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python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-inline-0.1.7-2.lbn36.noarch: Inline Matplotlib backend for Jupyter

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-marshmallow-sqlalchemy-1.0.0-1.lbn36.noarch: ********************** marshmallow-sqlalchemy **********************|pypi- package| |build-status| |docs| |marshmallow3| |black|Homepage: < integration with the marshmallow < (de)serialization library.Declare your models .. code- block:: python import sqlalchemy as sa from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker, relationship,...

python3-sqlalchemy-spanner-1.6.2-2.lbn36.noarch: Spanner dialect for SQLAlchemy represents an interface API designed to make it possible to control Cloud Spanner databases with SQLAlchemy API. The dialect is built on top of the Spanner DB API, which is designed in accordance with PEP-249. Known limitations are listed here. All supported features have been tested and verified to work with the test configurations. There may be configurations and/or data model variations that have not yet been covered by the tests and that show unexpected behavior. Please report any problems that you might encounter by creating a new issue. Cloud Spanner product documentation SQLAlchemy product documentation Quick Start In order to use this package, you first need to go through the following steps: Select or create a Cloud Platform project. Enable billing for your project. Enable the Google Cloud Spanner API. Setup Authentication. Installation Stable released version of the package is available on PyPi: pip install sqlalchemy-spanner To install a

python3-snowflake-connector-3.15.0-1.lbn36.x86_64: This package includes the Snowflake Connector for Python, which conforms to the Python DB API 2.0 specification: Documentation is available at: code is also available at: Release Notes- v2.8.2(November 18,2022) - Improved performance of OCSP response caching - During the execution of GET commands we no longer resolve target location on the local machine - Improved performance of regexes used...

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