semvec/pypi-packages huggingface dataset:
shiyong-gnuplot-kexue-zuotu 2024.3.2.0
使用GnuPlot科学作图
Requires Python: >=3.6

# 使用GnuPlot科学作图

## 下载

### Docker

```
docker pull apachecn0/shiyong-gnuplot-kexue-zuotu
docker run -tid -p <port>:80 apachecn0/shiyong-gnuplot-kexue-zuotu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyong-gnuplot-kexue-zuotu
shiyong-gnuplot-kexue-zuotu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyong-gnuplot-kexue-zuotu
shiyong-gnuplot-kexue-zuotu <port>
# 访问 http://localhost:{port} 查看文档
```
shiyanlou-wangluojiaocheng-heji-20160904 2024.3.2.0
实验楼网络教程合集 20160904
Requires Python: >=3.6

# 实验楼网络教程合集 20160904

## 下载

### Docker

```
docker pull apachecn0/shiyanlou-wangluojiaocheng-heji-20160904
docker run -tid -p <port>:80 apachecn0/shiyanlou-wangluojiaocheng-heji-20160904
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyanlou-wangluojiaocheng-heji-20160904
shiyanlou-wangluojiaocheng-heji-20160904 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyanlou-wangluojiaocheng-heji-erlingyiliulingjiulingsi
shiyanlou-wangluojiaocheng-heji-erlingyiliulingjiulingsi <port>
# 访问 http://localhost:{port} 查看文档
```
shiyanlou-suanfa-jiaocheng-heji-20160904 2024.3.2.0
实验楼算法教程合集 20160904
Requires Python: >=3.6

# 实验楼算法教程合集 20160904

## 下载

### Docker

```
docker pull apachecn0/shiyanlou-suanfa-jiaocheng-heji-20160904
docker run -tid -p <port>:80 apachecn0/shiyanlou-suanfa-jiaocheng-heji-20160904
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyanlou-suanfa-jiaocheng-heji-20160904
shiyanlou-suanfa-jiaocheng-heji-20160904 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyanlou-suanfa-jiaocheng-heji-erlingyiliulingjiulingsi
shiyanlou-suanfa-jiaocheng-heji-erlingyiliulingjiulingsi <port>
# 访问 http://localhost:{port} 查看文档
```
chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi 2024.3.2.0
传智播客 mybatis 框架课程讲义
Requires Python: >=3.6

# 传智播客 mybatis 框架课程讲义

## 下载

### Docker

```
docker pull apachecn0/chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi
docker run -tid -p <port>:80 apachecn0/chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi
chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi
chuanzhibo-ke-mybatis-kuangjia-kecheng-jiangyi <port>
# 访问 http://localhost:{port} 查看文档
```
distance-correlation 0.0.2
Computes distance covariances/correlations between vectors and distance covariance/correlation matrices for data matrices
Requires Python: >=3.9

# distance correlation

Distance correlation is a measure of dependence between two vectors. It's particularly known for the nice property that two uncorrelated vectors are guaranteed to be independent.

In slightly more detail: given X and Y random vectors, distance covariance is a metric (called the [energy distance](https://en.wikipedia.org/wiki/Energy_distance)) of how distant is the actual joint distribution (X,Y) from the alternate distribution that would hold if X and Y were independent. [Wikipedia has the technical details](https://en.wikipedia.org/wiki/Distance_correlation), but to illustrate its main properties we'll borrow the following chart, with depicts a few point cloud-type datasets and their distance correlation:

![Distance Correlation - examples](https://upload.wikimedia.org/wikipedia/commons/thumb/9/99/Distance_Correlation_Examples.svg/1024px-Distance_Correlation_Examples.svg.png)

- Unlike Pearson correlation, distance correlation is bounded between 0 and 1, and doesn't have an interpretation of linear directionality, Therefore negatively sloped lines have perfect correlation = 1.
- Distance correlation is quite adept at detecting nonlinear structures. The Pearson correlation for all the charts in the third row is 0, 

I've always been interested in distance correlation and tried to use it in my datasets, but it's quite computation-intensive in ways that aren't easy to vectorize for numpy optimizations. This package relies on a custom-written C++ extension that leverages [pybind11](https://github.com/pybind/pybind11) for Python integration and the excellent [Eigen3](https://eigen.tuxfamily.org/index.php?title=Main_Page) library for C++ linear algebra that saves the heartache of manual memory management.
chrome-kaifagongju-zhinan 2024.3.2.0
Chrome 开发工具指南
Requires Python: >=3.6

# Chrome 开发工具指南

## 下载

### Docker

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

### PYPI

```
pip install chrome-kaifagongju-zhinan
chrome-kaifagongju-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g chrome-kaifagongju-zhinan
chrome-kaifagongju-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```
shiyanlou-sql-jiaocheng-heji-20160904-fix1 2024.3.2.0
实验楼 SQL 教程合集 20160904 fix1
Requires Python: >=3.6

# 实验楼 SQL 教程合集 20160904 fix1

## 下载

### Docker

```
docker pull apachecn0/shiyanlou-sql-jiaocheng-heji-20160904-fix1
docker run -tid -p <port>:80 apachecn0/shiyanlou-sql-jiaocheng-heji-20160904-fix1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyanlou-sql-jiaocheng-heji-20160904-fix1
shiyanlou-sql-jiaocheng-heji-20160904-fix1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyanlou-sql-jiaocheng-heji-erlingyiliulingjiulingsi-fix1
shiyanlou-sql-jiaocheng-heji-erlingyiliulingjiulingsi-fix1 <port>
# 访问 http://localhost:{port} 查看文档
```
ceh-v8-labs-module-all-in-one 2024.3.2.0
CEH v8 Labs Module All-In-One
Requires Python: >=3.6

# CEH v8 Labs Module All-In-One

## 下载

### Docker

```
docker pull apachecn0/ceh-v8-labs-module-all-in-one
docker run -tid -p <port>:80 apachecn0/ceh-v8-labs-module-all-in-one
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ceh-v8-labs-module-all-in-one
ceh-v8-labs-module-all-in-one <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ceh-v8-labs-module-all-in-one
ceh-v8-labs-module-all-in-one <port>
# 访问 http://localhost:{port} 查看文档
```
shiyanlou-spark-jiaocheng-heji-20160904 2024.3.2.0
实验楼 Spark 教程合集 20160904
Requires Python: >=3.6

# 实验楼 Spark 教程合集 20160904

## 下载

### Docker

```
docker pull apachecn0/shiyanlou-spark-jiaocheng-heji-20160904
docker run -tid -p <port>:80 apachecn0/shiyanlou-spark-jiaocheng-heji-20160904
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyanlou-spark-jiaocheng-heji-20160904
shiyanlou-spark-jiaocheng-heji-20160904 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyanlou-spark-jiaocheng-heji-erlingyiliulingjiulingsi
shiyanlou-spark-jiaocheng-heji-erlingyiliulingjiulingsi <port>
# 访问 http://localhost:{port} 查看文档
```
category-theory-for-scientists-mit-18-s996 2024.3.2.0
Category Theory for Scientists  (MIT 18.S996)
Requires Python: >=3.6

# Category Theory for Scientists  (MIT 18.S996)

## 下载

### Docker

```
docker pull apachecn0/category-theory-for-scientists-mit-18-s996
docker run -tid -p <port>:80 apachecn0/category-theory-for-scientists-mit-18-s996
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install category-theory-for-scientists-mit-18-s996
category-theory-for-scientists-mit-18-s996 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g category-theory-for-scientists-mit-yiba-sjiujiuliu
category-theory-for-scientists-mit-yiba-sjiujiuliu <port>
# 访问 http://localhost:{port} 查看文档
```
calculus-with-applications-mit-18-013a 2024.3.2.0
calculus with applications (MIT 18.013A)
Requires Python: >=3.6

# calculus with applications (MIT 18.013A)

## 下载

### Docker

```
docker pull apachecn0/calculus-with-applications-mit-18-013a
docker run -tid -p <port>:80 apachecn0/calculus-with-applications-mit-18-013a
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install calculus-with-applications-mit-18-013a
calculus-with-applications-mit-18-013a <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g calculus-with-applications-mit-yiba-lingyisana
calculus-with-applications-mit-yiba-lingyisana <port>
# 访问 http://localhost:{port} 查看文档
```
shiyanlou-scala-jiaocheng-heji-20160904 2024.3.2.0
实验楼 Scala 教程合集 20160904
Requires Python: >=3.6

# 实验楼 Scala 教程合集 20160904

## 下载

### Docker

```
docker pull apachecn0/shiyanlou-scala-jiaocheng-heji-20160904
docker run -tid -p <port>:80 apachecn0/shiyanlou-scala-jiaocheng-heji-20160904
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyanlou-scala-jiaocheng-heji-20160904
shiyanlou-scala-jiaocheng-heji-20160904 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyanlou-scala-jiaocheng-heji-erlingyiliulingjiulingsi
shiyanlou-scala-jiaocheng-heji-erlingyiliulingjiulingsi <port>
# 访问 http://localhost:{port} 查看文档
```
cnbc 1.0.0
A Python package that wraps CNBC API endpoints and returns financial data in JSON.
Requires Python: >=3.12

# CNBC

A Python package that wraps CNBC API endpoints and returns financial data in JSON. The API queries business news and live market data to streamline the development of financial applications.
<table><tr><td>

#### IMPORTANT LEGAL DISCLAIMER

---

cnbc is **not** affiliated, endorsed, or vetted by CNBC, Inc. It is
an open-source tool that uses a publicly available API and is
intended for research and educational purposes.
</td></tr></table>

## Installation

```shell
pip install cnbc
```

## [Subscribe to CNBC API](https://rapidapi.com/apidojo/api/cnbc/ 'CNBC API')

## Utilization

### APIWrapper
The `APIWrapper` class is used to make requests to the CNBC API. <br>
Note: A majority of the CNBC API endpoints require parameters. These must be set by an additional instruction.

```python
from cnbc import APIWrapper, Endpoints

api_wrapper = APIWrapper(
    api_key='YOUR_API_KEY',
    endpoint=Endpoints.TRANSLATE
)
# The APIWrapper class will supply the required parameters for the configured CNBC API endpoint.
api_wrapper_params = api_wrapper.params
api_wrapper_params['symbol'] = 'AAPL'
# The APIWrapper class will make a request to the CNBC API and return the response in JSON.
json_response = api_wrapper.request()

# The APIWrapper class can be repurposed to make multiple requests to the CNBC API.
api_wrapper.endpoint = Endpoints.GET_SUMMARY
api_wrapper_params = api_wrapper.params
api_wrapper_params['issueIds'] = json_response['issueId']
json_response = api_wrapper.request()
```

#### Translate Endpoint
A majority of the CNBC API endpoints require an `issueId` or `issueIds` parameter. The translate endpoint is used to convert a symbol to an `issueId`. <br>
Note: The `APIWrapper` class contains a translation table which can be loaded and saved to a file to reduce the number of requests to the CNBC API.
calculus-for-beginners-mit 2024.3.2.0
Calculus for Beginners (MIT)
Requires Python: >=3.6

# Calculus for Beginners (MIT)

## 下载

### Docker

```
docker pull apachecn0/calculus-for-beginners-mit
docker run -tid -p <port>:80 apachecn0/calculus-for-beginners-mit
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install calculus-for-beginners-mit
calculus-for-beginners-mit <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g calculus-for-beginners-mit
calculus-for-beginners-mit <port>
# 访问 http://localhost:{port} 查看文档
```