Saravanan666/pypi-org huggingface dataset:
Package: pandadjango-polls
Polls is a simple Django app to conduct Web-based polls. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: pandadoc-python-client
# The Official PandaDoc Python client SDKPandaDoc SDK spans a broad range of functionality to help you build incredible documents automation experiences inside your product.DocsOfficial public API documentationRequirementsPython >= 3.6Installationpip installIf the python package is hosted on a repository, you can install directly using:pipinstallpandadoc-python-client(you may need to runpipwith root permission:sudo pip install pandadoc-python-client)Then import the package:importpandadoc_clientGetting Startedfrompprintimportpprintimportpandadoc_clientfrompandadoc_client.apiimporttemplates_api# Configure API key authorization: apiKeyapi_key="YOUR_API_KEY"# Defining the host is optional and defaults to https://api.pandadoc.com# See configuration.py for a list of all supported configuration parameters.cfg=pandadoc_client.Configuration(host="https://api.pandadoc.com",api_key={"apiKey":f"API-Key{api_key}"},)# Enter a context with an instance of the API clientwithpandadoc_client.ApiClient(cfg)asapi_client:# Create an instance of the API classapi_instance=templates_api.TemplatesApi(api_client)try:resp=api_instance.list_templates(tag=["doe-inc-proposals"])pprint(resp)exceptpandadoc_client.ApiExceptionase:pprint("Exception when calling TemplatesApi->list_templates:%s\n"%e)AuthorizationapiKeyType: API keyAPI key parameter name: AuthorizationLocation: HTTP headeroauth2Type: OAuthFlow: accessCodeAuthorization URL:https://app.pandadoc.com/oauth2/authorizeScopes:read+write: Useread+writeto create, send, delete, and download documents, andreadto view templates and document details.ExamplesCreate and send document from a templateCreate and send document from the pdf urlDocsOfficial PandaDoc public API docshttps://developers.pandadoc.com/reference/aboutLicenseSDK is licensed under the followingLicense.
Package: pandadocs
pandadocsEasy pythonic integration with Pandadocs’ REST APIFree software: MIT licenseDocumentation:https://pandadocs.readthedocs.io.FeaturesTODOCreditsCreated with love byAlokin!History0.1.2 (2017-01-20)New year special release0.1.1 (2016-07-17)More complete mapping of Pandadoc API0.1.0 (2016-07-09)First release on PyPI.
Package: panda-grove
Panda GroveA lightweight package for easier management of multiple Pandas DataFrames
during data analysis and exploration.The philosophy of Panda Grove is to assist without getting in the way or
creating yet another API on top of Pandas.It also depends exclusively on the Pandas package for minimal overhead.Feature Highlights:ballot_box_with_check: Multi-merges (merge across multiple DataFrames at once):ballot_box_with_check:Collectionclass to encapsulate and manage multiple DataFrames:ballot_box_with_check: DataFrame sanity checks:black_square_button: Support all types of joins (currently only inner join):black_square_button: Datatype management (work in progress)InstallationInstall the PyPI package:pipinstallpanda-groveRequirementspython >= 3.7pandas >= 1.1.0IPython >= 7 is not required but will add pretty printing in Jupyter notebooks
Package: panda-gym
panda-gymSet of robotic environments based on PyBullet physics engine and gymnasium.DocumentationCheck out thedocumentation.InstallationUsing PyPIpipinstallpanda-gymFrom sourcegitclonehttps://github.com/qgallouedec/panda-gym.git
pipinstall-epanda-gymUsageimportgymnasiumasgymimportpanda_gymenv=gym.make('PandaReach-v3',render_mode="human")observation,info=env.reset()for_inrange(1000):action=env.action_space.sample()# random actionobservation,reward,terminated,truncated,info=env.step(action)ifterminatedortruncated:observation,info=env.reset()env.close()You can alsoEnvironmentsPandaReach-v3PandaPush-v3PandaSlide-v3PandaPickAndPlace-v3PandaStack-v3PandaFlip-v3Baselines resultsBaselines results are available inrl-baselines3-zooand the pre-trained agents in theHugging Face Hub.CitationCite as@article{gallouedec2021pandagym,title={{panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning}},author={Gallou{\'e}dec, Quentin and Cazin, Nicolas and Dellandr{\'e}a, Emmanuel and Chen, Liming},year=2021,journal={4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeurIPS},}Environments are widely inspired fromOpenAI Fetch environments.
Package: pandahouse
PandahousePandas interface for Clickhouse HTTP APIInstallpipinstallpandahouseUsageWriting dataframe to clickhouseconnection={'host':'http://clickhouse-host:8123','database':'test'affected_rows=to_clickhouse(df,table='name',connection=connection)Reading arbitrary clickhouse query to pandasdf=read_clickhouse('SELECT * FROM{db}.table',index_col='id',connection=connection)
Package: pandahouse-v2
Pandas interface for Clickhouse HTTP APIInstallpipinstallpandahouseUsageWriting dataframe to clickhouseconnection={'host':'http://clickhouse-host:8123','database':'test'}affected_rows=to_clickhouse(df,table='name',connection=connection)Reading arbitrary clickhouse query to pandasdf=read_clickhouse('SELECT * FROM{db}.table',index_col='id',connection=connection)
Package: pandahub
pandahub is a data hub for pandapower and pandapipes networks based on MongoDB. It allows you to store pandapower and pandapipes networks as well as timeseries in a MongoDB. pandahub allows you to access the database directly for local development, but also includes a client/server architecture that exposes all relevant methods as an API based on FastAPI. Access through the API is managed with a user management implementation based on FastAPI Users.Setup a local pandahub apiSteps to test the client/server structure locally:Start a MongoDB on localhost:27017 (or another custom port)Start the uvicorn server that exposes the API by navigating to "pandahub/api" and running:# windows
set SECRET=secret & python main.py

# linux
SECRET=secret python main.pyor if you don't run mongodb on the default port (27017)# windows
set MONGODB_URL=mongodb://localhost:[mongo-port] & set SECRET=secret & python main.py

# linux
MONGODB_URL=mongodb://localhost:[mongo-port] SECRET=secret python main.pyThe API should now run onhttp://localhost:8002NoteA full documentation of all api endpoints can be seen athttp://localhost:8002/docsNote 2You can avoid always setting the environment variables for SECRET and MONGODB_URL by creating an.envfile inpandahub/api/with the following content:SECRET=secret
MONGODB_URL=mongodb://localhost:[mongo-port]Develop with Dockerdocker compose upstarts a mongodb container alongside pandahub with live reload available athttp://localhost:8002.If you want to connect to a running database, set the database url and specify only docker-compose.yml:MONGODB_URL=mongodb://localhost:[mongo-port] docker compose -f docker-compose.yml upUse pandahub api with pandahub clientLogin with the pandahub clientThere are two ways to login with pandahub clientIf you installed pandahub by pip or with setup.py just runpandahub-loginin your shell.ORRun the following in your python or IPython shell:from pandahub.client.user_management import login
login()This will guide you through the login process.You only need to login once. After you logged in successfully a configuration file (pandahub.config) containing an authentication token is created in your home directory and will be used every time you use the pandahub client. You only need to login again if you want to use a different instance of the pandahub api.
Package: panda-jedi
Core package for JEDIPanDA documentationhttps://panda-wms.readthedocs.io/en/latest/JEDI installation instructionshttps://panda-wms.readthedocs.io/en/latest/installation/jedi.htmlRelease notesSee ChangeLog.txtJEDI cluster details for ATLAShttps://github.com/PanDAWMS/panda-jedi/wiki/ATLAS-production-JEDI-servers-%5BSeptember-2023%5D
Package: pandalchemy
pandalchemy: an intuitive combination of Pandas and sqlalchemy to manipulate sql databases with pandasWhat is it?pandalchemyis a Python package that lets Data Scientists create and manipulte sql databases with the Pandas package
that they know and love without needing to learn the ins and outs of sqlalchemy.Main FeaturesHere are just a few of the things that pandalchemy does:Pulls down any sql table as a Pandas DataFrame with sqlalchemy and maintains all data types, keys, and indexes
after you push your changes.Make changes to Pandas DataFrame as you normally would then push any changes (new columns, delete columns, new rows, updated rows) to sql database.Adds a primary key to a new sql table, something pandas to_sql method does not do.Add or delete columns in a database table thanks to sqlalchemy-migrate.Where to get itThe source code is currently hosted on GitHub at:https://github.com/eddiethedean/pandalchemy# PyPIpipinstallpandalchemyDependenciespandassqlalchemy==1.3.18sqlalchemy-migratenumpy[tabulate]Example codefromsqlalchemyimportcreate_engineimportpandalchemyasba# Use sqlalchemy to create an engine to connect to existing databaseengine=create_engine('postgresql://scott:tiger@localhost:5432/mydatabase')# Initialize a pandalchemy DataBase objectdb=ba.DataBase(engine)# Accessing a table by name gives you a DataFrame like Table objecttbl=db['test_table']# Make changes to the Table just like you would a pandas DataFrametbl['age']=[11,12,13,14,15]# Use the push method to push all your changes to your databasedb.push()
Package: pandalib
PandalibA python module to get informations from the api of the popular agent PandabuyInstallationInstall the package using pippipinstallpandalibSince Pandabuy requires login for basically every api request you will need your pandabuy bearer token and your useridGet your bearer token and user idYou can get your bearer token from the headers of any api requestYou can get your user id from the profile infoUsage/ExamplesGet an item infofrompandalibimportpandalib,pandautilitieslib=pandalib("my bearer token","my user id")item=lib.get_item("https://item.taobao.com/item.htm?id=634959757581")print(item)Get your account balancefrompandalibimportpandalib,pandautilitieslib=pandalib("my bearer token","my user id")balance=lib.get_balance()print(balance)Get your account infofrompandalibimportpandalib,pandautilitieslib=pandalib("my bearer token","my user id")info=lib.get_user_info()print(info)Get your account cartfrompandalibimportpandalib,pandautilitieslib=pandalib("my bearer token","my user id")cart=lib.get_cart()print(cart)PandautilitiesYou can use pandautilities to estimate the shipping prices and convert cny to usdConvert CNY to USDfrompandalibimportpandalib,pandautilitiesutilities=pandautilities("my bearer token","my user id")usd=utilities.cny_to_usd(230)print(usd)Estimate shipping pricesTo estimate the shipping prices you can only input the weight or you can also input width, hight, lengthfrompandalibimportpandalib,pandautilitiesutilities=pandautilities("my bearer token","my user id")shipping_prices=utilities.estimate(weight=1200,length=30,hight=30,width=30)print(shipping_prices)
Package: pandalyse
A toolkit for an analysis environment with pandas.Documentation[![Documentation Status](https://readthedocs.org/projects/pandalyse/badge/?version=latest)](https://pandalyse.readthedocs.io/en/latest/?badge=latest)What is pandalysePandalyse offers an analysis environment and tools for pandas.
The main features are:Selector: Define and store cuts in a Selector object:cutted_df = selector(df)Trainer: Train multiple mva with scikit-learn interface in the way:trainer.fit(signal_df, background_df)Analysis: Store and retrive Selectors, Trainings, numpy-arrays and dataframes in predefined locations:df = ana.data.get(“MySignalData”)
Package: panda-model
panda-modelpanda-model allows the offline use of theModelclass fromlibfrankawithout a connection to the master control unit. To do this, a shared library needs to be downloaded from an FCI enabled Franka Emika master control unit using the included tools.To get startet install panda-model as described below and check out thedocumentationas well as the examples.InstallationUsing pippip install panda_modelFrom SourcePythonClone the repository and install the package using pip by executing the following from the root directory:pip install .This will install the command line scriptpanda-model-downloadas well as Python bindings for the modifiedModelclass.C++To use the model in C++ you can build the necessary library by running:mkdir build && cd build
cmake .. -DBUILD_CPP=ON
cmake --build .You can then install the library usingsudo make installor by building a deb package:cpack -G DEB
sudo dpkg -i panda_model*.debRequirementsBuilding from source requires POCO C++ libraries and Eigen3. You can install the necessary requirements on Ubuntu by running:sudo apt-get install libpoco-dev libeigen3-dev
Package: pandance
PandancePandance provides advanced relational operations forpandasDataFrames,
enabling powerful and efficient joins (aka merges).HighlightsPandance extends the set of standard join operations in pandas
(inner, outer, cross, left, right) with:fuzzy joins: Match columns with a tolerance. Supports numerical and datetime values.inequality join: Match one column's values that are less / greater than the other column's values.theta joins:
Allows the user to specify arbitrary matching conditions on which to joinPandance is designed with performance in mind, aiming to provide fast implementations
whenever possible.InstallationpipinstallpandanceUsageSee thedocumentation
Package: pandansheet
PandanSheet(Korean.ver)PandanSheet는 google sheeet의 내용을 읽는 패키지 입니다.주요 기능Google Sheets 문서로부터 데이터를 읽어 pandas DataFrame으로 변환설치PandanSheet패키지는 pip를 통해 쉽게 설치할 수 있습니다.pipinstallpandansheet함수 소개함수명소개ReadSheet구글 시트의 내용을 가져온다인자소개함수명ReadSheet인자명소개필수여부기본값URL시트링크Oheader열 이름으로 사용할 행의 위치X0사용방법importpandansheetaspsurl='https://docs.google.com/spreadsheets/d/1LK5N0kdoEXdHEVKXn1ZGbtxCbZ8V9QDxumyjXVWqKfk/edit?usp=sharing'#구글 시트 링크를 입력한다. 단 해당 시트는 공개상태여야 할것print(ps.ReadSheet(url))url='https://docs.google.com/spreadsheets/d/1LK5N0kdoEXdHEVKXn1ZGbtxCbZ8V9QDxumyjXVWqKfk/edit#gid=1717818648'#두번째 시트의 내용을 가져온다print(ps.ReadSheet(url),header=2)주의비공개 시트에 접근하려고 할 때 에러가 발생합니다. 시트를 공개 상태로 변경하세요.업데이트 내역버전내역0.0.1ReadSheet 함수를 만들었습니다.PandanSheet (English.ver)PandanSheetis a package for reading content from Google Sheets.Key FeaturesReads data from Google Sheets documents and converts it into a pandas DataFrame.InstallationPandanSheet can be easily installed via pippipinstallpandansheetFunction IntroductionFunction NameDescriptionReadSheetFetches content from a Google Sheet.Argument IntroductionFunction NameReadSheetArgument NameDescriptionRequiredDefault ValueURLLink to the sheetYesheaderPosition of the row to be used as column namesNo0How to Useimportpandansheetaspsurl='https://docs.google.com/spreadsheets/d/1LK5N0kdoEXdHEVKXn1ZGbtxCbZ8V9QDxumyjXVWqKfk/edit?usp=sharing'# Enter the link to the Google Sheet. Note: The sheet must be public.print(ps.ReadSheet(url))url='https://docs.google.com/spreadsheets/d/1LK5N0kdoEXdHEVKXn1ZGbtxCbZ8V9QDxumyjXVWqKfk/edit#gid=1717818648'# Fetches content from the second sheetprint(ps.ReadSheet(url,header=2))CautionAttempting to access a private sheet will result in an error. Please change the sheet to public status.Update HistoryVersionDetails0.0.1Created the ReadSheet function.
Package: panda-producer
Function to calculate mean average precision (mAP) in CSV format for post detection prediction calculation.Requirementspython 3.*, numpy, cv2,Installationpip install panda_producer
pip install panda_producer -i https://pypi.python.org/simpleUsage example:from panda_producer import gen_panda_head
import cv2

front_img = cv2.imread("front.jpg", 0)
res = gen_panda_head(front_img, "text", hyper=128)
Package: panda-python
panda-pyFinally, Python bindings for the Panda. These will increase your productivity by 1000%, guaranteed[^1]!Getting startedTo get started, check out thetutorial paper, Jupyternotebooksand other examples you can run directly on your robot. For more details on the API, please refer to thedocumentation.Extensionsdm_robotics_pandaPanda model and tools for reinforcement learning indm_roboticsInstallpip install panda-pythonThis will install panda-py and all its requirements. The pip version ships with libfranka 0.9.2, the newest version for the Franka Emika Robot. Please refer to the section below if you use an older system version or the more recent Franka Research 3 robot.libfranka VersionThere are currently two robot models available from Franka Emika: the Franka Emika Robot (FER, formerly known as Panda) and the Franka Research 3 (FR3). Depending on the installed firmware, the FER supports version <0.10 while the FR3 requires version >=0.10. For details, refer tothiscompatibility matrix. If you need a libfranka version different from the default 0.9.2, download the respective zip archive from therelease page. Extract the archive and install the wheel for your Python version with pip, e.g., runpip install panda_python-*libfranka.0.7.1-cp310*.whlto install the binary wheel for libfranka 0.7.1 and Python 3.10.CitationIf you use panda-py in published research, please consider citing theoriginal software paper.@article{elsner2023taming,
title = {Taming the Panda with Python: A powerful duo for seamless robotics programming and integration},
journal = {SoftwareX},
volume = {24},
pages = {101532},
year = {2023},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2023.101532},
url = {https://www.sciencedirect.com/science/article/pii/S2352711023002285},
author = {Jean Elsner}
}[^1]: Not actually guaranteed. Based on a sample size of one.
Package: pandaq
pandaq: An easy pandas query-string builderThis library providesqmethod for easy querying ofpandas.DataFrame.
Internally,qgenerates the query string forpandas.DataFrame.query.The goal ofpandaqis to save time when querying.Installationpip install pandaqUsagepandaqprovides two ways to selectpandas.DataFramerows by query string.A. Generate a query-stringfrompandaqimportQimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')qstr=Q().q(PassengerId=1)# -> "PassengerId==1"df.query(qstr)B. Addqmethod topandas.DataFrameimportpandaq.patchimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')df.q(PassengerId=1)How to construct a queryBasicsQuery TypeHow It Worksq("str")Equivalent topd.DataFrame.query("str")q(dict)Works like q(**dict)q(k=v)Where the column namedkis equal tovq(k=[v1, ...])Where the column namedkis in [v1, ...]q(k=(op1, v1, ...))Where the column namedkmeets the conditionk op1 v1 and ...q(k="!str")Where the column namedkis NOTstr(seeAdvance)q(k="?str")Where the column namedkcontainsstr(seeAdvance)q(k="!?str")Where the column namedkdoes NOT containstr(seeAdvance)q(k="/str")Where the column namedkmatch the regexstr(seeAdvance)ExampleTypeKindpandaq.qEquivalent to query-stringNumberEqualq(a=1)"a==1"NumberContainq(a=[1, 2])"a==1 or a==2"NumberInequalityq(a=(">", 1))"a>1"NumberInequalityq(a=(">", 1, "<=", 3))"a>1 and a<=3"BoolEqualq(a=True)"a==True"StrFull-Matchq(a="text")'a=="text"'StrNot Full-Matchq(a="!text")'a!="text"'StrPartial-Matchq(a="?text")'a.str.contains("text", regex=False, na=False)'StrNot Partial-Matchq(a="!?text")'not a.str.contains("text", regex=False, na=False)'StrRegex-Matchq(a="/textA|textB")'a.str.contains("textA|textB", regex=True, na=False)'DatetimeEqualq(a=dt.date(1970,1,1))'a=="1970-01-01"'DatetimeInequalityq(a=(">", dt.date(1970,1,1)))'a>"1970-01-01"'Combinationpandaq.qEquivalent to query-stringq(a=1, b=1)"(a==1 & b==1)"q(a=1).q(b=1)"a==1 & b==1"Advanced UsageTo set theprefixindividually, configure the following settings.frompandaqimportconfig# Default settingsconfig.str_prefix=StringPrefix(neq="!",# The prefix for not equalregex="/",# The prefix for regular expressionspartial="?",# The prefix for partial matchneq_partial="!?",# The prefix for not equal partial match)Licensepandaqis distributed under the terms of theMITlicense.
Package: pandarallel
Pandaral·lelWithout parallelizationWith parallelizationPandaral.lelprovides a simple way to parallelize your pandas operations on all your
CPUs by changing only one line of code. It also displays progress bars.MaintainersManu NALEPAtill-mInstallationpipinstallpandarallel[--upgrade][--user]Quickstartfrompandarallelimportpandarallelpandarallel.initialize(progress_bar=True)# df.apply(func)df.parallel_apply(func)UsageBe sure to check out thedocumentation.ExamplesAn example of each availablepandasAPI is available:ForMac & LinuxForWindows
Package: pandare
pandare: PANDA's Python Interface - PyPI ReleaseOverviewpandareis a Python 3 interface to PANDA. With pandare (also called PyPANDA), you can quickly develop plugins
to analyze behavior of a running system, record a system and analyze replays, or do
nearly anything you can do using PANDA's C/C++ APIs.Autogenerated documentation is available atdocs.panda.re.InstallationNote the PANDARE package provided on pypi only a binary distribution and will certainly only work onx86_64hosts as pre-built PANDA libraries from Ubuntu 20.04 are included.Prior to using pandareyou must install some dependencies, these are fully described in the code's dependencies folder, i.e.,the list for ubuntu:20.04 is here. On our development systems, we typically find we just need to install:apt install libvdeplug-dev libpng16-16 libsdl2-2.0-0Then simply install withpip3 install pandare. From there, you'll be able to use PANDA with:from pandare import Panda
panda = Panda(generic='i386')
...This is a beta release - Last updated Dec 2021.Although PANDA is fairly stable, this interface is new and subject to change significantly prior to version 1.0.This package is manually generated and may fall behind the code on GitHub.
Package: pandarss
#PandaRSSPandaRSS 是一个基于 ToughRADIUS V2版本 API 的自助服务系统。## 快速指南### 运行环境LinuxPython 2.7pipTwisted>=15.0.0 可选bottle>=0.12.7### 安装pip  install -Uhttps://github.com/PandaPark/PandaRSS/archive/master.zip### 配置新增加一个配置文件 /etc/pandarss.conf,内容如下[system]
host = 0.0.0.0
port = 1819
home_site = www.mydomain.com
api_url =http://x.x.x.x:1816/api/v1api_key = CRTCcMB7tfnXU8aXIyfavfuqruvXkNng
session_secret = CRTCcMB7tfnXU8aXIyfavfuqruvXkNng[alipay]
alipay_key = jrid3242fs52234scxdzqoajmww
alipay_partner = 2342342342342
alipay_seller_email =mypay@xxxx.comalipay_return_url =http://www.mydomain.com/alipay/returnalipay_notify_url =http://www.mydomain.com/order/verifyapi_url： 请填写部署的ToughRADIUS的服务器地址，替换ip，端口即可api_key： 请填写部署的ToughRADIUS的安全密钥session_secret： 一个32位的字符串，用来做cookie加密支付宝配置参数，请根据你的支付宝申请的直接到账支付提供的参数，要使用支付宝支付，服务器需要绑定域名，建议通过nginx等代理服务器实现。### 运行输入 pandarss 会直接以非守护进程模式运行，按Ctrl+C可退出。若要以守护进程模式运行，可使用nohup命令：nohup pandarss &默认的 pandarss  运行模式性能不高，可以使用基于twisted异步高性能网络框架来运行nohup pandarss_txrun &### 绑定到ToughRADIUS服务运行如果你的ToughRADIUS是以本地模式安装的，可以在ToughRADIUS的服务进程中配置pandarss进程。修改 /etc/toughradius.conf, 加入以下内容[program:pandarss]
command=pandarss_txrun
startretries = 10
autorestart = true
redirect_stderr=true
stdout_logfile=/var/toughradius/pandarss.log这样pandarss可以随toughradius的进程启动停止。