Saravanan666/pypi-org huggingface dataset:
Package: nbadge
nbadgeA Python CLI to generate Jupyter notebook badges for different services.ReferencesAdd a direct Binder link for built HTML notebooksblog post by Eric J. Ma.Launch repositories in Deepnotedocumentation.Making Jupyter notebooks Google Colab readyblog post by Tim Sainburg.Open in Colabwebsite.Developmentpdm config python.use_venv False+pdm config python.use_venvpdm installpdm run nbadge --helppdm run nbadge https://github.com/feedzai/feedzai-altair-theme/blob/master/demo.ipynbpdm run black .+pdm run isort .NotesPEP 582: Python local packages directory.eval "$(pdm --pep582)"(source).Awesome PDM.PDMdocumentation.Publish the project to PyPI.License.Commands:pdm --version.pdm init.pdm config python.use_venv Falseandpdm install.pdm config python.use_venv Trueandpdm install.pdm add click pyperclip.pdm add -dG format black isort.pdm build --no-isolation --verbose.Setup PDM for GitHub Action.Copier templateandCopier template by pawamoy.giturlparsepackage.curl -sSL https://raw.githubusercontent.com/pdm-project/pdm/main/install-pdm.py | python3 - -v 2.0.2.
Package: nba-gateway-pdenno
Author:Peter Denno <podenno@gmail.com>homepage:https://github.com/pdenno/mznbProvides communication between the Jupyter notebook and the MiniZinc nb-agent.The module requires an existing installation of the nb-agent (https://github.com/pdenno/nb-agent)  and MiniZinc.InstallationYou can install or upgrade this module via pippip install -U mznbBasic usage%load_ext mznb
Package: nbagent
nbagentnbagentis aNullboardbackup agent for Unix systems.It works in the same way that the officialNullboard Backup Agentfor Windows.Installation$pipinstallnbagentUsageInvokenbagent:$nbagentImmediately you'll see an output similar to this:*[i]Usingdatadirectory/Users/luis/.local/share/nbagent*[i]Configsavedto/Users/luis/.local/share/nbagent/app-config.json*[!]Nullboardtoken:d6606ecaaae54612906cc56a75583b61*[i]Serverlistening0.0.0.0:10001...Note the line:* [!] Nullboard token: d6606ecaaae54612906cc56a75583b61Just copy the auth token (in this exampled6606ecaaae54612906cc56a75583b61) into Nullboard,as per the instructionsand you're ready to go.If for any reason you need to reset the auth token use:$nbagent--reset-tokenYou can also set a custom token if you want:$nbagent--override-token"123-456-789"Overriding defaultsnbagentallows you to specify several settings if you're not comfortable with the defaults.Note thatnbagentdon't use hardcoded paths for the data. It tries to use the standard directory$XDG_DATA_HOME. If not defined, it defaults to$HOME/.local/share.`$nbagent--help

Usage:nbagent[OPTIONS]StartlisteningforNullboardrequests.

Options:--addrTEXTBindtoaddress[default:0.0.0.0]--portINTEGERUsecustomport[default:10001]--dataTEXTDirectoryfordata[default:~/.local/share/nbagent]--reset-tokenGenerateanewrandomauthtoken--override-tokenTEXTUseacustomauthtoken--helpShowthismessageandexit.
Package: nbagile
nbagileMakingnbdevmore Agile-FriendlyInstallpip install nbagileGoals:When exporting source code usingdocments, attach and format the docstring to Numpy formatExport tests and throw them into a pytest-compatible file formatMake documentation run on SphynxTests are exported to atestsfolder, source code to a source code folder, and Markdown/Sphynx documentation to adocsfolderIndividual notebooks can then be recreated from these three itemsCurrent CapabilitiesUsing the CLIReplacenbdev_commands withnbagile_to use it's capabilities.Currently Supported:nbagile_build_lib: Exports code and converts it to black-style + NumPy docstringsnbagile_diff_nbs: A special version ofnbdev_diff_nbsto support how nbagile worksnbagile_build_docs: Builds the docs usingnbverboseExporting code fromdocmentsto NumPydocmentsis a very efficient way of documenting the parameters for your code, and is akin to how javascript is documented. We utilize comment-blocks and typing to describe how parameters are utilized. For example, we have the following:defaddition(a:int,# The first number to addb:(int,float),# The second number to add)->int:# The sum of a and b"Adds a and b"returna+bBut this is not the commonly accepted way of documenting our code, and as a whole looks quite ugly.nbagilesupports building yournbdevbuilt libraries to instead automatically convert this code into a more NumPy-styled documentation string and definition, with the added bonus of it mimicing the Black format:defaddition(a,b):"""Adds a and bParameters----------a : intThe first number to addb : (int,float)The second number to addReturns----------intThe sum of a and b"""returna+bThis works for functions, classes, as well as functions wrapped around decorators.Optional__all__for nbdevIf you are not a fan of nbdev's__all__format in each file, there is an additional setting you can add to yoursettings.ini:use_all.If set toFalse, you won't get the__all__being generated in each python file
Package: nbaHeadshots
nbaHeadshotsA package for downloading NBA player headshots from stats.NBA.comDevelopment Version: v0.0.1nbaHeadshotswas created to make player headsnhots fromwww.stats.nba.commore accessible and easilly downloadable.Installationpip install nbaHeadshotsExample UsageCurrently the package only consists of two functions:getHeadshotById(id, saveFolder, fileName=None)getAllHeadshots(saveFolder)Player Id's are based onwww.stats.nba.complayer ids.nba_apiis an easy way to get a list of these player ids and its also useful for getting nab statistics. There is a json file of basic player information loacted atsrc/playerInfo.json. Image files are saved in .png format. Different formats can be used by specifying fileName.fromnbaHeadshotsimportgetHeadshotById,getAllHeadshots# Lebron James headshot will be saved as lebron.jpg in saveHere foldergetHeadshotById(2544,folder="saveHere/",fileName="lebron.jpg")# Lebron James headshot will be saved as 2544.png in current directorygetHeadshotById(2544)# All headshots will be saved as "id".png in saveHere foldergetAllHeadshots("saveHere/")
Package: nba-player
NBA Player Data into JSONInstallationpip install nba-playerUsageimport datanbaGet All Playersdata = datanba.player_stats.AllOfBasketball(arr)arr is the years that you want in the normal year format for NBA (i.e ["2018-19"])To get all player data into JSONdata = datanba.player_stats.AllOfBasketball(["2018-19"])
data.gather("players.json", exists=False)Only use this if you have nothing in your players.json file or it does not exist at all.However, since you will be polling a lot, you might have your connection work or the NBA stats API will limit you, so if scraping stops in the middle, you can change your code to:data = datanba.player_stats.AllOfBasketball(["2018-19"])
data.gather("players.json", exists=True)Then, scraping will continue from where it left off.Use Player JSON dataAfter polling once, you can reuse the output form the JSON file as long as you would like(until the data that you have polled has become old), by using these commands.import datanba.player_stats as datanba
data = datanba.AllOfBasketball(["2018-19"])
players = data.players_from_json('players.json')Now you have all NBA players in yourplayersvariable.
Package: nbapy
nbapy -stats.nba.comAPI for pythonSummaryA python facing API forstats.nba.comWarningstats.nba.comis notorious for being extremely unreliable. Please report any issues you find.UsageAll data is returned as a pandas dataframe (check out thestarter docsif you're new to pandas). For example:fromnbapyimportgameimportpandasaspdgame_id='0021900017'# taken from 'https://stats.nba.com/game/0021900017/'stats=pd.DataFrame(game.BoxScore(game_id).players_stats())If you want to cache results so you don't have to reach the api every time, you can userequests-cachefromnbapyimportgameimportpandasaspdimportrequests_cacherequests_cache.install_cache('nbapy_cache')game_id='0021900017'stats=pd.DataFrame(game.BoxScore(game_id).players_stats())DocumentationAn ongoing process, but check outthe jupyter notebook docs, or feel free to poke around the codebase.InstallationTo install from pypi:$python-mpipinstallnbapyElse:Download from source (git clone, zipped package)Run from the root directory:$python-mpipinstall.Contributing1. Fork the repository and create a feature/bug fix branch2. Install development requirements$python-mpipinstall-e.".[dev]"3. Hack awayCoding conventionsblackfor formattinggoogle docstringsflake8for lintingmypyfor static typing analysisconventional commitsfor commit style.Optional (but recommended)nbapyhas apre-commitfile that you can install to automatically enforce these conventions prior to committing via a git hook.To install:$ pre-commit installYou can also use$ pre-commit run -ato run the checks manually.For commit messages, I recommend usingcommtizen. It is automatically installed in the dev dependencies, so to commit, you just runcz cand follow the prompts.4. Create some tests5. Make sure everything looks good$ pytest --cov*$ pre-commit run -a(if you didn't install the pre-commit git hook)* note the first time you run this, it may take a few minutes. However, the requests will cache, and subsequent runs should be much faster.6. Submit a pull requestOther ways to contribute involve submitting any issues or adding some documentation!To-DoFinish Jupyter Notebook documentationAuthorsThis is orginally based off ofhttps://github.com/seemethere/nba_pyso a lot of the work was done by those guys. My goal with this project is to clean up the code, add some proper documentation, and keep it up to date.
Package: nba_scrape
# nba_scrapeAn easy-to-use Python utility to scrape professional basketball data off stats.nba.com using Selenium and BeautifulSoup.## Installation:pip install nba_scrape## Usage:from nba_scrape import NBA#### Example:>>> league = NBA()>>> lebron = league.get_player(‘lebron james’)>>> lebron.get_stat(‘pts’, ‘2016-17’, playoffs=True)>>> 32.8>>> lebron.get_stats([‘pts’, ‘reb’, ‘ast’, ‘ts%’], ‘2015-18’, mode=’playoffs’)>>> {‘2015-16’: (26.3, 9.5, 7.6, 0.585), ‘2016-17’: (32.8, 9.1, 7.8, 0.649), ‘2017-18’: (34, 9.1, 9, 0.619)}#### Get an instance of the NBA class:league = NBA()#### Get a player:player = league.get_player(player_name)ORplayer = league.get_player_by_id(id_number)#### Get a single stat:player.get_stat(stat_name, season)#### Get multiple stats (formatted as a dict with tuples as items):player.get_stats([stat1, stat2, stat3], season_range, mode=mode)(Possible modes are ‘season’, ‘playoffs’, or ‘both’; ‘season’ is the default.)## Current functionality:Compile a list of all NBA players and their IDs when initializing the NBA class.Easily load all regular season and playoff stats off a player’s career page to a SQLite database. Only the player’s name is required as input.Retrieve all traditional stats and select advanced ones (such as True Shooting Percentage) via database queries; only the requested stats and seasons are required as input.Browser-agnostic; uses the best available browser or raises an error if no supported browser is available.Test suite to ensure correct statistics are returned.
Package: nba-scraper
nba_scraperThis is a package written in Python to scrape the NBA's api and produce the
play by play of games either in acsvfile or apandasdataframe. This package
has two main functionsscrape_gamewhich scrapes an individual game or a list
of specific games, andscrape_seasonwhich scrapes an entire season of regular
season games.The scraper goes back to the 1999-2000 season and will pull the play by play along
with who was on the court at the time of each play. Some other various statistics may
be calculated as well.As of version 1.0.8 the scraper will now scrape WNBA games as well as NBA games.
Just callwnba_scrape_gameinstead ofscrape_game. The parameters and usage is
exactly the same asscrape_gamefunction. As of right now I know it goes
back to the 2005 season maybe further just haven't tested.
Be warned it is much slower than the nba scraper due to the extra api calls
needed to pull in player names that are readily available in the NBA api itself.InstallationTo install this package just type this at the command line:pip install nba_scraperUsagescrape_gameThe default data format is a pandas dataframe you can change this to csv
with thedata_formatparameter. The default file path is the
users home directory you can change this with thedata_dirparameterimport nba_scraper.nba_scraper as ns

# if you want to return a dataframe
# you can pass the function a list of strings or integers
# all nba game ids have two leading zeros but you can omit these
# to make it easier to create lists of game ids as I add them on
nba_df = ns.scrape_game([21800001, 21800002])

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_game([21800001, 21800002], data_format='csv', data_dir='file/path')scrape_seasonThedata_formatanddata_dirkey words are used the excat same way asscrape_game. Instead of game ids though, you would pass the season you want
scraped to the function. This season is a four digit year that must be an
integer.import nba_scraper.nba_scraper as ns

#scrape a season
nba_df = ns.scrape_season(2019)

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_season(2019, data_format='csv', data_dir='file/path')scrape_date_rangeThis allows you to scrape allregular seasongames in the date range passed to
the function. As of right now it will not scrape playoff games. Date format must
be passed in the formatYYYY-MM-DD.import nba_scraper.nba_scraper as ns

#scrape a season
nba_df = ns.scrape_date_range('2019-01-01', 2019-01-03')

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_date_range('2019-01-01', 2019-01-03', data_format='csv', data_dir='file/path')ContactIf you have any troubles or bugs pleaseopen an issue/bug report. If you have
any improvements/suggestions pleasesubmit a pull request. If it falls outside
those two areas please feel free to email me atmatt@barloweanalytics.com.
Package: nb-as-module
nb_as_modulepip install nb_as_moduleLoad a Jupyter Notebook as a module object.fromnb_as_moduleimportnb_as_modulemy_module=nb_as_module('path/to/notebook.ipynb',name='my_module')my_module.hello()# where `hello` is a function defined in the notebook.Google ColabThere is also a helper module for Google Colab.To install in Google Colab, run in a cell:!pip install nb_as_moduleimportnb_as_module.colab# Mount Google Drive, where Colab notebooks are saved.# (Note you will be asked permission for access here.)nb_as_module.colab.mount_drive()# Get a list of your notebooks:list_of_notebooks=nb_as_module.colab.list_nbs()print(list_of_notebooks)#> ['hello.ipynb', ...]# Load one of the notebooks in the list as a module:hello=nb_as_module.colab.as_module('hello.ipynb','hello')# Run a function from the module.hello.hello()#> Hello, world.docPut <!--docstring--> at the beggining of markdown cells,
to use them as__doc__documentation. (If multiple cells are used,
they will be concatenated.)
Package: nba-transactions-scraper
NBA Trades and Transactions ScraperThis is a simple library to scrape NBAProSportstrades and transactions data.Usagepipinstallnba-transactions-scraperMethodsrun(team, start, end)returns a Pandas dataframe of NBA transactions for a specific team from start date to end date (defaults to current date). Date format: ('yyyy-mm-dd')Examplefrom nba_transactions_scraper.main import ProSports_Scraperscraper = ProSports_Scraper()results = scraper.run('Lakers', '2021-01-01')Next AdditionsMulti-team argumentExtracting trade $ valuesDraft Pick transactionsCategory of transactionsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: nb-autodoc
nb-autodoc简体中文·English简介nb-autodoc 是一个从 Python 源码的类型注解和Docstring自动生成 API 文档的工具。本工具从包里查找所有的模块并导入，解析各模块的抽象语法树、运行时类型，链接内部对象，解析函数签名和 docstring 语法树，最终生成完整、可靠、带有链接的 API 文档。主要特性基于 AST 的类型分析系统现代的类型展示，如X | Y、list[str]、(*args) -> Anystub (.pyi) 支持TYPE_CHECKING 支持Re-export 支持，从 AST 解析导入引用Overload 重载函数支持用法从 pip 安装:pip install nb-autodoc运行 nb-autodoc:nb-autodoc {package_name}其他命令行选项:Usage: nb-autodoc [OPTIONS] MODULE

Options:
  -o, --output-dir DIRECTORY      [default: build]
  -s, --skip TEXT                 skip import modules
  -u, --undoc TEXT                undocument modules
  --markdown-linkmode [heading_id|vuepress]
                                  [default: heading_id]
  --help                          Show this message and exit.提示:使用--undoc而不是--skip，后者会跳过模块的导入和分析。
Package: nbawebstats
NBAWebStats is a Python interface to the stats.nba.com HTTP API, compatible with
Python 3.3-3.5.NBAWebStats was developed from existing packages for the same purpose such aspy-goldsberry, without which NBAWebStats would not be possible. Compared to
py-goldsberry, NBAWebStats offers the following attributes:Interface adherent toPEP 8.Smaller, more maintainable code footprint due to reduced boilerplate (single
~200 line python file vs 10 files totalling ~2350 lines).Centralized documentation of permitted request types and parametersonline.Descriptive package name.Support for Python 3 (but not for Python 2).DetailsSource repository:http://github.com/agirardeaudale/nbawebstatsDocumentation:https://nbawebstats.readthedocs.org.Free software: MIT license
Package: nbb-api
nbb_apiPython package for easy access to Brazilian basketball data through scraping ofLNBwebsite.This allows users to obtain statistics, standings, and scores for various seasons and phases of the following tournaments:NBB(Novo Basquete Brasil)LDB(Liga de Desenvolvimento de Basquete)Liga OuroInstallingViapipThis library was written as an exercise for creating my first PyPi package. Hopefully you will find it valuable!
Install with the following command:pip install nbb-apiDocumentationFor documentation about the API methods refer tothe documentation.Example of usefrom nbb_api import nbbnbb.get_stats('2022-23','regular','cestinhas').head()Output:JogadorEquipeJOMinPts3P2PLLCamisaTemporada0McClanahanFortaleza B. C.2734.1120.896.1110.893.89222022-231AntonioUNIFACISA2631.7417.966.008.853.12112022-232ThomasCorinthians2732.1317.675.448.154.0702022-233Lucas MarianoSesi Franca2227.4117.597.237.642.73282022-234Lucas DiasSesi Franca2030.6517.505.858.603.0592022-23Use it wisely!
Package: nb-black
# nb_black[![PyPI](https://img.shields.io/pypi/v/nb_black.svg)]()
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nb_black.svg)]()A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically usingBlack.Please note that since theBlackpackage only supports Python 3.6+, soYAPFpackage will
be used for the lower versions. If you edit the code while running the cell, the formatting is
not submitted to the Jupyter notebook and instead silently suppressed, so you have to stick with
the edited, but unformatted code.## InstallationYou can install this package using [pip](http://www.pip-installer.org):` $ [sudo] pip install nb_black `## UsageFor Jupyter Notebook:` %load_ext nb_black `For Jupyter Lab:` %load_ext lab_black `Just put this code into the first cell in your Notebook, and that’s all. :)
Package: nb-black-only
nb_blackA simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically usingBlack.Please note that theBlackpackage only supports Python 3.6+.
If you edit the code while running the cell, the formatting is
not submitted to the Jupyter notebook and instead silently suppressed, so you have to stick with
the edited, but unformatted code.InstallationYou can install this package usingpip:$ [sudo] pip install nb_blackUsageFor Jupyter Notebook:%load_ext nb_blackFor Jupyter Lab:%load_ext lab_blackJust put this code into the first cell in your Notebook, and that's all. :)
Package: nbbrowserpdf
# nbbrowserpdf
> LaTeX-free PDF generation for Jupyter Notebooks## Installation
> TODO: pypi package`shell pip installhttps://github.com/Anaconda-Server/nbbrowserpdf/archive/master.zippython-mnbbrowserpdf.install `Enable the extension for every notebook launch:`shell python-mnbpresent.install--enable`Installing withcondawill handle the installation and enabling (in user
space):`shell conda install--channelnbcio nbbrowserpdf `## In-Browser Usage
In the Notebook application menu bar, click inFile->Download As…->PDF via Headless Browser (.pdf).## CLI
> TODO: document CLI usage## Development
> TODO: document development processes
Package: nbcc_loading
NBCC LoadingsLibrary to obtain NBCC LoadingsView Demo·Documentation·Report Bug·Request FeatureTable of Contents1. About the Project2. Getting Started2.1. Prerequisites2.2. Dependencies2.3. Installation3. Usage4. Roadmap5. License6. Contact7. Acknowledgements1. About the ProjectA library to supply NBCC loadings, to be incorporated into other calculations2. Getting Started2.1. Prerequisites2.2. DependenciesThe repo comes pre-compiled with all dependencies.2.3. InstallationInstall from pypipython-mvenv.venv

.venv\Scripts\activate

pipinstallnbcc_loading3. Usage4. RoadmapSnow LoadingsNBCC 2020NBCC 2015NBCC 2010Wind LoadingsNBCC 2020NBCC 2015NBCC 2010Seismic LoadingsNBCC 2020NBCC 2015NBCC 20105. LicenseSeeLICENSE.txtfor more information.6. ContactArun Kishore -@rpakishoreProject Link:https://github.com/rpakishore/nbcc-loadings7. AcknowledgementsAwesome README TemplateBanner MakerShields.ioCarbon
Package: nbcelldiff
nb_cell_diffSimple tools for ad hoc, in notebook, code cell differencing.Based on the Googlediff-match-patchPython3 package.%load_ext nbcelldiffThe package currently provides cell block magics that let you compare the current cell with the previously executed cell, an earlier executed cell from its cell run index number, or the contents of the clipboard; and a line magic that lets you compare the contents of two previously executed cells from the cell run index numbers.See thedemo.ipynbfor examples.TO DO:clipboard demo is broken in MyBinder?
Package: nb-cell-execution-status
nb_cell_execution_statusIndicators relating to cell execution statusUse colour to display cell execution status of notebook code cells:white: not executedlight cyan: awaiting executiongreen: successfully executedpink: failedVisual cell execution status indicators are based on the cell exeustion status indicators found in thelc_multi_outputspython package (viaNII-cloud-operation/Jupyter-multi_outputs).Audible feedback for successful and unsuccessful cell completion can also be enabled from the notebook extension configurator menu:There is also an optional heartbeat pulse (default period 5s; configurable) that can be used to indicate the continuing run status of long running cells.Thejupyter-a11yextensionalso includes audible accessibility support eg for navigation,kernel activity humetc.InstallInstall via:pip install nb_cell_execution_statuspip install --upgrade git+https://github.com/innovationOUtside/nb_cell_execution_status.gitThe extension should be automatically installed and enabled.If you need to do things manually:jupyter nbextension uninstall cell_execution_status
jupyter nbextension install cell_execution_status --user
jupyter nbextension enable cell_execution_status/index
Package: nbcheck
Notebook format validation utilityThis project is a simple utility wrapper aroundnbformatto check the validity of notebook files.
The utility will return a non zero return code if any of the notebooks has validations errors.
This may be useful in pre-commit scripts to detect problematic notebooks.UsageUsage: nbcheck [OPTIONS] [PATH]...Check notebooks for validation errorsOptions:-r, --recurse  Recurse to sub directories-v, --verbose  Print validation errors--help         Show this message and exit.Example> nbcheck extraspass extras/valid-notebook.ipynbfail extras/broken-notebook.ipynbfail extras/nbformat-read.ipynbInstallationYou can install the latest version of this module withpiporpipxpipx install git+ssh://git@github.com/furechan/nbcheck.gitRelated ResourcesThe Jupyter Notebook Formatnbfornat API