semvec/pypi-packages huggingface dataset:
h5dataframe 0.2.3
Drop-in replacement for pandas DataFrames that allows to store data on an hdf5 file and manipulate data directly from that hdf5 file without loading it in memory.
Requires Python: >=3.10

# h5dataframe

Drop-in replacement for pandas DataFrames that allows to store data on an hdf5 file and manipulate data directly from that hdf5 file without loading it in memory.

# Warning !

This is very much a **work in progress**, some features might not work yet or cause bugs.
**Save** your data elsewhere before converting it to an H5DataFrame.

If you miss a feature from pandas DataFrames, please fill an issue or feel free to contribute.

# Overview

This library provides the `H5DataFrame` object, replacing the regular `pandas.DataFrame`.

An `H5DataFrame` can be created from a `pandas.DataFrame` or from a dictionnary of (column_name -> column_values).

```python
>>> import pandas as pd
>>> from h5dataframe import H5DataFrame
>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, 
                      index=['r1', 'r2', 'r3'])
>>> hdf = H5DataFrame(df)
>>> hdf
    a  b
r1  1  4
r2  2  5
r3  3  6
[RAM]
[3 rows x 2 columns]
```

At this point, all the data is still loaded in RAM, as indicated by the second-to-last line. To write the data to an hdf5 file, use the `H5DataFrame.write()` method.

```python
>>> hdf.write('path/to/file.h5')
>>> hdf
    a  b
r1  1  4
r2  2  5
r3  3  6
[FILE]
[3 rows x 2 columns]
```

The `H5DataFrame` is now backed on an hdf5 file, only loading data in RAM when requested.

Alternatively, an `H5DataFrame` can be read directly from an previously created hdf5 file with the `H5DataFrame.read()` method.

```python
>>> from h5dataframe import H5Mode
>>> H5DataFrame.read('path/to/file.h5', mode=H5Mode.READ)
    a  b
r1  1  4
r2  2  5
r3  3  6
[FILE]
[3 rows x 2 columns]
```

The default mode is `READ` (`'r'`) which creates a **read-only** `H5DataFrame`. To modify the data, use `mode=H5Mode.READ_WRITE` (`'r+'`).

# Installation

From pip:
```shell
pip install h5dataframe
```

From source:
```shell
git clone git@github.com:Vidium/h5dataframe.git
```
work-calendar 1.1.0
Determines if a day is a working day in Russia
Requires Python: <4.0,>=3.10
Keywords: utility, python, official, work, calendar, Russia

[![license: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![PyPI](https://img.shields.io/pypi/v/work-calendar?label=pypi%20work-calendar)
![ruff](https://github.com/Polyrom/work-calendar/actions/workflows/linter.yml/badge.svg)
![tests](https://github.com/Polyrom/work-calendar/actions/workflows/tests.yml/badge.svg)

# Work Calendar

A simple no-nonsense library to find out whether a day is a working day in Russia.

Data obtained from [consultant.org](https://www.consultant.ru). I try to parse it as soon as the official calendar for the following year is published, which is normally late summer or early autumn.

Data available **for years 2015-2027**. Feel free to use the [raw json file](work_calendar/total.json).

## Installation

```bash
pip install work-calendar
```

## Usage

```python
from datetime import date

import work_calendar

new_years_holiday = date(2020, 1, 2)
weekend = date(2021, 4, 4)
workday = date(2020, 6, 3)
for day in [new_years_holiday, weekend, workday]:
    if work_calendar.is_workday(day):
        print(f"{day.strftime('%A %d.%m.%Y')} is a workday in Russia")
    else:
        print(f"{day.strftime('%A %d.%m.%Y')} is not a workday in Russia")

dt_out_of_bounds = dt = date(2090, 1, 2)
try:
    work_calendar.is_workday(dt_out_of_bounds)
except work_calendar.NoDataForYearError:
    print(f"No data found for date {dt_out_of_bounds} in work calendar!")
```

**Output**

```
Thursday 02.01.2020 is not a workday in Russia
Sunday 04.04.2021 is not a workday in Russia
Wednesday 03.06.2020 is a workday in Russia
No data found for date 2090-01-02 in work calendar!
```
containmint 0.6.0
Create multi-arch containers using native cloud builds.
Requires Python: <3.14,>=3.11

containmint
===========

Create multi-arch containers using native cloud builds.

Q&A
===

Why another tool?
-----------------

Most existing tools and services rely on QEMU to perform container builds on other architectures.
These builds are much slower, often running 15x longer than native builds.

Additionally, using customizable virtual machines allows for builds which dedicated build services may not support.

How does it work?
-----------------

Ephemeral virtual machines are provisioned through the Ansible Core CI service [#ansible_core_ci]_.
These virtual machines are used to perform native container builds.
The resulting images are pushed to a container registry.

After the container images are pushed, a manifest list referencing the container images is created.
The manifest list is then pushed to a container registry.

.. rubric:: Footnotes

.. [#ansible_core_ci] Authentication is required.
   An API key must be provided, or the tool must be run from an approved organization at a supported CI provider.

Usage Examples
==============

Configure container registry credentials
----------------------------------------

The credentials [#no_login]_ for the container registry [#one_registry]_ are set using environment variables:

.. code-block::

   export CONTAINMINT_USERNAME = 'my-username'
   export CONTAINMINT_PASSWORD = 'my-password'

.. rubric:: Footnotes

.. [#no_login] Use the ``--no-login`` option to allow operation without credentials.
   This option is only usable when not pushing to a container registry.

.. [#one_registry] Only one container registry can be used with each invocation.
   Multiple repositories from the same registry can be used.

Build and push a multi-arch container
-------------------------------------

The following steps can be performed in parallel:

.. code-block::

   containmint build --push --tag quay.io/my_org/scratch_repo:my_tag-x86_64 --arch x86_64
   containmint build --push --tag quay.io/my_org/scratch_repo:my_tag-aarch64 --arch aarch64

Once the steps above have been completed:

.. code-block::

   containmint merge --push \
     --tag quay.io/my_org/final_repo:my_tag \
           quay.io/my_org/scratch_repo:my_tag-x86_64 \
           quay.io/my_org/scratch_repo:my_tag-aarch64

This results in three tags:

* ``quay.io/my_org/final_repo:my_tag`` -- This manifest list contains x86_64 and aarch64 images.
* ``quay.io/my_org/scratch_repo:my_tag-x86_64`` -- This image is x86_64 only.
* ``quay.io/my_org/scratch_repo:my_tag-aarch64`` -- This image is aarch64 only.
django-ldaporm 1.3.0
A Django ORM-like interface for ldap objectclasses
Requires Python: >=3.10
Keywords: django, ldap

# django-ldaporm

`django-ldaporm` provides  Django ORM-like module that allows you to treat ldap
object classes like RDBMS tables.  This allows you to use Django forms, fields
and views natively with ldap models.

## Installing django-ldaporm

`django-ldaporm` is a pure python package.  As such, it can be installed in the
usual python ways.  For the following instructions, either install it into your
global python install, or use a python [virtual
environment](https://python-guide-pt-br.readthedocs.io/en/latest/dev/virtualenvs/)
to install it without polluting your global python environment.

### Install via pip

    pip install django-ldaporm