semvec/pypi-packages huggingface dataset:
KFinanceDataReader 1.1.0
한국 주요 금융 데이터 추출을 위한 라이브러리
Requires Python: >=3.7

# KFinanceDataReader

## Install
```
pip install KFinanceDataReader
```

## 요약

한국의 주요 금융 데이터 추출을 위한 library

- 거시경제 지표 [ECOS](https://ecos.bok.or.kr/api/#/)
- 시장 지표 [KRX](https://data.krx.co.kr/)
- 기업 재무제표 지표 [DART](https://dart.fss.or.kr/)

## 장점

본 프로젝트는 다양한 source의 금융데이터를 활용하기 편한 형식으로 수집한다.
1. 데이터 수집 파라미터 형식의 통일
2. 추출된 데이터 형식의 통일


## 요구사항
- 거시경제 지표 추출을 위해 `ECOS API KEY`가 필요하다.
- 기업 재무제표 지표 추출을 위해 `DART API KEY`가 필요하다.

## example을 확인하세용.

```py
from KFinanceDataReader import KFinanceDataReader

k_finance_data_reader = KFinanceDataReader()


# 거시 경제 지표(한국은행 API-KEY 필요)

MacroDataReader = k_finance_data_reader.MacroDataReader
macro_data_reader = MacroDataReader(ecos_api_key)

## 금리 (기준금리, 예금금리, 대출금리)
interest_rate_df = macro_data_reader.get_interest_rate_df(start_date, end_date)

## 채권 (국고채 1년 / 국고채 3년 / 국고채 5년 / 국고채 10년 / 국고채 20년)
bond_yield_df = macro_data_reader.get_bond_yield_df(start_date, end_date)

## 환율 관련 (원/달러, 원/엔화, 원/위안, 원/유로)
exchange_rate_df = macro_data_reader.get_exchange_rate_df(start_date, end_date)

## 물가 지수 (소비자 물가지수 / 생산자 물가지수 / 수출 물가지수 / 수입 물가지수)
price_index_df = macro_data_reader.get_price_index_df(start_date, end_date)

## 무역 (무역수지 / 수출 총지수 / 수출 총액 / 수입 총지수 / 수입 총액)
trade_balance_df = macro_data_reader.get_trade_balance_df(start_date, end_date)

"""------------------------------------------------------------------"""

# 시장 데이터
MarketDataReader = k_finance_data_reader.MarketDataReader
market_data_reader = MarketDataReader()


kospi_info_df = market_data_reader.get_kospi_info_df()
kospi_ohlcv_df = market_data_reader.get_kospi_ohlcv_df(start_date, end_date)
kospi_trader_df = market_data_reader.get_kospi_trader_df(start_date, end_date)

kosdaq_info_df = market_data_reader.get_kosdaq_info_df()
kosdaq_ohlcv_df = market_data_reader.get_kosdaq_ohlcv_df(start_date, end_date)
kosdaq_trader_df = market_data_reader.get_kosdaq_trader_df(start_date, end_date)

"""------------------------------------------------------------------"""

# 기업 재무제표 (DART API-KEY 필요)
CorpDataReader = k_finance_data_reader.CorpDataReader
corp_data_reader = CorpDataReader(dart_api_key)


reprt_codes = [
    '11011',  # 사업보고서
    '11012',  # 반기보고서
    '11013',  # 1분기보고서
    '11014',  # 3분기보고서
]
years = [
    '2024',
    '2023',
    '2022',
    '2021',
    '2020',
]

reprt_code = '11011'
year = '2023'

fundamentals_df = corp_data_reader.get_fundamentals_df(stock_codes, reprt_code, year)
```
serieset 0.1.2
Easy Time Series Dataset with PyTorch.
Requires Python: >=3.8

<div align="center">

# SerieSet: Easy Time Series Dataset with PyTorch

</div>

## 💡 What's SerieSet?

We need an easy and (relatively) general dataset builder for time series model training. This project only relies on `PyTorch`, `Pandas`, and `Numpy`. 


## ⬇️ Installation

```shell
pip install serieset
```

## </> API details

> **data** (pandas.DataFrame): dataframe with single or multiple time series, `group_id`, `date_col`, `target_col` columns are expected, `features` columns are optional.

> **inp_len** (int): input sequence length. i.e. 96.

> **pred_len** (int): prediction sequence length. i.e. 14.

> **target_col** (str): target time series column name (i.e., airport volume, store sales).

> **date_col** (str): date column name. i.e. "date".

> **group_id** (Union[str, List[str]]): group id column name. i.e. "store_name" or ["store_name", "product_id"].

> **features** (Optional[Union[str, List[str]]]): feature column name. i.e. "volume" or ["volume", "price"]. All features should be numeric.

> **train_val_split_date** (str): date for train-validation split. i.e. "2019-01-01". Default is None. If 'last', the last inp_len + pred_len data will be used for validation.

> **dtype** (str): data type of torch data tensor. Default is "float32".

> **mode** (str): train or validation.

## 💡 Example

```python
import pandas as pd
from serieset import TimeSeriesDataset

data = pd.read_csv("./data/ETTh1.csv")
data["group_id"] = "ETTh1"

print(data.head())
print(f"minimum date: {data['date'].min()}")
print(f"maximum date: {data['date'].max()}")

params = {
    'target_col': 'OT',
    'features': ["HUFL", "HULL"],
    'group_id': 'group_id',
    'date_col': 'date',
    'inp_len': 36,
    'pred_len': 12,
    'train_val_split_date': '2018-01-01 00:00:00',
    'mode': 'train',
}

torch_dataset = TimeSeriesDataset(
    data=data,
    **params
)
```
jupyterlab_henanigans 0.4.2
Henanigans theme for Jupyter Lab.
Requires Python: >=3.10
Keywords: jupyter, jupyter-lab, theme

# jupyterlab_henanigans

[![Github Actions Status](https://github.com/theNewFlesh/jupyterlab_henanigans/workflows/Build/badge.svg)](https://github.com/theNewFlesh/jupyterlab_henanigans/actions/workflows/build.yml)
A JupyterLab extension.

## Requirements

- JupyterLab >= 4.0.0

## Install

To install the extension, execute:

```bash
pip install jupyterlab_henanigans
```

## Uninstall

To remove the extension, execute:

```bash
pip uninstall jupyterlab_henanigans
```

## Contributing

### Development install

Note: You will need NodeJS to build the extension package.

The `jlpm` command is JupyterLab's pinned version of
[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use
`yarn` or `npm` in lieu of `jlpm` below.

```bash
# Clone the repo to your local environment
# Change directory to the jupyterlab_henanigans directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm build
```

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

```bash
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab
```

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

```bash
jupyter lab build --minimize=False
```

### Development uninstall

```bash
pip uninstall jupyterlab_henanigans
```

In development mode, you will also need to remove the symlink created by `jupyter labextension develop`
command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions`
folder is located. Then you can remove the symlink named jupyterlab_henanigans` within that folder.

### Packaging the extension

See [RELEASE](RELEASE.md)
ezname 0.1.3
A simple tool for generating random, memorable names - better than UUIDs
Requires Python: >=3.8

# ezname

A simple tool for generating random, memorable names - better than UUIDs

## Overview

`ezname` is a Python tool that generates random, human-readable names that are much more memorable than UUIDs or timestamps. Use them for experiments, temporary resources, projects, or anywhere you need a distinctive identifier.

## Installation

```bash
pip install ezname
```

## Usage

- Python

```python
import ezname

ezname.generate()
# happy-friday

ezname.generate(prefix="run", suffix="v1.0", delimiter="_")
# run_sleepy_penguin_v1.0

ezname.generate(as_tuple=True)
# ("bad", "cop")

ezname.generate_batch(3)
# ['desirable-none', 'old-code', 'troublesome-rod']
```

- Terminal

```bash
$ ezname
>> witty-calculator
```

## Features

- Generates names that are easy to pronounce and understand
- Provides customizable naming options with prefixes, suffixes, and delimiters
- Supports tuple output format for structured naming

## Word Selection

The vocabulary used in ezname is carefully curated to include words that are:

- Easy to pronounce and recognize across different cultures
- Simple enough to be understood by non-native English speakers

We would like to acknowledge the vocabulary list from Dr. Shin Ishikawa, Kobe University, which helped inform our word selection process.

## License

MIT License
korede-data-validator-tool 0.2
A simple data validation package
Requires Python: >=3.6

# **Personal Data Validator**  

## **Overview**  
Personal Data Validator is a Python package designed to efficiently validate various types of personal data, including:  
- **Email addresses**  
- **Phone numbers**  
- **Dates**  
- **URLs**  

This package follows **Object-Oriented Programming (OOP)** principles, ensuring modularity, reusability, and easy integration into different projects.  

---

## **Installation**  
Install the package using:  
```bash
pip install datavalidator
```

---

## **Usage**  

### **1️⃣ Import the Validator Class**  
```python
from datavalidator.validator import DataValidator
```

### **2️⃣ Validate an Email**  
```python
data = "example@email.com"
validator = DataValidator(data)
print(validator.validate_email())  # Output: True
```

### **3️⃣ Validate a Phone Number**  
```python
data = "+1-800-555-1234"
validator = DataValidator(data)
print(validator.validate_phone())  # Output: True
```

### **4️⃣ Validate a Date**  
```python
data = "2024-03-14"
validator = DataValidator(data)
print(validator.validate_date())  # Output: True
```

### **5️⃣ Validate a URL**  
```python
data = "https://example.com"
validator = DataValidator(data)
print(validator.validate_url())  # Output: True
```

---

## **Running Tests**  
The package includes unit tests to ensure proper functionality. Run tests using:  
```bash
pytest tests/
```

---

## **Contributing**  
Contributions are welcome! To contribute:  
1. **Fork** the repository  
2. **Create** a feature branch:  
   ```bash
   git checkout -b feature-branch
   ```  
3. **Commit** your changes:  
   ```bash
   git commit -m "Added new feature"
   ```  
4. **Push** to the branch:  
   ```bash
   git push origin feature-branch
   ```  
5. **Open a Pull Request**  

---

## **License**  
This project is licensed under the **MIT License**.  

---

## **Author**  
👤 **Oluwakorede Oyewole**  
📧 Email: damisonoyewole@gmail.com  

---