semvec/pypi-packages huggingface dataset:
bide-linqu-xuangu-zhanlue 2022.10.21.1
彼得林区选股战略
Requires Python: >=3.6

# 彼得林区选股战略

## 下载

### Docker

```
docker pull apachecn0/bide-linqu-xuangu-zhanlue
docker run -tid -p <port>:80 apachecn0/bide-linqu-xuangu-zhanlue
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bide-linqu-xuangu-zhanlue
bide-linqu-xuangu-zhanlue <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bide-linqu-xuangu-zhanlue
bide-linqu-xuangu-zhanlue <port>
# 访问 http://localhost:{port} 查看文档
```
nonebot-plugin-abbrreply 1.1.3
缩写查询器

<!--
 * @Author         : anlen123
 * @Date           : 2021-03-01 00:00:00
 * @LastEditors    : anlen123
 * @LastEditTime   : 2021-03-01 00:00:00
 * @Description    : None
 * @GitHub         : https://github.com/anlen123/nonebot_plugin_abbrreply
-->

<p align="center">
  <a href="https://v2.nonebot.dev/"><img src="https://v2.nonebot.dev/logo.png" width="200" height="200" alt="nonebot"></a>
</p>

<div align="center">

# nonebot_plugin_abbrreply

_✨ NoneBot 短句回复 查看插件 ✨_

</div>

<p align="center">
  <a href="https://raw.githubusercontent.com/cscs181/QQ-Github-Bot/master/LICENSE">
    <img src="https://img.shields.io/github/license/cscs181/QQ-Github-Bot.svg" alt="license">
  </a>
  <a href="https://pypi.python.org/pypi/nonebot-plugin-status">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-status.svg" alt="pypi">
  </a>
  <img src="https://img.shields.io/badge/python-3.7+-blue.svg" alt="python">
</p>

## 使用方式
sx lsp

缩写 lsp

lsp

['老色批', '恋尸癖', '老山炮', 'Lovesick Puppies（游戏名称）']

## 安装
pip install nonebot_plugin_abbrreply

## 使用
nonebot.load_plugin('nonebot_plugin_abbrreply')


# 已适配nonebot beta版本
corpus-cite 0.0.2
Initializer for decisions, citations, and voting tables.
Requires Python: >=3.10,<4.0

# Corpus Cite

Validator of inputs from raw files for the following sqlite db tables:

1. Decisions
2. Citations
3. Votelines
4. Titletags

## Clean raw ponente string

Each `ponente` name stored in `decisions_tbl` of the database has been made uniform, e.g.:

```python
>>> from corpus_vote import RawPonente
>>> RawPonente.clean("REYES , J.B.L, Acting C.J.") # sample name 1
"reyes, j.b.l."
>>> RawPonente.clean("REYES, J, B. L. J.") # sample name 2
"reyes, j.b.l."
```

We can see  most common names in the `ponente` field and the covered dates, e.g. from 1954 to 1972 (dates found in the decisions), there have been 1053 decisions marked with `jbl` (as cleaned):

```python
>>> from corpus_cite.helpers import most_popular
>>> [i for i in most_popular(db)] # excluding per curiams and unidentified cases
[
    ('1994-07-04', '2017-08-09', 'mendoza', 1297),
    ('1921-10-22', '1992-07-03', 'paras', 1287),
    ('2009-03-17', '2021-03-24', 'peralta', 1243),
    ('1998-06-18', '2009-10-30', 'quisumbing', 1187),
    ('1999-06-28', '2011-06-02', 'ynares-santiago', 1184),
    ('1956-04-28', '2008-04-04', 'panganiban', 1102),
    ('1936-11-19', '2009-11-05', 'concepcion', 1058),
    ('1954-07-30', '1972-08-18', 'reyes, j.b.l.', 1053),
    ('1903-11-21', '1932-03-31', 'johnson', 1043),
    ('1950-11-16', '1999-05-23', 'bautista angelo', 1028),
    ('2001-11-20', '2019-10-15', 'carpio', 1011),
    ...
]
```

This is retrieved through the following sql query:

```sql
select min(date), max(date), raw_ponente, count(*) num
from decisions_tbl
where raw_ponente is not null
group by raw_ponente
order by num desc
```