Saravanan666/pypi-org huggingface dataset:
Package: datavision-beeyard-sdk
Release notesRelease notes are available on the documentation web pagehttps://docs.beeyard.ai/docs/reference/sdk/python/release-notes/.InstallationTo use the package, just install it via pip (or preferred package manager):pipinstalldatavision-beeyard-sdkor use Poetry package manager to create your virtual environment and just add the SDK with:poetryadddatavision-beeyard-sdkNow all functionalities are ready to be imported inside your Python project!Initialize a BeeYard clientAuthentication is with authenticated client:frombeeyard_sdkimportAuthenticatedClientclient=AuthenticatedClient('https://demo.beeyard.ai/hive/',username,password)It is possible to specify themax_waiting_time_msparameter (in milliseconds) when creating the client object in order to avoid connectivity problems.
It specifies the max time within which the client tries to repeatedly send the request, in case of connectivity errors. After that time,
a ConnectionError will be raised. Default value is 0.client=AuthenticatedClient('https://demo.beeyard.ai/hive/',username,password,max_waiting_time_ms=1000)As login endpoint will be updated, some optional parameters are provided, that can be set to point to the new endpoint:client=AuthenticatedClient('https://demo.beeyard.ai/hive/',username,password,max_waiting_time_ms=1000,client_id="byard",client_secret="",login_url=None)wherelogin_urlis the new endpoint uri.It is possible to log in using client credentials as follows:client=AuthenticatedClient('https://demo.beeyard.ai/hive/',grant_type="client_credentials",client_id="the_client_id",client_secret="the_client_secret",login_url="https://demo.beeyard.ai/id/")Another option is to use a valid access token to initialize the client:client=AuthenticatedClient('https://demo.beeyard.ai/hive/',use_token=True,token="valid_token")Example usagefromdatavision_beeyard_sdk.modelsimportWorkspaceDescriptorDtofromdatavision_beeyard_sdk.api.workspaceimportcreate_workspaceworkspace_desc=WorkspaceDescriptorDto(name="test",namespace="test")create_workspace.create(client=client,request_body=workspace_desc)DocumentationComplete documentation can be found athttps://docs.beeyard.ai/docs/reference/sdk/python/methods_reference/.
Package: datavistaflow
Datavistaflow[This python package is under development]InstallationInstall by typing the following command in the terminalpipinstalldatavistaflowUsageLine plot animationA line graph plotter to create animated graphical visuals.Parameters:input : list of tuples containing data to be plotted as a line graph. Each tuple represents a line graph. ex: [(x1_data, y1_data), (x1_data, y1_data)]plot_style (optional): defines the plot's style.xlim (optional): defines the x axis limitylim (optional): defines the y axis limittitle (optional): Title of the plotxlabel (optional): defines the x axis labelylabel (optional): defines the y axis labeln_frames (optional): total number of frames in the animationframe_interval (optional): time interval between two consecutive frames in milli seconds. Default: 100mssave (optional): save animation as a GIFfromdatavistaflowimportplot_line_animationy1=[10,20,30,25,60,70,30,12,155,245,223,123,454,22,455,2,22,31]x1=list(range(len(y1)))y2=[12,-23,31,-23,-62,72,-32]x2=list(range(len(y2)))plot_line_animation(input=[(x1,y1),(x2,y2)],n_frames=len(x1),ylim=[-100,500],xlim=[0,20],xlabel='x axis',ylabel='y axis',title='Test',legend_label=['y1','y2'],plot_style='ggplot',save=True,save_animation_name='line_test_animation.gif')Scatter plot animationA scatter graph plotter to create animated graphical visuals.Parameters:input : list of tuples containing data to be plotted as a line graph. Each tuple represents a line graph. ex: [(x1_data, y1_data), (x1_data, y1_data)]plot_style (optional): defines the plot's style.xlim (optional): defines the x axis limitylim (optional): defines the y axis limittitle (optional): Title of the plotxlabel (optional): defines the x axis labelylabel (optional): defines the y axis labeln_frames (optional): total number of frames in the animationframe_interval (optional): time interval between two consecutive frames in milli seconds. Default: 100mssave (optional): save animation as a GIFfromdatavistaflowimportplot_scatter_animationy1=[10,20,30,25,60,70,30,12,155,245,223,123,454,22,455,2,22,31]x1=list(range(len(y1)))y2=[12,-23,31,-23,-62,72,-32]x2=list(range(len(y2)))plot_scatter_animation(input=[(x1,y1),(x2,y2)],n_frames=len(x1),ylim=[-100,500],xlim=[0,20],xlabel='x axis',ylabel='y axis',title='Test',legend_label=['y1','y2'],plot_style='ggplot',save=True,save_animation_name='line_test_animation.gif')
Package: dataVisualisation
Intelligent PredictionA simplelinear regressionmodel that lets you select aGoogle Sheetfrom the drive and calculates all the optimised parameters of the model usingGradient_Descent_Algorithmand lets you save all the parameters and evaluation metrics of the model to yourGoogle Drive.It not just calculates the optimised parameters of your model but lets you graphically visualise the relationship between various other parameters.It lets you save files to your drive after your proper authentication.You Come up  with a Dataset and walk out with the most optimised parameters of the model...Everything else(Data Preprocessing and Data Modelling) is taken care of.It's kind of a drag and drop model.How to install?pipinstalldataVisualisationHow to use:import the modulecall the main function using the moduleimportdataVisualisationdataVisualisation.main()Prerequisties required for the module to run successfully:Before you call the module make sure you have installed all the following modulespipinstallpydrivepipinstallgspreadpipinstallpandaspipinstallsklearnpipinstallmatplotlibSet-Up:Before you go ahead with running the module...there are a few steps that needs to be followed:Create a  new project ongoogle cloud consoleActivate the Google Drive APICreateCredentialsfor the google drive APISave the downloaded file ascredentials.jsonShare the desired google sheet with the client email in credentials.json fileActivate the google sheets APICreateoAuth2 Client credentialsand save the file asclient_secrets.jsonUsage and functionality:This module can be used with any of the non categorical dataset but make sure to have the target variable at the last column of the spreadsheet.Make sureclient_secrets.jsonandcredentials.jsonis inside the same directory as the Python program.The directory(path) should not contain any subdirectories inside it.Make sure to share the google sheet to client_email from the drive.References:Creating a python libraryHosting the library on PyPIworking with google sheetsworking with markdown LanguageLicense:This Package is distributed under theMIT license
Package: dataVisualization
Data VisualisationHow to use:SetupTo run the software first clone and then in the directory create a virtual
environment (tutorial here)Runpip install -r requirements.txtto get all of the required packages for
the software and in the correct versions.Runpython -m src.main.main.py <input_dir>to run the softwareCommand line arguments--ignore-barsskips generation of bar charts--ignore-piesskips generation of pie chartsTestingTo test the code base runpytestin the data visualisation directory.
This will usetests/resources/test_data.xlsxas test data to generate the test charts to be compared.Once generated these are saved inresult_imagesand are compared againsttests/baseline_images/where there is 2 directorys, 1 for bars and 1 for pies.Once all the comparisons are done pytest will give you a rundown of what tests passed and what ones failed with brief descriptions on why they may have failed.Updating baseline ImagesBaseline images should not be changed unless there is a change in design or an issue is found, they baseline images directory is quite large so this can take a whileTo update the baseline images first locally run pytest and find your output data (result_images/) copy any baselines you wish to update (e.g. test_bars) and paste it intests/baseline_images/replacing the previous version.Then commit your changes, your commit should inlcude a reason for updating the baseline. and push them to the gitlab.Format Sheet:HeadingUse CaseExampleSheet No.Indicates the sheet this row is for1,2,3Slide TitleThe title of the slideexample title hereChart Typeindicates the type of chart the be generated"Stacked Bar", "100% Stacked Bar"Chart Sizewhat width should the chartfull, halfValues Font SizeThe font size for values8,10,21Legendshould a legend be present on the chart"yes","no","shared"Gridlines/X-Axisshould gridlines be used on the chartyes, noSegment Valuesshould individual segments have valuesyes, noTotal Valuesshould total values be shown at the end of a rowyes, noCommentany additional comments for researchersblah blah blahGraphs:Example of all the chart types can be seen in 'tests/resources/test_data.xlsx'All charts must follow these rules otherwise it can cause issues with the code:Data should start at A1.Tables should be formatted with headers along the top with data as rows.Extra information should not be placed beyond the last row of data, this can cause issues in the code that means files don't generate properly.
Package: data-visualizer-JavierRodriguezPosada
IntroductionData visualization tool based in Dash framework. Allows to easly visualize 3D data sets from a data matrix in the source file. Currently supported graphics are:3D ScatterGetting StartedInstallation processpip install --upgrade data-visualizer-JavierRodriguezPosadaRun the apppython -m data_visualizerAccess the urlhttp://127.0.0.1:8050/Software dependenciesPython 3numpy==1.19.3xlrd==1.2.0pandas==1.1.4dash==1.17.0Latest releasesBuild and TestIts recommended to install all dev depdendencies in a virtual environment.python -m venv .\.env
.\.env\Scripts\activate
pip install -r .\requirements.dev.txtTo run the app:python .\data_visualizer\__main__.pyGenerate packageDependenciespython -m pip install --user --upgrade setuptools wheel twineCompilepython setup.py sdist bdist_wheelUploadpython -m twine upload --repository pypi dist\*ContributeTODO
Package: datavita-open-python-sdk
华创证券VitaX Python 工具套件欢迎使用华创证券开发者工具套件（SDK）。环境准备要使用VitaX Python SDK ，您需要联系华创证券金融科技部工作人员申请获取Access Key ID和Access Key Secret。SDK 获取和安装使用 pip 安装(推荐)pipinstalldatavita-open-python-sdk# 安装华创DataVita开放Python SDK开始调用以下这个代码示例向您展示了调用阿里云 Python SDK 的3个主要步骤：创建 Client 实例获取所需数据发起请求并处理异常# -*- coding: utf8 -*-fromdatavita.andes.andes_dataimportAndesDatafromdatavita.core.authimportAuth# 创建 AcsClient 实例access_key_id="1"access_secret="1"auth=Auth(access_key_id,access_secret)ad=AndesData(auth=auth,timeout=1,max_retries=1)df=ad.ind_data_list(ind_code='xxxx')print(df)在创建 Auth 实例时，您需要填写 2 个参数：Access Key ID 和 Access Secret。Access Key ID 和 Access Secret 申请获取；
Package: datavita-python-sdk
华创证券VitaX Python 工具套件欢迎使用华创证券开发者工具套件（SDK）。环境准备要使用VitaX Python SDK ，您需要联系华创证券金融科技部工作人员申请获取Access Key ID和Access Key Secret。SDK 获取和安装使用 pip 安装(推荐)pipinstalldatavita-python-sdk# 安装阿里云 SDK 核心库开始调用以下这个代码示例向您展示了调用阿里云 Python SDK 的3个主要步骤：创建 Client 实例获取所需数据发起请求并处理异常# -*- coding: utf8 -*-fromdatavita.andes.andes_dataimportAndesDatafromdatavita.core.authimportAuth# 创建 AcsClient 实例access_key_id="1"access_secret="1"auth=Auth(access_key_id,access_secret)ad=AndesData(auth=auth,timeout=1,max_retries=1)df=ad.get_dataset_by_tagcode(tag_code='xxxx')print(df)在创建 Auth 实例时，您需要填写 2 个参数：Access Key ID 和 Access Secret。Access Key ID 和 Access Secret 申请获取；
Package: dataviz
DataVizDataViz helps you to create plots quickly. You declare what information (the
data source) to display and how to display it (the plot type and style).
DataViz tries to be as close as possible to theVega-Litevisualization grammar. Available backends for plotting arematplotlibandbokeh.InstallationInstall via pip:$pipinstalldatavizExampleTo display a plot in Jupyter notebook:importdatavizasdvspec={"width":600,"height":300,"data":{"values":[{"time":"2021-01-01","value":28},{"time":"2021-01-02","value":55},{"time":"2021-01-03","value":43},{"time":"2021-01-04","value":91},{"time":"2021-01-05","value":81},{"time":"2021-01-06","value":53},]},"mark":"line","encoding":{"x":{"field":"time","type":"temporal"},"y":{"field":"value","type":"quantitative"},"color":{"value":"red"},}}fig=dv.figure(spec,'matplotlib')fig.show()This will produce the following plot:ContributeIssue Tracker:https://gitlab.com/librecube/lib/python-dataviz/-/issuesSource Code:https://gitlab.com/librecube/lib/python-datavizTo learn more on how to successfully contribute please read the contributing
information in theLibreCube guidelines.SupportIf you are having issues, please let us know. Reach us atMatrixor viaEmail.LicenseThe project is licensed under the MIT license. See theLICENSEfile for details.
Package: datavoreclient
Datavore-client - Python Client library to get data
from LOPS Datavore services
=============================================================================Installation :pip install datavoreclientUsage example:>>> from datavoreclient import Datalaps
>>> dl = Datalaps()
>>> idxlist = dl.searchIndexes('.*smos.*argo', return_id_only=False)
>>> dl.getTimeserie(idxlist[0][0], 'SSSSAT', '-20,-20,20,20', groupby='yyyy-MM')
Package: datavortex
Classes utilizadas para o processamento, tratamento e análises de dados.INSTALAÇÃOpip install datavortexMODO DE IMPORTAÇÃOfrom datavortex import DataProcess, Treatment, Analyzes ou from datavortex import *DOCUMENTAÇÃOEncontre a documentação aqui:https://datavortexdocs.netlify.app/
Package: data-watcher-tray
Data WatcherThe Data Watcher class is a PyQt system tray application with three main components:Ability to specify a background task to run (i.e.watchdog observer)Ability to manually upload a file using the context menu of the tray icon.Logging displayExample UsageUsing thewatchdoglibrary combined with theFileUploadDialogclass,
you can create a program that watches a directory for new files and runs any tasks in theparse_filefunction.You can use the pyinstaller library to create a frozen exe to distribute.importloggingimportsysfrompathlibimportPathfromwatchdog.eventsimportFileSystemEventHandlerfromwatchdog.observersimportObserverfromdata_watcherimportDataWatcher,FileUploadDialoglogger=logging.getLogger()icon_path=Path("icon.png").absolute()app_name="Data Logger"# Main parsing methoddefparse_file(path:Path):logger.info(path)classCreatedHandler(FileSystemEventHandler):defon_created(self,event):ifnotevent.is_directoryand".txt"inevent.src_path:parse_file(event.src_path)definit_watcher():event_handler=CreatedHandler()observer=Observer()try:observer.schedule(event_handler,path=str(Path.cwd()),recursive=False)observer.start()exceptFileNotFoundError:logging.error('Directory not found')logging.info('Watcher Started')returnobserverdefupload_file():FileUploadDialog(app_name=app_name,on_select=parse_file,allowed_files_filter='Text Files (*.txt);;All Files (*)')app=DataWatcher(app_name=app_name,icon_path=icon_path,background_task=init_watcher,upload_callback=upload_file)sys.exit(app.start())
Package: datawhispers
DataWhispers ModuleDocumentation:Pypi:Streamlit:TheDataWhispersmodule is used for solving regression and statistical problemsIt was programmed for exams in advanced programming and data visualisation at DHBW MannheimSolve regression problems (lin, poly, trig, free, least_squares), plot the results and mnist numbers or do some statistical analysis with chi2, pearson or student-tFeel free to check out theDocumentationTo install just writepip install datawhispersin your command prompt of choice
Package: data-whiz
Calculate the distance between points in curved surfaceDownload the library!pip install curved-surface-distanceImporting the libraryfrom curved_surface_distance import distanceHSV_distanceHSV_distance is used to Calculate the distance between the HSV color codes.H represents Hue ranges from 0 to 360, S represents Saturation and V represents Value(brightness) both ranges from 0 to 100.
syntax  -   distance.HSV_distance([H1,S1,V1],[H2,S2,V2])example -   distance.HSV_distance([0,0,0],[360,100,100])distancedistance is used to find the distance between two points or between two list of points in curved surface.1. This is to find distance between two list of points 
      
      syntax  -   distance.distance([p11,p12,p13],[p21,p22,p23])
      
      example -   distance.distance([0,0,0],[360,360,360])

  2. This is to find distance between two points in the curved surface. 
      
      syntax  -   distance.distance(p1,p2)
      
      example -   distance.distance(0,500)LicenseMIT
Package: datawhys
DataWhys Python SDKDataWhys Python SDK is a python wrapper for the DataWhys API that provides additional functionality such as dataframe ingest and one-off processingInstallationUse the package managerpipto install datawhys.pipinstalldatawhysDependenciespython3Install from sourceIn themondobrain-pythondirectory (same directory as this README.md file), run this command in your terminal:pipinstall-e.Usageimportdatawhysasdw# Set your credentialsdw.api_key="<API-KEY>"# Build a pandas dataframe and store in `df` (not shown)# Convert your pandas df to a datawhys dfdwf=dw.DataWhysFrame(df)# Select a column as your outcome column & specify a target classoutcome=dwf["column_name"]# for a discrete columnoutcome.target_class="Some_modality"# for a continuous column the value should be `min` or `max`outcome.target_class="max"# Get a dataframe of all columns you want to explorexplorable=dwf[["column_a","column_b"]]# Create a solver instancesolver=dw.Solver()# Fit your datasolver.fit(explorable,outcome)# Check your resultssolver.ruleSee documentation andSDK Example.ipynbin themondobrain-pythondirectory for more in depth examples.The package includes documentation to provide explanation and examples of usage.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Take a look atCONTRIBUTING.mdfor more infoPlease make sure to update tests as appropriate.LicenseMIT
Package: datawhys-dashboard
DataWhys Dashboard Python SDKDataWhys Dashboard SDK allows users to create a DataWhys Dashboard directly within their Python notebooks.InstallationUse the package managerpipto install datawhys-dashboard.pipinstalldatawhys-dashboardUsageimportdatawhys_dashboardasdwdash# Set your credentialsdwdash.api_key="<API_KEY>"# Ask your datawhys rep for this key# Your API key is stack specific. The SDK defaults to the main US stack, but you can# change this by setting `api_base` as follows. Ask your datawhys rep for the api base.dwdash.api_base="https://<DOMAIN>/api/v0.2/"# Build a pandas dataframe and store in `df` (not shown)# Create a dashboardfromdatawhys_dashboardimportcreate_dashboardcreate_dashboard(df,outcome="Risk")ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Take a look atCONTRIBUTING.mdfor more infoPlease make sure to update tests as appropriate.LicenseMIT
Package: datawire-mdk
The Datawire Microservices Development Kit is an open source SDK for
building microservices. The MDK includes native APIs forservice
discovery,
resilience (includingcircuit
breakersanddeadlines),
and distributed logging.The MDK isnota platform; it is designed to augment your existing
webapp framework. For more information, visithttps://www.datawire.io.
Package: data-wizard
Django Data Wizardis an interactive tool for mapping tabular data (e.g. Excel, CSV, XML, JSON) into a normalized database structure viaDjango REST FrameworkandIterTable.  Django Data Wizard allows novice users to map spreadsheet columns to serializer fields (and cell values to foreign keys) on-the-fly during the import process.  This reduces the need for preset spreadsheet formats, which most data import solutions require.The Data Wizard supports straightforward one-to-one mappings from spreadsheet columns to database fields, as well as more complex scenarios likenatural keysandEntity-Attribute-Value(or "wide") table mappings.DocumentationDjango Data Wizard provides aweb interface,JSON API, andCLIfor specifying adata sourceto import (e.g. a previously-uploaded file), selecting aserializer, mapping the datacolumnsandidentifiers, and (asynchronously) importing thedatainto any target model in the database.Data Wizard is designed to allow users to iteratively refine their data import flow.  For example, decisions made during an initial data import are preserved for future imports of files with the same structure.  The includeddata modelmakes this workflow possible.Getting StartedInstallationInitial ConfigurationTarget Model Registration (required)Usagewq Framework Integration (optional)API DocumentationRun APIAdmin ScreensCommand-Line InterfaceAdvanced CustomizationDjango SettingsData ModelCustom SerializersCustom Data SourcesCustom TasksTask Backends
Package: datawords
datawordsThis is a library oriented to common and uncommon NLP tasks.Datawordsemerge after two years of solving different projects that required NLP techniques
like training and saving Word2Vec (Gensim) models, finding entities on text (Spacy), ranking texts (scikit-network), indexing it (Spotify Annoy), translating it (Hugging Face).Then to use those libraries some pre-processing, post-processing tasks and transformations were also required. For this reasons,datawords exists.Sometimes it’s very opinated (Indexing happens over text, and not over vectors besides Annoy allows it), and sometimes gives you freedom and provide you with helper classes and functions to use freely.Another way to see this library is as an agreggator of all that excellent libraries mentioned before.In a nutshell,Datawords let’s you:Train Word2Vec models (Gensim)Build Indexes for texts (Annoy, SQLite)Translate texts (Transformers)Rank texts (PageRank)Table of ContentsInstallationLicenseInstallationpip install datawordsTo use transformes fromHuggingFaceplease do:pip install datawords[transformers]Quickstartdeepnlp:fromdatawords.deepnlpimporttranslatorsmn=translators.build_model_name("es","en")rsp=transform_mp("es","en",model_path=fp,texts=["hola mundo","adios mundo","notias eran las de antes","Messi es un dios para muchas personas"])Licensedatawordsis distributed under the terms of theMPL-2.0license.
Package: dataworkspaces
Data Workspacesis an open source framework for maintaining the
state of a data science project, including data sets, intermediate
data, results, and code. It supports reproducability through snapshotting
and lineage models and collaboration through a push/pull model
inspired by source control systems like Git.Data Workspaces is installed as a Python 3 package and provides a
Git-like command line interface and programming APIs. Specific data
science tools and workflows are supported through extensions
calledkits. Currently, this includes Scikit-learn, TensorFlow,
and Jupyter Notebooks. The goal is to provide the reproducibility and collaboration
benefits with minimal changes to your current projects and processes.Data Workspaces runs on
Unix-like systems, including Linux, MacOS, and on Windows via the
Windows Subsystem for Linux.Quick StartPlease see theQuickstart Sectionof the documentation.DocumentationThe documentation is available here:https://data-workspaces-core.readthedocs.io/en/latest/. The source for the documentation is underdocs. To build it locally, installSphinxand run the following:cd docs
pip install -r requirements.txt # extras needed to build the docs
make htmlTo view the local documentation, open the filedocs/_build/html/index.htmlin your
browser.LicenseThis code is copyright 2018 - 2021 by the Max Planck Institute for Software Systems and Benedat LLC.
It is licensed under the Apache 2.0 license. See the file LICENSE.txt for details.