semvec/pypi-packages huggingface dataset:
NamespaceDistribution 0.0.2
A small example package
License: MIT
Requires Python: >=3.3

# **Namespace Distribution**
The purpose of the distribution is to demonstrate how to install namespace packages. Namespace packages do not require \_\_init__.py files and allow modules to be stored in separate directories but logically appear to look as if they are installed in one directory.

To understand Git Mark down files [click here](https://docs.github.com/en/github/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)

## 1. What does it do?
This python distribution installs two namespace packages called SampleDistribution_1/pkg1 and SampleDistribution_2/pkg1. The two pkg1 directories logically look like one directory even though physically they are separate. Notice how the module SampleDistribution_2/pkg1/mod2 imports mod3 with the code "from . import mod3". 

## 2. How is this accomplished.
The python search path, sys.path, is modified to include the directories ...\PythonXX\site-packages\SampleDistribution_1 and ...\PythonXX\site-packages\SampleDistribution_2. The logic is coded in SampleDistribution_1/pkg2/main so that the environment varible PYTHONPATH does not have to be manually changed.

## 3. What versions of python support namespace packages
python 3.3 and later

## 4. How to install
pip install NamespaceDistribution

## 5. How do you run it?
SDRun <br>

## 6. What is the output?
in mod1.py <br>
...\PythonXX\site-packages\SampleDistribution_1\pkg1\mod3.py <br>
...\PythonXX\site-packages\SampleDistribution_2\pkg1\mod2.py

## 7. Why is the program named SDRun. 
SDRun is short for Sample Distribution Run. Instead of running python -m SampleDistribution_1/pkg2/main, the console script entry point SDRun has been created for ease of use.
racebert 1.1.0
Race and Ethnicity Prediction from names
License: MIT

# RaceBERT -- A transformer based model to predict race and ethnicty from names

# Installation

```
pip install racebert
```
Using a virtual environment is highly recommended!
You may need to install pytorch as instructed here: https://pytorch.org/get-started/locally/

# Paper
Todo

# Usage
raceBERT predicts race (U.S census race) and ethnicity from names. 

```python
from racebert import RaceBERT

model = RaceBERT()

# To predict race
model.predict_race("Barack Obama")
```

```
>>> {"label": "nh_black", "score": 0.5196923613548279}
```

The race categories are:
| Race                             | Label    |
|----------------------------------|----------|
| Non-hispanic White               | nh_white |
| Hispanic                         | hispanic |
| Non-hispanic Black               | nh_black |
| Asian & Pacific Islander         | api      |
| American Indian & Alaskan Native | aian     |


```python
# Predict ethnicity
model.predict_ethnicty("Arjun Gupta")
```
```
>>> {"label": "Asian,IndianSubContinent", "score": 0.9612812399864197}
```
The ethnicity categories are:

| Ethnicity                             |
|---------------------------------------|
| GreaterEuropean,British               |
| GreaterEuropean,WestEuropean,French   |
| GreaterEuropean,WestEuropean,Italian  |
| GreaterEuropean,WestEuropean,Hispanic |
| GreaterEuropean,Jewish                |
| GreaterEuropean,EastEuropean          |
| Asian,IndianSubContinent              |
| Asian,GreaterEastAsian,Japanese       |
| GreaterAfrican,Muslim                 |
| Asian,GreaterEastAsian,EastAsian      |
| GreaterEuropean,WestEuropean,Nordic   |
| GreaterEuropean,WestEuropean,Germanic |
| GreaterAfrican,Africans               |

## GPU

If you have a GPU, you can speed up the computation by specifying the CUDA device when you instantiate the model. 

```python
from racebert import RaceBERT

model = RaceBERT(device=0)

# predict race in batch
model.predict_race(["Barack Obama", "George Bush"])
```
```
>>>
[
        {"label": "nh_black", "score": 0.5196923613548279},
        {"label": "nh_white", "score": 0.8365859389305115}
]
```

```python
# predict ethnicity in batch
model.predict_ethnicity(["Barack Obama", "George Bush"])
```
# HuggingFace 

Alternatively, you can work with the transformers models hosted on the huggingface hub directly.

- Race Model: https://huggingface.co/pparasurama/raceBERT
- Ethnicity Model: https://huggingface.co/pparasurama/raceBERT-ethnicity

Please refer to the [transformers](https://huggingface.co/transformers/) documentation.
anime.py 0.5.7
Make cool animations with PyNime! Check PyPi page for more info: https://pypi.org/project/anime.py/
License: MIT
Requires Python: >=3.0,<4.0
Keywords: animations,console

# hello!
## PyNime is a console tool to allow easy access to console animations.
```bash
pip install --upgrade pip
pip3 install pyfiglet
pip3 install cursor
pip3 install getkey
pip3 install anime.py
```
# OR
```py
import os
downloads = """pip install --upgrade pip
pip3 install pyfiglet
pip3 install cursor
pip3 install getkey
pip3 install anime.py""".split("\n")
# OR
downloads = """curl -sSL https://raw.githubusercontent.com/sdispater/poetry/master/get-poetry.py | python
poetry add anime.py""".split("\n")
print("",file=open("pyproject.toml","w"),end="")
# then:
for i in downloads:
  os.system(i)
```
## Then

```py
from anime_py import Screen
screen = Screen()
screen.example() # this will run an example
```
## Things you can do:
```py
from anime_py import Screen
screen = Screen() 
print(screen.usable_functions()) # print this for all functions. If the function has an odd name like "THE_THING" or "func" then it's because it's an auxiliary function for another one.
```
terminaltables-whl 3.1.4
Generate simple tables in terminals from a nested list of strings. (has wheels!)
License: MIT
Requires Python: >=2.6
Keywords: Shell,Bash,ANSI,ASCII,terminal,tables

## terminaltables

Forked because original project's repo is ARCHIVED and there are no wheels for the old project in pypi.

# What is it

Easily draw tables in terminal/console applications from a list of lists of strings. Supports multi-line rows.

- Python 2.6, 2.7, PyPy, PyPy3, 3.3, 3.4, and 3.5 supported on Linux and OS X.
- Python 2.7, 3.3, 3.4, and 3.5 supported on Windows (both 32 and 64 bit versions of Python).

📖 Full documentation: https://robpol86.github.io/terminaltables


Quickstart
==========

Install:

```bash
pip install terminaltables
```

Usage:

```python
from terminaltables import AsciiTable
table_data = [
    ['Heading1', 'Heading2'],
    ['row1 column1', 'row1 column2'],
    ['row2 column1', 'row2 column2'],
    ['row3 column1', 'row3 column2']
]
table = AsciiTable(table_data)
print table.table
```
```bash
+--------------+--------------+
| Heading1     | Heading2     |
+--------------+--------------+
| row1 column1 | row1 column2 |
| row2 column1 | row2 column2 |
| row3 column1 | row3 column2 |
+--------------+--------------+
```

Example Implementations
=======================
[Example Scripts Screenshot](docs/examples.png?raw=true)

Source code for examples: 
[example1.py](https://github.com/Robpol86/terminaltables/blob/master/example1.py)
`example2.py <https://github.com/Robpol86/terminaltables/blob/master/example2.py>`_, and
`example3.py <https://github.com/Robpol86/terminaltables/blob/master/example3.py>`_

.. changelog-section-start