Saravanan666/pypi-org huggingface dataset:
Package: work
Work time logworkallows manual time tracking with an interaction model inspired bygit:Text files are used for storage. This makes it easy to track the log withgit.The tool does not run continously and instead only modifies thework statuson disk.Thework statusis global, meaning any terminal can be used to check or update it.Checksums are used to verify that the log was not modified by another tool.FeaturesTime trackingTime track while working and (optionally) add a category and message.Retroactively add and modify any entry.AnalysesCalculate and check the hours worked over arbitrary periods.List tracked entries by date or category with optional filters.Overtime and undertimeConfigure "expected hours" and view the accumulated over-/undertime.(Optionally) store vacations or holidays.Export entries as CSV.Read MoreFor more information, including examples and the release history, check thewebsite.
Package: workadays
Workadays:Pacote para calcular dias úteis, dias corridos e dias na base 360 (30/360).Há calendários de feriados do Brasil, Estados Unidos, Luxemburgo, Reino Unido e Argentina.Considera o novo feriado dos EUA, Juneteenth. Consulte:https://pt.wikipedia.org/wiki/JuneteenthConsidera o novo feriado nacional de 20/Nov (Conciência Negra)Calendário de dias úteis:import datetime as dt
from workadays import workdays as wdDias corridosd1 = dt.date(2018, 1, 7)
d2 = dt.date(2020, 12, 31)
print(wd.days(d1, d2))                          # 1089Dias corridos, base 30U/360, método americanod1 = dt.date(2018, 1, 7)
d2 = dt.date(2020, 12, 31)
print(wd.days360(d1, d2, method_eu=False))     # 1074Dias corridos, base 30E/360, método europeud1 = dt.date(2018, 1, 7)
d2 = dt.date(2020, 12, 31)
print(wd.days360(d1, d2, method_eu=True))     # 1073Diferença de dias úteis entre duas datasd1 = dt.date(2018, 1, 7)
d2 = dt.date(2020, 12, 31)
print(wd.networkdays(d1, d2))                # 750Diferença de dias úteis entre duas datas sem calendário de feriados (considera apenas os finais de semana como dia não útil)d1 = dt.date(2018, 1, 7)
d2 = dt.date(2020, 12, 31)
print(wd.networkdays(d1, d2, country=None))  # 779# Soma 252 dias úteis com calendário de feriados padrão do Brasild1 = dt.date(2018, 1, 7)
print(wd.workdays(d1, 252))                  # 09/01/2019Soma 252 dias úteis a data de referência utilizando o calendário Brasil e de SPd1 = dt.date(2018, 1, 7)
print(wd.workdays(d1, 252, country=None))    # 25/12/2018Verifica se é feriadod1 = dt.date(2018, 1, 7)
print('É feriado? ', wd.is_holiday(d1, country='BR', years=range(2020, 2021)))Verifica se é final de semanad1 = dt.date(2018, 1, 7)
print('É final de semana? ', wd.is_weekend(d1))Verifica se é dia útild1 = dt.date(2018, 1, 7)
print('É dia útil? ', wd.is_workday(d1, country='BR', years=range(2020, 2021)))Verifica se é ano bissextoprint('É ano bissexto? ', wd.is_leap_year(2008))Exibe a lista de feriados do Brasil e de São Paulofor date in wd.get_holidays(country='BR', state='SP', years=range(2020, 2022)):
    print(date)Exibe a lista de feriados da Inglaterra e das Libor'sfor date in wd.get_holidays(country='England', years=range(2019, 2023)):
    print(date)
Package: workbench
WorkbenchA scalable python framework for security research and development teams.Workbench focuses on simplicity, transparency, and easy on-site
customization. As an open source python project it provides light-weight
task management, execution and pipelining for a loosely-coupled set of
python classes.Workbench VideosGetting Started with WorkbenchWorkbench Command InterfaceWorkbench Command Interface 2Workbench Robust Client/ServerDive into PCAPs with WorkbenchCorrelating Yara Sigs with WorkbenchWorkbench ExamplesPCAP to Graph(A short teaser)Workbench DemoAdding a new Worker(super hawt)PCAP to DataframePCAP DriveBy AnalysisUsing Neo4j for PE File Sim GraphGenerator Pipelines NotebookNetwork Stream Analysis NotebookPE File Static Analysis NotebookMemory Analysis NotebookBig ThanksYara Sigs: AlienVault, Crowdstrike, Mandiant, Viper, KevTheHermitPull the repositoryWarning!: The repository contains malcious data samples, be careful, exclude the workbench directory from AV, etc…git clone https://github.com/supercowpowers/workbench.gitRead the DocumentationWorkbench documentation:Workbench DocsEmail Lists (Forums)Users Email List:workbench-usersDevelopers Email List:workbench-devsDocumentationThe full documentation is athttp://workbench.rtfd.org.History0.1 (2014-06-10)Release of workbench for alpha developers and users.0.1.5 (2014-06-10)Release of workbench for alpha developers and users.0.2.5 (2014-07-07)Release of workbench for alpha developers and users.0.2.6 (2014-07-11)Release of workbench for alpha developers and users.0.2.9 (2014-07-27)Release of workbench for alpha developers and users.0.3.1 (2014-08-03)Release of workbench for alpha developers and users.0.3.3 (2014-08-29)Release of workbench for alpha developers and users.0.3.4 (2014-11-23)Bug fixes related to ‘drift’ of python modules.Release of workbench for alpha developers and users.
Package: workbench_cli
Workbench Command Line InterfaceA Command Line Interface for the Workbench Project.Workbench focuses on simplicity, transparency, and easy on-site
customization. As an open source python project it provides light-weight
task management, execution and pipelining for a loosely-coupled set of
python classes.Workbench VideosGetting Started with WorkbenchWorkbench Command InterfaceWorkbench Command Interface 2Workbench Robust Client/ServerDive into PCAPs with WorkbenchCorrelating Yara Sigs with WorkbenchWorkbench ExamplesPCAP to Graph(A short teaser)Workbench DemoAdding a new Worker(super hawt)PCAP to DataframePCAP DriveBy AnalysisUsing Neo4j for PE File Sim GraphGenerator Pipelines NotebookNetwork Stream Analysis NotebookPE File Static Analysis NotebookMemory Analysis NotebookPull the repositoryWarning!: The repository contains malcious data samples, be careful, exclude the workbench directory from AV, etc…git clone https://github.com/supercowpowers/workbench.gitRead the DocumentationWorkbench documentation:Workbench DocsEmail Lists (Forums)Users Email List:workbench-usersDevelopers Email List:workbench-devsDocumentationThe full documentation is athttp://workbench.rtfd.org.
Package: workbench-crm
Recommended contents of.env(add some random value forSECRET_KEYand copy theOAUTH2_CLIENT_*values from the.envfile on the production server):DATABASE_URL=postgres://localhost:5432/workbench
CACHE_URL=hiredis://localhost:6379/1/?key_prefix=workbench
SECRET_KEY=...
SENTRY_DSN=
ALLOWED_HOSTS=["*"]
DEBUG=True
NAMESPACE=feinheit
OAUTH2_CLIENT_ID=...
OAUTH2_CLIENT_SECRET=...Local setup:fab setup
fab pull_db:fhDevelopment:fab devCode style & prettification:tox
yarn prettier
yarn eslintCompile the bootstrap library:yarn build-bootstrap
Package: workbenchdata-fastparquet
fastparquet is a python implementation of theparquet
format, aiming integrate
into python-based big data work-flows.Not all parts of the parquet-format have been implemented yet or tested
e.g. see the Todos linked below. With that said,
fastparquet is capable of reading all the data files from theparquet-compatabilityproject.IntroductionDetails of this project can be found in thedocumentation.The original plan listing expected features can be found inthis issue.
Please feel free to comment on that list as to missing items and priorities,
or raise new issues with bugs or requests.Requirements(all development is against recent versions in the default anaconda channels)Required:numba (requiresLLVM 4.0.x)numpypandascythonsixOptional (compression algorithms; gzip is always available):snappy (aka python-snappy)lzobrotlilz4zstandardInstallationInstall using conda:conda install -c conda-forge fastparquetinstall from pypi:pip install fastparquetor install latest version from github:pip install git+https://github.com/dask/fastparquetFor the pip methods, numba must have been previously installed (using conda).UsageReadingfromfastparquetimportParquetFilepf=ParquetFile('myfile.parq')df=pf.to_pandas()df2=pf.to_pandas(['col1','col2'],categories=['col1'])You may specify which columns to load, which of those to keep as categoricals
(if the data uses dictionary encoding). The file-path can be a single file,
a metadata file pointing to other data files, or a directory (tree) containing
data files. The latter is what is typically output by hive/spark.Writingfromfastparquetimportwritewrite('outfile.parq',df)write('outfile2.parq',df,row_group_offsets=[0,10000,20000],compression='GZIP',file_scheme='hive')The default is to produce a single output file with a single row-group
(i.e., logical segment) and no compression. At the moment, only simple
data-types and plain encoding are supported, so expect performance to be
similar tonumpy.savez.HistorySince early October 2016, this fork ofparquet-pythonhas been
undergoing considerable redevelopment. The aim is to have a small and simple
and performant library for reading and writing the parquet format from python.
Package: workbenchdata-pandas
pandasis a Python package providing fast, flexible, and expressive data
structures designed to make working with structured (tabular, multidimensional,
potentially heterogeneous) and time series data both easy and intuitive. It
aims to be the fundamental high-level building block for doing practical,real worlddata analysis in Python. Additionally, it has the broader goal
of becomingthe most powerful and flexible open source data analysis /
manipulation tool available in any language. It is already well on its way
toward this goal.pandas is well suited for many different kinds of data:Tabular data with heterogeneously-typed columns, as in an SQL table or
Excel spreadsheetOrdered and unordered (not necessarily fixed-frequency) time series data.Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
column labelsAny other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structureThe two primary data structures of pandas, Series (1-dimensional) and DataFrame
(2-dimensional), handle the vast majority of typical use cases in finance,
statistics, social science, and many areas of engineering. For R users,
DataFrame provides everything that R’sdata.frameprovides and much
more. pandas is built on top ofNumPyand is
intended to integrate well within a scientific computing environment with many
other 3rd party libraries.Here are just a few of the things that pandas does well:Easy handling ofmissing data(represented as NaN) in floating point as
well as non-floating point dataSize mutability: columns can beinserted and deletedfrom DataFrame and
higher dimensional objectsAutomatic and explicitdata alignment: objects can be explicitly
aligned to a set of labels, or the user can simply ignore the labels and
letSeries,DataFrame, etc. automatically align the data for you in
computationsPowerful, flexiblegroup byfunctionality to perform
split-apply-combine operations on data sets, for both aggregating and
transforming dataMake iteasy to convertragged, differently-indexed data in other
Python and NumPy data structures into DataFrame objectsIntelligent label-basedslicing,fancy indexing, andsubsettingof large data setsIntuitivemergingandjoiningdata setsFlexiblereshapingand pivoting of data setsHierarchicallabeling of axes (possible to have multiple labels per
tick)Robust IO tools for loading data fromflat files(CSV and delimited),
Excel files, databases, and saving / loading data from the ultrafastHDF5
formatTime series-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions,
date shifting and lagging, etc.Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas is
the ideal tool for all of these tasks.
Package: workbench-jupyterlab
workbench_jupyterlabThe workbench_jupyterlab JupyterLab plugin provides features for integrating JupyterLab with Posit Workbench.FeaturesProvides a button on the main toolbar that will bring the user to the Posit Workbench homepageProvides the command "Return to Posit Workbench Home" that can be accessed through the Command Palette to bring the user to the Posit Workbench homepage.Provides a "Proxied Servers" sidebar view that allows users to access their remote servers from Posit Workbench environments.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallworkbench_jupyterlabUninstallTo remove the extension, execute:pipuninstallworkbench_jupyterlab
Package: workbooky
workbookyA module providing utilities to work with EXce; workbooks and worksheets.InstallationpipinstallworkbookyUsagefromworkbookyimportWorkbookworkbook=Workbook(path)Methodsget_worksheet(sheet_name)save()Version HistoryVersion 0.0.5 4 Dec 2023Expose WorksheetVersion 0.0.4 4 Dec 2023Add asyncio and worksheet errorsVersion 0.0.3 10 May 2023RefactorAdded testsVersion 0.0.2 10 May 2023Added path to save()Version 0.0.1 10 May 2023Added workbook saveVersion 0.0.0 10 May 2023Initial version
Package: workbox-cli
The Confluence WorkBox CLI allows you to work with your WorkBox tasks
via the command line.ConfigurationTo use workbox, you’ll need to create a .workbox configuration file in your
home directory. In this configuration file you can specify as many Confluence
servers as you would like, each requiring a base URL, username and password.An example configuration file:[MyConf]
base_url = "https://foo.com/wiki"
username = "xxx"
password = "yyy"CommandsAvailable commands for WorkBox CLI:$ workbox list #list your tasks
$ workbox add "Task 1" "Task 2" #add tasks
$ workbox complete 2 #complete a task
$ workbox rm 2 #remove a taskSupportTo raise a bug, please visithttp://bitbucket.org/samtardif/workbox-cli
Package: work-calendar-ru
Russian work calendarDescriptionSimple library for checking current day as a holiday or weekend and current time as a working time or not.Setuppip install work_calendar_ruImport packagefrom work_calendar_ru import WCRUsing packagewcr = WCR(start_hour=9, start_minutes=0, end_hour=19, end_minutes=30)Wherestart_hourandstart_minutesis a time of the start of working day.Thereforeend_hourandend_minutesis a time of the end of working day.Work time nowwcr.is_work_time()Holiday todaywcr.is_holiday()Work day todaywcr.is_work_day()Short work day todaywcr.is_short_work_day()Work weekend day todaywcr.is_work_weekend()Weekend day todaywcr.is_weekend()LicenseMIT license
Package: workday-calc
workday-calc概要土日、日本の祝日を加味してworking dayを計算してくれるツールです。細かい話ではありますが、workking dayつまりは稼働日という考え方なので、開始日: 2023/8/27、終了日: 2023/8/27と入力した場合は1daysと返ってくる仕様になっています。産休申請をする際に期間の他にworkdayも必要と言われたので、ツールを探したのですがパッと見つからなかったので作りました。インストール方法pip3 install https://github.com/konono/workday-calc使い方--start(-s): 開始日時を入力します(オプションです、入力をしなかった場合今の日付が入力されます)--end(-e): 終了日時を入力します--holidays: 日本の祝日以外の祝日(E.g. company holidayなど)を入力します。スペース区切りでdateフォーマットを入力してください。dateの入力については以下のフォーマットが許容されています:YYYY-MM-DD, YYYY-M-DD, YYYY-M-D, YYYY/MM/DD, YYYY/M/DD,YYYY/M/D, YYYY.MM.DD, YYYY.M.DD, YYYY.M.D, YYYYMMDD--with-holiday(-w):土日、祝日を加味しない開始日を含む、開始日から終了日までの日数を計算します。--debug: 与えられた期間にある祝日を表示します❯ python3 cli.py --help
usage: python3 cli.py -s <date> -e <date> [option]
Available date formats is following:
YYYY-MM-DD, YYYY-M-DD, YYYY-M-D, YYYY/MM/DD, YYYY/M/DD,YYYY/M/D, YYYY.MM.DD, YYYY.M.DD, YYYY.M.D, YYYYMMDD

options:
  -h, --help            show this help message and exit
  --with-holiday, -w
                        To calculate the number of days with holiday.
  --debug               debug option

date:
  --start START_DATE, -s START_DATE
  --end END_DATE, -e END_DATE
  --holidays [HOLIDAYS ...]
                        A list of date format, space delimiter使用例❯ workday-calc -s 2023/10/18 -e 2024/01/31
start_date: 2023/10/18
end_date: 2024/01/31
workdays: 72 days

❯ python3 cli.py -s 2023/10/18 -e 2024/01/31 --holidays 2024/1/2 2024/1/3
start_date: 2023/10/18
end_date: 2024/01/31
workdays: 70 days

❯ workday-calc -s 2023/10/18 -e 2024/01/31 -w
start_date: 2023/10/18
end_date: 2024/01/31
days: 106 days

❯ workday-calc -s 2023/10/18 -e 2024/01/31 --debug
start_date: 2023/10/18
end_date: 2024/01/31
workdays: 72 days
holidays:
(datetime.date(2023, 11, 3), '文化の日')
(datetime.date(2023, 11, 23), '勤労感謝の日')
(datetime.date(2024, 1, 1), '元日')
(datetime.date(2024, 1, 8), '成人の日')
Package: workday-duration
两个时间点间实际工作时间（去除周六日、节假日）两个时间点间实际工作时间（去除周六日、节假日）。
支持 2004年 至 2023年，包括 2020年 的春节延长。安装pip install workday-duration详细使用示例请参考代码tests中的测试用例样例1：时间段包含周六日fromdatetimeimportdatetimefromworkday_duration.utilsimportduration_daysstart_datetime=datetime.strptime('2020-07-31 12:0:0',"%Y-%m-%d%H:%M:%S")end_datetime=datetime.strptime('2020-08-03 12:0:0',"%Y-%m-%d%H:%M:%S")work_days,total_days=duration_days(start_datetime,end_datetime)# 区间全部在休息日)assert1==work_days,"8月1号，2号是周六日，所以实际工作时间为1天"asserttotal_days==3,"绝对时间间隔为3天"样例2：时间区间包含节假日fromdatetimeimportdatetimefromworkday_duration.utilsimportduration_daysstart_datetime=datetime.strptime('2020-09-30 12:00:00',"%Y-%m-%d%H:%M:%S")end_datetime=datetime.strptime('2020-10-02 12:00:00',"%Y-%m-%d%H:%M:%S")work_days,total_days=duration_days(start_datetime,end_datetime)assert0.5==work_days,"10.1开始为国庆节，所以实际工作时间只有0.5天"asserttotal_days==2样例3：带时区的时间间隔（自动转为北京时间后计算）fromworkday_duration.utilsimportduration_days2work_days,total_days=duration_days2('2020-09-30 16:00:00 +0000','2020-10-02 04:00:00 +0000','%Y-%m-%d%H:%M:%S %z')assert0==work_days,"10.1开始为国庆节，所以实际这个区间都在休假"asserttotal_days==1.5
Package: workdays
Extend python datetime with excel-like workday addition/subtractionfunctionality:NETWORKDAYS(start_date,end_date,holidays)Returns the number of whole working days between start_date and
end_date (inclusive of both start_date and end_date). Working days
exclude weekends and any dates identified in holidays. Use NETWORKDAYS
to calculate employee benefits that accrue based on the number of
days worked during a specific term.WORKDAY(start_date,days,[holidays])Returns a number that represents a date that is the indicated number
of working days before or after a date (the starting date). Working
days exclude weekends and any dates identified as holidays. Use
WORKDAY to exclude weekends or holidays when you calculate invoice
due dates, expected delivery times, or the number of days of work
performed.This module has similarities with the BusinessHours module - you may
want to check it out as well to see which one better fits your needs.
Package: workdir
A simple module for easily isolating temporary file I/O to a directory.  Ever developed a command-line tool which required a lot of file manipulation?  Caching, perhaps?  You don’t want to use the cwd and keeping all of your paths straight if you’re working outside of it can be a pain.  This tool may be the pain-reliever you need!A tale of sin and woeWell, not really.  I’ve just developed a bunch of tools that rely on various bits of temporary or isolated file I/O, and found myself implementing this pattern over and over again.  Eventually, I got tired of doing it and decided to develop this handy module.  Now I’m passing the savings on to you!CompatibilityAs far as I know,workdiris compatible with every version of Python from 2.6 through 3.5.  It should also be compatible with all major platforms including Linux, OSX, and Windows.  If not, let me know!Installationpip install workdirExamplesUse it as a staging area for downloaded archives:importworkdirimportshutilworkdir.options.path='~/.myfilecache'withworkdir.as_cwd():download_remote_archive('somefile')unpack_archive('somefile','somedir')shutil.copy(workdir.path_to_file('somedir','unpackedfilefromarchive'),os.path.join('otherdir','unpackedfilefromarchive'))Use it as a working directory in your git repo (added to.gitignore, of course):importworkdirworkdir.options.path='.gitrepo.work'workdir.sync()withworkdir.as_cwd():futz_with_source_tree()more_futzing()
Package: workdown
WorkdownA system to write Markdown and have it published and hosted viaCloudflare WorkersPre-requisitesPython 3InstallwranglerUsagepip3 install workdownRunwrangler generate site https://github.com/eldridgea/workdown-example-site. This will generate a folder calledsitecontaining the folder structureworkdownrequires to work.Configure wrangler.toml with your Cloudflare details, make sure your route ends in a wildcard e.g.route = "https://example.com/*".Create a KV Namespace and bind in topagesin yourwrangler.tomlfile.Make a markdown file ending in.mdin thecontent/directory If you want it to be your main page, name itindex.md.From thesitefolder (or whatever you named it) runworkdownDone!CSSCSS should go in theassets/directory. They will be available as their full filename undercss. For example if you makemain.css, it will beexample.com/css/main.cssPartialsHere is where you can customize the header and footer of pages in HTML. Currently all partials apply to all pages generated. Good things to do here would be if you have a static CSS file incss/or would like to include a a fonts CDN, add it into the<head>in thepartials/header.htmlso it will be usable on your pages.NotesThe path for content will be the name of the markdown file with no extension. For example if you makecontact.md, it will beexample.com/contact.Install directly from Githubpip3 install git+https://github.com/eldridgea/workdownManual Installgit clone https://github.com/eldridgea/workdowncd workdownpython3 setup.py install
Package: worker
simple workerNote: this package is still in alpha. Use with caution !QuickstartInstall worker:pip install workerUse worker:data=[{'hello':'world'},]*40info={'worker':'xxx.yyy.worker_do_sth'}worker=Worker(mode='thread')resp=worker.work(data,info)print(resp)defworker_do_sth(data,info):print(data)returndatadefcallback(results):print('final')print(results)deftest_celery():data=['u11','u22','u33','u44','u21','u22','u23','u24','u31','u32','u33','u34','u41','u42','u43','u44','u51','u52','u53','u54',]*8info={'celery_worker':'test.functional.test_celery.simple','worker':'test.functional.test_celery.worker_do_sth','celery_max_workers':4,'celery_chunk_size':80,'chunk_size':20,'final_callback':'test.functional.test_celery.callback','queue':'123'}worker=Worker(mode='celery')resp=worker.work(data,info)returnresp
Package: worker-bunch
Worker-Bunch... is a tasks/jobs/rules engine, primarily intended for use in a smarthome environment.Worker-Bunchprovides a programming infrastructure for creating tasks/jobs/rules with proprietary functionality.
These tasks/jobs/rules are called "workers" here. Each worker runs as a separate thread.The worker base class is supposed to get overwritten. The most functionality goes into 2 functions with limited scope:subscribe_notificationsand_work. Seedummy_worker.pyandmain.py.The following infrastructure parts are already implemented:Starting and stopping the workersLoggingConfiguration and validation of configuration file (extendable for your job configuration; JSON schema based)Subscriptions to timer and cron events.Subscriptions to MQTT topics and publish MQTT messages. MQTT messages get debounced (configurable time span).Command line argumentsOther characteristics:Runs as Linux service.Additional prepacked is a Postgres and MQTT client.
This is a quite opinionated decision due to the special lifecycle of the MQTT client (among others).Ready to use is a database worker, which is fully configurable (cron, sql statements, sql scripts, text replacements).
Seedatabase_worker.UsageHave a look atWorker-Bunch-SampleMaintainer & LicenseMIT ©Raul RosenlöcherThe code is available atGitHub.