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python3-psycopg-c-3.2.4-1.lbn36.x86_64
PostgreSQL database adapter for Python -- C optimisation distribution
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python3-psycopg-pool-3.3.0.dev1-1.lbn36.x86_64
PostgreSQL database adapter for Python - Connection Pool
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python3-raven-6.10.0-14.lbn36.noarch
Raven is a Python client for Sentry <http://getsentry.com>. It provides full out-of-the-box support for many of the popular frameworks, including Django, and Flask. Raven also includes drop-in support for any WSGI-compatible web application.
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python3-raven+flask-6.10.0-14.lbn36.noarch
This is a metapackage bringing in flask extras requires for python3-raven. It makes sure the dependencies are installed.
Located in LBN / … / Business / BastionLinux 36
python3-rich-13.9.4-1.lbn36.noarch
English readme • 简体中文 readme • 正體中文 readme • Lengua española readme • Deutsche readme • Läs på svenska • 日本語 readme • 한국어 readme • Français readme • Schwizerdütsch readme • हिन्दी readme • Português brasileiro readme • Italian readme • Русский readme • Indonesian readme • فارسی readme • Türkçe readme • Polskie readme Rich is a Python library for rich text and beautiful formatting in the terminal. The Rich API makes it easy to add color and style to terminal output. Rich can also render pretty tables, progress bars, markdown, syntax highlighted source code, tracebacks, and more — out of the box. For a video introduction to Rich see calmcode.io by @fishnets88. See what people are saying about Rich. Compatibility Rich works with Linux, macOS and Windows. True color / emoji works with new Windows Terminal, classic terminal is limited to 16 colors. Rich requires Python 3.8 or later. Rich works with Jupyter notebooks with no additional configuration required. Installing Install with pip or y
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python3-rich-argparse-1.3.0-1.lbn36.noarch
rich-argparse Format argparse and optparse help using rich. rich-argparse improves the look and readability of argparse's help while requiring minimal changes to the code. Table of contents Installation Usage Output styles Colors Group names Highlighting patterns "usage" --version Subparsers Third party formatters (ft. django) Optparse (experimental) Legacy Windows Installation Install from PyPI with pip or your favorite tool. pip install rich-argparse Usage Simply pass formatter_class to the argument parser import argparse from rich_argparse import RichHelpFormatter parser = argparse.ArgumentParser(..., formatter_class=RichHelpFormatter) ... rich-argparse defines equivalents to argparse's built-in formatters: rich_argparse formatter equivalent in argparse RichHelpFormatter HelpFormatter RawDescriptionRichHelpFormatter RawDescriptionHelpFormatter RawTextRichHelpFormatter RawTextHelpFormatter ArgumentDefaultsRichHelpFormatter ArgumentDefaultsHelpFormatter
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python3-rich-click-1.8.3-1.lbn36.noarch
Richly rendered command line interfaces in click.
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python3-rich-dataframe-0.2.0-1.lbn36.noarch
Rich DataFrame Create animated and pretty Pandas Dataframe or Pandas Series, as shown below: Installation pip install rich-dataframe Usage Minimal example from sklearn.datasets import fetch_openml from rich_dataframe import prettify speed_dating = fetch_openml(name='SpeedDating', version=1)['frame'] table = prettify(speed_dating) If you want to pass a non-dataframe object, rich_dataframe got it covered too! from rich_dataframe import prettify var = {'a': 1, 'b': 3} prettify(var) Parameters df: pd.DataFrame The data you want to prettify row_limit : int, optional Number of rows to show, by default 20 col_limit : int, optional Number of columns to show, by default 10 first_rows : bool, optional Whether to show first n rows or last n rows, by default True. If this is set to False, show last n rows. first_cols : bool, optional Whether to show first n columns or last n columns, by default True. If this is set to False, show last n rows. delay_time : int, optional How fast is the
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python3-serpy-0.3.1-13.lbn36.noarch
serpy is a super simple object serialization framework built for speed. serpy serializes complex datatypes (Django Models, custom classes, ...) to simple native types (dicts, lists, strings, ...). The native types can easily be converted to JSON or any other format needed. The goal of serpy is to be able to do this simply, reliably, and quickly. Since serializers are class based, they can be combined, extended and customized with very little code duplication. Compared to other popular Python serialization frameworks like marshmallow or Django Rest Framework Serializers serpy is at least an order of magnitude faster. Python 3 version.
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python3-trytond+completion-7.6.4-1.lbn36.noarch
This is a metapackage bringing in completion extras requires for python3-trytond. It makes sure the dependencies are installed.
Located in LBN / … / Business / BastionLinux 36

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