semvec/pypi-packages huggingface dataset:
classicdata 0.1.0a2
Loaders for classic datasets commonly used in Machine Learning
Requires Python: >=3.9, <4
Keywords: machine learning

# Classic Datasets

Loaders for classic datasets commonly used in Machine Learning:

| Name                                              |   # Samples |   # Features |   # Classes |
|---------------------------------------------------|-------------|--------------|-------------|
| [Ionosphere](https://archive.ics.uci.edu)         |         351 |           34 |           2 |
| [Letter Recognition](https://archive.ics.uci.edu) |       20000 |           16 |          10 |
| [Telescope](https://archive.ics.uci.edu)          |       19020 |           10 |           2 |
| [Pen Digits](https://archive.ics.uci.edu)         |       10992 |           16 |          10 |
| [Robot Navigation](https://archive.ics.uci.edu)   |        5456 |           24 |           4 |
| [Segmentation](https://archive.ics.uci.edu)       |        2310 |           16 |           7 |
| [USPS](http://www.gaussianprocess.org/gpml/data/) |        9298 |          256 |          10 |

## Installation

```
pip install classicdata
```

Run `python -m classicdata.info` to list all implemented datasets.

## Example Usage

```python
from classicdata import Ionosphere

ionosphere = Ionosphere()

# Use ionosphere.points and ionosphere.labels...
```
VCFfrom23AndMe 0.1.0
@csonido2 An retooling of github @david-wb's "23andme-to-vcf" command line code to work with inline python code.

An retooling of github @david-wb's "23andme-to-vcf" command line code to work with inline python code.
    
Developed to enhance business capabilities for the government contractor Spinsys.

Usage:
<br/>
<code>
import vcf_from_23andme
</code>
<br/>
<code>
vcf_from_23andme.from_file("C:/input_name.23andme", "C:/desired_output_name.vcf", "C:/my_fasta_file.fa.fasta", "C:/my_human_genome_file.fa.fai")
</code>
OpenTamPy 0.10.1
OpenTamPy - A Python library to interface with https://intranet.tam.ch/
Requires Python: >=3.10

# OpenTamPy

[![Documentation Status](https://readthedocs.org/projects/opentampy/badge/?version=latest)](https://opentampy.readthedocs.io/?badge=latest)
![build](https://github.com/neonfighter28/OpenTamPy/actions/workflows/main.yml/badge.svg)

## A Python Library to simplify the access to the [Intranet](https://intranet.tam.ch/) website

[Full Documentation](https://opentampy.readthedocs.io/)

Example snippet, more can be found in the documentation as well as in [examples](https://github.com/neonfighter28/OpenTamPy/tree/master/examples)/

```python
from OpenTamPy import Intranet

    username = "YOURUSERNAME"
    password = "YOURPASSWORD"
    school = "krm"

    instance = Intranet(username, password, school)
    timetable = instance.get_timetable()
    for lesson in timetable:
        print(lesson.courseName)
```

## Development

### Installing with pip

`python3 -m pip install OpenTamPy`

### Installing from source

1. Clone the repo from [https://github.com/neonfighter28/OpenTamPy](https://github.com/neonfighter28/OpenTamPy)
2. Install dependencies from requirements.txt with `pip install -r requirements.txt`

### Check typehints and pep8

Please make sure your PR is pep8 compliant. More about pep8 can be found [here](https://pep8.org/)

1. Make sure you have ``mypy`` and ``pycodestyle`` installed
2. run ``mypy ./src/OpenTamPy.py``
3. run ``pycodestyle OpenTamPy.py --max-line-length=119``

### Pushing to GitHub

1. Fork the project
2. Commit your changes
3. Create a Pull Request
docov 0.0.3
Light-weight docstring coverage analysis for python modules
Requires Python: >=3.6, <4
Keywords: coverage,development

# docov

[![docov](https://ripaul.github.io/docov/docov.svg)](https://ripaul.github.io/docov/docov.txt)

Light-weight, recursive docstring coverage analysis for python modules. 

## Overview

`docov` provides light-weight docstring coverage analysis for python modules. 
It recursively collects symbols defined by the specified module and checks whether these
symbols have docstrings and if they are longer than at least 20 symbols.

The docstring coverage is computed simply as the fraction of symbols which are deemed
to have a sufficient docstring and the total number of symbols found.

The command-line utility will generate a badge indicating the docstring coverage and 
a report listing all symbols found in the module, which do not have sufficient
docstrings.

In order to not distort the docstring coverage, symbols and modules (and their symbols)
can be ignored in the analysis. 
This allows for some fine-tuning in really only considering the desired module's symbols.
Builtin types are currently always ignored, meaning that e.g. methods of a string object
in a module will not be assessed.

## Installation

`docov` is available via [PyPI](https://pypi.org/project/docov/):
```
$ pip install docov
```

## Usage

Once installed, `docov` can be used from the command-line
```
$ docov <module-name>
```
which will produce a badge and a report on symbols, which were deemed to have insufficient docstring.

A short help text on available options can be found with

```
$ docov -h
usage: docov [-h] [-o DIR] [-p PREFIX] [-i SYMBOL [SYMBOL ...]] [-d DEPTH] [--no-badge] [--no-report] MODULE

Command line utility to analyze docstring coverage of python modules.
Works by recursively fetching the module's symbol's docstrings and 
checking that they consist of at least 20 characters.

Generates a SVG-badge and a report if not specified otherwise, stored in
DIR/[PREFIX_]docov.svg and DIR/[PREFIX_]docov.txt respectively.

positional arguments:
  MODULE                The module which is to be analyzed.

optional arguments:
  -h, --help            show this help message and exit
  -o DIR, --output DIR  Output directory.
  -p PREFIX, --prefix PREFIX
                        Output file prefix: PREFIX_docov.[txt,svg]
  -i SYMBOL [SYMBOL ...], --ignore SYMBOL [SYMBOL ...]
                        Ignore symbols. If the symbol names a module, all its types will be ignored in the recursion.
  -d DEPTH, --depth DEPTH
                        Analysis recursion depth.
  --no-badge            Suppress badge generation.
  --no-report           Suppress report generation.

```