DealStat Utilities
Project description
=================
DealStat Utilities
=================
.. image:: https://img.shields.io/pypi/v/dealstat.svg
:target: https://pypi.python.org/pypi/dealstat
.. image:: https://img.shields.io/travis/ecatkins/dealstat.svg
:target: https://travis-ci.org/ecatkins/dealstat
.. image:: https://readthedocs.org/projects/dealstat/badge/?version=latest
:target: https://dealstat.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/ecatkins/dealstat/shield.svg
:target: https://pyup.io/repos/github/ecatkins/dealstat/
:alt: Updates
DealStat Utilities
* Free software: MIT license
Dealstat
--------
Generic functions that may be moved to specific modules at some point:::
from dealstat.dealstat import *
# generate random letter based ID of given length
my_id = unique_id(30)
Boto
--------
Simplifies some of AWS' Boto3 functionality (at the moment, just S3)
* First save `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` as environment variables::
from dealstat.boto import Boto
s3 = Boto('s3')
location1 = {'bucket':'<some-bucket>', 'key': '<some-key'>}
location2 = {'bucket':'<some-other-bucket>', 'key': '<some-other-key'>}
# Get temporary pre-signed url
url = s3.get_temp_url(location1)
# Move object from one bucket/key to another
s3.move_object(location1, location2)
# Upload file
file_path = '/some/file/path.txt'
s3.upload_file(file_path, location1)
# Download file
destination_file_path = 'local/file/path2.txt'
s3.download_file(destination_file_path, location2)
# List contents of bucket
# Use prefix='some-prefix' to search for specific key prefixes
# Use exclude_dirs=True to not return directories in result
s3.list_bucket('<some-bucket'>, prefix=None, exclude_dirs=True)
Machine Learning
--------
Random utilities useful in ML prototyping
* Download and use Standform NLP Glove Embedings ::
from dealstat.ml import Embeddings
Embed = Embeddings()
# Download embeddings
Embed.download_embeddings()
# Unzip embeddings
Embed.extract_embeddings()
# Generate embeddings look up dict for given dimension
dim = 200
embedding_dict = Embed.generate(loc='.', dim=dim)
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.1.0 (2018-08-23)
------------------
* First release on PyPI.
DealStat Utilities
=================
.. image:: https://img.shields.io/pypi/v/dealstat.svg
:target: https://pypi.python.org/pypi/dealstat
.. image:: https://img.shields.io/travis/ecatkins/dealstat.svg
:target: https://travis-ci.org/ecatkins/dealstat
.. image:: https://readthedocs.org/projects/dealstat/badge/?version=latest
:target: https://dealstat.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/ecatkins/dealstat/shield.svg
:target: https://pyup.io/repos/github/ecatkins/dealstat/
:alt: Updates
DealStat Utilities
* Free software: MIT license
Dealstat
--------
Generic functions that may be moved to specific modules at some point:::
from dealstat.dealstat import *
# generate random letter based ID of given length
my_id = unique_id(30)
Boto
--------
Simplifies some of AWS' Boto3 functionality (at the moment, just S3)
* First save `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` as environment variables::
from dealstat.boto import Boto
s3 = Boto('s3')
location1 = {'bucket':'<some-bucket>', 'key': '<some-key'>}
location2 = {'bucket':'<some-other-bucket>', 'key': '<some-other-key'>}
# Get temporary pre-signed url
url = s3.get_temp_url(location1)
# Move object from one bucket/key to another
s3.move_object(location1, location2)
# Upload file
file_path = '/some/file/path.txt'
s3.upload_file(file_path, location1)
# Download file
destination_file_path = 'local/file/path2.txt'
s3.download_file(destination_file_path, location2)
# List contents of bucket
# Use prefix='some-prefix' to search for specific key prefixes
# Use exclude_dirs=True to not return directories in result
s3.list_bucket('<some-bucket'>, prefix=None, exclude_dirs=True)
Machine Learning
--------
Random utilities useful in ML prototyping
* Download and use Standform NLP Glove Embedings ::
from dealstat.ml import Embeddings
Embed = Embeddings()
# Download embeddings
Embed.download_embeddings()
# Unzip embeddings
Embed.extract_embeddings()
# Generate embeddings look up dict for given dimension
dim = 200
embedding_dict = Embed.generate(loc='.', dim=dim)
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.1.0 (2018-08-23)
------------------
* First release on PyPI.
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