semvec/pypi-packages huggingface dataset:
vnpy_sqlite 1.1.3
SQLite database adapter for VeighNa quant trading framework.
License: MIT
Requires Python: >=3.10
Keywords: algotrading, investment, quant, quantitative, trading

# VeighNa框架的SQLite数据库接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-1.1.3-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows|linux|macos-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.10|3.11|3.12|3.13-blue.svg" />
</p>

## 说明

基于peewee开发的SQLite数据库接口，无需另外安装配置数据库软件，易于使用适合初学者用户。

## 使用

在VeighNa中使用SQLite时，需要在全局配置中填写以下字段信息：

|名称|含义|必填|举例|
|---------|----|---|---|
|database.name|名称|是|sqlite|
|database.database|实例|是|database.db|
greenophy-client 0.1.4
Python client for the hosted Greenophy ESG substantiveness classification API.
License: MIT
Requires Python: >=3.9

# Greenophy Client

Python helper for calling the hosted Greenophy ESG substantiveness API.

## Installation

```bash
pip install .
```

## Usage

```python
from greenophy_client import SubstantivenessClient

# Uses the default hosted base URL. Add an API key if your partner token requires it.
client = SubstantivenessClient(api_key="your-shared-token")

results = client.classify_esg_text("We transitioned 70% of our fleet to EVs in 2023.\nWe value teamwork.")
for item in results:
    print(item.index, item.label_name, item.sentence)
```

Use `client.close()` when you are done (or create it with a context manager).

### Generic classification

Need a neutral rubric instead of the ESG-focused labels? Call `classify_generic_text("...")`, which targets `/api/generic_classification`.

### Environment-variable configuration

You can also set connection details once for the whole environment. Override the hosted endpoint only if you are targeting a private deployment.

```bash
export GREENOPHY_API_KEY="your-shared-token"
# export GREENOPHY_API_BASE_URL="https://your-private-service"  # optional
```

Then simply instantiate `SubstantivenessClient()` without arguments.
hyperpanda 0.1.2
A utility to convert pandas DataFrames to Tableau .hyper extracts using the Tableau Hyper API
License: MIT
Requires Python: >=3.7
Keywords: tableau, hyper, pandas, dataframe, extract

# HyperPanda

**A quick and easy way to convert a Pandas DataFrame to a Tableau .hyper extract using the Tableau Hyper API with support for spatial columns.**

---

## Overview

HyperPanda is a Python utility to convert Pandas DataFrames into Tableau `.hyper` extract files. It refactors and modernizes the original [pandleau](https://github.com/bwiley1/pandleau) project by using the up-to-date Tableau Hyper API instead of the deprecated Tableau SDK. Additionally, it supports spatial/geospatial columns mapped to Tableau's `GEOGRAPHY` type.

---

## Features

- Converts standard Pandas DataFrame columns to appropriate Tableau Hyper SQL types.
- Supports nullable and big integers.
- Automatically converts columns with numeric data stored as `object` dtype.
- Supports spatial data by casting specified columns as Tableau `GEOGRAPHY`.
- Cleans up old extract logs and temporary files before writing.

---

## Installation

```shell
    pip install hyperpanda
```

---

## Usage

```python
    import pandas as pd
    from hyperpanda import HyperPanda

    # Create your pandas DataFrame
    df = pd.DataFrame({...})

    # Specify columns that contain spatial data
    geo_columns = ['location_column']

    # Create HyperPanda object
    hp = HyperPanda(df, geo_columns)

    # Convert to Tableau .hyper extract
    hp.to_tableau("output.hyper")
```

---

## Credit

This package is a refactored and enhanced continuation of the [pandleau](https://github.com/bwiley1/pandleau) project originally developed by Benjamin Wiley (@jamin4lyfe). It updates the project to use the Tableau Hyper API for better compatibility and future-proofing.

---

## License

This project is licensed under the MIT License. See the LICENSE file for details.
vnpy_tqsdk 3.8.6.0
TQsdk datafeed for VeighNa quant trading framework.
License: MIT
Requires Python: >=3.10
Keywords: algotrading, investment, quant, quantitative, trading

# VeighNa框架的天勤TQSDK数据服务接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-3.8.6.0-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows|linux|macos-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.10|3.11|3.12|3.13-blue.svg"/>
    <img src ="https://img.shields.io/github/license/vnpy/vnpy.svg?color=orange"/>
</p>

## 说明

基于天勤TQSDK模块的3.8.6版本开发，支持以下中国金融市场的K线数据：

* 期货：
  * CFFEX：中国金融期货交易所
  * SHFE：上海期货交易所
  * DCE：大连商品交易所
  * CZCE：郑州商品交易所
  * INE：上海国际能源交易中心
* 股票：
  * SSE：上海证券交易所
  * SZSE：深圳证券交易所

注意：需要使用相应的数据服务权限，可以通过[该页面](https://www.shinnytech.com/tqsdk_professional/)申请试用或者付费购买。


## 安装

安装环境推荐基于4.0.0版本以上的【[**VeighNa Studio**](https://www.vnpy.com)】。

直接使用pip命令：

```
pip install vnpy_tqsdk
```


或者下载源代码后，解压后在cmd中运行：

```
pip install .
```


## 使用

在VeighNa中使用天勤TQSDK时，需要在全局配置中填写以下字段信息：

|名称|含义|必填|举例|
|---------|----|---|---|
|datafeed.name|名称|是|tqsdk|
|datafeed.username|用户名|是|test|
|datafeed.password|密码|是|12345678|