semvec/pypi-packages huggingface dataset:
pandashark 1.1.1.1
Apache Accumulo and Apache HDFS Python Connector
Requires Python: >=3.6

# ![logo](https://www.sharkbite.io/wp-content/uploads/2017/02/sharkbite.jpg) Sharkbite 
[![Documentation Status](https://readthedocs.org/projects/sharkbite/badge/?version=latest)](https://docs.sharkbite.io/en/latest/?badge=latest)

**S**harkbite is an HDFS and native client for Apache Accumulo ccumulo, with design liberties
that make it usable across other key/value stores. 

**P**andashark is a pandas linkage for sharkbite.
bioezy 0.0.6
Contains several bioinformatics scripts automating base level genomics analysis tasks
Requires Python: >=3.6

# Bioez Package v 0.0.6

### [PyPi](https://pypi.org/project/bioezy/#description)

### [Github](https://github.com/Rukhan4/bioezy-package)


## Installation: `pip install bioezy`

## Usage: `from bioezy import bzy`


## Package details

Contains several bioinformatics scripts automating base level genomics analysis tasks. Methods mainly runs on genomic and Dna data, 
which are fully documented. Also contains an extensive analysis on E.Coli
SF4wD 0.0.2
four component stochastic frontier model with determinants

# SF4wD #

four-component stochastic frontier model with determinants

## Motivation ##

This package was developed to complement four-component stochastic frontier that consider 

determinants in mean and variance parameters of inefficiency distributions 

by Ruei-Chi Lee. 

## Features ##

* **SF4wD**: main.py - set method and model to run simulation or real data

* **HMC**: Hamilton Monte Carlo designed for determinants parameters. 

* **DA**: Data augmentation for the model

* **TK**: Two-parametrization method originally proposed by Tsiona and Kunmbhaker (2014) for four-component model without determinants. 

* **PMCMC**: Particle MCMC for the model (perferred approach) - speed up by GPU parallel computation


## License ##

Ruei-Chi Lee is the main author and contributor.

Bug reports, feature requests, questions, rants, etc are welcome, preferably 
on the github page.
ossd 1.0.8
OpenSubtitles Subtitles Downloader

# OpenSubtitles Subtitles Downloader - OSSD
This package allows you to download subtitles as text from [www.opensubtitles.com](https://www.opensubtitles.com). All you need to have is [**Selenium**](https://selenium-python.readthedocs.io) with Chrome driver, **requests** package (*pip install requests*) and this **OSSD** package.
## Instalation
```
pip install ossd
```
PyPi page - [https://pypi.org/project/ossd/](https://pypi.org/project/ossd/)
## Usage
    import ossd
    ossd.get_subtitles(driver, movie_name)

* driver -- [Chrome Driver (Selenium)](https://chromedriver.chromium.org)
* movie_name -- Name of movie which subtitles you want to download

## Methods
**get_subtitles** -- returns most downloaded subtitles text, if error occurs then returns " " (whitespace) and prints error code
**parse_subtitles** -- parses script lines from default format into one line only text format, skips ads on the script start

## Example
     import ossd
     from selenium import webdriver  
     driver = webdriver.Chrome(executable_path=driverPath)
     text = ossd.get_subtitles(driver, "Star Wars: A New Hope")

#### Output (in variable text)
Same as [here](https://www.opensubtitles.org/en/subtitles/3182984/star-wars-episode-iv-a-new-hope-en).
> 0 \
> 00:02:40,680 --> 00:02:42,557 \
> Did you hear that? \
> 
> 1 \ 
> 00:02:42,680 --> 00:02:45,319 \
> They shut down the main reactor. \ 
> We'll be destroyed for sure. \
> 
> 2 \
> 00:02:45,440 --> 00:02:46,873 \
> This is madness. \
> 
> 3 \
> 00:02:56,560 --> 00:02:57,913 \
> We're doomed. \
> ....
---
	one_text = ossd.parse_subtitles(text)
#### Output (in variable one_text)
> Did you hear that? They shut down the main reactor. We'll be destroyed for sure. This is madness. We're doomed. ...
## Licence - MIT
Free for use, editing, selling, just mention author

## Development

+ Clean ununsed: `rm -rf build/ dist/ *egg* **.pyc __pycache__`
+ Build package: `python setup.py sdist bdist_wheel`
+ deploy package: `python -m twine upload dist/*`
cherrypicker 0.4.0
Pluck and flatten complex data.
Keywords: cherrypicker data etl extract flatten jquery

Cherrypicker
------------

*Flatten complex data.*

``cherrypicker`` aims to make common ETL tasks (filtering data and
restructuring it into flat tables) easier, by taking inspiration from jQuery
and applying it in a Pythonic way to generic data objects.

.. code-block:: bash

    pip install cherrypicker

``cherrypicker`` provides a chainable filter and extraction interface to
allow you to easily pick out objects from complex structures and place them in
a flat table. It fills a similar role to jQuery in JavaScript, enabling you to
navigate complex structures without the need for lots of complex nested for
loops or list comprehensions.

Examples
++++++++

.. code-block:: python

    >>> from cherrypicker import CherryPicker
    >>> import json
    >>> with open('climate.json', 'r') as fp:
    ...     data = json.load(fp)
    >>> picker = CherryPicker(data)

.. code-block:: python

    >>> picker['id', 'city'].get()
    [[1, 'Amsterdam'], [2, 'Athens'], [3, 'Atlanta GA'], ...]

.. code-block:: python

    >>> picker(city='B*')['info'](
    ...     population=lambda n: n > 2000000,
    ...     area=lambda a: a < 2000
    ... )['area', 'population'].get()
    [[1568, 8300000], [891, 3700000], [203, 2800000]]

More complex filtering and flattening of nested structures is possible. Learn
more in the documentation: https://cherrypicker.readthedocs.io.