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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.
Located in
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Data Science
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BastionLinux 36
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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.
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Data Science
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BastionLinux 36
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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.
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Data Science
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BastionLinux 36
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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.
Located in
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Data Science
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BastionLinux 36
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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.
Located in
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Data Science
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BastionLinux 36
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python3-pandas-datareader-0.10.0-2.fc36.noarch
Data readers extracted from the pandas codebase, should be compatible with
recent pandas versions.
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Data Science
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BastionLinux 36
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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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Data Science
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BastionLinux 36
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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:
Located in
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Data Science
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BastionLinux 36
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python3-croniter-5.0.1-2.lbn36.noarch
croniter provides iteration for the datetime object with a cron like format.
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Core Linux
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BastionLinux 36
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haveged-1.9.8-1.lbn25.x86_64
A Linux entropy source using the HAVEGE algorithm
Haveged is a user space entropy daemon which is not dependent upon the
standard mechanisms for harvesting randomness for the system entropy
pool. This is important in systems with high entropy needs or limited
user interaction (e.g. headless servers).
Haveged uses HAVEGE (HArdware Volatile Entropy Gathering and Expansion)
to maintain a 1M pool of random bytes used to fill /dev/random
whenever the supply of random bits in /dev/random falls below the low
water mark of the device. The principle inputs to haveged are the
sizes of the processor instruction and data caches used to setup the
HAVEGE collector. The haveged default is a 4kb data cache and a 16kb
instruction cache. On machines with a cpuid instruction, haveged will
attempt to select appropriate values from internal tables.
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BastionLinux 25
