Saravanan666/pypi-org huggingface dataset:
Package: bank-statement-reader
DescriptionThis script allows to read PDF bank statements from theGLS Bank.
Even so, the banks online manager supports exporting of CSV files, this works only for the past three month whereas PDF
account statements are kept for at least two years.So if, you forced to automatically analyse past bank transactions, this script will help you.The tool also supports reading the CSV files (which include more information), so you can analyse them.I also tested it with Banking records from theTriodos Bankand it works well. They both using the same banking system, so maybe also other
"Volksbank" or "Raiffeisenbank" work as well. Write me an issueI tested it with the following Banking records so far:GLS 2014-2020Triodos 2020Dependenciespython >= 3.6pdftotext(part ofpoppler-utils)jupyter-notebook[Optional]InstallationVariant OneInstall usingpip install bank-statement-readerAdvantage:Easy to installDisadvantage:some features like thebooking/personal.pyfile depend on modifying the package
the package source before installing, which won't work using this
methodVariant TwoClone the repro locally, create and activate a new virtual environment
and runpip install -e .within the project folder.UsageAfter installation, you have a new commandstatement2csvavailable.usage: statement2csv [-h] [--out out.csv] statement.pdf [statement.pdf ...]

Convert banking statements (PDF & CSV) to an analysed standard csv form.

positional arguments:
  statement.pdf  files to open and convert

optional arguments:
  -h, --help     show this help message and exit
  --out out.csv  csv file to write the results to

        If no filename is given, the file will be saved to
            basename_first_file_%date_string%.csv.
        %date_string% will be always replaced to 'YYYY-mm-dd_to_YYYY-mm-dd'
                                                 start date  to   end dateAnother way to use the project is to usejupyter-notebookfor fast analysing data.
Seeexample.ipynbfor an idea how to use it.Data Protection NoteAs bank statement data is highly sensitive, only very general rules for categorizing were pushed to this git.Usesrc/bank_statement_reader/bookings/personal.pyfor customizations of categories and payees.
You only to create this file with a content like the following, and it will be used automatically.frombank_statement_reader.booking.booking_baseimportBookingBaseclassBooking(BookingBase):def_set_payee(self,value:str):"""your custom functions here"""super()._set_payee(value)def_get_category(self):"""your custom stuff here"""returnsuper()._get_category()
Package: bankvpn
MIT LicenseCopyright (c) [year] [fullname]Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: banner
<div align="center"><img src="https://raw.githubusercontent.com/will-rowe/banner/master/misc/logo/banner-logo-with-text.png" alt="banner-logo" width="250"><hr><a href="https://travis-ci.org/will-rowe/banner"><img src="https://travis-ci.org/will-rowe/banner.svg?branch=master" alt="travis"></a><a href='http://hulk.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/hulk/badge/?version=latest' alt='Documentation Status' /></a><a href="https://github.com/will-rowe/banner/blob/master/LICENSE"><img src="https://img.shields.io/badge/license-MIT-orange.svg" alt="License"></a><a href="https://zenodo.org/badge/latestdoi/144629592"><img src="https://zenodo.org/badge/144629592.svg" alt="DOI"></a></div>***```BANNER is still under development - features and improvements are being added, so please check back soon.```***## Overview`BANNER` is a tool that lives inside [HULK](https://github.com/will-rowe/hulk) and aims to make sense of **hulk sketches**. At the moment, it trains a [Random Forest Classifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) using a set of labelled **hulk sketches**. It can then use this model to predict the label of microbiomes as they are sketches by ``HULK``.For example, you could train `BANNER` using a set of microbiomes from patients that either have or haven't received antibiotic treatment. You can then use `BANNER` to predict whether a new microbiome sample exhibits signs of antibiotic dysbiosis. I will post more information and examples soon...## Installation### Bioconda```conda install banner```> note: if using Conda make sure you have added the [Bioconda](https://bioconda.github.io/) channel first#### Pip```pip install banner```## Quick Start`BANNER` is called by typing **banner**, followed by the subcommand you wish to run. There are two main subcommands: **train** and **predict**. This quick start will show you how to get things running but it is recommended to follow the [HULK documentation](http://hulk-documentation.readthedocs.io/en/latest/?badge=latest).```bash# Train a random forest classifierbanner train -m hulk-banner-matrix.csv -o banner.rfc# Predict the label for a hulk sketchhulk sketch -f mystery-sample.fastq --stream -p 8 | banner predict -m banner.rfc```## Notes* only supports 2 labels at the moment* there is very limited checking and not many unit tests...
Package: banner88x31
banner88x31banner88x31 is a Python package designed to simplify the process of scraping 88x31 banners. These small banners, commonly used for displaying affiliations or links, can be challenging to collect manually. With banner88x31, you can automate this task and retrieve these banners effortlessly.FeaturesEasy to Use: banner88x31 provides a simple and intuitive interface for scraping 88x31 banners.Robust: banner88x31 is built on top of reliable web scraping libraries, ensuring stability and consistent results.InstallationYou can install banner88x31 via pip:pipinstallbanner88x31UsageHere's a quick example of how to use banner88x31 to scrape 88x31 banners:frombanner88x31importBanner88x31scraper=Banner88x31()result=scraper.get_all()ContributingContributions are welcome! If you find a bug or have a feature request, pleaseopen an issue. If you'd like to contribute code, please fork the repository and submit a pull request.Licensebanner88x31 is licensed under the MIT License. See theLICENSEfile for details.ContactIf you have any questions, suggestions, or need assistance, feel free to contact me.Happy scraping with banner88x31! 😄
Package: banner_comment
[![Build Status](https://travis-ci.org/jbn/banner_comment.svg?branch=master)](https://travis-ci.org/jbn/banner_comment)[![Coverage Status](https://coveralls.io/repos/github/jbn/banner_comment/badge.svg?branch=master)](https://coveralls.io/github/jbn/banner_comment?branch=master)<!-- LOL: Coverage on a one function module --># What is this?I like banner comments in my code. Modern IDE's have fancier ways of doing this like code collapsing and such. And, Python style guides generally prohibit this style of commenting. But, I like to see the big test while scrolling (or, in [`subl`](https://www.sublimetext.com/)).# ExampleRunning this:```pythonfrom banner_comment import *banner_comment("hello")```Prints:```################################################################################                             _          _ _                                  ##                            | |__   ___| | | ___                             ##                            | '_ \ / _ \ | |/ _ \                            ##                            | | | |  __/ | | (_) |                           ##                            |_| |_|\___|_|_|\___/                            ##                                                                             ################################################################################```The package also installs a command line entry point, allowing:```bashbanner_comment "hello"```# InstallationThis (one function) package just wraps [figlet](http://www.figlet.org/). You need to install figlet first. It exists on lots of package managers. E.g.```shbrew install figlet    # for OSXapt-get install figlet # for ubuntu```After installing figlet, do```shpip install banner_comment```
Package: banner-snatcher
## banner-snatcher```❯ ./banner-snatcher.py --helpusage: banner-snatcher.py [-h] --host HOST [HOST ...] -p PORT [PORT ...][-o FILE] [--debug] [-q]Python program that snatches banners of accessible portsoptional arguments:-h, --help            show this help message and exit--host HOST [HOST ...]host(s) to scan-p PORT [PORT ...], --port PORT [PORT ...]port(s) to scan-o FILE, --outfile FILEoutput to file--debug               print error messages-q, --quiet           suppress all output```
Package: banrep
BanRep: Analítica de Textobanrepes una librería en Python para analizar conjuntos de documentos textuales. Ofrece las funciones usadas recurrentemente en elBanco de la Repúblicapara el preprocesamiento y análisis de texto.📖Cómo usarVisite ladocumentaciónpara información detallada de uso.InstalaciónCómo instalar en su equipoExtracciónCómo usar para extracción de texto en documentosUso generalFuncionalidad principal de la libreríaInstalaciónSe requiere tener instaladoPython 3.7.Si es la primera vez que va a instalar este lenguaje de programación, se recomienda instalarlo usandoAnaconda3oMiniconda3. Siga las instrucciones de instalación para su sistema.Se recomienda instalar en un entorno virtual para no interferir con otras instalaciones de python.Tanto Anaconda como Miniconda instalan un programa llamadoconda, para crear y activar un entorno virtual que instalepip.Desde lalínea de comandos(Terminalen macOS,Anaconda Prompten windows):# crear un entorno...~$condacreate--nameentornopython=3.7pipjupyterlab# confirmar que quiere descargar lo solicitado...Proceed([y]/n)?y# activar el entorno creado...~$condaactivateentornopipUna vez activado el entorno, instalar usandopip. Esto instalará automáticamente las librerías quebanreprequiere.~$pipinstall--upgradebanrepModelo de Lenguaje NaturalSe requiere un modelo pre-entrenado deSpacy, que depende del idioma del texto que se quiera procesar.Existen diversas formas de instalar, la más fácil es usandodownload.~$python-mspacydownloades_core_news_mdCuando se piensa usar el mismo modelo para diferentes proyectos, una alternativa es hacer unainstalación manual: descargar elarchivo del modelo, guardarlo en el directorio deseado, y crear unvínculo simbólicoa dicho modelo.Verificar instalaciónPuede verificar sibanrepinstaló correctamente usandopythonojupyter labdesde la línea de comandos:~$python
>>>frombanrep.linguisticaimportDocumentos
>>>Si no aparece ningún error quiere decir que la instalación fue exitosa.
Package: banrural-bank-parser-gt
Banrural ScrapperPython library to scrap Banrural WebsiteThe intended use of this library is to make it easy to access Banrural bank data to allow the automatisation of processes that currently banks doesn't allow.How to use itInstallationpip install banrural-scrapper-gtIf you are using requirements.txt just addbanrural-scrapper-gtinto the fileUsagePrecautionsDon't access to Banrural website or mobile app while executing some script usign this library.Login with the library will fail if you are logged in from the website or mobile app.LoginThe recommended method for login is usingwithstatement of Python. We initialize the class in the with and will make automatically login with the provided credentials and will logout after finishing all the work and if some exception happens we ensure that logout method is called.frombanrural_bank_gtimportBanruralBank,UserPasswordBankLogincredentials=UserPasswordBankLogin(username="ENTER YOUR USERNAME",password="ENTER YOUR PASSWORD")withBanruralBank(credentials)asconnection:# Do your work here# We are logout nowGet accountsfrombanrural_bank_gtimportBanruralBank,UserPasswordBankLogincredentials=UserPasswordBankLogin(username="ENTER YOUR USERNAME",password="ENTER YOUR PASSWORD")withBanruralBank(credentials)asconnection:accounts=connection.fetch_accounts()# Fetch all accountsforainaccounts:print(a)Get movementsWe can retrieve the movements of an account usingfetch_movementsmethod inBanruralBankAccountimportdatetimefrombanrural_bank_gtimportBanruralBank,UserPasswordBankLogincredentials=UserPasswordBankLogin(username="ENTER YOUR USERNAME",password="ENTER YOUR PASSWORD")withBanruralBank(credentials)asconnection:accounts=connection.fetch_accounts()forainaccounts:mov=a.fetch_movements(datetime.date.today()-datetime.timedelta(days=60),datetime.date.today()+datetime.timedelta(days=1),)forminmov:print(m)LogoutIt's important to logout after you finish your operations otherwise you can lock yourself and that won't allow you to login from the website, that's due security protections of Banrural.Even thoughwithstatement is the recommended way to make sure logout method is called. You can also execute it manually.bank=BanruralBank(credentials)try:bank.login()# Do your workexcept:passfinally:bank.logout()ContributingFork itCreate your feature branchCommit your changesPush your branchCreate a pull requestFAQWhy I need to enter my credentials? It is safe?We need your credentials to authenticate in the bank, those credentials are not stored in any place. This is open source library so you can check the code.Can you make some program for me using this library?Of course, this library provides a base for another utilities and potentially any thing that is on the website of banrural is under subject for automatization, if you need some customization or build a program on top of this library please contact me at dev at csimon (dot) dev and I will be happy to help you.
Package: banshee
BansheeA command dispatcher and message bus implementation for python.🛠 Installingpoetry add banshee📚 HelpSee theDocumentationor ask questions on theDiscussionboard.⚖️ LicenceThis project is licensed under theMIT licence.All documentation and images are licenced under theCreative Commons Attribution-ShareAlike 4.0 International License.📝 MetaThis project usesSemantic Versioning.
Package: bansoko
Bansokois a reimagined, space-themed 🚀 clone of MS-DOS Soko-Ban from 1984 created in Python usingPyxel.You can download :package: for both Windows and Linux from:Introduction 📖Welcome to Bansoko!You are a scavenger. You collect cargos that people have left in abandoned ships in deep space.It's a dangerous job, that's why we're using robots.Your goal is to use a remotely controlled robot to push all crates to cargo bays.Remember, you can onlypushthem (you cannotpull)Fortunately, in case of mistake you can undo as many steps as you need.Good luck!Installation 🎮WindowsInstallPython(version 3.8 or higher) and make sure that python is added to PATH.Install Bansoko by running:pipinstall-UbansokoRun the game:bansokoLinuxInstallpython3(version 3.8 or higher),python3-pipand required SDL2 libraries (libsdl2-2.0-0andlibsdl2-image-2.0-0).On Ubuntu, this can be done by running:sudoaptinstallpython3python3-piplibsdl2-2.0-0libsdl2-image-2.0-0Install Bansoko by running:pip3install-UbansokoRun the game:bansokoDevelopment setup 💻1. Install prerequisitesWindowsInstall 32-bitPython(version 3.8 or higher) and make sure that python is added to PATH.Additionally, installpipenvfor managing virtual environments and project dependencies:pipinstallpipenvLinuxInstallpython3(version 3.8 or higher),python3-pip,pipenvand required SDL2 libraries (libsdl2-2.0-0andlibsdl2-image-2.0-0).On Ubuntu, this can be done by running:sudoaptinstallpython3python3-pippipenvlibsdl2-2.0-0libsdl2-image-2.0-02. Set up the projectClone the repository:gitclonehttps://github.com/kfurtak1024/bansokoNavigate to the directory Bansoko was cloned to.Create virtual environment to isolate development:pipenvshellInstall all dependencies needed for development:pipenvinstall--dev3. Run the gameRun Bansoko from virtual environment created in the previous step:Windowspython-mbansokoLinuxpython3-mbansokoModding 🧰Bansokois heavily modifiable thanks to included resource builder. More information on how to 'mod' it can be found onBansoko modding page.How to contribute 🤝Submitting an issueUse the issue tracker to submit bug reports and feature/enhancement requests.
When submitting a report, please select the appropriatetemplate.Submitting a 'mod'If you have created a modification for Bansoko which you would like to publish then please contact me viacontact@krzysztoffurtak.devLicense ⚖️This project is licensed under the MIT License - see theLICENSE.mdfile for details.Copyright © 2020-2022 Krzysztof Furtak
Package: banyan-python
BanyanBanyan Python is an extension to the Python programming language that seamlessly
scales existing libraries and code to massive data and parallel cloud computing.Getting StartedEnsure you have Python>=3.8 installed. Install Banyan for Python as follows:pip install banyan-pythonImport and configure as follows:import banyan as bn

bn.configure()VisitBanyan Computingfor full documentation.
Package: baoshare
It’s easy to use because most of the data returned are pandas DataFrame objectsWe have our own data server, efficient and stable operationFree china stock market dataFriendly to machine learning and data miningTarget UsersChina Financial Market AnalystFinancial data analysis enthusiastsQuanters who are interested in china stock marketInstallationpip install baoshareUpgradepip install baoshare –upgradeQuick Startimport baostock as bs
import pandas as pd

# 登陆系统
lg = bs.login(user_id="anonymous", password="123456")
# 显示登陆返回信息
print(lg.error_code)
print(lg.error_msg)
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data("sh.601398",
    "date,code,open,high,low,close,volume,amount,adjustflag",
    start_date='2017-01-01', end_date='2017-01-31',
    frequency="d", adjustflag="3")
print(rs.error_code)
print(rs.error_msg)
# 获取具体的信息
result = pd.DataFrame(columns=["date","code","open","high","low","close","volume","amount","adjustflag"])
while (rs.error_code == '0') & rs.next():
    # 分页查询，将每页信息合并在一起
    result = result.append(rs.get_row_data(), ignore_index=True)
result.to_csv("D:\history_k_data.csv", index=False)
print(result)
# 登出系统
bs.logout()return:date       code    open    high     low   close preclose     volume
0   2017-01-03  sh.601398  4.4000  4.4300  4.3900  4.4300   4.4100  104161632
1   2017-01-04  sh.601398  4.4200  4.4400  4.4100  4.4300   4.4300  118923425
2   2017-01-05  sh.601398  4.4300  4.4500  4.4200  4.4400   4.4300   87356137
3   2017-01-06  sh.601398  4.4400  4.4500  4.4300  4.4400   4.4400   87008191
4   2017-01-09  sh.601398  4.4500  4.4800  4.4300  4.4600   4.4400  117454094
5   2017-01-10  sh.601398  4.4500  4.4700  4.4400  4.4600   4.4600   63663257
6   2017-01-11  sh.601398  4.4600  4.4800  4.4500  4.4700   4.4600   52395427
7   2017-01-12  sh.601398  4.4600  4.4700  4.4400  4.4700   4.4700   62166279

             amount adjustflag      turn tradestatus
0    460087744.0000          3  0.038634           1
1    526408816.0000          3  0.044109           1
2    387580736.0000          3  0.032401           1
3    386138112.0000          3  0.032272           1
4    523539392.0000          3  0.043564           1
5    283646224.0000          3  0.023613           1
6    233898107.0000          3  0.019434           1
7    277258304.0000          3  0.023058           1
Package: baostock
It’s easy to use because most of the data returned are pandas DataFrame objectsWe have our own data server, efficient and stable operationFree china stock market dataFriendly to machine learning and data miningTarget UsersChina Financial Market AnalystFinancial data analysis enthusiastsQuanters who are interested in china stock marketInstallationpip install baostockUpgradepip install baostock –upgradeQuick Startimport baostock as bs
import pandas as pd

# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print(lg.error_code)
print(lg.error_msg)
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data("sh.601398",
    "date,code,open,high,low,close,volume,amount,adjustflag",
    start_date='2017-01-01', end_date='2017-01-31',
    frequency="d", adjustflag="3")
print(rs.error_code)
print(rs.error_msg)
# 获取具体的信息
result_list = []
while (rs.error_code == '0') & rs.next():
    # 分页查询，将每页信息合并在一起
    result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
result.to_csv("D:/history_k_data.csv", encoding="gbk", index=False)
print(result)
# 登出系统
bs.logout()return:date       code    open    high     low   close preclose     volume
0   2017-01-03  sh.601398  4.4000  4.4300  4.3900  4.4300   4.4100  104161632
1   2017-01-04  sh.601398  4.4200  4.4400  4.4100  4.4300   4.4300  118923425
2   2017-01-05  sh.601398  4.4300  4.4500  4.4200  4.4400   4.4300   87356137
3   2017-01-06  sh.601398  4.4400  4.4500  4.4300  4.4400   4.4400   87008191
4   2017-01-09  sh.601398  4.4500  4.4800  4.4300  4.4600   4.4400  117454094
5   2017-01-10  sh.601398  4.4500  4.4700  4.4400  4.4600   4.4600   63663257
6   2017-01-11  sh.601398  4.4600  4.4800  4.4500  4.4700   4.4600   52395427
7   2017-01-12  sh.601398  4.4600  4.4700  4.4400  4.4700   4.4700   62166279

             amount adjustflag      turn tradestatus
0    460087744.0000          3  0.038634           1
1    526408816.0000          3  0.044109           1
2    387580736.0000          3  0.032401           1
3    386138112.0000          3  0.032272           1
4    523539392.0000          3  0.043564           1
5    283646224.0000          3  0.023613           1
6    233898107.0000          3  0.019434           1
7    277258304.0000          3  0.023058           1
Package: bap-elk-backends
Bitergia Analytics GrimoireELK backendsGrimoireELK backends installed in Bitergia Analytics:public-inbox: software for efficiently managing and archiving public mailing
lists using Git repositories.Topicbox: email discussion platform that facilitates organized group
communication through dedicated email groups.Pontoon: web-based localization platform that facilitates collaborative
translation of software and documentationRequirementsPython >= 3.8You will also need some other libraries for running the tool, you can find the
whole list of dependencies inpyproject.tomlfile.InstallationThere are several ways to install grimoire-elk-public-inbox on your system: packages or source
code using Poetry or pip.PyPIgrimoire-elk-public-inbox can be installed using pip, a tool for installing Python packages.
To do it, run the next command:$ pip install grimoire-elk-public-inboxSource codeTo install from the source code you will need to clone the repository first:$ git clone https://github.com/bitergia-analytics/grimoirelab-elk-public-inbox
$ cd grimoirelab-elk-public-inboxThen use pip or Poetry to install the package along with its dependencies.PipTo install the package from local directory run the following command:$ pip install .In case you are a developer, you should install grimoire-elk-public-inbox in editable mode:$ pip install -e .PoetryWe usepoetryfor dependency management and
packaging. You can install it following itsdocumentation.
Once you have installed it, you can install grimoire-elk-public-inbox and the dependencies in
a project isolated environment using:$ poetry installTo spaw a new shell within the virtual environment use:$ poetry shellLicenseLicensed under GNU General Public License (GPL), version 3 or later.