Full documentation available at wrangles.io.
Wrangles are a set of modular transformations for data cleaning and enrichment. Each Wrangle is optimized for a particular job, many of which are backed by sophisticated machine learning models.
With Wrangles, you can:
Wrangles are system independent, and allow you to pull data from one system, transform it and push it to another. Wrangles can be incorporated directly into python code, or an automated sequence of wrangles can be run as a recipe.
The python package can be installed using pip.
pip install wrangles
Once installed, import the package into your code.
import wrangles
Some Wrangles use cloud based machine learning models. To use them a WrangleWorks account is required.
Create a WrangleWorks account: Register
There are two ways to provide the credentials:
The credentials can be saved as the environment variables:
WRANGLES_USER
WRANGLES_PASSWORD
The credentials can be provided within the python code using the authenticate method, prior to calling other functions.
wrangles.authenticate('<user>', '<password>')
Wrangles can be used as functions, directly incorporated into python code.
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.
# Extract alphanumeric codes from a free text strings - e.g. find all part numbers in a set of product description
>>> import wrangles
>>> wrangles.extract.codes('replacement part ABCD1234ZZ')
['ABCD1234ZZ']
>>> wrangles.extract.codes(['replacement part ABCD1234ZZ', 'NNN555BBB this one has two XYZ789'])
[
['ABCD1234ZZ'],
['NNN555BBB', 'XYZ789']
]
Recipes are written in YAML and allow a series of Wrangles to be run as an automated sequence.
Recipes can be triggered either from python code or a terminal command.
# PYTHON
import wrangles
wrangles.recipe.run('recipe.wrgl.yml')
# TERMINAL
wrangles.recipe recipe.wrgl.yml
# file: recipe.wrgl.yml
# ---
# Convert a CSV file to an Excel file
# and change the case of a column.
read:
- file:
name: file.csv
wrangles:
- convert.case:
input: my column
case: upper
write:
- file:
name: file.xlsx