Package: larrydata
LarryDataLarryData is a library of utilities for common data tasks using AWS for data science and data engineering projects.
While boto3 is a great interface for interacting with AWS services, it can be overly complex for data scientists and
others who want to perform straightforward operations on data. Boto3 is powerful but often requires you spend time
worrying about API-specific interactions and parameters. LarryData makes it easy to use services like S3, MTurk,
and other data-oriented AWS services in a far morefunctionalmanner to let you focus on the data rather than
syntax. This library is designed to make getting tasks completed in Jupyter Notebooks or AWS Lambda functions as
easy as possible by providing simplified interfaces while still giving you access to the underlying boto3 libraries
when you need them.Installationpip install larrydataIn addition, you can easily add LarryData to your AWS Lambda functions by adding one of the following public Layers:LarryData:arn:aws:lambda:us-west-2:981332165467:layer:LarryData:2LarryData with Jinja2:arn:aws:lambda:us-west-2:981332165467:layer:LarryDataWithJinja:2LarryData with Jinja2 and Pillow:arn:aws:lambda:us-west-2:981332165467:layer:LarryDataWithJinjaPillow:2Configuring your AWS sessionBy default LarryData creates a boto3 session using your default AWS credentials that can be configured using theAWS CLI. To use a different profile, you can change using the following:importlarrydataasldld.set_session(profile_name='my_profile')Alternatively, you can pass AWS credentials directlyimportlarrydataasldld.set_session(aws_access_key_id='XXXXXXXXXX',aws_secret_access_key='XXXXXXXXXXXXX')Functional S3 interactionsWhen using boto3 alone, the following is how you would read in a JSON formatted object from S3 into a dict:importboto3importjsonresource=boto3.resource('s3')obj=resource.Bucket('mybucket').Object(key='myfile.json').get()contents=obj['Body'].read()my_dict=json.loads(contents.decode('utf-8'))In contrast, LarryData takes care of all those steps for you and let's you simply call one function to get your data.importlarrydataasldmy_dict=ld.s3.read_dict(bucket='mybucket',key='myfile.json')In addition to accessing data using bucket/key pairs, you can S3 URIs like those commonly used in SageMaker.my_dict2=ld.s3.read_dict(uri='s3://mybucket/myfile.json')To write files to S3, simply callwrite_objectto write your object out in the appropriate format:# Write json to S3my_dict={'key':'val'}ld.s3.write_object(my_dict,bucket='mybucket',key='myfile.json')# Write a list of strings to S3 as rowsmy_list=['a','b','c','d']ld.s3.write_object(my_list,bucket='mybucket',key='myfile.txt')# Write a JSON lines file to S3 (commonly used for SageMaker manifest files)my_dictlist=[{'a':1},{'b':2},{'c':3}]ld.s3.write_object(my_dictlist,bucket='mybucket',key='myfile.jsonl')Powerful MTurk extensionsLarryData is especially useful for services like MTurk which have more complex interaction patterns and legacy aspects
of their APIs. The MTurk module includes a number of features to make using MTurk much easier:Easy toggling between sandbox and production clients (no more copy/pasting in endpoint urls)Worker answers are converted into easy to access dict objects (no more QuestionFormAnswer XML!)Potentially expensive operations such as list_hits and list_assignments_for_hit return generatorsUtilities are included to easily generate HTMLQuestion and ExternalQuestion XML objects with integrated Jinja2 templatingHelpers to enable state data in the RequesterAnnotation fieldThe combination of these features means that creating a HIT in MTurk is as easy as the following:importlarrydataasld# Indicate we want to use the production environmentld.mturk.use_production()# Load a template from S3 and populate it with valuestask_data={'image_url':'https://mywebsite.com/images/233.jpg'}question_xml=ld.mturk.render_jinja_template_question(task_data,template_uri='s3://mybucket/templates/imageCat.html')# Format the source data so it can be stored in the RequesterAnnotation field for use in trackingtask_data['request_id']='MY_TRACKING_ID'annotation_payload=ld.mturk.prepare_requester_annotation(task_data)# Create a HIThit=ld.mturk.create_hit(title='Test task',description='Categorize images',reward='0.05',max_assignments=5,lifetime=86400,assignment_duration=600,question=question_xml,annotation=annotation_payload)# Display where the HIT can be viewed on the Worker websitehit_id=hit['HITId']hit_type_id=hit['HITTypeId']print('HIT{}created, preview at{}'.format(hit_id,ld.mturk.preview_url(hit_type_id)))Getting the results from that task is as simple as the following:importlarrydataasldhit_id='HIT_ID_FROM_EARLIER'# Indicate we want to use the production environmentld.mturk.use_production()# retrieve the HIThit=ld.mturk.get_hit(hit_id)# retrieve the requester annotation datatask_data=ld.mturk.retrieve_requester_annotation(hit=hit)# get the resultsforassignmentinld.mturk.list_assignments_for_hit(hit_id):print('Worker{}responded with{}'.format(assignment['WorkerId'],assignment['Answer']['category']))More features will be added over time, feel free to submit your feature suggestions.