semvec/pypi-packages huggingface dataset:
creevey 2.3.0
Bulk image processing

# Creevey

> **Note 6/15/20:** Our team previously had a tradition of naming projects with terms or characters from the Harry Potter series, but we are disappointed by J.K. Rowling’s persistent transphobic comments. In response, we will be renaming this repository, and are working to develop an inclusive solution that minimizes disruption to our users.

Creevey is a file processing framework. It is designed for IO-bound workflows that involve reading files into memory, doing some processing on their contents, and writing out the results. It makes running those workflows faster and more reliable by parallelizing them across files, optional skipping files that have already been processed, handling errors, and keeping organized records of what happened.

Creevey was developed for deep learning computer vision projects, so in addition to the general framework it also provides predefined components for image processing. However, it can be used for any project that involves processing data from many sources.

See [the docs](https://creevey.readthedocs.io/) for more details.
lonny-sql 0.1.0
A library for building inline SQL queries safely using python callables.
Requires Python: >=3.8,<4.0

# `lonny_sql`

A library for building inline SQL queries safely using python `callables`.

## Installation:

```bash
pip install lonny_sql
```

## Usage:

Usage is very straightforward. Please see example below:

```python
from lonny_sql import build

table = "TABLE"
value = "VALUE"

sql, params = build(lambda w: f"""
    SELECT * FROM {table}
    WHERE value = {w(value)}
""")
```

We simply pass `build` a callable that takes a `wrapper` argument. This wrapper is itself a `callable` that returns the substituted parameter name whilst simultaneously adds the value to the parameter dictionary to be returned along with the finalized SQL.
evekeys 0.0.2
A set of functions that uses sklearn to conduct a TF-IDF analysis to generate keywords from event-based / grouped textual corpus.
Keywords: tf-idf,keyword extraction,event-based corpus

# evekeys: Isolate keywords from an event-based and custom-grouped textual corpus

By Chris Lindgren <chris.a.lindgren@gmail.com>

Distributed under the BSD 3-clause license. See LICENSE.txt or http://opensource.org/licenses/BSD-3-Clause for details.

**Documentation**: [https://evekeys.readthedocs.io/en/latest/](https://evekeys.readthedocs.io/en/latest/)

## Overview

A set of functions that uses scikit-learn to conduct a TF-IDF analysis to isolate keywords from event-based documents. It answers the following questions:

1. What keywords represent a particular period of content?
2. What keywords represent a particular group of content from a particular period?

It assumes you have:

- imported your corpus as a pandas DataFrame,
- included metadata information, such as a list of dates and list of groups to reorganize your corpus, and
- pre-processed your documents.

It functions only with Python 3.x and is not backwards-compatible.

**Warning**: evekeys performs little to no custom error-handling, so make sure your inputs are formatted properly. If you have questions, please let me know via email.

## System requirements

* pandas
* sklearn
* tqdm

## Installation
```pip install evekeys```

## Known Issues or Limitations

- Please contact me if you discover any issues.

## Example notebooks

- Coming soon.

## Distribution update terminal commands

<pre>
# Create new distribution of code for archiving
sudo python setup.py sdist bdist_wheel

# Distribute to Python Package Index
python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
</pre>