attempt on failure.
messages		list	Overall prompts to pass additional instructions.
url		str	Override the endpoint. Must implement the OpenAI chat completions API schema with function calling.

Attributes

Extract numeric attributes such as lengths or voltages.

Tabset

Samples

>>> wrangles.extract.attributes('it is 15mm long')
{'length': ['15mm']}

>>> wrangles.extract.attributes(['it is 15mm long', 'the voltage is 15V'])
[{'length': ['15mm']}, {'electric potential': ['15V']}]

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for attributes.
responseContent		span / object	Default span. If span, returns original text, if object returns an object of value and dimension.
type		angle / area / current / force / length / power / pressure / electric potential / volume / mass	Specify which types of attributes to find. If omitted, a dictionary of all attributes types is returned

Codes

Extract alphanumeric codes.

Tabset

Samples

>>> wrangles.extract.codes('test ABCD1234ZZ test')
['ABCD1234ZZ']

>>> wrangles.extract.codes(['test ABCD1234ZZ test', 'NNN555BBB this one has two XYZ789'])
[['ABCD1234ZZ'], ['NNN555BBB', 'XYZ789']]

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for codes.

Custom

Extract entities using a custom model. An extraction wrangle must be created to be able to use this.

Tabset

Samples

>>> wrangles.extract.custom('test skf test', '<model id>')
['SKF']

>>> wrangles.extract.custom(['test skf test', 'festo is hidden in here'], '<model id>')
[['SKF'], ['FESTO']]

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for custom entities.
model_id	✓	str	ID of the model to run.

Properties

Extract categorical properties such as colours or materials.

Tabset

Samples

>>> wrangles.extract.properties('yellow submarine')
{'Colours': ['Yellow']}

>>> wrangles.extract.properties(['yellow submarine', 'the green mile'])
[{'Colours': ['Yellow']}, {'Colours': ['Green']}]

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for properties.
type		colours / materials / shapes / standards	The type of property to return. If omitted, a dictionary with all results will be returned.

Lookup

Lookups can be used to look up data from a saved lookup wrangle. They can either be key (exact) or semantic (most similar meaning) based matches.

Exact Lookups

Exact lookups look up exact matches (from your list of values) in a saved lookup wrangle.

Tabset

Samples

>>> wrangles.lookup(["Key1", "Key2"], "<model id>", "Value1")
["Key1's Value1", "Key2's Value1"]

>>> wrangles.lookup("Key1", "<model id>")
{"Value1": "Key1's Value1", "Value2": "Key1's Value2"}

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for properties.
model_id	✓	str	ID of the model to run.
columns		str, list	The columns to be returned. If not provided, all columns will be returned as a dict.

Semantic Lookups

Semantic lookups look up the most similar matches (from your list of values) in a saved lookup wrangle.

Tabset

Samples

>>> wrangles.lookup(["KeyOne", "KeyTwo"], "<model id>", "Value1")
["Key1's Value1", "Key2's Value1"]

>>> wrangles.lookup("KeyOne", "<model id>")
{"Value1": "Key1's Value1", "Value2": "Key1's Value2"}

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be searched for properties.
model_id	✓	str	ID of the model to run.
columns		str, list	The columns to be returned. If not provided, all columns will be returned as a dict.

Standardize

Standardize text data, such as replacing abbreviations. A standardization wrangle must be created to be able to use this.

Tabset

Samples

>>> wrangles.standardize('It will arrive asap.', '<model id>')
'It will arrive as soon as possible.'

>>> wrangles.standardize(['It will arrive asap.', 'I live in the USA'])
['It will arrive as soon as possible.', 'I live in the United States']

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be standardized.
model_id	✓	str	ID of the model to run.

Translate

Translate text between languages.

Requires a WrangleWorks Account and DeepL API Key (A free account for up to 500,000 characters per month is available).

Tabset

Samples

>>> wrangles.translate('My name is Chris', 'ES')
Mi nombre es Chris

>>> wrangles.translate(['My name is Chris', 'I live in Austin'], 'DE')
['Mein Name ist Chris', 'Ich wohne in Austin']

Parameters

Parameter	Required	Data Type	Notes
input	✓	str, list	The text(s) to be classified.
target_language	✓	str	A code for the target language. Available Codes
source_language		str	A code for the source language. If omitted, the language will be inferred from the contents.
case		lower / upper / title	Allow changing the case of the input prior to translation.