A Collection of Methods for Data Collection & Processing
Project description
labPack
A Collection of Methods for Data Collection & Processing
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Top-Level Classes
labID: A class of methods for uniquely identifying objects
labDT: A class of methods for transforming datetime data
labRandom: A class of methods for generating random data
Features
Unique IDs which do not conflict nor leak record origin
Transformations of datetime data between popular formats
Randomization using best current algorithms
Installation
From PyPi:
$ pip install labpack
From GitHub:
$ git clone https://github.com/collectiveacuity/labPack $ cd labPack $ python setup.py install
Getting Started
This module is designed to make the process of retrieving, managing and processing data more uniform across a variety of different sources and structures. A number of module methods are implementations of built-in python packages and standard python imports which have been optimized for data management and compensate for the messy state of real data. The methods in this module aggregate and curate python resources and online APIs to provide a set of best practices for handling data.
Create an unique ID for records:
from labpack.records import labID id = labID() url_safe_id_string = id.id48 id_datetime = id.epoch
For more details about how to use labPack, refer to the Reference Documentation on GitHub
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