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.
Predict which categories items belong to. A classification wrangle must be created to be able to use this.
>>> wrangles.classify('ball bearing', '<model id>')
MechPT
>>> wrangles.classify(['ball bearing', 'spanner'], '<model id>')
['MechPT', 'Tools']
| Parameter | Required | Data Type | Notes |
|---|---|---|---|
| input | ✓ | str, list | The text(s) to be classified. |
| model_id | ✓ | str | ID of the model to run. |
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']]
| 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. |
Extract numeric attributes such as lengths or voltages.
>>> 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']}]
| 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 |
Extract alphanumeric codes.
>>> wrangles.extract.codes('test ABCD1234ZZ test')
['ABCD1234ZZ']
>>> wrangles.extract.codes(['test ABCD1234ZZ test', 'NNN555BBB this one has two XYZ789'])
[['ABCD1234ZZ'], ['NNN555BBB', 'XYZ789']]
| Parameter | Required | Data Type | Notes |
|---|---|---|---|
| input | ✓ | str, list | The text(s) to be searched for codes. |
Extract entities using a custom model. An extraction wrangle must be created to be able to use this.
>>> wrangles.extract.custom('test skf test', '<model id>')
['SKF']
>>> wrangles.extract.custom(['test skf test', 'festo is hidden in here'], '<model id>')
[['SKF'], ['FESTO']]
| 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. |
Extract categorical properties such as colours or materials.
>>> wrangles.extract.properties('yellow submarine')
{'Colours': ['Yellow']}
>>> wrangles.extract.properties(['yellow submarine', 'the green mile'])
[{'Colours': ['Yellow']}, {'Colours': ['Green']}]
| 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. |
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 look up exact matches (from your list of values) in a saved lookup wrangle.
>>> 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"}
| 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 look up the most similar matches (from your list of values) in a saved lookup wrangle.
>>> 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"}
| 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 text data, such as replacing abbreviations. A standardization wrangle must be created to be able to use this.
>>> 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']
| Parameter | Required | Data Type | Notes |
|---|---|---|---|
| input | ✓ | str, list | The text(s) to be standardized. |
| model_id | ✓ | str | ID of the model to run. |
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).
>>> 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']
| 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. |