Saravanan666/pypi-org huggingface dataset:
Package: tubelearns
Tube-Data: YouTube Video Transcript ExtractorTube-Data is a Python script designed for extracting and cleaning YouTube video transcripts for preprocessing in machine learning. This versatile tool streamlines the process of acquiring high-quality text data from YouTube videos, making it ideal for various natural language processing tasks, sentiment analysis, speech recognition, and more.FeaturesExtracts video transcripts from YouTube videos.Saves cleaned transcripts into separate text files.Supports individual video URLs, batch processing from a list of URLs, and entire playlists.Streamlines the dataset collection process for machine learning applications.New Feature: Tokenization and Punctuation Removal for text preprocessing and cleaning.InstallationYou can install TubeLearns using pip:pipinstalltubelearnsUsagePlaylist GrabbingfromtubelearnsimportAcquisition# Initialize the Acquisition classmodel=Acquisition()# Grab transcripts from a YouTube playlistplaylist_url='https://www.youtube.com/your_playlist_url'model.playlist_grab(playlist_url,name="raw_data")Extract Video Links from Playlist# Extract video links from a YouTube playlistplaylist_url='https://www.youtube.com/your_playlist_url'model.play2text(playlist_url)Tokenization and Cleaningfromtubelearns.tokenizersimportTokenization,Cleaning# Initialize the Tokenization classtokenizer=Tokenization()cleaner=Cleaning()# Tokenize text datatext_data="Your input text here."tokenized_data=tokenizer.tokenize_raw(text_data)cleaned_data=cleaner.punct_raw(tokenized_data)Refer to theTubeLearns documentationfor detailed usage instructions and examples.ContributingIf you'd like to contribute to TubeLearns or report issues, please check out theGitHub repository.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.AcknowledgmentsYouTube Transcript APIPyTubespaCynum2wordsEnjoy using TubeLearns! If you have any questions or encounter issues, please don't hesitate toget in touch.
Package: tuber
TuberThis project finds an easy way to find the root of a python project's repo.  It
relies on several common markers, like.gitorpyproject.tomlto determine
this.>>>fromtuberimportget_root>>>get_root("/")Traceback (most recent call last):...tuber.RootException:No root found before actual root>>>get_root(".")PosixPath("/path/to/repo")LicenseTuber is copyrightTim Hatch, and licensed under
the MIT license.  I am providing code in this repository to you under an open
source license.  This is my personal repository; the license you receive to
my code is from me and not from my employer. See theLICENSEfile for details.
Package: tuberia
Data engineering meets software engineering:books:Documentation:https://aidictive.github.io/tuberia:keyboard:Source Code:https://github.com/aidictive/tuberia🤔 What is this?Tuberia is born from the need to bring the worlds of data and software
engineering closer together. Here is a list of common problems in data
projects:Loooooong SQL queries impossible to understand/test.A lot of duplicate code due to the difficulty of reusing it in SQL queries.Lack of tests, sometimes because the used framework does not facilitate
testing tasks.Lack of documentation.Discrepancies between the existing documentation and the latest deployed code.A set of notebooks deployed under the Databricks Share folder.A generic notebook with utility functions.Use of drag-and-drop frameworks that limit the developer's creativity.Months of intense work to migrate existing pipelines from one orchestrator to
another (e.g. from Airflow to Prefect, from Databricks Jobs to Data
Factory...).Tuberia aims to solve all these problems and many others.🤓 How it works?You can view Tuberia as if it were a compiler. Instead of compiling a
programming language, it compiles the steps necessary for your data pipeline to
run successfully.Tuberia is not an orchestrator, but it allows you to run the code you write in
Python in any existing orchestrator: Airflow, Prefect, Databricks Jobs, Data
Factory....Tuberia provides some abstraction of where the code is executed, but defines
very well what are the necessary steps to execute it. For example, this shows
how to create a PySpark DataFrame from therangefunction and creates a Delta
table.importpyspark.sql.functionsasFfromtuberiaimportPySparkTable,runclassRange(PySparkTable):"""Table with numbers from 1 to `n`.Attribute:n: Max number in table."""n:int=10defdf(self):returnself.spark.range(self.n).withColumn("id",F.col(self.schema.id)classDoubleRange(PySparkTable):range:Range=Range()defdf(self):returnself.range.read().withColumn("id",F.col("id")*2)run(DoubleRange())!!! warningPrevious code may not work yet and it can change. Please, notice this
project is in an early stage of its development.All docstrings included in the code will be used to generate documentation
about your data pipeline. That information, together with the result of data
expectations/data quality rules will help you to always have complete and up to
date documentation.Besides that, as you have seen, Tuberia is pure Python so doing unit tests/data
tests is very easy. Programming gurus will enjoy data engineering again!
Package: tube-scrapy
TubeScrapyA python package to webscrap for youtube video data as title, video url, thumbnail url, and number of views. The package runs packagesSeleniumandWebdriverto access Youtube page and copy videos informations. The process is expensive and remains about 5 seconds for search. The first search could take a little more time because packageWebdriverwill download and install the web driver to control Chrome browser.Python minimal versionPython 3.8Python packages requiredSelenium 4.8.2Webdriver-manager 3.8.5ExamplesFirst of all, import module:importtube_scrapyCreate a new instance ofTubeScrapy:ts=TubeScrapy()Create a variable to save video info searched using methodsearch_video_info():videos_infos=ts.search_video_info(search_title='Cheia de Manias Raça Negra',# search title or queryadd_to_search='karaoke',# will basiclly be concatened with search_titlesearch_range=5# quantity of videos searched)The videos informations will be returned in alistof objectsScrapedVideoInfo, which have this structure:ScrapedVideoInfo(title=title,# youtube video titlevideo_url=video_url,# youtube video urlthumbnail_url=thumbnail,# video thumbnail urlviews=views# video number of views)This is it, good coding! ;-) For more information, visit the projectrepositoryand relate anyissue, please.
Package: tubesml
A package that allows for flexible ML pipelines, model validation, and model inspection.The project started to productize code written for Kaggle competitions and it is mostly designed to facilitate the creation of flexible processing and modeling pipeline. Everything is built around scikit-learn transformers and methods, but it is possible to extend the concept to other ML packages.The main characteristic that makes the pipeline components oftubesMLflexible is the fact that it always preserves the pandas DataFrame structure, hence making it easy to, for example, create a feature within a pipeline and test it in a grid search.InstallationDependenciestubesML requires:Python (>= 3.7)Matplotlib (>= 3.5.3)Pandas (>= 1.3.5)Scikit-Learn (>= 1.0.2)Seaborn (>= 0.12.0)These requirements are good for a Kaggle notebook, however the package has been developed with the following requirementsPython (>= 3.10)Matplotlib (>= 3.6.0)Pandas (>= 1.5.0)Numpy (>= 1.23.1)Scikit-Learn (>= 1.1.2)Seaborn (>= 0.12.0)User installationIf you already have a working installation of pandas and sklearn,
the easiest way to install scikit-learn is usingpippip install -U tubesmlImportant linksOfficial source code repo:https://github.com/lucabasa/tubesMLDownload releases:https://pypi.org/project/tubesml/Documentation:https://tubesml.readthedocs.io/en/latest/
Package: tubetone
Tone TubeAnalyzes youtube videos usingpytube,transcriptsanddostoevsky. Can save values into the db.pip install tonetubeThen you'll need to download binary model fordostoevsky:python -m dostoevsky download fasttext-social-network-modelSeetestsdirectory for more details.Run to load the given playlist video tones:tubetone --id=PLL5fTqvjKv3JaH8SofJ89geu2VwJL5R04For environment variables,
seeconstants file.
Package: tubex
TubexA simple Command Line Interface to download mp3 from youtube and mp4 from oreilly.tubex download-mp3 <youtube_playlist_url> --album <album_name> --artist <artist_name>
tubex --username <username> --password <password> download-oreilly <oreilly_learning_url>Example# To learn more about pipx: https://github.com/pipxproject/pipx$pipxinstalltubex
$pipxupgradetubex# Download mp3$tubexdownload-mp3\https://www.youtube.com/playlist?list=PLSdmnemG6jbv1-GRf23jwDsYWkjz7mblP\--albumtest--artistmixed# Enjoy the music (e.g using `vlc`)$vlctubex_out# Download mp4 (video)$tubex--outdir20210110download-mp4https://www.youtube.com/watch?v=aUrmkKDJ1t8# Download oreilly$tubex--username<username>--password<password>\download-oreillyhttps://learning.oreilly.com/videos/modern-python-livelessons/9780134743400# Enjoy learning$vlctubex_out/Modern_Python_LiveLessons_-_Big_Ideas_and_Little_Code_in_Python/
Package: tublub
tublubConvert or view tabular data files usingTablib.
Tublub is just a simple CLI wrapper around Tablib.$ tublub --list
Available formats: json xlsx xls yaml csv tsv ods dbf html jira latex df rst cli

$ tublub input.json
Username |Identifier|First name|Last name
---------|----------|----------|---------
booker12 |9012      |Rachel    |Booker
grey07   |2070      |Laura     |Grey
jenkins46|9346      |Mary      |Jenkins
johnson81|4081      |Craig     |Johnson
smith79  |5079      |Jamie     |Smith

$ tublub input.json output.xlsx

$ file output.xlsx
output.xlsx: Microsoft Excel 2007+

$ tublub input.json --format csv
Username,Identifier,First name,Last name
booker12,9012,Rachel,Booker
grey07,2070,Laura,Grey
jenkins46,9346,Mary,Jenkins
johnson81,4081,Craig,Johnson
smith79,5079,Jamie,SmithNews and ChangesPlease see thechangelogfor more details.
Package: tubtools
tubtoolsA collection of scrapers for the TU Berlin web services. They are mostly intended for purposes of automating reoccurring
tasks in teaching at TU Berlin. We provide commandline utilities for convenient scripting.Login credentials can be saved securely using thekeyringpackage.InstallationThis is a python package that can be installed usingpip:gitclonegit@git.tu-berlin.de:tkn/tubtools.gitcdtubtools
sudo-Hpipinstall.ISISSearch for courses by name withisis searchDownload assignment submissions for a course withisis dlAuto-extracts '.tar.gz' and 'zip' submissionsBy default, this assumes group submissions with one archive fileLook up course participants and their group membership withisis ptDownload grading tables from a course e.g. to see who passed withìsis gradingModify group membership withisis groupsMosesLook up a person on Moses withmoses pplincluding their email and student id. Batch processing of emails / student
id / usernames with the--batchflag, e.g. if you have a list of student ids and want to email them.NOTE: This requires administrator role in Moses. You should see the 'Administration' tab in the top.Examples# Find course id of `Rechnernetze und Verteilte Systeme (no login required)isissearch"Rechnernetze und Verteilte Systeme"# Count number of participants in course 21032isispt-uMyUserName-r'Teilnehmer/in'21032|wc-l# Find all group members of group T01G01isispt-uMyUserName-G"T01G01"21032# This is also possible with the groups command# Probably faster, but less detailed informationisisgroups-uMyUserName-g"T01G01"--show21032# You can add somebody to a group via their emailisisgroups-uMyUserName-g"T01G01"--addbraeuer@tu-berlin.de21032# ..or via their student id (if you are a TU Berlin staff member)isisgroups-uMyUserName-g"T01G01"--add34982921032# A batch mode is also available, which might be useful for assigning lots of ppl to one groupisisgroups-uMyUserName-g"QISPOS-angemeldet"--batch-add21032<ids_from_qispos.txt# Download all group submissions for a selected assignment (interactive)isisdl-uMyUserName21032# Copy the student id of all students that passed more than 3 out of 5 programming# assignments in the RNVS course to the clipboardisisgrading-uMyUserName--no-feedback-f"Abgabe"21032\|awk-F","'$8 + $9 + $10 + $11 + $12 >= 3 {print $6}'\|sed's/^16900//g'\|xclip-selectionclipboard# This filters all gradings that start with 'Abgabe'.# awk sums the relevant columns and prints the student id# sed filters the irrelevant prefix 16900# xclip puts it into the clipboard# Get the emails to all student ids that you have in a filemosesppl-uMyUserName--batchid--extended<need_to_be_notified.txt# Find out who is named Bräuer in the universitymosesppl-uMyUserName--surnameBräuer
Package: tubular
Tubular pre-processing for machine learning!tubularimplements pre-processing steps for tabular data commonly used in machine learning pipelines.The transformers are compatible withscikit-learnPipelines. Each has atransformmethod to apply the pre-processing step to data and afitmethod to learn the relevant information from the data, if applicable.The transformers intubularwork with data inpandasDataFrames.There are a variety of transformers to assist with;cappingdatesimputationmappingcategorical encodingnumeric operationsHere is a simple example of applying capping to two columns;fromtubular.cappingimportCappingTransformerimportpandasaspdfromsklearn.datasetsimportfetch_california_housing# load the california housing datasetcali=fetch_california_housing()X=pd.DataFrame(cali['data'],columns=cali['feature_names'])# initialise a capping transformer for 2 columnscapper=CappingTransformer(capping_values={'AveOccup':[0,10],'HouseAge':[0,50]})# transform the dataX_capped=capper.transform(X)InstallationThe easiest way to gettubularis directly frompypiwith;pip install tubularDocumentationThe documentation fortubularcan be found onreadthedocs.Instructions for building the docs locally can be found indocs/README.ExamplesTo help get started there are example notebooks in theexamplesfolder in the repo that show how to use each transformer.To open the example notebooks inbinderclickhereor click on thelaunch bindershield above and then click on the directory button in the side bar to the left to navigate to the specific notebook.IssuesFor bugs and feature requests please open anissue.Build and testThe test framework we are using for this project ispytest. To build the package locally and run the tests follow the steps below.First clone the repo and move to the root directory;gitclonehttps://github.com/lvgig/tubular.gitcdtubularNext installtubularand development dependencies;pipinstall.-rrequirements-dev.txtFinally run the test suite withpytest;pytestContributetubularis under active development, we're super excited if you're interested in contributing!See theCONTRIBUTINGfile for the full details of our working practices.
Package: tucao
个人比较喜欢《瞎扯,如何正确的吐槽》这个栏目,所以写了一个脚本获取当天或者指定日期的内容.Installsudo pip install tucaoUsageget the lastest version$ tucao
===============================================================

Q1: 过度中二是怎样一种体验？

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作者: 道林，答完这道题，我就要回老家结婚了

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我……我独自一人吃肉的时候，会用嘴把肉叼住然后仰脖子吞下去……
这让我觉得自己是头恐龙。get the specific date version$  tucao 20161002
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Q1: 工作中，你捅过多大的娄子？结果你怎么样了？

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作者: 匿名用户

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儿子玩手机把一个讽刺小三的图片发给了大老板，偏偏他正因为小三焦头烂额。LICENSEMIT
Package: tuchuang
介绍TuChuang是一个基于SunPics开发的免费图床库，可以上传图片并获取图片链接。
TuChuang is a free image hosting library based on SunPics. It allows users to upload images and obtain the corresponding image links.安装使用以下命令通过pip安装图床库：pip install tuchuang示例以下是使用图床库上传图片并获取图片链接的简单示例：fromtuchunagimportuploadfile_path=input("请输入文件名或目录：")image_url=upload.upload_image(file_path)ifimage_url:print("上传成功")print("图片链接："+image_url)else:print("上传失败")这段代码示例通过调用upload_image函数上传图片，并打印出上传结果和图片链接。
Package: tuck
TuckSemi-automated Python formatting.The aim of this tool is to build up developer-assistance tooling for python
formatting. In general it will only format things when it needs to or when
directly instructed to.UsageMost usage of Tuck is expected to be within editor extensions:VSCode Tuck ExtensionTuck can be also used as a command line tool:python-mtuck--positions<line>:<col>--file.pyStyleThe wrapped statement style which Tuck targets aims to reduce diff noise without
concern for vertical space.Example: Function definitiondeffoo(bar:str,quox:int=0)->float:return4.2wraps to:deffoo(bar:str,quox:int=0,)->float:return4.2Example: List comprehension[xforxin'aBcD'ifx.isupper()]wraps to:[xforxin'aBcD'ifx.isupper()]
Package: tucker-conv
Tucker Convolutional LayersPyTorch implementation of Tucker Convolutional Layers as introduced inMobileDets: Searching for Object Detection Architectures for Mobile Accelerators. Ross Wightman's timm library has been used for some helper functions and inspiration for syntax style.Installation$pipinstalltucker-convUsagefromtucker_conv.convimportTuckerConvimporttorchtucker=TuckerConv(30,30,in_comp_ratio=0.25,out_comp_ratio=0.75)input=torch.randn([1,30,512,512])output=tucker(input)
Package: tucker-riemopt
Tucker RiemoptPython implementation of the Tucker toolbox. Package allows users to manipulate
tensors in Tucker and SF-Tucker[1]formats. It also provides tools
for implementing first-order optimization methods of the Riemannian
optimization on the manifolds of tensors of fixed Tucker rank or fixed SF-Tucker rank.
For instance, package implements a method for efficiently computing the Riemannian
gradient of any smooth function via automatic differentiation.The library is compatible with several computation frameworks, such as PyTorch and
JAX, and can be easily integrated with other frameworks.InstallationNumPy, SciPy andopt-einsumare required for installation. Additionally, you need to install your special
computation framework: PyTorch or JAX.Package may be installed usingpip install tucker_riemopt[torch/jax]with corresponding computation framework.Use casesSeethis repositoryfor examples of package usage.Default computation framework is PyTorch. For using JAX you shouldInstall JAX;Enable JAX backend usingfromtucker_riemoptimportset_backendset_backend("jax")DocumentationDetailed information may be foundhere.LicenseMIT License
Package: tuco
A simple class based finite state machine with parsing time validationFree software: MIT licenseInstallationpip install tucoDocumentationhttps://python-tuco.readthedocs.io/DevelopmentMake sure you have a running redis instance and to run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.3.0Fix: Propagate existing __classcell__ to fix a DeprecationWarning.0.2.0Feature: Add get_all_states() method.0.1.0Initial release.
Package: tucuxi
FeaturesTODORequirementsTODOInstallationYou can installTucuxiviapipfromPyPI:$pipinstalltucuxiUsageTODOContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMITlicense,Tucuxiis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: tudaesasII
TU Delft, Aerospace Engineering, Stability and Analysis of Structures II (tudaesasII)
—Github Actions status:[![Actions Status](https://github.com/saullocastro/tudaesasII/workflows/pytest/badge.svg)](https://github.com/saullocastro/tudaesasII/actions)Coverage status:[![Codecov Status](https://codecov.io/gh/saullocastro/tudaesasII/branch/master/graph/badge.svg?token=KD9D8G8D2P)](https://codecov.io/gh/saullocastro/tudaesasII)TU DelftAerospace EngineeringAerospace Structures and MaterialsStability and Analysis of Structures IIAE4-ASM-511This Python module can be used by the students to assist in the course assignments.DocumentationThe documentation is available on:https://saullocastro.github.io/tudaesasII/Citing this repositoryIf you use any content of this repository, you can cite it as:Castro SGP, Sodja J. Supporting code for Stability and Analysis of Structures II, Department of Aerospace Structures and Materials, Delft University of Technology, 2024. [DOI: 10.5281/zenodo.2583004](https://doi.org/10.5281/zenodo.2583004).LicenseDistrubuted under the 3-Clause BSD license
(https://raw.github.com/saullocastro/tudaesasII/master/LICENSE).Contacts:Saullo G. P. Castro,S.G.P.Castro@tudelft.nlJurij Sodja,J.Sodja@tudelft.nl
Package: tudo
A simple command-line based TODO-app in PythonCommandsmain.py add <task_description><...>: Add new tasks. Default priorities: Important = No/0, Urgent = No/0main.py add--prio<task_description> <important_val> <urgent_val><...>: Add new tasks with non-default priorities (possible priority values: 0 => No, 1 => Yes)main.py list: List all non-finished tasksmain.py list all: List all tasks including the finished onesmain.py list--prio[all] <important_val> <urgent_val>: List tasks, filter for specific important/urgent valuesmain.py rm <task_id><...>: Delete tasksmain.py done <task_id><...>: Set a task as done/finishedmain.py stats: Display number of finished tasks per daymain.py eisenhower: Display open tasks in an Eisenhower matrix according to set priorities (seehttps://en.wikipedia.org/wiki/Time_management#The_Eisenhower_Method)How to continue development (tested on Windows)Navigate to the root directory of this repositorypip install-e.Now, you can usetudo listetc. while still applying changes to the underlaying source codeTo uninstall, usepip uninstall tudoHow to publish to PyPi (already done)Register on PyPi (or, for tests, on TestPyPi) the  and create a personal.pypircfile in your HOME directory (seehttps://packaging.python.org/distributing/#create-an-account)Register the package withpython setup.py register-r<testpypi|pypi>(seehttps://packaging.python.org/distributing/#register-your-project)Attention: This does not work with the live PyPi version (Server response (410): This API is no longer supported, instead simply upload the file.). Using the register form (https://pypi.python.org/pypi?%3Aaction=submit_form) works.Upload the package withtwine upload dist/*-r<testpypi|pipy>(seehttps://wiki.python.org/moin/TestPyPI)Install the package withpip install[-ihttps://testpypi.python.org/pypi]tudoTo uninstall, usepip uninstall tudoAlso probably a good guide:http://peterdowns.com/posts/first-time-with-pypi.htmlOpen issuesdatabase.dbis currently created & saved in the same directory from where thetudocommand was executed (leads to multipledatabase.dbfiles). But our app should always use the same database location! (What is a good location? The user directory?)Editing tasks