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python3-jeepney-0.8.0-1.lbn36.noarch
Jeepney is a pure Python implementation of D-Bus messaging. It has an I/O-free core, and integration modules for different
event loops.
D-Bus is an inter-process communication system, mainly used in Linux.
To install Jeepney:
pip install jeepney
Jeepney docs on Readthedocs
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python3-jsonschema-spec-0.2.4-1.lbn36.noarch
Object-oriented JSONSchema
Key features
Traverse schema like paths
Access schema on demand with separate dereferencing accessor layer
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python3-looker-sdk-22.20.0-1.lbn36.noarch
Looker SDK The Looker SDK for Python provides a convenient way to communicate
with the Looker API available on your Looker server. The library requires
python3.6+ and is annotated using the typing module.The SDK uses a plug-in
architecture (also known as dependency injection) for initializing that
supports run-time specific transports (currently only RequestsTransport) and
different...
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python3-monotonic-1.6-1.lbn36.noarch
This module provides a ``monotonic()`` function which
returns the
value (in fractional seconds) of a clock which never goes
backwards.
On Python 3.3 or newer, ``monotonic`` will be an alias of
``time.monotonic`` from the standard library. On older versions,
it will fall back to an equivalent platform specific implementation.
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python3-mypy-boto3-redshift-data-1.26.0.post1-1.lbn36.noarch
<a id"mypy-boto3-redshift-data"></a> mypy-boto3-redshift-data[ 2006-2022, Liran Funaro.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the...
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python3-pandas-gbq-0.19.1-1.lbn36.noarch
pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an
interface to the Google BigQuery API from pandas.- Library Documentation_ -
Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:
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python3-papermill-2.6.0-3.lbn36.noarch
papermill is a tool for parameterizing, executing, and analyzing Jupyter
Notebooks.
Papermill lets you:
- parameterize notebooks
- execute notebooks
This opens up new opportunities for how notebooks can be used. For example:
- Perhaps you have a financial report that you wish to run with different
values on the first or last day of a month or at the beginning or end of the
year, using parameters makes this task easier.
- Do you want to run a notebook and depending on its results, choose a
particular notebook to run next? You can now programmatically execute a
workflow without having to copy and paste from notebook to notebook manually.
Papermill takes an opinionated approach to notebook parameterization and
execution based on our experiences using notebooks at scale in data pipelines.
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python3-pathable-0.4.3-1.lbn36.noarch
pathableAbout Object-oriented pathsKey features ************* Traverse
resources like paths * Access resources on demand with separate accessor
layerUsage .. code-block:: python from pathable import DictPath d { "parts": {
"part1": { "name": "Part One", }, "part2": { "name": "Part Two", }, }, dp
DictPath(d) Concatenate paths with / parts dp / "parts" Stat path keys "part2"
in parts Open path...
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python3-pure-sasl-0.6.2-1.lbn36.noarch
This package provides a reasonably high-level SASL client written in pure
Python. New mechanisms may be integrated easily, but by default, support for
PLAIN, ANONYMOUS, EXTERNAL, CRAM-MD5, DIGEST-MD5, and GSSAPI are provided.
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