semvec/pypi-packages huggingface dataset:
Kinho 1.3.1
A library to classify images with deep learning.
License: MIT
Keywords: dev, web

The library features the "Neural" model, which is a Convolutional Neural Network (CNN) for image classification. It supports both CPU and GPU, providing excellent performance for large networks. You can also export and import Deep models and continue training on other machines. The exported file is generated in the ".brain" format, which is a proprietary data type of this project. For more information, please visit our repository.
nvdlib 0.8.3
National Vulnerability Database CPE/CVE API Library for Python
License: MIT
Requires Python: >=3.11.0

## Simple NIST NVD API wrapper library

<img src="docs/source/logo.png" width=300 style="padding-right:15px">

**NVDlib** is a Python library that allows you to interface with the [NIST National Vulnerability Database](https://nvd.nist.gov/) (NVD), pull vulnerabilities (CVEs), and [Common Platform Enumeration](https://nvd.nist.gov/products/cpe) (CPEs) into easily accessible objects.

![License](https://img.shields.io/github/license/bradleeriley/nvdlib) 
[![Written](https://img.shields.io/badge/Python%203.11.0-https%3A%2F%2Fpypi.org%2Fproject%2Fnvdlib%2F-yellowgreen)](https://pypi.org/project/nvdlib/)
[![Documentation Status](https://readthedocs.org/projects/nvdlib/badge/?version=latest)](https://nvdlib.readthedocs.io/en/latest/?badge=latest)

---

### Features

- Search the NVD for CVEs using all parameters allowed by the NVD API (recently updated to utilize version 2 of the API). Including search criteria such as CVE publish and modification date, keywords, severity, score, or CPE name.
- Search CPE names by keywords, CPE match strings, or modification dates. Then pull the CVE ID's that are relevant to those CPEs. 
- Retrieve details on individual CVEs, their relevant CPE names, and more.
- Built in rate limiting according to [NIST NVD recommendations](https://nvd.nist.gov/developers/start-here). <br> Get an API key (https://nvd.nist.gov/developers/request-an-api-key) to allow for a delay argument to be passed. Otherwise it is 6 seconds between requests by default.

### Install
```bash
$ pip install nvdlib
```


### Demo
```python
>>> import nvdlib
>>> r = nvdlib.searchCVE(cveId='CVE-2021-26855')[0]
>>> print(r.v31severity + ' - ' + str(r.v31score))
CRITICAL - 9.8
>>> print(r.descriptions[0].value)
Microsoft Exchange Server Remote Code Execution Vulnerability This CVE ID is unique from CVE-2021-26412, 
CVE-2021-26854, CVE-2021-26857, CVE-2021-26858, CVE-2021-27065, CVE-2021-27078.
>>> print(r.v31vector)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H 
```


### Development

Run the tests with

```bash
$ pip install -e '.[dev]'
$ pytest
```

### Documentation

https://nvdlib.com

#### More information

This is my first attempt at creating a library while utilizing all my Python experience from classes to functions.

For more information on the NIST NVD API for CPE and CVEs, see the documentation here: 
https://nvd.nist.gov/General/News/New-NVD-CVE-CPE-API-and-SOAP-Retirement

---

This product uses data from the NVD API but is not endorsed or certified by the NVD.
pyclimaterisk 1.1.1
This package provides climate risk data derived from newspapers data
License: MIT
Keywords: Climate, Risk, Newspapers, Finance

This mini package provides access to latin american climate risk indices at different categoriies. Those indices are derived from a large corpus of newspapers archives and available at monthly and daily frequency. It further provides certain climate related events (laws, legistlation, presidential decrees).

The package provides three different functions. Python library `Polars` is assumed to be installed. 

```
pip install pyclimaterisk
```

```
import pyclimaterisk as cr
```

```
cr.get_monthly_data()
```

and

```
cr.get_daily_data()
```
 and 
 
```
cr.get_events_data()
```


If you use this data set, please kindly cite our work as:
- Muhsin Ciftci, and Christina Kolerus. "Climate News and Asset Valuations: Insights from Latin America", IMF Working Papers 2025, 037 (2025), https://doi.org/10.5089/9798229000932.001