semvec/pypi-packages huggingface dataset:
mysql-f 0.1.4
高性能MySQL操作工具类库
Requires Python: >=3.7

# MySQL_F - 高效 MySQL 数据库操作封装库

`MySQL_F` 是一个 Python 库，封装了 MySQL 数据库的常用操作，提供了简洁易用的 API，支持连接池、批量插入、条件查询、更新、删除等功能。通过智能分块插入和 LRU 缓存优化，大幅提升了数据库操作的性能。

## 安装

使用 pip 安装：

```bash
pip install mysql_f
```

## 快速开始

### 初始化数据库连接

```python
from mysql_f import MySQL_F as my

# 初始化数据库连接
db = my.MySQL_F(
    dbName="test_db",        # 数据库名
    tableName="users",       # 默认表名
    host='localhost',        # 数据库地址
    user='root',             # 用户名
    password='123456',       # 密码
    charset='utf8mb4',       # 字符集
    poolSize=20              # 连接池大小（默认CPU核心数×5）
)
```

### 创建数据表

```python
# 创建表
db.create({
    "id": "INT AUTO_INCREMENT PRIMARY KEY",
    "name": "VARCHAR(255) NOT NULL",
    "age": "INT",
})
```

### 插入数据

```python
# 单条插入
db.add({"name": "张三", "age": 25})  

# 批量插入（智能分块优化）
db.add([
    {"name": "李四", "age": 28},
    {"name": "王五", "age": 32},
    {"name": "赵六", "age": 30},
    {"name": "钱七", "age": 22},
    {"name": "孙八", "age": 27},
]) 
```

### 查询数据

#### 基础查询

```python
# 查询所有字段
db.get()

# 指定返回字段
db.get(fields=["name", "age"]) 
```

#### 条件查询

```python
# 等值查询
db.get({"age": 25})  # 查询 age = 25 的数据

# IN 查询
db.get({"id": [1, 3, 5]})  # 范围查询 id 是 (1, 3, 5)

# LIKE 查询
db.get({"name": ("LIKE", "%张%")})  # 模糊查询 name 包含 '张' 的数据

# 组合条件查询
db.get({
    "age": (">=", 18),
    "name": ("LIKE", "%张%"),
})
# 查询 年龄大于等于 18 且 name 包含 '张' 的数据
```

### 删除数据

```python
# 条件删除
db.dels({"name": "张三"})  # 删除 name = '张三' 的数据

# 清空表数据（慎用！）
db.dels() 
```

### 更新数据

```python
# 单字段更新
db.set({"age": 26}, {"name": "张三"}) # 修改 年龄 = 26 条件下 name = '张三' 的数据

# 多字段更新
db.set(
    {
        "age": 30,
        "name": "new_name",
    }, 
    {
        "id": ("BETWEEN", (5, 10))
    }
)  # 修改 id 在 (5, 10) 范围内的 age = 30, name = 'new_name' 的数据
```

### 执行原生 SQL

```python
# 查询操作
db.sql("SELECT * FROM users WHERE age > %s", (20,))
```

## 功能特性

- **连接池支持**：通过 `PooledDB` 实现高效的数据库连接池管理。
- **智能分块插入**：自动将大批量数据分块插入，避免单次插入数据量过大。
- **LRU 缓存优化**：对查询结果进行缓存，提升重复查询的性能。
- **简洁易用的 API**：提供统一的 API，简化数据库操作。
- **错误处理**：详细的错误日志输出，方便调试和排查问题。

## 依赖

- `mysqlclient`
- `dbutils`

## 许可证

本项目采用 [MIT 许可证](LICENSE)。

## 贡献

欢迎提交 Issue 和 Pull Request！如有任何问题或建议，请随时联系作者。

---

**GitHub 仓库**: [https://github.com/cxfjh/mysql_f](https://github.com/cxfjh/mysql_f)  
**PyPI 包**: [https://pypi.org/project/mysql_f](https://pypi.org/project/mysql_f)
```

### 说明：
1. **标题和简介**：简洁明了地介绍了库的功能和特点。
2. **安装**：提供了安装命令。
3. **快速开始**：通过示例代码展示了库的基本用法。
4. **功能特性**：列出了库的主要功能和优势。
5. **依赖**：列出了项目的依赖项。
6. **许可证**：声明了项目的许可证。
7. **贡献**：鼓励用户提交 Issue 和 Pull Request。
8. **GitHub 仓库和 PyPI 包**：提供了项目链接。

将此内容保存为 `README.md` 文件，与 `setup.py` 放在同一目录下即可。
fraud-detection 0.2
AI phát hiện gian lận kế toán
Requires Python: >=3.7

# Fraud Detection AI Package

## Taá gia: Tran Minh Tam

## Mô tả:
Đây là package AI phát hiện gian lận kế toán dựa trên các giao dịch. Sử dụng mô hình RandomForest để dự đoán gian lận từ các giao dịch.

## Cài đặt:
Sử dụng lệnh dưới để cài đặt package:
  pip install accounting-fraud-detector


## Cách sử dụng:
1. **Huấn luyện mô hình**:
   ```python
   from fraud_detection.model import train_fraud_model
   train_fraud_model()

2. **Du doan gian lan**
  ```python
  from fraud_detection.predict import predict_fraud
  import pandas as pd

  # Đọc file CSV với dấu phân cách là dấu chấm phẩy
  df = pd.read_csv("Du_lieu_du_doan_gian_lan_ke_toan.csv", delimiter=";")

  # In ra danh sách các cột trong DataFrame để kiểm tra
  print(df.columns)


  # Dự đoán cho tất cả các giao dịch
  result = predict_fraud(df)

  # In kết quả hoặc lưu vào file mới
  print(result)
  result.to_csv("result_predictions.csv", index=False)  # Lưu kết quả dự đoán vào file CSV mới

3. **API**
  python fraud_detection\api.py
verified 0.0.0
Verified as an alternative to verification
Requires Python: <4.0,>=3.8

# verified

[![Release](https://img.shields.io/github/v/release/AlgoDistill/verified)](https://img.shields.io/github/v/release/AlgoDistill/verified)
[![Build status](https://img.shields.io/github/actions/workflow/status/AlgoDistill/verified/main.yml?branch=main)](https://github.com/AlgoDistill/verified/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/AlgoDistill/verified/branch/main/graph/badge.svg)](https://codecov.io/gh/AlgoDistill/verified)
[![Commit activity](https://img.shields.io/github/commit-activity/m/AlgoDistill/verified)](https://img.shields.io/github/commit-activity/m/AlgoDistill/verified)
[![License](https://img.shields.io/github/license/AlgoDistill/verified)](https://img.shields.io/github/license/AlgoDistill/verified)

Verified as an alternative to verification

- **Github repository**: <https://github.com/AlgoDistill/verified/>
- **Documentation** <https://AlgoDistill.github.io/verified/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:AlgoDistill/verified.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/AlgoDistill/verified/settings/secrets/actions/new).
- Create a [new release](https://github.com/AlgoDistill/verified/releases/new) on Github.
- Create a new tag in the form `*.*.*`.
- For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
verification 0.0.0
Verification for post-training LLMs
Requires Python: <4.0,>=3.8

# verification

[![Release](https://img.shields.io/github/v/release/AlgoDistill/verification)](https://img.shields.io/github/v/release/AlgoDistill/verification)
[![Build status](https://img.shields.io/github/actions/workflow/status/AlgoDistill/verification/main.yml?branch=main)](https://github.com/AlgoDistill/verification/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/AlgoDistill/verification/branch/main/graph/badge.svg)](https://codecov.io/gh/AlgoDistill/verification)
[![Commit activity](https://img.shields.io/github/commit-activity/m/AlgoDistill/verification)](https://img.shields.io/github/commit-activity/m/AlgoDistill/verification)
[![License](https://img.shields.io/github/license/AlgoDistill/verification)](https://img.shields.io/github/license/AlgoDistill/verification)

Verification for post-training LLMs

- **Github repository**: <https://github.com/AlgoDistill/verification/>
- **Documentation** <https://AlgoDistill.github.io/verification/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:AlgoDistill/verification.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/AlgoDistill/verification/settings/secrets/actions/new).
- Create a [new release](https://github.com/AlgoDistill/verification/releases/new) on Github.
- Create a new tag in the form `*.*.*`.
- For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).