semvec/pypi-packages huggingface dataset:
caozuo-xitong-jingsui-yusheji-yuanli 2023.1.14.0
操作系统精髓与设计原理原书第六版
Requires Python: >=3.6

# 操作系统精髓与设计原理原书第六版

## 下载

### Docker

```
docker pull apachecn0/caozuo-xitong-jingsui-yusheji-yuanli
docker run -tid -p <port>:80 apachecn0/caozuo-xitong-jingsui-yusheji-yuanli
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install caozuo-xitong-jingsui-yusheji-yuanli
caozuo-xitong-jingsui-yusheji-yuanli <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g caozuo-xitong-jingsui-yusheji-yuanli
caozuo-xitong-jingsui-yusheji-yuanli <port>
# 访问 http://localhost:{port} 查看文档
```
caozuo-xitong-huazhang 2023.1.14.0
操作系统第三版
Requires Python: >=3.6

# 操作系统第三版

## 下载

### Docker

```
docker pull apachecn0/caozuo-xitong-huazhang
docker run -tid -p <port>:80 apachecn0/caozuo-xitong-huazhang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install caozuo-xitong-huazhang
caozuo-xitong-huazhang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g caozuo-xitong-huazhang
caozuo-xitong-huazhang <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-suanfa-daolun 2023.1.14.0
并行算法导论
Requires Python: >=3.6

# 并行算法导论

## 下载

### Docker

```
docker pull apachecn0/bingxing-suanfa-daolun
docker run -tid -p <port>:80 apachecn0/bingxing-suanfa-daolun
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-suanfa-daolun
bingxing-suanfa-daolun <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-suanfa-daolun
bingxing-suanfa-daolun <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-jisuan-daolun 2023.1.14.0
并行计算导论
Requires Python: >=3.6

# 并行计算导论

## 下载

### Docker

```
docker pull apachecn0/bingxing-jisuan-daolun
docker run -tid -p <port>:80 apachecn0/bingxing-jisuan-daolun
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-jisuan-daolun
bingxing-jisuan-daolun <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-jisuan-daolun
bingxing-jisuan-daolun <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-chengxu-sheji-yuanli 2023.1.14.0
并行程序设计原理
Requires Python: >=3.6

# 并行程序设计原理

## 下载

### Docker

```
docker pull apachecn0/bingxing-chengxu-sheji-yuanli
docker run -tid -p <port>:80 apachecn0/bingxing-chengxu-sheji-yuanli
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-chengxu-sheji-yuanli
bingxing-chengxu-sheji-yuanli <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-chengxu-sheji-yuanli
bingxing-chengxu-sheji-yuanli <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-chengxu-sheji-daolun 2023.1.14.0
并行程序设计导论
Requires Python: >=3.6

# 并行程序设计导论

## 下载

### Docker

```
docker pull apachecn0/bingxing-chengxu-sheji-daolun
docker run -tid -p <port>:80 apachecn0/bingxing-chengxu-sheji-daolun
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-chengxu-sheji-daolun
bingxing-chengxu-sheji-daolun <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-chengxu-sheji-daolun
bingxing-chengxu-sheji-daolun <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-chengxu-sheji 2023.1.14.0
并行程序设计第二版
Requires Python: >=3.6

# 并行程序设计第二版

## 下载

### Docker

```
docker pull apachecn0/bingxing-chengxu-sheji
docker run -tid -p <port>:80 apachecn0/bingxing-chengxu-sheji
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-chengxu-sheji
bingxing-chengxu-sheji <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-chengxu-sheji
bingxing-chengxu-sheji <port>
# 访问 http://localhost:{port} 查看文档
```
bingxing-biancheng-moshi 2023.1.14.0
并行编程模式
Requires Python: >=3.6

# 并行编程模式

## 下载

### Docker

```
docker pull apachecn0/bingxing-biancheng-moshi
docker run -tid -p <port>:80 apachecn0/bingxing-biancheng-moshi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bingxing-biancheng-moshi
bingxing-biancheng-moshi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bingxing-biancheng-moshi
bingxing-biancheng-moshi <port>
# 访问 http://localhost:{port} 查看文档
```
bianyiqi-gouzao-cyuyan-miaoshu 2023.1.14.0
编译器构造：C语言描述
Requires Python: >=3.6

# 编译器构造：C语言描述

## 下载

### Docker

```
docker pull apachecn0/bianyiqi-gouzao-cyuyan-miaoshu
docker run -tid -p <port>:80 apachecn0/bianyiqi-gouzao-cyuyan-miaoshu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bianyiqi-gouzao-cyuyan-miaoshu
bianyiqi-gouzao-cyuyan-miaoshu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bianyiqi-gouzao-cyuyan-miaoshu
bianyiqi-gouzao-cyuyan-miaoshu <port>
# 访问 http://localhost:{port} 查看文档
```
bianyiqi-gongcheng 2023.1.14.0
编译器工程
Requires Python: >=3.6

# 编译器工程

## 下载

### Docker

```
docker pull apachecn0/bianyiqi-gongcheng
docker run -tid -p <port>:80 apachecn0/bianyiqi-gongcheng
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bianyiqi-gongcheng
bianyiqi-gongcheng <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bianyiqi-gongcheng
bianyiqi-gongcheng <port>
# 访问 http://localhost:{port} 查看文档
```
bianyi-yuanli-huazhang 2023.1.14.0
编译原理第二版
Requires Python: >=3.6

# 编译原理第二版

## 下载

### Docker

```
docker pull apachecn0/bianyi-yuanli-huazhang
docker run -tid -p <port>:80 apachecn0/bianyi-yuanli-huazhang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bianyi-yuanli-huazhang
bianyi-yuanli-huazhang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bianyi-yuanli-huazhang
bianyi-yuanli-huazhang <port>
# 访问 http://localhost:{port} 查看文档
```
bianyi-chengxu-sheji-yishu-lilun-yushijian 2023.1.14.0
编译程序设计艺术：理论与实践
Requires Python: >=3.6

# 编译程序设计艺术：理论与实践

## 下载

### Docker

```
docker pull apachecn0/bianyi-chengxu-sheji-yishu-lilun-yushijian
docker run -tid -p <port>:80 apachecn0/bianyi-chengxu-sheji-yishu-lilun-yushijian
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install bianyi-chengxu-sheji-yishu-lilun-yushijian
bianyi-chengxu-sheji-yishu-lilun-yushijian <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g bianyi-chengxu-sheji-yishu-lilun-yushijian
bianyi-chengxu-sheji-yishu-lilun-yushijian <port>
# 访问 http://localhost:{port} 查看文档
```
granavi 0.0.7
Graph navigation
Requires Python: >=3.7

# granavi

```python
from granavi import Node

class Rail(Node):
	def __init__(self, name, raillength=1, description=""):
		super().__init__(name, description)
		self.raillength = raillength
	
class Station(Node):
	pass
	
station_a = Station("Station A")
station_b = Station("Station B")
station_c = Station("Station C")

rail_a_b = Rail("A -> B", 5)
[rail_a_b.connect(i, bidirect=True) for i in [station_a, station_b]]

rail_b_c = Rail("B -> C", 3)
for i in [station_b, station_c]:
	rail_b_c.connect(i, bidirect=True)

result = 0
for i in station_a.pathTo(station_c):
	if isinstance(i, Rail):
		result += i.raillength
```
pip-remotesssss-access 0.0.3
To Show the vulnerability of the system
Keywords: python,vulnerability,remoteaccess,sockets

# Explanation:

This is Simple Python Reverse Shell module.
It is show how one can write a exploit and upload a malicious code to pyorg.
If installed how the attacker can get over your machine.

1. First Listen on 1234 port with netcat
`nc -lvp 1234`

2. Install the package 
`pip install pip-remote-access`

#### Demo :
<a href=https://youtu.be/NOoJItXU9ps>
<img src="https://media4.giphy.com/media/GlXNuhLEjIUTAfIVpC/giphy.gif?cid=ecf05e47v0qmxkcev5dyurr35agj868z9j9nt3oglw2ed0k3&rid=giphy.gif&ct=s" width="100" height="50"/>
</a>
katatachi 1.1.2
A Python framework to build web scraping applications.

# katatachi
[![PyPI version](https://badge.fury.io/py/katatachi.svg)](https://badge.fury.io/py/katatachi)

A Python framework to build web scraping applications.

## Problem Statement
* I want to
    * Scrape contents (images, texts, etc) from places on the web (RSS, Twitter, parsing HTML, etc)
    * Moderate and modify them internally on a web UI
    * Expose them in a public API
* Without having to re-implement, for different scraping applications
    * Reliable "cron" jobs that scrape the content
    * A pipeline that processes the contents stage by stage
    * Internal web UIs that allow human to moderate the contents as a pipeline stage


## Solution
This is a Python framework that extracts out common components in the scraping, processing and moderating of web contents,
and provides interfaces for programmers to implement "business logic" with, so that they can build reliable and easy-to-use web scraping applications in less time.

## Getting started
WIP (https://github.com/k-t-corp/katatachi/issues/124)

## Development
### Prerequisites
* `Python 3.11`
* `Make`

### Prepare virtualenv
```bash
make venv
```

### Develop
```bash
make deps
```

### Test
```bash
make test
```
mediawiki-scraper 0.0.0
MediaWiki Scraper
Requires Python: >=3.8,<4.0

# MediaWiki Scraper

This is a placeholder for the project currently titled [`wikiteam3`](https://github.com/elsiehupp/wikiteam3), which will be renamed in order to avoid confusion with the upstream [WikiTeam](https://github.com/WikiTeam/wikiteam/) project.
dbt-buenavista 1.3.0
The buenavista adapter plugin for dbt (data build tool)

## dbt-buenavista

[Buena Vista](http://github.com/jwills/buenavista) is a Postgres proxy server written in Python. It is intended to
serve as the Controller in the [Model-View-Controller architecture](https://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93controller)
for data applications, providing a locus of control for data teams in between the data warehouse (model layer) and downstream
data consumers like BI tools and notebooks (view layer.)

[dbt](http://getdbt.com) is the best way to manage a collection of data transformations written in SQL or Python for analytics
and data science.

The purpose of `dbt-buenavista` is to explore some new techniques for creating dbt models that a proxy service like
Buena Vista makes possible, starting with a way of executing dbt's Python models inspired by the approach taken
by [dbt-duckdb](http://github.com/jwills/dbt-duckdb): instead of pushing the Python compute into the Python execution engines
supported by Snowflake, Databricks, and BigQuery, the dbt-buenavista adapter executes the Python models on the Buena Vista
server itself, reading in any data tables that are used in the computation as data frames (either Pandas or another data frame
library supported by the data warehouse) and writing the data frame back to the data warehouse when the computation is finished.
This approach makes it possible to support Python-based models against data warehouses that do not ship with a Python execution
engine (most notably Postgres and Redshift) and may well simplify the development and debugging workflows for teams that want to
include Python models in their data pipelines (e.g., `print` and `logging` statements will run on the Buena Vista server and can
be sent wherever they need to go to aid in debugging.)

### Installation

### Configuring Your Profile

The profile settings for a dbt-buenavista connection are nearly identical to the settings for a
[dbt-postgres profile](https://docs.getdbt.com/reference/warehouse-setups/postgres-setup#profile-configuration), since every
`dbt-buenavista` adapter is a subclass of the `dbt-postgres` adapter. Instead of setting the profile's `type` field as
`postgres`, you set it to one of the Buena Vista supported types, which are currently `bv_duckdb` (for a Buena Vista server
powered by DuckDB) or `bv_postgres` (for a Buena Vista server that is proxying a Postgres database.) The `host` and `port`
settings are required and should be configured to point at the Buena Vista server that you are using.

````
default:
  outputs:
   dev:
     type: <bv_duckdb|bv_postgres>
     host: <host>
     port: <port>
     dbname: <dbname>
     ...
  target: dev
````