semvec/pypi-packages huggingface dataset:
ExcelPar 0.1.7
Analyze by python, and create Excel PAR

# Excel Par

Excel Par Sourcecode.
v0.1.3 DD 231107

# How to use

import ExcelPar as ep  
ep.run()

# History

v0.0.4, 231019 : 세부계정 월별순증분석 상단/하단 Logic 변경.  
v0.0.5, 231019 : FS Line 추가생성 구현  
v0.0.6, 231023 : 상하단 Logic 재변경_이예린SM (Mean ± 2Std)  
v0.1.2, 231104 : Improve with Parquet  
v0.1.3, 231104 : GL Slicing import 구현  
#0.1.5, 231108 : ADD README  

# README

## 설계도

모듈.메서드()  
실제 호출은 메서드를 바로 호출

1. TB 전처리부 : PreGL.RunPreTB() 
- 분석을 위해 rawTB를 읽어서 분석form으로 전처리 (어댑터 패턴)
- 사전에 지정된 excel form을 통해 필요한 변수를 입력받음

2. GL 전전처리부 (rawGL이 직접 Read할 수 없게 되어 있는 경우)

2-1. Excel 합치기 : ConcatExcelFiles.RunConcateExcelFiles()
- rawGL이 다수의 Excel파일(다수 시트 포함)일 경우 직접 읽어서 합치는 메서드
- pandas 사용
- 일체 전처리 없는 단순 합치기 (Column name은 trim함)

2-2. Text 합치기 : ConcatTextFiles.RunConcateTextFiles()
- rawGL이 다수의 Text파일일 경우 직접 읽어서 합치는 메서드
- pandas 사용
- 일체 전처리 없는 단순 합치기 (Column name은 trim함)

2-3. Text를 Slicing해서 합치기 : SliceAndSaveGL.RunSliceAndSaveGL()
- text를 필요한 컬럼만 slicing해서 합치는 메서드 => 가장 효율적
- dask 사용
- 대상컬럼은 현재 하드코딩되어 있으므로 향후 보완필요

2-4. BKPF+BSEG Join : JoinBKPFBSEG
- BKPF & BSEG를 다른 테이블로 주는 경우 join하는 구문
- 범용 tool이 아니므로 메서드로 구현하지는 않고 소스만 들어있음 (직접 호출 불가)

3. GL 전처리부 : PreGL.RunPreGL()
- 전전처리를 통해 읽을수 있게 된 rawGL을 읽어서 분석form으로 전처리 (어댑터 패턴)
- 사전에 지정된 excel form을 통해 필요한 변수를 입력받음
- FSLine 계정과목을 붙여준 후, TB/GL Recon data 자동 생성함 (실제 recon은 직접 엑셀에 붙여서 수행)

4. Excel Par Main분석부 : ExcelPar.RunEP()
- 전처리 완료된 TB/GL을 읽어서 분석 후 산출물 생성
- 최초코드를 리팩토링함
niu-download 1.1.0
A tool that can download Hugging Face model(s)
Requires Python: >=3.6

# NiuDownload
> This is a tool that can automatically download models from Hugging Face(Windows or Linux).You can download single model or make batch download. The project is powered by NEU(China) NLPLAB master students: Peinan Feng, Junhao Ruan and Peizhuo Liu. Any issue is welcomed.
**Only pytorch version models are supported now**
## Use in python script
### Single Download
    from sao_download import download as sd
    sd.batch_download_models('models', 'gpt2')
   In this way, the whole gpt2 model in pytorch version will be stored in "models" directory.
### Batch Download
    from sao_download import download as sd
    sd.batch_download_models('models', ['roberta-base', 'gpt2', 'bert-base-uncased'])
    #or 
    sd.batch_download_models('models', 'roberta-base, gpt2, bert-base-uncased')
  In this way, roberta-base、 gpt2、 bert-base-uncased will be stored in "models" directory.
### Use in command line
     niudownload -m bert-base-uncased,gpt2 -d /mnt/lpz/download/
  "-m" means the model(s) from Huggingface you want to download
  "-d" means the output dirct you want to store the model(s)
msaBase 0.0.136
General Package for Microservices based on FastAPI like Profiler, Scheduler, Sysinfo, Healtcheck, Error Handling etc.
Requires Python: >=3.9.0,<3.11

<p align="center">
  <img src="http://logos.u2d.ai/msaBase_logo.png?raw=true" alt="msaBase
</p>

------
<p align="center">
    <em>msaBase- General Package for Microservices based on FastAPI like Profiler, Scheduler, Sysinfo, Healtcheck, Error Handling etc.</em>
<br>
    Optimized for use with FastAPI/Pydantic.
<br>
  <a href="https://pypi.org/project/msaBase" target="_blank">
      <img src="https://img.shields.io/pypi/v/msaBase?color=%2334D058&label=pypi%20package" alt="Package version">
  </a>
  <a href="https://pypi.org/project/msaBase" target="_blank">
      <img src="https://img.shields.io/pypi/pyversions/msaBase.svg?color=%2334D058" alt="Supported Python versions">
  </a>
</p>

------

**Documentation**: <a href="https://msaBase.u2d.ai/" target="_blank">Documentation (https://msaBase.u2d.ai/)</a>

------

## Features
- **MSABaseExceptionHandler**: Central exception handler which sends formatted exception to logger
- **Filehandler utilities**: Classes for FileDelete, FileUpload async with chunking, Archive pack/unpack formats, helper functions
- **mkdocs code reference helper**: Create virtual mkdocs navs for code reference and used libraries from requirements.txt
- **MSAHealthCheck**: Healthcheck class with internal own thread, which checks url for health
- **logger intercept handler**: allows to change handler from all logger and define specific output format with loguru
- **Models for files and health classes**: reusable pydantic models for file handling and dealing with healthcheck status
- **MSAProfilerMiddleware**: PyInstrument Profiler as Middleware to create a html for an admin Dashboard
- **MSAAppSettings**: API oriented settings class with environment vars and `.env` file support
- **Service oriented System Info**: Classes and functions to get pydantic model response about system and gpu information

## License Agreement

- `msaBase`Based on `MIT` open source and free to use, it is free for commercial use, but please show/list the copyright information about msaBase somewhere.


## How to create the documentation

We use mkdocs and mkdocsstring. The code reference and nav entry get's created virtually by the triggered python script /docs/gen_ref_pages.py while ``mkdocs`` ``serve`` or ``build`` is executed.

### Requirements Install for the PDF creation option:
PDF Export is using mainly weasyprint, if you get some errors here pls. check there documentation. Installation is part of the msaBase, so this should be fine.

We can now test and view our documentation using:

    mkdocs serve

Build static Site:

    mkdocs build


## Build and Publish
  
Build:  

    python setup.py sdist

Publish to pypi:

    twine upload dist/*