Saravanan666/pypi-org huggingface dataset:
Package: mbpls
An easy to use Python package for (Multiblock) Partial Least Squares
prediction modelling of univariate or multivariate outcomes. Four state
of the art algorithms have been implemented and optimized for robust
performance on large data matrices. The package has been designed to be
able to handle missing data, such that application is straight forward
using the commonly known Scikit-learn API and its model selection
toolbox.The documentation is available athttps://mbpls.readthedocs.ioand elaborate (real-world) Jupyter Notebook examples can be found athttps://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS/tree/master/examplesThis package can be cited using the following reference.Baum et al., (2019). Multiblock PLS: Block dependent prediction modeling for Python. Journal of Open Source Software, 4(34), 1190InstallationInstall the package for Python3 using the following command. Some
dependencies might require an upgrade (scikit-learn, numpy and
scipy).$ pip install mbplsNow you can import the MBPLS class by typingfrom mbpls.mbpls import MBPLSQuick StartUse the mbpls package for Partial Least Squares (PLS) prediction modelingimportnumpyasnpfrommbpls.mbplsimportMBPLSnum_samples=40num_features=200# Generate random data matrix Xx=np.random.rand(num_samples,num_features)# Generate random reference vector yy=np.random.rand(num_samples,1)# Establish prediction model using 2 latent variables (components)pls=MBPLS(n_components=2)pls.fit(x,y)y_pred=pls.predict(x)The mbpls package for Multiblock Partial Least Squares (MB-PLS) prediction modelingimportnumpyasnpfrommbpls.mbplsimportMBPLSnum_samples=40num_features_x1=200num_features_x2=250# Generate two random data matrices X1 and X2 (two blocks)x1=np.random.rand(num_samples,num_features_x1)x2=np.random.rand(num_samples,num_features_x2)# Generate random reference vector yy=np.random.rand(num_samples,1)# Establish prediction model using 3 latent variables (components)mbpls=MBPLS(n_components=3)mbpls.fit([x1,x2],y)y_pred=mbpls.predict([x1,x2])# Use built-in plot method for exploratory analysis of multiblock pls modelsmbpls.plot(num_components=3)
Package: mbpprice
MBPPriceMBPPrice用來抓取二手Macbook Pro的價格和相關資訊SetuppipinstallmbppriceUtility# 搜尋 PTT 二手 Macbook Pro 資訊python3-mmbppricesearchpttExamplefrommbpprice.searchimportMBPSearchresults=MBPSearch('ptt').list(limit=10)for(i,item)inenumerate(results):print('{:03d}:{}'.format(i,item['url']))print('價格:{}'.format(item['price']))print('年份:{}/ 螢幕尺寸:{}/ 電池循環:{}'.format(item['year'],item['screen'],item['bat_count']))print('CPU:{}/ RAM:{}/ 硬碟容量:{}'.format(item['cpu'],item['ram'],item['hdd']))print()
Package: mbpro
mbpro é um módulo criado principalmente para aqueles que não gostam de ter muito trabalho com códigosEXEMPLOS#<--------------------------------------------------->#
#Função rand()
#< Com a rand() você pode gerar um número aleatório ou até mesmo uma letra do alfabeto >##rand() com números:
from mbpro import randnumber = rand(7, 888) #gera um número aleatório de 7 a 888
print(number)#Obs: Os números podem ser em string também#rand() com letras:letra = rand('e', 'm') #gera uma letra de E a M
print(letra)#<--------------------------------------------------->#
#Função pega()
#< Com a pega() você pode capturar um determinado texto dentro de um HTML por exemplo >#
from mbpro import pega
str = 'spanf Aqui está um exemplo da pega():hsgs 1$MBPRO t35'dados = pega(str, '1$', 'te5') #retorna o texto MBPRO
print(dados)#<--------------------------------------------------->#
#Função connect()
#< Com a connect() você pode testar a conexão de um proxy no GOOGLE >#
from mbpro import connect
str = connect('36.66.235.147:57550') #retorna um JSON com os dados e status do proxy#a connect() testa proxies com no máximo 7000 ms, caso ultrapassar o retorno será DIE#<--------------------------------------------------->#
#Função curl()
#< Com a curl() você pode realizar um request tanto GET quanto POST de maneira fácil >#
from mbpro import curl
postfields = 'post1=carlos&post2=jeferson&post3=antonio&post4=pedro'data = curl('http://proxy.mbpro.in/data.php', post=postfields).text()
print(data)
#Atributos ao curl:
#url -> urlencode
#header -> em forma de dict (opcional)
#timeout -> em inteiro (opcional)
#post (opcional)#<--------------------------------------------------->#
#Função pretty_print()
#< Com a pretty_print() você pode printar um json em PRETTY_PRINT >#
from mbpro import pretty_printjson = {"nome":"carlos","idade":"19 anos","sexo":"masculino"} #O json também pode vir em strpretty = pretty_print(json)print(pretty)CopyrightTodos os direitos reservados a equipe MBPro Services and RevolutionsBy: Master Bank
Package: mbpython
MBPythonMiniblink is a lighter browser widget base on chromium,
just one file, faster browser kernel of blink to integrate HTML UI in your app.MBPython is an open source project founded by lochen to provide python bindings for the Miniblink. Examples of embedding Miniblink browser are available for many popular GUI toolkits including: wxPython, PyQt, PySide, Kivy, Panda3D, PyGTK, PyGObject, PyGame/PyOpenGL and PyWin32.By using C interface, you can create a browser just some line code.
There are many use cases for MBPython. You can embed a web browser control based on MBPython with great HTML 5 support. You can use it to create a HTML 5 based GUI in an application, this can act as a replacement for standard GUI toolkits like wxWidgets, Qt or GTK. You can render web content off-screen in application that use custom drawing frameworks. You can use it for automated testing of existing applications. You can use it for web scraping or as a web crawler, or other kind of internet bots.Latest releaseOSPy332bit64bitRequirementsWindows3.6+YesYesWindows 7+Installationpipinstall--userMBPythonHow to useDownload the node.dll add to your project.The free version can only be called on the UI thread.Create a simple WindowfromMBPythonimportminiblinkmbpython=miniblink.Miniblinkmb=mbpython.init('node.dll')wke=mbpython(mb)window=wke.windowwebview=window.wkeCreateWebWindow(0,0,0,0,860,760)window.wkeShowWindow(webview)window.wkeRunMessageLoop()PyInstallerpyinstallxxx.py--noconsoleExamplesSee thetestsfolderTutorialSee thedocumentsfolderWhat about CEF?In short: I do not like CEF, it is too big, comes with too many dependency resolution library, and I think we can make a better and more intuitive one. Here are a few things that I don't like.Resourcesif you have any questions,you can contact me,and i will try my best to help youIf you would like to support general MBPython development efforts by making a donation then please scan to pay by the alipayProject WebsiteIssue TrackerOfficial WebsiteQQ Group-198671899
Package: mbq.atomiq
Installation$pipinstallmbq.atomiqGetting startedAddmbq.atomiqtoINSTALLED_APPSin your django application’s settingsAddATOMIQspecific settings to that same settings file. Those are used for metrics.ATOMIQ={'env':CURRENT_ENV,'service':YOUR_SERICE_NAME,}Set up consumers for each queue type that your app needs.mbq.atomiqprovides a handy management command for that:python-mmanageatomic_run_consumer--queuesnspython-mmanageatomic_run_consumer--queueceleryNote that atomiq will use the celery tasknameattribute to import and call the task. By default, celery sets the task name to be thepath.to.task.module.task_function_name. Overriding the name of a task will cause atomiq to break, so plz don’t do this.To make sure we’re not holding on to successfully executed or deleted tasks we also have a clean up management command, that by default will clean up all processed tasks that are older than 30 days. That default can be overriden.python-mmanageatomic_cleanup_old_tasksorpython-mmanageatomic_cleanup_old_tasks--daysNorpython-mmanageatomic_cleanup_old_tasks--minutesNUse it!importmbq.atomiqmbq.atomiq.sns_publish(topic_arn,message)mbq.atomiq.celery_publish(celery_task,*task_args,**task_kwargs)Monitoring<https://app.datadoghq.com/dash/895710/atomiq>TestingTests are automatically inTravis CIhttps://travis-ci.org/managedbyq/mbq.atomiqbut you can also run tests locally usingdocker-compose.
We now usetoxfor local testing across multiple python environments. Before this usepyenvto install the following python interpreters: cpython{2.7, 3.5, 3.6} and pypy3$docker-composeuppy36-pg|py36-mysql|py37-pg|py37-mysqlTesting in Other ServicesWhen using atomiq in other services, we don’t want to mock out atomiq’s publish functions. This is because atomiq includes functionality to check that all usages are wrapped in a transaction, and can account for transactions added by Django in test cases. To allow you to test that the tasks you expect have been added the queue, we expose atest_utilsmodule.Shipping a New ReleaseBump the version in__version__.pyGo toReleasesin GitHub and “Draft a New Release”After creating a new release, Travis CI will pick up the new release and ship it to PyPi
Package: mbq.client
Installation$pipinstallmbq.clientGetting StartedKey ConceptsServiceClientwraps python’s requests library to enable token based service to service authenticationAuthenticatorprovides Auth0 token based authenticationTokenManagerA manager that stores refreshable tokens with support for different persistent storage backends.Django IntegrationDjangoCacheStorageis a thin wrapper that stores token in your django project’s cache.Examplefromdjango.core.cacheimportcachefrommbq.clientimportServiceClient,Authenticator,TokenManager,DjangoCacheStoragetoken_manager=TokenManager(settings={'api_ids':{'my_service':'id'},'client_id':'client_id','client_secret':'your_secret''domain':'auth0domain'},storage=DjangoCacheStorage(cache),)my_service_client=ServiceClient(auth=Authenticator(service_name='my_service',token_manager=token_manager))my_service_client.get()my_service_client.post()my_service_client.patch()my_service_client.put()my_service_client.delete()access_token.refresh()access_token()#retrieves tokenThe Permissions ClientOSCore=ServiceClient(settings.OS_CORE_API_URL,auth=Authenticator(service_name="os-core",token_manager=token_manager),)frommbq.client.contribimportpermissionspermissions_client=permissions.PermissionsClient(permissions.OSCoreServiceClient(OSCore),cache_period_seconds=0)# query for a user's permission at company 15permissions_client.has_permission(person_id,"read:messages",15,"company")# returns True if person_id has that permissionYou can find additional information in the permissions documentation for developershere
Package: mbqc-scheduling
Python wrapper around  mbqc_scheduling crateThis package provides an interface to the thembqc_scheduling crate. It is usually used
together with thePauli tracker package.DocumentationTo the docsSince this here is mainly a wrapper, we did not document everything extensively, but
rather refer to the documentation of the underlying Rust crates, for now.ExamplesSeeexamples/simple.pyfor a very basic example.examples/compile_flow.pyis a
specific example describing how the Pauli tracking and the scheduling can be used when
combining/stitching graphs that, for example, represent parts of a larger circuit (it's a
specific example for the project for which this library was initially developed).InstallationYou can install the package from PyPI, e.g., withpipinstallmbqc-schedulingThe package contains pre-built wheels for manylinux_2_28_x86_64 (works on most Linux
distribuitions), latest Windows and latest MacOS (latest with respect to when the package
was built) for Python 3.8 to 3.12. Additionally, there is an manylinux_2_28_x86_64
abi3 wheel for Python >= 3.8. You can also build the package from source, e.g., force it
during a pip install withpip install --no-binary mbqc-scheduling mbqc-scheduling,
however, note that this requires Python >= 3.8 and a Rust toolchain >= 1.65.At the moment, you may also find a more up-to-date wheel in the artifacts of the latest
"pypackage" github actions workflow; this is unstable though.Manually BuildingThe package has to be build withmaturin. Themake packagecommands builds it through
a docker container such that it is compatible with manylinux_2_28_x86_64 for Python >=
3.8. Withmake update_docsthe documentation can be build. The output of both make
commands is in thedistdirectory.VersioningThe Python package followsSemVer, however, the underlying Rust crate is unstable.
Package: mbq-django-cid
Logging is important. Anyone who has had a call at 3am to say the site is down
knows this. Without quality logging it is almost impossible to work out what
on earth is happening.Even with plenty of logs it can be hard to track down exactly what the effects
of a particular request are. Enter Django Correlation IDs. The approach
is quite a simple one. Incoming requests are assigned a unique id (a uuid).
This can either happen in say your public facing web server (e.g. nginx) or be
applied as soon as it hits django.Thiscidis then available through the django request/response cycle. We
provide filters for logging witch adds thecidto the logging record so you
can add it to your formatting string. We also provide wrappers around all the
standard database backends which adds thecidas a comment before each SQL
request.FeaturesProcessing/Generation of a correlation idDatabase wrappers to add correlation id to each sql callLogging filter to inject the correlation id into logsA template context processor to make correlation id available in templatesOutput correlation id as a headerDocumentation can be found at:http://django-correlation-id.readthedocs.org/History0.1.0 (2014-08-05)First release on PyPI.0.1.2 (2016-12-01)Made CID repsonse header configurable, and optional (thanks @dbaty)0.2.0 (2016-12-06)Added support for Django 1.10 middleware (thanks @qbey)
Package: mbq.env
Installation$pipinstallmbq.env🚀✨Guaranteed fresh.Getting started$env...MY_VAR=baconMY_INT_VAR=123MY_BOOL_VAR=1MY_CSV_VAR=a,b,cMY_TOKEN_VAR=abcMY_KEY_VAR=-----BEGINCERTIFICATE-----...-----ENDCERTIFICATE-----...In[1]:frommbqimportenvIn[2]:env.get('MY_VAR',default='BACON')Out[2]:'bacon'In[3]:env.get_int('MY_INT_VAR',default=12)Out[3]:123In[4]:env.get_bool('MY_BOOL_VAR',default=False)Out[4]:TrueIn[5]:env.get_csv('MY_CSV_VAR',default=['abc','123'])Out[5]:['a','b','c']In[6]:env.get_tokens('MY_TOKEN_VAR',default=['abc','123'])Out[6]:['a','b','c']In[7]:env.get_key('CERTIFICATE','MY_KEY_VAR')Out[7]:'-----BEGIN CERTIFICATE-----\n...\n-----END CERTIFICATE-----'
Package: mbq.pubsub
mbq.pubsubInstallation$pipinstallmbq.pubsub
🚀✨Guaranteed fresh.Configuration# settings.pySERVICE_NAME="my-service"PUBSUB={"ENV":mbq.get_environment("ENV_NAME"),"SERVICE":SERVICE_NAME,"QUEUES":["foo-updates","bar-updates",],"MESSAGE_HANDLERS":{"foo.updated":"path.to.handlers.handle_foo_updated","bar.updated":"path.to.handlers.handle_bar_updated",},}INSTALLED_APPS={...'mbq.pubsub',...}# convox.ymlservices:foo-consumer:image:{{DOCKER_IMAGE_NAME}}command:newrelic-admin run-python -m manage pubsub consume --queue foo-updatesinit:trueenvironment:-"*"bar-consumer:image:{{DOCKER_IMAGE_NAME}}command:newrelic-admin run-python -m manage pubsub consume --queue bar-updatesinit:trueenvironment:-"*"Using PubSub Without a DatabaseIf your service does not have a database - fear not. The Infra team hath taken pity on you and your brethren.
Simply add the setting"USE_DATABASE": Falseto yourPUBSUBconfig.Things to keep in mind:There will be no Django admin for viewing/deleting/replaying DLQ messages.To replay the DLQ use the management command./manage.py pubsub replay --queue <queue_name> <number_of_messages>.Local DevelopmentAdd127.0.0.1 pubsub.lcl.mbq.ioto your/etc/hosts.Find theenv/pubsub/lcl.envSecure Note in 1Password and copy it toenv/lcl.env.$pbpaste>env/lcl.envRun migrations and bring up admin the server:$docker-composeruntests-adminpython-mmanagemigrate
$docker-composeuptests-adminTo use the Django admin, you'll need a superuser:$docker-composeruntests-adminpython-mmanagecreatesuperuserTo end-to-end test mbq.pubsub, you can publish a test message from SNS with:$docker-composeruntests-adminpython-mmanagepublish_exceptionThen bring up the consumer:$docker-composeuppubsub-pubsub-consume-updatesThe test message will eventually land on the DLQ, which you can inspect & replay fromhttp://pubsub.lcl.mbq.io:8080/admin/pubsub/undeliverablemessage
Package: mbq.ranch
RanchStandardized tooling, monitoring, and retry logic for use with CeleryInstallationRanch is a Django application. To use Ranch with Celery, add the following to your settings file:INSTALLED_APPS=[...'mbq.ranch']RANCH={'env':ENV_NAME,# e.g. production, development'service':MY_SERVICE_NAME,# e.g. os-core}FeaturesMetricsAny application with Ranch installed will have Celery metrics available inthe Celery/Ranch DataDog Dashboard.MonitorsTODO: Include link to Invoice's monitors once they're builtYou may set up monitors for your application using the metrics provided by Ranch. To get started, you might want to copy Invoicing's monitors.Dead Letter QueueCelery jobs that fail will be stored in the application's database for inspection and reprocessing. Ranch provides an Admin interface for this.SeeOS Core's Ranch Adminfor an example.Correlation IDsRanch can flow correlation IDs through your Celery jobs. Ranch willnotchange any of your logging configuration, so you'll still need to do that as part of your correlation ID implementation.To use the correlation ID functionality, add the following settings:RANCH={...,'correlation':{'getter':getter_fn,# callable with no args that returns the current correlation ID'setter':setter_fn,# callable with one arg which should be set as the current correlation ID},}Supplemental Error TaggingRanch provides a hook to add additional tagging information to error item metrics. This is used in OS Core to tag each error as belonging to a specific team.To use this feature, add the following settings:RANCH={...,# tags_fn takes a single arg (the Ranch Task object that failed)# and should return a list of strings in the format "tag_name:tag_value"# See OS Core's usage for an example'extra_error_queue_tags_fn':tags_fn,}
Package: mbq.tokens
Installation$pipinstallmbq.tokens🚀✨Guaranteed fresh.Getting startedfrommbqimporttokenstokens.init(certificate=settings.FORMATTED_CERTIFICATE,allowed_audiences=set(settings.ALLOWED_AUDIENCES),)try:decoded_token=tokens.decode(token)excepttokens.TokenError:# will only ever raise TokenErrorlogger.exception('Failed to decode token')decoded_token=tokens.decode_header(request.META['HTTP_AUTHORIZATION'])
Package: mbs
MBSSimple Python library for loading and plotting MB Scientific ARPES detector data in both KRX and TXT format.InstallationTo install usingpip, simply runpip install mbs.To install usingconda, please followthese instructions, asmbsis not directly published onconda.Usage & DocumentationFor some simple examples, seembs_example.ipynb. Full documentation is still a work in progress will be foundhere.
Package: mbs-cn
mbs博客管理器，可以管理多个博客的文章。使用1 安装pipinstallmbs-cn2 命令usage:mbs[-h][-cs][-nCATEGORYMARKDOWN_FILE_PATH][-dTITLE][-scFOLDER][-uoPATH][-uaFOLDER]博客管理器

optionalarguments:-h,--help显示当前帮助信息，然后退出-cs,--categories显示所有分类-nCATEGORYMARKDOWN_FILE_PATH,--new-postCATEGORYMARKDOWN_FILE_PATH要上传的markdown文件的分类和路径-dTITLE,--deleteTITLE要删除的文章标题-scFOLDER,--scan-changed-filesFOLDER扫描目标文件中所有有变化的文件-uoPATH,--update-onePATH更新一个文件-uaFOLDER,--update-allFOLDER更新指定目录中的所有文件3 当前支持的博客博客园简书思否个人网站 / 博客简书的图片不支持外链，所以采用异步的方式将所有外链图片上传到简书的图床中。
对于支持外链图片的博客，默认使用外链图片，不会上传到对应博客的图床。4 问题部分代码写得难看，因为能使用，暂时就没有优化5 debug默认日志是保存在文件里，不在终端输出，但有时可能会遇到执行一条命令后终端没有任何输出，查看日志或者启动 debug 可能会有帮助。日志文件路径：windows%APPDATA%\mbs\mbs.logLinux/Mac$HOME/.config/mbs/mbs.log开启 debug 模式可以在终端也输出日志，因为日志文件一样可以看，所以此功能作用不大。开启方式为在当前终端设置环境变量MBS_DEBUG=1。6 注意当前仅对使用mbs上传的文章进行管理，其他已经上传的文章，需要自己想办法将有关数据添加到数据库中。数据库文件与日志文件在同一个目录中。7 效果图检查更新
Package: mb-scripts
Creator : Sathya Krishnan SureshDESCRIPTIONThis is a python package to quicken the modelling and data analysis process.PyPi :https://pypi.org/project/mb-scripts/Version 0.1.1 additionsfeature selectionFeature selection sub-package has been added which consists of UnivariateFeatureSelection and
CombinationFeatureSelection.generate_time_seriesThis function can be used to test out new Recurrent Neural Architectures.Plotting feature importances using RandomForestThis function helps selecting the features that are most correlated with the target variable with
the help of RandomForestDecompositionThe new model in ðŸ”¥DecompositionðŸ”¥ contains standard decomposition methods that help with
dimensionality reduction.metrics updateA lot of regression metrics have been added along with multiclass precision metrics
variations.Installing and using mb_scriptsThe package can be downloaded usingpip install mb-scripts==<latest_version>Latest version :0.1.0.Once you have installed mb_scripts you can begin using it.Here are some examples for using mb_scripts.train_validation_curve_for_rf- used to monitorRandomForestClassifier's overfittingplot_decision_boundaryis used to visually look at the decision boundary of classification functionsclassifiers_metricsreturns a dataframe that consists of precision, recall, accuracy_score and f1_score for all the classification models passed.I am writing scripts regularly so the versions will keep changing for the next one month. Stay tuned.
Package: mbsim
mbsimA Modbus SimulatorThis is the command line to to control mbsim.  There is a addon system for mbsim.InstallationTo install mbsim you can install fromgitlab package registryoruse pippip install mbsimUsagesmbsim [-h] [-l {DEBUG,INFO,ERROR,WARNING,CRITICAL}] {sim,ui} ...

mbsim ui {ui1,ui2,...} # a ui addon need to be installed for this command

mbsim sim [-h] {tcp,udp,rtu} ...

mbsim sim tcp [-h] [-a SIM_ADDRESS] [-p SIM_PORT]

mbsim sim udp [-h] [-a SIM_ADDRESS] [-p SIM_PORT]

mbsim sim rtu [-h] [-b SIM_BUAD] [--bytesize SIM_BYTE]
  [-s SIM_STOPBITS] [-p {N,O,E}] [-t SIM_TIMEOUT]
  [-x {0,1}] [-r {0,1}]
  [path]Addonsmbsim looks for the following entry points under the group mbsim_command and mbsim_ui.Under mbsim_ui will call the function to start the uiUnder mbsim_command will call the function and pass parser from argparse and expect a function to be returned that is
expecting a Namespace from argparseFor examples seesimanduiNoteThis project has been set up using PyScaffold 4.3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: mbsim-core
mbsim-coreCore package for mbsim to deal with some of the boiler plate necessary for prototyping.This is the module that will allow you to quickly make prototypes and simulate modbus devices and clients.If you are developing software for production environment, I strongly recommend to usepymodbusdirectly.InstallationTo install mbsim you can install fromgitlab package registryoruse pippip installmbsim-coreUsageWe have created a couple of examples to demonstrate making modbus simulators.
They can be foundhereMain PackageThe command line utils can be found in the packagembsim.
This package will have utils for modbus server and clients.Making Changes & ContributingThis project usespre-commit, please make sure to install it before making any
changes:pip install pre-commit
cd mbsim-core
pre-commit installIt is a good idea to update the hooks to the latest version:pre-commit autoupdateDon’t forget to tell your contributors to also install and use pre-commit.NoteThis project has been set up using PyScaffold 4.1.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: mbstrdecoder
mbstrdecoderSummaryInstallationInstall from PyPIInstall from PPA (for Ubuntu)UsageDependenciesSummarymbstrdecoderis a Python library for multi-byte character string decoder.InstallationInstall from PyPIpip install mbstrdecoderInstall from PPA (for Ubuntu)sudo add-apt-repository ppa:thombashi/ppa
sudo apt update
sudo apt install python3-mbstrdecoderUsageSample Code:frommbstrdecoderimportMultiByteStrDecoderencoded_multibyte_text="マルチバイト文字".encode("utf-8")decoder=MultiByteStrDecoder(encoded_multibyte_text)print("encoded bytes:{}".format(encoded_multibyte_text))print("unicode:{}".format(decoder.unicode_str))print("codec:{}".format(decoder.codec))Output:encoded bytes: b'\xe3\x83\x9e\xe3\x83\xab\xe3\x83\x81\xe3\x83\x90\xe3\x82\xa4\xe3\x83\x88\xe6\x96\x87\xe5\xad\x97'
unicode: マルチバイト文字
codec: utf_8DependenciesPython 3.7+Python package dependencies (automatically installed)