Saravanan666/pypi-org huggingface dataset:
Package: nflgame
nflgame is an API to retrieve and read NFL Game Center JSON data. It can
work with real-time data, which can be used for fantasy football.nflgame works by parsing the same JSON data that powers NFL.com’s live
GameCenter. Therefore, nflgame can be used to report game statistics
while a game is being played.The package comes pre-loaded with game data from every pre- and regular
season game from 2009 up until the present (I try to update it every
week). Therefore, querying such data does not actually ping NFL.com.However, if you try to search for data in a game that is being currently
played, the JSON data will be downloaded from NFL.com at each request
(so be careful not to inspect for data too many times while a game is
being played). If you ask for data for a particular game that hasn’t
been cached to disk but is no longer being played, it will be
automatically cached to disk so that no further downloads are required.Here’s a quick teaser to find the top 5 running backs by rushing yards
in the first week of the 2013 season:#!python
import nflgame

games = nflgame.games(2013, week=1)
players = nflgame.combine_game_stats(games)
for p in players.rushing().sort('rushing_yds').limit(5):
    msg = '%s %d carries for %d yards and %d TDs'
    print msg % (p, p.rushing_att, p.rushing_yds, p.rushing_tds)And the output is:L.McCoy 31 carries for 184 yards and 1 TDs
T.Pryor 13 carries for 112 yards and 0 TDs
S.Vereen 14 carries for 101 yards and 0 TDs
A.Peterson 18 carries for 93 yards and 2 TDs
R.Bush 21 carries for 90 yards and 0 TDsOr you could find the top 5 passing plays in the same time period:#!python
import nflgame

games = nflgame.games(2013, week=1)
plays = nflgame.combine_plays(games)
for p in plays.sort('passing_yds').limit(5):
    print pAnd the output is:(DEN, DEN 22, Q4, 3 and 8) (4:42) (Shotgun) P.Manning pass short left to D.Thomas for 78 yards, TOUCHDOWN. Penalty on BAL-E.Dumervil, Defensive Offside, declined.
(DET, DET 23, Q3, 3 and 7) (5:58) (Shotgun) M.Stafford pass short middle to R.Bush for 77 yards, TOUCHDOWN.
(NYG, NYG 30, Q2, 1 and 10) (2:01) (No Huddle, Shotgun) E.Manning pass deep left to V.Cruz for 70 yards, TOUCHDOWN. Pass complete on a fly pattern.
(NO, NO 24, Q2, 2 and 6) (5:11) (Shotgun) D.Brees pass deep left to K.Stills to ATL 9 for 67 yards (R.McClain; R.Alford). Pass 24, YAC 43
(NYG, NYG 20, Q1, 1 and 10) (13:04) E.Manning pass short middle to H.Nicks pushed ob at DAL 23 for 57 yards (M.Claiborne). Pass complete on a slant pattern.If you aren’t a programmer, then thetutorial for non
programmersis for you.If you need help, please come visit us at IRC/FreeNode on channel#nflgame. If you’ve never used IRC before, then you canuse a web
client. (Enter any
nickname you like, make sure the channel is#nflgame, fill in the
captcha and hit connect.)Failing IRC, the second fastest way to get help is toopen a new issue
on the tracker.
There are several active contributors to nflgame that watch the issue
tracker. We tend to respond fairly quickly!
Package: nflgame3
nflgame is an API to retrieve and read NFL Game Center JSON data. It can
work with real-time data, which can be used for fantasy football.nflgame works by parsing the same JSON data that powers NFL.com’s live
GameCenter. Therefore, nflgame can be used to report game statistics
while a game is being played.The package comes pre-loaded with game data from every pre- and regular
season game from 2009 up until the present (I try to update it every
week). Therefore, querying such data does not actually ping NFL.com.However, if you try to search for data in a game that is being currently
played, the JSON data will be downloaded from NFL.com at each request
(so be careful not to inspect for data too many times while a game is
being played). If you ask for data for a particular game that hasn’t
been cached to disk but is no longer being played, it will be
automatically cached to disk so that no further downloads are required.Here’s a quick teaser to find the top 5 running backs by rushing yards
in the first week of the 2013 season:#!python
import nflgame

games = nflgame.games(2013, week=1)
players = nflgame.combine_game_stats(games)
for p in players.rushing().sort('rushing_yds').limit(5):
    msg = '%s %d carries for %d yards and %d TDs'
    print msg % (p, p.rushing_att, p.rushing_yds, p.rushing_tds)And the output is:L.McCoy 31 carries for 184 yards and 1 TDs
T.Pryor 13 carries for 112 yards and 0 TDs
S.Vereen 14 carries for 101 yards and 0 TDs
A.Peterson 18 carries for 93 yards and 2 TDs
R.Bush 21 carries for 90 yards and 0 TDsOr you could find the top 5 passing plays in the same time period:#!python
import nflgame

games = nflgame.games(2013, week=1)
plays = nflgame.combine_plays(games)
for p in plays.sort('passing_yds').limit(5):
    print pAnd the output is:(DEN, DEN 22, Q4, 3 and 8) (4:42) (Shotgun) P.Manning pass short left to D.Thomas for 78 yards, TOUCHDOWN. Penalty on BAL-E.Dumervil, Defensive Offside, declined.
(DET, DET 23, Q3, 3 and 7) (5:58) (Shotgun) M.Stafford pass short middle to R.Bush for 77 yards, TOUCHDOWN.
(NYG, NYG 30, Q2, 1 and 10) (2:01) (No Huddle, Shotgun) E.Manning pass deep left to V.Cruz for 70 yards, TOUCHDOWN. Pass complete on a fly pattern.
(NO, NO 24, Q2, 2 and 6) (5:11) (Shotgun) D.Brees pass deep left to K.Stills to ATL 9 for 67 yards (R.McClain; R.Alford). Pass 24, YAC 43
(NYG, NYG 20, Q1, 1 and 10) (13:04) E.Manning pass short middle to H.Nicks pushed ob at DAL 23 for 57 yards (M.Claiborne). Pass complete on a slant pattern.If you aren’t a programmer, then thetutorial for non
programmersis for you.If you need help, please come visit us at IRC/FreeNode on channel#nflgame. If you’ve never used IRC before, then you canuse a web
client. (Enter any
nickname you like, make sure the channel is#nflgame, fill in the
captcha and hit connect.)Failing IRC, the second fastest way to get help is toopen a new issue
on the tracker.
There are several active contributors to nflgame that watch the issue
tracker. We tend to respond fairly quickly!
Package: nflgame-redux
nflgame is an API to retrieve and read NFL Game Center JSON data. It can
work with real-time data, which can be used for fantasy football.nflgame works by parsing the same JSON data that powers NFL.com’s live
GameCenter. Therefore, nflgame can be used to report game statistics
while a game is being played.The package comes pre-loaded with game data from every pre- and regular
season game from 2009 up until the present (I try to update it every
week). Therefore, querying such data does not actually ping NFL.com.However, if you try to search for data in a game that is being currently
played, the JSON data will be downloaded from NFL.com at each request
(so be careful not to inspect for data too many times while a game is
being played). If you ask for data for a particular game that hasn’t
been cached to disk but is no longer being played, it will be
automatically cached to disk so that no further downloads are required.Here’s a quick teaser to find the top 5 running backs by rushing yards
in the first week of the 2013 season:#!python
import nflgame

games = nflgame.games(2013, week=1)
players = nflgame.combine_game_stats(games)
for p in players.rushing().sort('rushing_yds').limit(5):
    msg = '%s %d carries for %d yards and %d TDs'
    print msg % (p, p.rushing_att, p.rushing_yds, p.rushing_tds)And the output is:L.McCoy 31 carries for 184 yards and 1 TDs
T.Pryor 13 carries for 112 yards and 0 TDs
S.Vereen 14 carries for 101 yards and 0 TDs
A.Peterson 18 carries for 93 yards and 2 TDs
R.Bush 21 carries for 90 yards and 0 TDsOr you could find the top 5 passing plays in the same time period:#!python
import nflgame

games = nflgame.games(2013, week=1)
plays = nflgame.combine_plays(games)
for p in plays.sort('passing_yds').limit(5):
    print pAnd the output is:(DEN, DEN 22, Q4, 3 and 8) (4:42) (Shotgun) P.Manning pass
short left to D.Thomas for 78 yards, TOUCHDOWN. Penalty on
BAL-E.Dumervil, Defensive Offside, declined.
(DET, DET 23, Q3, 3 and 7) (5:58) (Shotgun) M.Stafford pass short
middle to R.Bush for 77 yards, TOUCHDOWN.
(NYG, NYG 30, Q2, 1 and 10) (2:01) (No Huddle, Shotgun) E.Manning
pass deep left to V.Cruz for 70 yards, TOUCHDOWN. Pass complete on
a fly pattern.
(NO, NO 24, Q2, 2 and 6) (5:11) (Shotgun) D.Brees pass deep left to
K.Stills to ATL 9 for 67 yards (R.McClain; R.Alford). Pass 24, YAC
43
(NYG, NYG 20, Q1, 1 and 10) (13:04) E.Manning pass short middle
to H.Nicks pushed ob at DAL 23 for 57 yards (M.Claiborne). Pass
complete on a slant pattern.If you aren’t a programmer, then thetutorial for non
programmersis for you.If you need help, please come visit us in discord on channelhttps://discord.gg/G7uay2.Failing discord, the second fastest way to get help is toopen a new
issue on the
tracker. There
are several active contributors to nflgame that watch the issue tracker.
We tend to respond fairly quickly!
Package: nf-libdyson
Dyson Python LibraryThis library is still under development.This library is a refactor oflibpurecool. There is going to be some major changes:Initializing local connections no longer requires cloud login. Users can use the MQTT credentials directly.The interface of the library will be improved so that the same operation of different types of devices (e.g. PureCoolLink and PureCool) will have the same interface.Zeroconf based auto connection will be decoupled from device classes so that we can use only one zeroconf service to to auto connection for more than one device.The main purpose of this library is to support the HomeAssistant Dyson integration so that it'll only support Python 3.7+ and there is no plan for older versions of Python.
Package: nfllivepy
nfllivepyAboutPython package for obtaining live nfl play by play dataFor pbp data for past games, viewnfl-data-pywhich gets its data from thenflreadrpackage. This data contains postprocessing variables which are more detailed then the ones in the live data.Much of this package is inspired by the work ofnfl-nerd. For a full list of all the ESPN v2 NFL api endpoints, clickhere.InstallationThis project can be installed with pip by running:pip install nfllivepyUsagenfllivepy is very simple and easy to use, wth two simple methods to obtain live data.fromnfllivepy.requester.pbp_requesterimportPBPRequesterrequester=PBPRequester()# Get live data for all current gamesrequester.get_live_pbp_all_games()# Get live data for the Chicago Bearsrequester.get_live_pbp_for_team("CHI")
Package: nflows
nflowsnflowsis a comprehensive collection ofnormalizing flowsusingPyTorch.InstallationTo install from PyPI:pip install nflowsUsageTo define a flow:fromnflowsimporttransforms,distributions,flows# Define an invertible transformation.transform=transforms.CompositeTransform([transforms.MaskedAffineAutoregressiveTransform(features=2,hidden_features=4),transforms.RandomPermutation(features=2)])# Define a base distribution.base_distribution=distributions.StandardNormal(shape=[2])# Combine into a flow.flow=flows.Flow(transform=transform,distribution=base_distribution)To evaluate log probabilities of inputs:log_prob=flow.log_prob(inputs)To sample from the flow:samples=flow.sample(num_samples)Additional examples of the workflow are provided inexamples folder.DevelopmentYou can install all the dependencies using theenvironment.ymlfile to create a conda environment:conda env create -f environment.ymlAlternatively, you can install viasetup.py(thedevflag installs development and testing dependencies):pip install -e ".[dev]"Citing nflowsTo cite the package:@software{nflows,author={Conor Durkan andArtur Bekasov andIain Murray andGeorge Papamakarios},title={{nflows}: normalizing flows in {PyTorch}},month=nov,year=2020,publisher={Zenodo},version={v0.14},doi={10.5281/zenodo.4296287},url={https://doi.org/10.5281/zenodo.4296287}}The version number is intended to be the one fromnflows/version.py. The year/month correspond to the date of the release. BibTeX entries for other versions could be found onZenodo.If you're using spline-based flows in particular, consider citing theNeural Spline Flowspaper:[bibtex].Referencesnflowsis derived frombayesiains/nsforiginally published withC. Durkan, A. Bekasov, I. Murray, G. Papamakarios,Neural Spline Flows, NeurIPS 2019.[arXiv][bibtex]nflowshas been used inConor Durkan, Iain Murray, George Papamakarios,On Contrastive Learning for Likelihood-free Inference, ICML 2020.[arXiv].Artur Bekasov, Iain Murray,Ordering Dimensions with Nested Dropout Normalizing Flows.[arXiv].Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray,Density Deconvolution with Normalizing Flows.[arXiv].nflowsis used by the conditional density estimation packagepyknos, and in turn the likelihood-free inference frameworksbi.
Package: nflowscam
nflowsnflowsis a comprehensive collection ofnormalizing flowsusingPyTorch.InstallationTo install from PyPI:pip install nflowsUsageTo define a flow:fromnflowsimporttransforms,distributions,flows# Define an invertible transformation.transform=transforms.CompositeTransform([transforms.MaskedAffineAutoregressiveTransform(features=2,hidden_features=4),transforms.RandomPermutation(features=2)])# Define a base distribution.base_distribution=distributions.StandardNormal(shape=[2])# Combine into a flow.flow=flows.Flow(transform=transform,distribution=base_distribution)To evaluate log probabilities of inputs:log_prob=flow.log_prob(inputs)To sample from the flow:samples=flow.sample(num_samples)Additional examples of the workflow are provided inexamples folder.DevelopmentYou can install all the dependencies using theenvironment.ymlfile to create a conda environment:conda env create -f environment.ymlAlternatively, you can install viasetup.py(thedevflag installs development and testing dependencies):pip install -e ".[dev]"Citing nflowsTo cite the package:@software{nflows,author={Conor Durkan andArtur Bekasov andIain Murray andGeorge Papamakarios},title={{nflows}: normalizing flows in {PyTorch}},month=nov,year=2020,publisher={Zenodo},version={v0.14},doi={10.5281/zenodo.4296287},url={https://doi.org/10.5281/zenodo.4296287}}The version number is intended to be the one fromnflows/version.py. The year/month correspond to the date of the release. BibTeX entries for other versions could be found onZenodo.If you're using spline-based flows in particular, consider citing theNeural Spline Flowspaper:[bibtex].Referencesnflowsis derived frombayesiains/nsforiginally published withC. Durkan, A. Bekasov, I. Murray, G. Papamakarios,Neural Spline Flows, NeurIPS 2019.[arXiv][bibtex]nflowshas been used inConor Durkan, Iain Murray, George Papamakarios,On Contrastive Learning for Likelihood-free Inference, ICML 2020.[arXiv].Artur Bekasov, Iain Murray,Ordering Dimensions with Nested Dropout Normalizing Flows.[arXiv].Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray,Density Deconvolution with Normalizing Flows.[arXiv].nflowsis used by the conditional density estimation packagepyknos, and in turn the likelihood-free inference frameworksbi.
Package: nflpocketarea2023
NFL Big Data Bowl 2023Common CommandsCommandDescriptionpytestRun all unit tests.run jupyterStart a Jupyter notebook server (automatically launched).run install-python-requirementsInstall the latest Python requirements to your virtual environment.git checkout -b branch_nameCreate a new branch namedbranch_name.git statusCheck which files have been modified or added.git add .Add all changes.pre-commit runRun pre-commit on added changes.git commit -m  "message"Create a commit with a message and run pre-commit.git pushPush changes to remote.Setup InstructionsAccept the GitHub invitation granting you read and write access to this repositoryLogin to GitPodusing the same GitHub accountLogin to Kaggleand get your Kaggle credentialsClick on your profile picture in the top right corner and go to the "Your Profile" pageClick the "Account" tabScroll down to the "API" sectionClick the "Create New API Token" button, which will trigger a downloadSave the file to your computerOpen the file in a text editor to view your username and secret keyGo back to GitPod to save your Kaggle credentialsOpen theenvironment variablespageClick the "New Variable" buttonCreate a new variable for your Kaggle username:Name:KAGGLE_USERNAMEValue:your_usernameScope:vingkan/nflbigdatabowl2023Create a new variable for your Kaggle key:Name:KAGGLE_KEYValue:your_keyScope:vingkan/nflbigdatabowl2023Go tothe GitHub repository pageand click the GitPod button at the top of the README to start up a workspaceAutomatic setup commands will run on GitPod and then you can use the terminal an editor freelyRelease ProcessTo publish new versions to PyPi (reference):# Increment the version number in pyproject.toml# Build packagerm-rfdist
python3-mbuild# Upload to Test PyPipython3-mtwineupload--repositorytestpypidist/*
python3-mpipinstall--index-urlhttps://test.pypi.org/simple/--no-depsnflpocketarea2023# Upload to PyPipython3-mtwineuploaddist/*
python3-mpipinstall--no-depsnflpocketarea2023
Package: nfl-scraper
fantasy-scraperTrying to scrape all data from our fantasy league with NFL.com. Since we are moving to Sleeper. It might end up include myfantasyleague as wellSimple UsageFirst off you will need to ensure you have chromedriver installed in your pathStackOverflow.
You should be able to set your path withPATH="$PATH:/usr/local/bin/chromedriver"or where ever your chromedriver is. If you
canwhich chromedriverthen you are good.To use the package, you should be able to pull your league id from NFL.com, email, you know your password, and whatever you want to use for a name of the
league (this does not need to be the actually name of the league)_email="youremail@email.com"_pass="TheP@SSWordYouUse"_leagueid=123456_name="Some random name"python3-mpipinstall--index-urlhttps://test.pypi.org/simple/--no-depsnfl_scraper# Should worknfl_scraper-e$_email-p$_pass-i$_leagueid-n$_name# Could bepython3runnfl_scraper-e$_email-p$_pass-i$_leagueid-n$_nameDeveloper NotesIf you are familiar with Python. You can work with the repo directly. Below are some of the steps I used to get the project running, building, etc...Pipenv and PoetrySetupcurl-sSLhttps://instcurl-sSLhttps://install.python-poetry.org|python3-
pipenvinstall--python=/usr/loca/bin/python3.10
pipenvshell
poetrycompletionsbash>>~/.bash_completion#export PIP_PYTHON_PATH="$VIRTUAL_ENV/bin/python3"poetrynewnfl_scraper
poetrynewnfl_scraper#pipenv install --index=pip#pipenv install --index=distutilspoetryaddrequests
poetryaddhtml5lib
poetryaddbs4#pip uninstall -y setuptools#exit#deactivateRunning as locally Non Devpoetryinstall--withoutdev--sync
poetryrunpython-V# Helppoetryrunpythonmain.py-h# Sub out the paramspoetryrunpythonmain.py-e<email>-p<password>-i<id>-n<name># Test need to beef these uppoetryrunpytestRunning as Devpoetrycheck
poetrybuild#poetry update #gets latest package versionRunning in CICDpoetrycheck# output versionpoetryversion-s

poetryversionmajor|minor|patch--dry-run
Package: nflscraPy
This package was inspired by the creators of nflscrapR and nflfastR and the tremendous influence they have had on the open-source NFL communityThe functionality of nflscraPy was designed to allow Python users to easily ingest boxscore and seasonal data from publicly available resourses, in particular, Pro Football ReferenceHopefully this package builds upon the availabilty of open-source resources for the football and data analytics community
Package: nfl-statistic-book
nfl-statistic-bookPython application that retrieves information from MySportsFeed API based on NFL teamsNote: User must have login credentials forMySportsFeedand add them to a separate file “secret.txt” for full accessFeaturesCollect NFL Team data from the MySportsFeed APIAbility to see any team’s upcoming schedule as well as current rosterEasy to use text-based interfacePlayer search coming soon :)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-06-13)First release on PyPI.
Package: nflvid
A simple library to download, slice and search NFL game footage on a
play-by-play basis.This library comes with preloaded play-by-play meta data, which
describes the start time of each play in the game footage. However, the
actual footage does not come with this library and is not released by
me. This package therefore provides utilities to batch download NFL Game
Footage from the original source.Once game footage is downloaded, you can use this library to search
plays and construct a playlist to play invlcwith thenflvid.vlcsubmodule.
Package: nflwin
Estimate Win Probability (WP) for plays in NFL games:>>>importpandasaspd>>>fromnflwin.modelimportWPModel>>>standard_model=WPModel.load_model()>>>plays=pd.DataFrame({..."quarter":["Q1","Q2","Q4"],..."seconds_elapsed":[0,0,600],..."offense_team":["NYJ","NYJ","NE"],..."yardline":[-20,20,35],..."down":[1,3,3],..."yards_to_go":[10,2,10],..."home_team":["NYJ","NYJ","NYJ"],..."away_team":["NE","NE","NE"],..."curr_home_score":[0,0,21],..."curr_away_score":[0,0,10]...})>>>standard_model.predict_wp(plays)array([0.58300397,0.64321796,0.18195466])For full documentation, including information about methods and accuracy, clickhere.LicenseMIT. Seelicense file.
Package: nflx-jupyterlab-slackfeedback
nflx_jupyterlab_slackfeedbackA jupyter lab extension to allow users send feedback to the notebooks team using slack.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallnflx_jupyterlab_slackfeedbackUninstallTo remove the extension, execute:pipuninstallnflx_jupyterlab_slackfeedbackContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the nflx_jupyterlab_slackfeedback directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallnflx_jupyterlab_slackfeedbackIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namednflx-jupyterlab-slackfeedbackwithin that folder.Packaging the extensionSeeRELEASE
Package: nfmodelapis
nfmodelapisCentral repo for all model training, and pipeline tools for usage with all data collection APIs from nfflow.Example:from nfmodelapis.text.SentenceEmbedder import ModelSelect
trainer = ModelSelect(model_name,
                      model_output_path,
                      model_architecture=model_architecture
                       ).return_trainer()
trainer.train(data_path=os.path.join(
                save_timestamp,json_filename))