semvec/pypi-packages huggingface dataset:
WokeyTalky 0.1.3
A scalable and dynamic pipeline set for generation of spurious features.
Requires Python: >=3.9

```
# Package Name

Short description of what your package does.

## Installation

```bash
pip install package-name
```

## Usage

Provide examples of how to use your package. Include code snippets if applicable.

```python
import package_name

# Example of using your package
result = package_logs.function_name(param1, param2)
print(result)
```

## Features

- Feature 1
- Feature 2
- Feature 3

## Requirements

List the requirements if there are any specific dependencies that need to be installed separately.

## Contributing

Instructions for how others can contribute to the package. You can include guidelines for how to submit issues and pull requests.

## License

Specify the license under which your package is released.

## Authors

- Your Name - Initial work - [YourGitHub](https://github.com/YourGitHub)

## Acknowledgments

- Hat tip to anyone whose code was used
- Inspiration
- etc.
```
googletrends-ai 0.0.4
The graph shows how often the entered word or phrase is searched in Google Trend
Requires Python: >=3.8.10

入力した語句が、Googleトレンドで世界のさまざまな地域や、さまざまな言語での総検索ボリュームと比較してどのくらいの頻度で検索されているかをグラフで表します。語句を複数入力したい場合は「,」で区切ってください。（例：cat,dog）
事前に「pip install pytrends pandas matplotlib」を実行してください。



The graph shows how often the entered words or phrases are searched for in Google Trends compared to the total search volume in various regions of the world and in various languages.If you want to enter multiple words, please separate them with "," (e.g. cat,dog).
Please run [pip install pytrends pandas matplotlib] beforehand.
US-statemap 0.0.1
Displays a map of the United States as specified
Requires Python: >=3.8.10

アメリカの地図を指定した州、指定した色で表示します。
事前に「pip install geopandas matplotlib」を実行してください。

Displays a map of the United States in the specified state and specified color.
Please run "pip install geopandas matplotlib in advance.
nimbletl 0.1.14
Make your ETL easier and more nimble

# NimbleTL

NimbleTL is a Python package to facilitate ETL development with a focus on quick and easy initialization of MySQL database tables.

## Installation

To install NimbleTL, run:

```bash
pip install nimbletl

将配置文件写入环境变量中
nimbletl load-config-to-env --file ./log_drive_db_config.yml

刷新环境变量，需要知道写入到那个环境变量文件中
source ~/.zshrc
source ~/.bashrc
source ~/.bash_profile

nimbletl init-drive-db
cmip 0.0.9
An efficient information processing program.
Requires Python: >=3.8

# cmip
一个高效的信息处理库。

## 安装
```shell
pip install -U cmip
```

## 用法

### 1. 动态渲染异步爬虫
example: 
```python
from cmip.web import web_scraping
import asyncio
urls = [
        "https://baidu.com",
        "https://qq.com",
        # ...More URL
    ]
asyncio.run(web_scraping(urls, output_path="output", max_concurrent_tasks=10, save_image=True, min_img_size=200))
```
参数含义：

| urls | 网页链接（包含协议头） |
| --- | --- |
| output_path | 输出路径 |
| max_concurrent_tasks | 最大同时执行任务数，根据自身机器资源和网络情况调整 |
| save_image | 是否保存图片 |
| min_img_size | 当图片小于这个值时不爬取 |


### 2. 计算网页结构相似度
example:
```python
from cmip.web import html, simhash_array, hamming_distance_array
import requests

headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36"
}

html_text_baidu = requests.get("https://baidu.com", headers=headers).text
html_text_example_com = requests.get("https://example.com", headers=headers).text
html_text_example_org = requests.get("https://example.org", headers=headers).text
dom_tree_baidu = html.dom_tree(html_text_baidu)
dom_tree_example_com = html.dom_tree(html_text_example_com)
dom_tree_example_org = html.dom_tree(html_text_example_org)
simhash_baidu = simhash_array(dom_tree_baidu)
simhash_example_com = simhash_array(dom_tree_example_com)
simhash_example_org = simhash_array(dom_tree_example_org)

print("Similarity:", hamming_distance_array([simhash_example_com], [simhash_baidu, simhash_example_org]))
```
一般而言，当网页距离在4以内时，网页结构较为相似。