Saravanan666/pypi-org huggingface dataset:
Package: naimco
NaimCo (NaimController) is package to control Naim Mu-so sound systems.Nothing much to see for the moment but you can turn on the radio to preset #2:$ python scripts/naim_control.py 192.168.1.183 --preset 2 --volume 10

usage: naim_control.py [-h] [-i {IRADIO,DIGITAL1,SPOTIFY,USB,UPNP,TIDAL,FRONT} | -p PRESET | -o] [-v VOLUME] address

Turn on Radio on naim Mu-so

positional arguments:
  address               ip address of Mu-so

options:
  -h, --help            show this help message and exit
  -i {IRADIO,DIGITAL1,SPOTIFY,USB,UPNP,TIDAL,FRONT}, --input {IRADIO,DIGITAL1,SPOTIFY,USB,UPNP,TIDAL,FRONT}
                        Select input
  -p PRESET, --preset PRESET
                        Preset [1-40]
  -o, --off             Turn receiver off
  -v VOLUME, --volume VOLUME
                        Volume [0-100]MotivationNaim Mu-so implements DLNA to some extent and it is possible to control it in home automation systems.
Basic stuff like volume up down and play some media works.But there are functions that as far as I can tell can’t be controlled with upnp/DLNA such as:On/Off ( Standby off or standby on í Naim terms )Input selectioniRadioDigitalAnalogNaim does not publish an API for the Mu-so, but there is an App. So after 5 years of waiting for someone else to figure it out I decided to have a look at how it communicates with my Mu-so.CommunicationSome information found here:Sniffing
Package: naipy
Naipy(네이피)누구나 쉽게 네이버 API를누구나 손쉽게 네이버API를 사용할 수 있어요!동기 비동기 모두 지원합니다!현재 지원API : 검색API, 번역API, 언어감지API자세한 사용법은 여기를 참조해주세요!Installation$ pip install naipyExample검색(이미지)[동기]fromnaipyimportsyncnipy=sync.Search()# 필수 인자(API)가 들어가는 곳입니다. (샘플키지원)print(nipy.image("너구리").link)# 너구리라는 검색어 데이터의 link를 반환합니다.검색(이미지)[비동기]fromnaipyimportclientimportasyncioasyncdefmain():c=client.Search()# 필수 인자(API)가 들어가는 곳입니다. (샘플키지원)r=awaitc.image("너구리")# 너구리라는 검색어 데이터의 link를 반환합니다.print(r.link)asyncio.run(main())
Package: nairods
nairods介绍整理现阶段的项目

封装一些常用的工具包

提高开发效率(疑问?)安装pipinstallnairods源安装pipinstall-ihttps://pypi.tuna.tsinghua.edu.cn/simplenairodspipinstall-ihttps://mirrors.aliyun.com/pypi/simple/nairodspipinstall-ihttps://pypi.douban.com/simple/nairods文件结构nairods/# 顶级包│__init__.py│├─ConfigTools# 配置包│└─DatabaseConfig# 数据库配置│PgServe.yml# Pg服务器│Mysql.yml# Mysql服务器│└─Database_Sql# 数据库语句│CreatemysqlData.sql# 建表语句│├─CrawlerTools# 爬虫包│__init__.py│└─Example# 举个栗子│__init__.py│DlzbCrawler.py# 电力招标网│├─TestFile# 测试数据包│└─Xlsx# 表格数据│└─Txt# 文本数据│├─DatebaseTools# 数据库包│__init__.py│PgTools.py# Pg数据库工具类│├─DingtalkTools# 钉钉包│__init__.py│DingtalkBot.py# 机器人,推送文件,文本│├─Document# 文档│└─Docx# word文档│└─Pdf# pdf文件│└─Xlsx# 表格数据│├─FileTools# 常用文件包│__init__.py│ExcleTools.py# 表格操作│FolderTools.py# 文件夹操作│PdfTools.py# pdf操作│YmlTools.py# yml操作│ZipTools.py# zip操作│├─Image# 图片地址│└─Icon# 图标│└─Png# png图片│├─Log# log日志├─Temp# 临时文件夹│└─Csv# csv文件夹├─TimeTools# 时间包│__init__.py│DatetimeTools.py# 时间││README.en.md│README.md│version_update.md# 版本更新代码规范(试用版)变量命名一个单词组成: 小写connect多个单词组成: 下划线remote_folder_path函数命名一个单词组成: 小写test()多个单词组成: 下划线connect_test()类命名一个单词组成: 首字母大写Serve()多个单词组成: 首字母大写&末尾字母大写SoftwareTools()文件命名一个单词组成: 小写remove.py多个单词组成: 首字母大写&末尾字母大写DatabaseTools.py文件夹命名一个单词组成: 大写Temp多个单词组成: 首字母大写&末尾字母大写FileTools模块命名一个单词组成: 大写Serve多个单词组成: 首字母大写&末尾字母大写FileTools类调用一个单词组成: 前面两个字母大写 SE =Serve()多个单词组成: 首字母大写&末尾字母大写 ST =SoftwareTools()函数出参入参注释defread_yaml(yml_path=''):'''文件夹不存在则创建:param folder_path: 文件夹地址:return: yml文件地址'''returnyml_path类初始化函数注释classYamlCurd(obejct):'''yaml 操作'''def__init__(self):passdefread_yaml(self,yml_path=''):'''文件夹不存在则创建:param folder_path: 文件夹地址:return: yml文件地址'''returnyml_path文件要有函数入口测试classYamlCurd(obejct):'''yaml 操作'''def__init__(self):passdefread_yaml(self,yml_path=''):'''文件夹不存在则创建:param folder_path: 文件夹地址:return: 创建成功'''returnyml_pathif__name__=='__main__':YC=YamlCurd()YC.read_yml(F'demo.yml')变量值变量值   优先使用单引号 '    '这里主要是复制其他数据会是双引号如:json,xpath等等age=18name='小明'格式化字符串f-stringf或者F 优先使用 F + ' 'age=18name='小明'result=F'{name}今年{age}岁'转义字符 R或者r转义字符 使用优先使用大写 R路径类   使用优先使用大写 Ryml_path=R'./yml_path'使用说明导入相关包fromFileTools.YmlToolsimportYmlCurd初始化fromFileTools.YmlToolsimportYmlCurdYC=YmlCurd()使用相关函数fromFileTools.YmlToolsimportYmlCurdYC=YmlCurd()YC.read_yaml()
Package: nais
DocumentationNORSAR Artificial Inteligence System (NAIS, yes, the acronym was chosen first)This repository will eventually contain tools specific to aiding in AI research at NORSAR.This repository is based on Tensorflow and Keras.This repository will contain:Baseline models, for waveforms and spectrograms. Possibly pretrained for certain tasks.Standard datasets to test models. Development of new models can be difficult. Standard datasets eliminates errors in the data and lets you focus on developing the model.ClassificationRegressionMasking (eg. arrival picking)Augmentation methods.Installationpip install naisIf an error occurs when importingnais, likelysndfilelibrary is not installed (check the error), and needs to be:apt-get install libsndfile1-devQuick exampleimportnumpyasnpfromnais.ModelsimportAlexNet1DX=np.random.normal(size=(16,256,3))#16 examples of three channel data.y=np.random.randint(0,1,size=(16,))#Labelsmodel=AlexNet1D(num_outputs=1)#binarymodel.compile('adam','binary_crossentropy')model.fit(X,y)
Package: naislinter
naislinterA simple Python script to validate that your NAIS yaml file adheres to the NAIS specifications.GitHub ActionTo set up this as a GitHub Action, add the following as a step in your workflow:-name:naislint nais.yamluses:nais/naislinter@v1.0.2env:RESOURCE:nais.yamlThe action follows the same footprint asnais/deployfor injecting variables to templates, however some environment variables are not required, such asAPIKEY,CLUSTER.Local installationpipinstallnaislinterLocal usagenaislinternais.yamlWill print any path in the config tree that falls outside of the spec and return a non-zero code if any were encountered.Returns 0 if the file is valid according to the NAIS specifications.LimitationsThis does not validate value types, this is not in scope asnais/deployverifies types and rejects
a deployment if the NAIS yaml file is malformed in this fashion.Additionally, this tool requires a fully formed NAIS yaml file, after any template injections.
A file containing{{ image }}will therefore fail, unless the variable has been injected.
This is not relevant in the GitHub Action, as template injection happens beforenaislinterruns.Finally, in its current state, the tool is unable to verify objects within lists. For instance:spec:env:-name:KEYvalue:value-name:ANOTHER_KEYvalue:another_valuekey_outside_of_spec:hello!key_outside_of_specwill not be caught as an error since it's in an object within a list.
Package: nait
NaitProvides functions and classes for creating andtraining neural networks.ModelModel.trainModel.predictModel.mutateLayerLayer.forwardLayer.mutateNeuronNeuron.parseNeuron.mutateActivationActivation.calculateevaluatesaveloadModelclassSyntaxModel(layers)ArgumentslayerslistModel.trainmethodSyntaxtrain(x=[], y=[], epochs=5000, rate=1, batch_size=10, sample_size=None, square_loss=True, visual=True, loss_function=None)Argumentsxlistylistepochsintrateint / floatbatch_sizeintsample_sizeintsquare_lossboolvisualboolloss_functionfunctionModel.predictmethodSyntaxpredict(inputs)ArgumentsinputslistModel.mutatemethodSyntaxmutate(rate)Argumentsrateint / floatLayerclassSyntaxLayer(inputs=4, neurons=4, activation="linear")Argumentsinputsintneuronsintactivation"linear" / "step" / "relu" / "tanh" / "hardmax" / "sigmoid" / "softmax" / "leaky relu"Layer.forwardmethodSyntaxforward(inputs)ArgumentsinputslistLayer.mutatemethodSyntaxmutate(rate)Argumentsrateint / floatNeuronclassSyntaxNeuron(weights, bias)Argumentsweightslistbiasint / floatNeuron.parsemethodSyntaxparse(inputs)ArgumentsinputslistNeuron.mutatemethodSyntaxmutate(rate)Argumentsrateint / floatActivationclassSyntaxActivation(function)Argumentsfunction"linear" / "step" / "relu" / "tanh" / "hardmax" / "sigmoid" / "softmax" / "leaky relu"Activation.calculatemethodSyntaxcalculate(x)Argumentsxint / floatsavefunctionSyntaxsave(model, path)ArgumentsmodelModelpathstrloadfunctionSyntaxload(path)ArgumentspathstrChangelog - Nait 3.0.8Fixed 'save()'
Package: naitoon
NaiToon(네이툰)네이버 웹툰 크롤링 라이브러리동기 비동기 모두 지원합니다!현재 지원기능 : 작품리스트(요일별), ID검색 기능Installation$ pip install naitoonExample리스트 불러오기(비동기)fromnaitoonimportnaverimportasyncionaver=naver.Webtoon()asyncdefmain():data=awaitnaver.get_list()#요일웹툰 리스트를 가져옵니다. 파라미터가 비어있을시 오늘차 요일 리스트를 반환합니다.print(data)asyncio.run(main())리스트 불러오기(동기)fromnaitoonimportsyncnaver=sync.Webtoon()data=naver.get_list()#요일웹툰 리스트를 가져옵니다. 파라미터가 비어있을시 오늘차 요일 리스트를 반환합니다.print(data)Request sample[
  {
    'title': '화산귀환', 
    'author': 'LICO / 비가', 
    'star': '9.91', 
    'thumbnail': 'https://shared-comic.pstatic.net/thumb/webtoon/769209/thumbnail/thumbnail_IMAG10_13ff2e28-686a-42de-890e-9289550978bc.jpg', 
    'id': '769209', 
    'link': 'https://comic.naver.com/webtoon/list?titleId=769209&weekday=wed'
  }
]ID로 작품 불러오기(비동기)fromnaitoonimportnaverimportasyncionaver=naver.Webtoon()asyncdefmain():data=awaitnaver.get_info(758037)#작품의 정보를 가져옵니다. 작품을 찾지 못할 경우 TitleIdException 예외를 반환합니다.print(data)asyncio.run(main())ID로 작품 불러오기(비동기)fromnaitoonimportsyncnaver=sync.Webtoon()data=naver.get_info(758037)#요일웹툰 리스트를 가져옵니다. 파라미터가 비어있을시 오늘차 요일 리스트를 반환합니다.print(data)Request sample{
  'title': '참교육', 
  'author': '채용택 / 한가람', 
  'genre': '스토리, 액션', 
  'age': '15세 이용가', 
  'description': '무너진 교권을 지키기 위해 교권보호국 소속 나화진의 참교육이 시작된다!<부활남> 채용택 작가 X <신석기녀> 한가람 작가의 신작!', 
  'thumbnail': 'https://shared-comic.pstatic.net/thumb/webtoon/758037/thumbnail/thumbnail_IMAG06_794bcc1e-23aa-4c35-a335-b5d21b4bc2ab.jpg'
}
Package: naive-bayes
# Naive Bayes Text ClassifierText classifier based on Naive Bayes.## Instalation```bash$ pip install naive-bayes```## Usage example```pythonfrom naivebayes import NaiveBayesTextClassifierclassifier = NaiveBayesTextClassifier(categories=categories_list,stop_words=stopwords_list)classifier.train(train_docs, train_classes)predicted_classes = classifier.classify(test_docs)````NaiveBayesTextClassifier` is a simple wrapper around `scikit-learn` class `CountVectorizer`. You can put all arguments which support this class. For more information please check `scikit-learn` official documentation.## More examplesCheck examples at `examples` folder. Before run them, install requirements in this folder.Clone repository from github```bash$ git clone git@github.com:itdxer/naive-bayes.git$ cd naive-bayes/examples$ pip install -r requirements.txt```And run some example### Usenet 20 newsgroup```bash$ python 20newsgroup```### Kaggle IMDB reviews competition```bash$ python imdb_reviews```
Package: naivefea
NaiveFeaA simple python library for 2D finite element analysis.You can learn how to use it byexample.ipynbandexample_nonlinear.ipynb.Linear and nonlinear elasticity;Easy to calculate and plot user-defined showing data;Support multiprocessor;Plot mesh, undeformed, and deformed figure, where magnificient of deformed figure can be calculate automatically.Pre-processingPlot figure of mesh and restirct conditionSolvingSolving a FEA problem within 8 lines.mesh=meshio.read('abaqus_mesh.inp')fea=LinearFea(mesh)material=LinearElastic(E=10.0,nv=0.3)fea.uniform_material(material)fea.set_deform_conditions('fix',Uxy=[0,5,10,15,20])fea.set_force_conditions({14:(0.001,0)})fea.submit()fea.plot('stress','S12')Post-processingEasy to plot figures.Result of two material feaAbility to solve multi material problem.MarkThis project is a teaching material for students who want to implement FEA with Python ina post on Chaoli forum(in Chinese):Theory document of linear finite element analysis(in Chinese)
Package: naive-feature-selection
NFS: Naive Feature SelectionThis package solves the Naive Feature Selection problem described inthe paper.Installationpip install git+https://github.com/aspremon/NaiveFeatureSelectionUsageMinimal usage scriptTheDemoNFS.pyscript loads the20 newsgroupstext data set fromscikit-learnand reports accuracy of Naive Feature Selection, followed by SVC using the selected features.The package is compatible withscikit-learn'sFit-Transformparadigm. To demonstrate this,DemoNFS.pyruns the same test using thepipelinepackage fromscikit-learnand performs cross validation usingGridSearchCVfromsklearn.model_selection.To run theDemoNFS.pyscript, typepython DemoNFS.pyThis should produce the following outputTesting NFS ...
Loading 20 newsgroups dataset for categories:
['sci.med', 'sci.space']

Extracting features from the training data using a sparse vectorizer
n_samples: 1187, n_features: 21368

Extracting features from the test data using the same vectorizer
n_samples: 790, n_features: 21368

NFS accuracy:   0.843

Space features:
['aerospace', 'allen', 'ames', 'apollo', 'astronomy', 'billion', 'built', 'centaur', 'comet', 'command', 'commercial', 'cost', 'data', 'dc', 'dryden', 'earth', 'flight', 'funding', 'government', 'gravity', 'jupiter', 'landing', 'launch', 'launched', 'launches', 'lunar', 'mars', 'mary', 'mining', 'mission', 'missions', 'moon', 'nasa', 'orbit', 'orbital', 'pat', 'payload', 'planetary', 'program', 'project', 'proton', 'rocket', 'rockets', 'russian', 'satellite', 'satellites', 'shafer', 'shuttle', 'software', 'solar', 'space', 'spacecraft', 'ssto', 'station', 'sun', 'titan', 'vehicle']

Med features:
['allergic', 'banks', 'blood', 'brain', 'cadre', 'cancer', 'candida', 'chastity', 'diagnosed', 'diet', 'disease', 'diseases', 'doctor', 'doctors', 'drug', 'drugs', 'dsl', 'food', 'foods', 'geb', 'gordon', 'health', 'intellect', 'lyme', 'med', 'medical', 'medicine', 'msg', 'n3jxp', 'pain', 'patient', 'patients', 'pitt', 'seizures', 'shameful', 'skepticism', 'soon', 'surrender', 'symptoms', 'syndrome', 'therapy', 'treatment', 'yeast']

Pipeline accuracy:      0.843

Best cross validated k: 500
Package: naivenlp-datasets
smile-datasetsData pipelines for bothTensorFlowandPyTorch!If you want to load public datasets, try:tensorflow/datasetshuggingface/datasetsIf you want to load local, personal dataset with minimized boilerplate, usenaivenlp-datasets!installationpipinstall-Unaivenlp-datasetsIf you work with PyTorch, you should installPyTorchfirst.If you work with TensorFlow, you should installTensorFlowfirst.UsageHere are few examples to show you how to use this library.
Package: naivenmt-datasets
datasetsA dataset utils repository.For tensorflow>=2.0.0b only!Requirementspython 3.6tensorflow>=2.0.0bInstallationpipinstallnlp-datasetsContentsBuild dataset for seq2seq models.seq2seq_dataset.pyBuild dataset for NMT.nmt_dataset.pyBuild dataset for DSSM.dssm_dataset.pyBuild dataset for MatchPyramid.matchpyramid_dataset.pyUsageFor NMT taskfromnlp_datasetsimportNMTSameFileDataseto=NMTSameFileDataset(config=None,logger_name=None)train_files=[]# your files# train_dataset is an instance of tf.data.Datasettrain_dataset=o.build_train_dataset(train_files)fromnlp_datasetsimportNMTSeparateFileDataseto=NMTSeparateFileDataset(config=None,logger_name=None)feature_files=[]# your fileslabel_files=[]train_dataset=o.build_train_dataset(feature_files,label_files)For DSSM taskfromnlp_datasetsimportDSSMSameFileDataseto=DSSMSameFileDataset(config=None,logger_name=None)train_dataset=o.build_train_dataset(train_files=[])fromnlp_datasetsimportDSSMSeparateFileDataseto=DSSMSeparateFileDataset(config=None,logger_name=None)query_files=[]doc_files=[]label_files=[]train_dataset=o.build_train_dataset(query_files,doc_files,label_files)For MatchPyramid taskfromnlp_datasetsimportMatchPyramidSameFileDataseto=MatchPyramidSameFileDataset(config=None,logger_name=None)train_dataset=o.build_train_dataset(train_files=[])fromnlp_datasetsimportMatchPyramidSeparateFilesDataseto=MatchPyramidSeparateFilesDataset(config=None,logger_name=None)query_files=[]doc_files=[]label_files=[]train_dataset=o.build_train_dataset(query_files,doc_files,label_files)
Package: naivepdf
NaivePDFyet another pdf texts and tables extractorThis project is inspired bypdfminer,
and the pdf parts use, rewrite or redesign a lots of it's codes.The main purpose of this project is to provide a tool
that can naively extract text lines andbordered tablesfrom pdf files,
and write them into a html file.
In most cases it works well.On the other hand, it's pdf parts can be an alternative of pdfminer
that you can use it to extract texts, lines and shapes more simply.How to InstallPython3.6+ requiredpip install naivepdfExample of Usevery simple to use# encoding: utf-8fromnaivepdf.pdfdocumentimportPDFDocumentfromnaivepdf.reconstructorimportPageReconstructorfromnaivepdf.utils.htmlimporthtmldefmain():withopen('examples/1206061047.pdf','rb')asfp:data=[]doc=PDFDocument(fp)fori,pageinenumerate(doc.pages):# as an alternative of pdfminer, just:# data.append(page.data)reconstructor=PageReconstructor(page)data.extend(reconstructor.reconstruct())# as an alternative of pdfminer, just:# return datawithopen('examples/1206061047.html','w',encoding='utf-8')asfp:html(fp,data)if__name__=='__main__':main()
Package: naivepy
NaivePyThis Module Is No Longer Maintainedv1.1 is the last release.Naive Bayes :About Naive Bayes :Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.It is mainly used in text classification that includes a high-dimensional training dataset.Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.Formula Of Naive Bayes :Bayes' theorem is also known as Bayes' Rule or Bayes' law, which is used to determine the probability of a hypothesis with prior knowledge. It depends on the conditional probability.
The formula for Bayes' theorem is given as:
Naïve Bayes Classifier Algorithm
Where,$P(A|B)$ = ${P(B|A)P(A)} \over P(B)$P(A|B) is Posterior probability: Probability of hypothesis A on the observed event B.P(B|A) is Likelihood probability: Probability of the evidence given that the probability of a hypothesis is true.P(A) is Prior Probability: Probability of hypothesis before observing the evidence.P(B) is Marginal Probability: Probability of Evidence.Documentation:Read theDocs HereInstallation :To Install the modulepip install naivepyAbout Module:Naivepy module is built using python and pandas. It is and machine learning algorithm. This Module can take the target column and classifies it.Note: The Target Column must have 2 Types of values other wise MaxTargetColumnException will be occured.Examples :Code:from naivepy import Naive

n=Naive(filename="data.csv",sample_list=["red","suv","domestic"],target_column="stolen")
print(n.getans)
print(n.getdata)
print(n.getlabel)Output:Color    Type    Origin Stolen
   0     Red  Sports  Domestic    Yes
   1     Red  Sports  Domestic     No
   2     Red  Sports  Domestic    Yes
   3  Yellow  Sports  Domestic     No
   4  Yellow  Sports  Imported    Yes
   5  Yellow     SUV  Imported     No
   6  Yellow     SUV  Imported    Yes
   7  Yellow     SUV  Domestic     No
   8     Red     SUV  Imported     No
   9     Red  Sports  Imported    Yes
   No
   0.072Author : Prathamesh Dhande
Package: naivepyrunner
Naive Python RunnerWhat does it do?naivepyrunner is library to schedule a small amount of timed tasks that may need to perform repeatedly. It does not claim to always choose the right execution order of those tasks to minimize the overall delay if it is running under limited ressources, but it does try to do so.How does it work?Why is it naive?The runner assumes that the list with yet to execute tasks does not change while calculating the optimal position for the task. This may lead to not optimal positioningInstallationInstall it viapip:pipinstallnaivepyrunner-UClone this repo and build it yourself:pipinstallwheel-U# clone via httpsgitclonehttps://github.com/henningjanssen/naivepyrunner.git# or via sshgitclonegit@github.com:henningjanssen/naivepyrunner.gitcdnaivepyrunner# install via pippipinstall.# or register as developed packagepythonsetup.pydevelop# or build the package and place it with your packagesmakeDockerBuild it# build it yourselfdockerbuild-tnaivepyrunner.
dockerrun-v$PWD:/appnaivepyrunnerpythonmyapp.py# or use the prebuilt containerdockerrun-v$PWD:/apphenningj/naivepyrunnerpythonmyapp.pyExtend your Dockerfile:FROMhenningj/naivepyrunner:latest[...]pythonmyapp.pyUsageLicense
Package: naivert
# naivertnaivert is an pure Python implementation of ray tracing algorithm using Phong illumination model.
For geometry calculation, naivert uses the library of [Geometry3D](https://github.com/GouMinghao/Geometry3D).
Basic materials and light sources are given in the configuration file. You can also define the material and light source by yourself.
You may refer to the examples and documentation of Geometry3D to get to know how to use naivert.## Requirementsnumpy, Geometry3D, matplotlib and opencv-python## Installation`bash pip install naivert `## Examples
Run .py files under examples folder.
For example:`bash python example3.py `![sample3](sample3.png)`bash python example4.py `![sample3](sample4.png)`bash python example5.py `![sample3](sample5.png)Note that the calculation may be very slow. You can modify the resolution of the image to reduce the time cost.## Documentations
[HTML](https://naivert.readthedocs.io/en/latest/)
[PDF](https://naivert.readthedocs.io/_/downloads/en/latest/pdf/)
Package: naive-stopwords
naive-stopwordsStop words for Chinese.Installationpipinstall-Unaive-stopwordsUsage>>>fromnaive_stopwordsimportStopwords
>>>sw=Stopwords()>>>sw.size()2321>>>sw.contains('hello')True
>>>sw.add('的')>>>sw.size()2321>>>sw.remove('的')>>>sw.size()2320>>>sw.dump('file.txt')