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        "author": "Dave Schultz",
        "author_email": "djschult@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# LarryData\nLarryData is a library of utilities for common data tasks using AWS for data science and data engineering projects. \nWhile boto3 is a great interface for interacting with AWS services, it can be overly complex for data scientists and \nothers who want to perform straightforward operations on data. Boto3 is powerful but often requires you spend time\nworrying about API-specific interactions and parameters. LarryData makes it easy to use services like S3, MTurk, \nand other data-oriented AWS services in a far more **functional** manner to let you focus on the data rather than \nsyntax. This library is designed to make getting tasks completed in Jupyter Notebooks or AWS Lambda functions as \neasy as possible by providing simplified interfaces while still giving you access to the underlying boto3 libraries\nwhen you need them.\n\n## Installation\n```\npip install larrydata\n```\nIn addition, you can easily add LarryData to your AWS Lambda functions by adding one of the following public Layers:\n* LarryData: `arn:aws:lambda:us-west-2:981332165467:layer:LarryData:2`\n* LarryData with Jinja2: `arn:aws:lambda:us-west-2:981332165467:layer:LarryDataWithJinja:2`\n* LarryData with Jinja2 and Pillow: `arn:aws:lambda:us-west-2:981332165467:layer:LarryDataWithJinjaPillow:2`\n\n\n## Configuring your AWS session\nBy default LarryData creates a boto3 session using your default AWS credentials that can be configured using the \n[AWS CLI](https://aws.amazon.com/cli/). To use a different profile, you can change using the following:\n```python\nimport larrydata as ld\nld.set_session(profile_name='my_profile')\n```\nAlternatively, you can pass AWS credentials directly\n```python\nimport larrydata as ld\nld.set_session(aws_access_key_id='XXXXXXXXXX', aws_secret_access_key='XXXXXXXXXXXXX')\n```\n\n## Functional S3 interactions\nWhen using boto3 alone, the following is how you would read in a JSON formatted object from S3 into a dict:\n```python\nimport boto3\nimport json\n\nresource = boto3.resource('s3')\nobj = resource.Bucket('mybucket').Object(key='myfile.json').get()\ncontents = obj['Body'].read()\nmy_dict = json.loads(contents.decode('utf-8'))\n```\n\nIn contrast, LarryData takes care of all those steps for you and let's you simply call one function to get your data.\n```python\nimport larrydata as ld\n\nmy_dict = ld.s3.read_dict(bucket='mybucket', key='myfile.json')\n```\nIn addition to accessing data using bucket/key pairs, you can S3 URIs like those commonly used in SageMaker.\n```python\nmy_dict2 = ld.s3.read_dict(uri='s3://mybucket/myfile.json')\n```\n\nTo write files to S3, simply call `write_object` to write your object out in the appropriate format:\n```python\n# Write json to S3\nmy_dict = {'key': 'val'}\nld.s3.write_object(my_dict, bucket='mybucket', key='myfile.json')\n\n# Write a list of strings to S3 as rows\nmy_list = ['a','b','c','d']\nld.s3.write_object(my_list, bucket='mybucket', key='myfile.txt')\n\n# Write a JSON lines file to S3 (commonly used for SageMaker manifest files)\nmy_dictlist = [{'a': 1}, {'b': 2}, {'c': 3}]\nld.s3.write_object(my_dictlist, bucket='mybucket', key='myfile.jsonl')\n```\n\n## Powerful MTurk extensions\nLarryData is especially useful for services like MTurk which have more complex interaction patterns and legacy aspects\nof their APIs. The MTurk module includes a number of features to make using MTurk much easier:\n* Easy toggling between sandbox and production clients (no more copy/pasting in endpoint urls)\n* Worker answers are converted into easy to access dict objects (no more QuestionFormAnswer XML!)\n* Potentially expensive operations such as list_hits and list_assignments_for_hit return generators\n* Utilities are included to easily generate HTMLQuestion and ExternalQuestion XML objects with integrated Jinja2 templating\n* Helpers to enable state data in the RequesterAnnotation field\n\nThe combination of these features means that creating a HIT in MTurk is as easy as the following:\n```python\nimport larrydata as ld\n\n# Indicate we want to use the production environment\nld.mturk.use_production()\n\n# Load a template from S3 and populate it with values\ntask_data = {'image_url': 'https://mywebsite.com/images/233.jpg'}\nquestion_xml = ld.mturk.render_jinja_template_question(task_data, template_uri='s3://mybucket/templates/imageCat.html')\n\n# Format the source data so it can be stored in the RequesterAnnotation field for use in tracking\ntask_data['request_id'] = 'MY_TRACKING_ID'\nannotation_payload = ld.mturk.prepare_requester_annotation(task_data)\n\n# Create a HIT\nhit = ld.mturk.create_hit(title='Test task', description='Categorize images', reward='0.05', max_assignments=5,\n                       lifetime=86400, assignment_duration=600, question=question_xml, annotation=annotation_payload)\n\n# Display where the HIT can be viewed on the Worker website\nhit_id = hit['HITId']\nhit_type_id = hit['HITTypeId']\nprint('HIT {} created, preview at {}'.format(hit_id, ld.mturk.preview_url(hit_type_id)))\n```\nGetting the results from that task is as simple as the following:\n```python\nimport larrydata as ld\n\nhit_id = 'HIT_ID_FROM_EARLIER'\n\n# Indicate we want to use the production environment\nld.mturk.use_production()\n\n# retrieve the HIT\nhit = ld.mturk.get_hit(hit_id)\n\n# retrieve the requester annotation data\ntask_data = ld.mturk.retrieve_requester_annotation(hit=hit)\n\n# get the results\nfor assignment in ld.mturk.list_assignments_for_hit(hit_id):\n    print('Worker {} responded with {}'.format(assignment['WorkerId'], assignment['Answer']['category']))\n```\n\nMore features will be added over time, feel free to submit your feature suggestions.\n\n\n",
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