Saravanan666/pypi-org huggingface dataset:
Package: nepse
NEPSEThe Last Nepse Module You'll Ever NeedNepse is a realtime nepse scraper which communicates with newweb.nepalstock.com.np, to fetch and return required stats.FeaturesGet BrokersGet Realtime PricesMake Charts and Many MoreInstallationNepse requirespython3 and pipto install and run.pipinstallnepsePluginsNepse is currently extended with the following plugins.
Instructions on how to use them in your own application are linked below.PluginLINKMatplotlibhttps://matplotlib.org/Requestshttps://pypi.org/project/requests/Pandas (For Next Update)https://pandas.pydata.org/UsagefromnepseimportNEPSEinit=NEPSE()#GET ALL REGISTERED BROKERSbrokers=init.brokers()#GET ALL NEWS & ALERTS Published By NEPSE on Newwebnews=init.alerts()#Check IF MARKET IS OPENisOpen=init.isOpen()#Returns TRUE IF market is open#Check live price of specific scrip or get all pricesallPrices=init.todayPrice()cghPrice=init.todayPrice('CGH')#returns information for CGH#CHARTSchartHistory=init.getChartHistory('CGH')#Get History Prices for CGHchartHistoryButFiltered=init.getChartHistory('CGH',start_date='2021-03-04',end_date='2021-03-07')makeChart=init.createChart('CGH',theme='dark',high=False,low=False)#returns abspath of chart savedcsv=init.saveCSV('CGH',start_date='2021-02-07')#filename,start_date and  end_date are optionalindices=init.indices(sector='NEPSE Index',start_date='2021-03-04',end_date='2021-03-07')floorsheets=init.floorsheets()#CHECK IPOresult=init.checkIPO(scrip,boid){'success':False,'message':'Sorry, not alloted for the entered BOID. ','body':None}LicenseMIT
Package: nepse-api
NEPSE API WrapperThis python module fetches the data fromNepali Stock Siteand provides them in a pythonic
and usable way.AboutThis is a API wrapper for NEPSE API. This project was inspired fromPyPi Nepse.DocumentationThere is a detailed guide in my documentation page for getting started and installing.DocumentationHow to use?You can use this by package fromNepse API PyPipipinstallnepse-apiWhy use this?How is this better thanPyPi Nepse?It is asynchronous.Data can be taken as attributes rather than from dict.Data is fetched from the API rather than scraping the site.Data is cachedAPIs usedThe APIs that I used to create this API wrapper is:https://newweb.nepalstock.com/api/How to use?importasyncioimporthttpxfromnepseimportClientasyncdefmain():company_Symbol=input('Input Company Symbol (Uppercase): ')# Doing this is optional you can directly# Initialize using `client = Client()` as wellasyncwithhttpx.AsyncClient()asasync_client:# Initializes the clientclient=Client(httpx_client=async_client)# Gets the datadata=awaitclient.security_client.get_company(symbol=f"{company_Symbol}")# Prints the highest price for that company todayprint(f'High Price of{company_Symbol}: ',data.high_price)print(f'Low price of{company_Symbol}: ',data.low_price)print(f'Open Price of{company_Symbol}: ',data.open_price)# Run the functionloop=asyncio.get_event_loop()loop.run_until_complete(main())Why are the attributes so in-costistent?The attribues are in-consistent because the attributes are set according to the response set by the API.
I tried changing it up but that would be distruptive because the comability with the API would be broken.Want To Contribute?You can send a pull request or open an issue to contribute.
Check outCode Of Conductbefore contributing.
Package: nepsense
NEPSENSEComplete NEPSE solution in command linehttps://user-images.githubusercontent.com/52292457/123103383-a924c500-d455-11eb-94a4-b1fde8ff8c48.mp4Installationpip install nepsenseAfter the installation you can use the cli application with the priceof commandUsageusage: priceof [-h] [-c  [...]] [-f [path ...]] [-n] [-s] [-m symbol] [-ms] [-d] [-cu] [-ci] [--losers] [--gainers][--supply] [--demand] [--turnover] [--volume] [--transactions]STONK, complete NEPSE solution in command lineoptional arguments:-h, --help            show this help message and exit-c  [ ...], --company  [ ...]symbols of companies seperated by space-f [path ...], --floorsheet [path ...]Saves today's floorsheet as a csv file You can specify the path as argument. if no argumentis provided the csv file will be saved in the current directory-n, --nepse           get NEPSE index-s, --sub-indices     get subindices-m symbol, --market_depth symbolget market depth of a company-ms, --market_summaryget market summary-d , --get_detail     get the non price related detail of the company-cu , --custom        custom list in data.py-ci , --change_indexchange the value of id sent as a payload in the post request--losers              get the top losers--gainers             get the top gainers--supply              get the scripts with maximum supply--demand              get the scripts with maximum demand--turnover            get the scripts with maximum turnover--volume              get the scripts with maximum volume--transactions        get the scripts with maximum transactionsPlease change the index with -ci flag.If you get this type of message when using the cli. Review the following videohttps://user-images.githubusercontent.com/52292457/123106754-a1b2eb00-d458-11eb-924c-591f24bcb4e2.mp4Since i can't afford to pay for the nepse api(I don't think I would, even if I could, but anyways).
There are some caveats to retrieving data from api endpoints from the nepse's newweb website. Nepse decided
to change the request method for some api endpoints from get to post. The payload sent along with the
post request changes randomly at different intervals.
The payload looks like{
  "id" : 123
}Here 123 is a random number. This number can be changed using the -ci flag. To get the id we have to
inspect the network traffic while loading the nepse's newweb website. This is demonstrated in the video aboveFloorsheetpriceof -f [path]You can either specify a path or leave it blank. If you leave it blank the floorsheet csv file will be
saved in you current directory. The floorsheet file will be saved as floorsheet_todaysdata_weekday.csv
You can do different kind of analysis on the data using the floorsheet.ipynb notebook.
Package: nepserate
Introduction----------- Python module to get Nepse Price and other info.- Easy to install and Simple to use.- Thanks to nepalstock.com for the data.- Works on python 2.7+ and python 3- All the date are in ADTo install$ python setup.py installDependencies----------* BeautifulSoup4* requestsUsage---------->>> from nepserate import ScrapeRate>>> rs = ScrapeRate()>>> rs.getRate("ADBL") # 'ADBL' is the symbol of the orgranization for more reger to docs/SymbolList.rst{'high': u'Rs. 514', 'previous_close': u'Rs. 506', 'percent_change': u'-0.20', 'net_change': u'Rs.-1', 'last_traded_date': '2014-10-22', 'ADBL': 'Agricultural Development Bank Ltd', 'last_traded_price': '505', 'low': u'Rs. 495'}Also you can request to the data of previous date>>> rs.getRate("ADBL", "2013-12-10"){'previous_close': u'396', 'amount': u'11,496,761', 'min_price': u'391', 'total_share': u'28,690', 'date': '2013-12-10', 'number_of_transaction': u'192', 'closing_price': u'400', 'difference_rs': u'4', 'ADBL': 'Agricultural Development Bank Ltd', 'max_price': u'405'}Reference----------- Symbol list of the Banks and Organization (https://github.com/acsudeep/nepserate/blob/master/docs/SymbolList.rst)Problem---------Tweet me [@acsudeep](http://twitter.com/acsudeep)
Package: nepse-scraper
Nepse scraperPython module that provides convenient access to the Nepal Stock Exchange (NEPSE) API, enabling developers and analysts to retrieve stock market data and improve investment decisions.If you're looking for more detailed documentation on how to use my project called "nepse_scraper," I would recommend checking out the project's GitHub page at [https://github.com/polymorphisma/nepse_scraper/].Table of contentIntroductionFeaturesInstallationUsing PipUsageDocumentationLicenseIntroductionWelcome tonepse_scraper, the Python module that provides easy access to the Nepal Stock Exchange (NEPSE) API. With just a few lines of code, you can retrieve today's stock prices, market summary, head indices and etc to help you make better investment decisions. The code is well-documented and organized, making it easy to understand and modify for your specific use case.nepse_scraperis open-source, which means you can contribute and improve the code by adding new functions to retrieve additional data, enhancing existing functions with more options, or improving the error handling and exception handling to make it more robust. Your contributions can help makenepse_scrapereven more useful for developers and analysts who work with data from the Nepal Stock Exchange.So what are you waiting for? Try outnepse_scraperand see how it can simplify your stock market analysis and investment strategies.FeaturesGet Today PriceGet Head IndicesGet Market SummaryGet Sector SummaryGet Sector DetailGet Broker DetailsGet NewsMany More ....InstallationUsing Pippip install nepse-scraperUsagefromnepse_scraperimportNepse_scraper# create object from Nepse_scraper classrequest_obj=Nepse_scraper()# getting today's price from nepsetoday_price=request_obj.get_today_price()print(today_price)# "Please refer to the documentation for more detailed usage instructions."LicenseMITFree Software, Hell Yeah!
Package: nept
nept: Neuroelectrophysiology toolsFormerlyvdmlab, renamed to emphasize general abilities of this library.Getting startedIf you don’t already have python 3, we recommend you download it using Miniconda
fromContinuum Analytics.We recommend using a separate python environment.Open anewterminal, create and activate a new conda environment:conda create -n yourenv python=3.5
activate yourenv [Windows] or source activate yourenv [Linux]Install package dependencies:conda install matplotlib jupyter scipy numpy pandas seaborn pytest coverageFor Shapely, try:pip install shapelyIf that fails, in Windows, download the most recent wheel filehere.
Once downloaded, install with wheel.pipinstallyourshapelyinstall.whlInstallationClone nept from Github and use a developer installation:git clone https://github.com/vandermeerlab/nept.git
cd nept
python setup.py developDocumentationUsersCheckGitHub Pagesfor the latest version of the nept documentation.DevelopersEnsure you have sphinx, numpydic, and mock:conda install ghp-import sphinx numpydoc sphinx_rtd_themeInstall nbsphinx so notebooks in the documentations can be executed:pip install nbsphinx --userBuild the latest version of the documentation using
in the nept directory prior to pushing it to Github:sphinx-build docs docs/_buildAnd push it to Github:docs/update.shTestingRun tests withpytest.Check coverage withcodecov.LicenseThe nept codebase is made available under made available
under theMIT licensethat allows using, copying and sharing.The filenept/neuralynx_loaders.pycontains code fromnlxioby Bernard Willers,
used with permission.Projects using neptemi_shortcutemi_biconditional
Package: neptune-apex-classic
neptune_apex_classicPython module for getting the state of and controlling a classic Neptune Apex from your LANRequirementsThis module requires the "XML Access" to be set on the Apex in order for the status of probes and outlets to be retrieved. Outlets can be set between OFF, AUTO, and ON if the ApexConnection object is created with a username and password.HTTP access is governed by anaiohttp.ClientSessionsupplied by the caller.Example Usageasyncdefdo_apex_stuff(clientSession:aiohttp.ClientSession)->None:# Create an ApexConnection - one connection per Apex is all that's requiredconn=ApexConnection("ip-address",clientSession,"admin","1234")# The hardware serial number is an ID unique to each Apex and can be used# to differentiate between different units on the same networkserial_number=awaitconn.get_serial_number()# Fetch probe and outlet dataawaitconn.refresh()# Iterate through connected probesforprobeinget_connected_probes(conn):print(probe.name)print(probe.value)# Iterate through connected outlets and print the stateforoutletinget_connected_outlets(conn):print(outlet.name)print(outlet.value)# Probe values and outlet states are cached and are only updated when the ApexConnection is refreshed!conn.refresh()outlet=get_connected_outlets(conn)[0]awaitoutlet.set_state(Outlet.ON)
Package: neptune-cache-redis
Neptune Resolver RestThis module is made as a caching middleware for Neptune DNS serverInstallationpip3 install neptune_cache_redisConnect to serverIn config.py edit CACHE variable. Add'neptune_cache_redis'It will look like thisCACHE = 'neptune_cache_redis'Then you need to set settings for this cacher. Those settings are set by creating variable   in config.py with upper module name like thisNEPTUNE_CACHE_REDIS = {'host':'localhost', 'database': 1}
Package: neptune-contrib
neptune-contribDocumentationSeeneptune-contrib documentation siteInstallationGet prerequisitespython versions3.5.6/3.6are supportedInstall libpipinstallneptune-contribInstall additional packages depending on which submodule you want to use.
For example:pipinstallneptune-contrib[monitoring]Installation options are: [bots], [hpo], [monitoring], [versioning], [viz] and [all].Getting helpIf you get stuck, don't worry we are here to help.
The best order of communication is:neptune-contrib readthedocsneptune community forumneptune community SpectrumGithub issuesContributingIf you see something that you don't like you are more than welcome to contribute!
There are many options:Participate in discussions onneptune community forumorneptune community SpectrumSubmit a feature request or a bug here, on GithubSubmit a pull request that deals with an open feature-request or bugSpread a word about neptune-contrib in your community
Package: neptune-dns-server
Neptune DNS ServerNeptune is asynchronous modular DNS server made for flexibility and easy modifications
Its based on Triton dns client and uses different modules to extend it to full serverInstallationFrom PYPIpip3 install neptune-dns-serverFrom this repogit clone https://git.best-service.online/yurzs/neptune
cd neptune
python3 setup.py installUsagePlease edit config.py file for your configuration. Basic install uses 1.1.1.1 and 8.8.8.8 as a resolvers with local redis cacheTo start server use a simple commandimport neptune
neptune.start_server()TODO write more docs
Package: neptune-experimental
Neptune Experimental PackageThis package consists of experimental features that are not yet ready for production use. The API is subject to change without notice.Neptune FetcherNeptune Fetcher is a Python package designed for efficient fetching and manipulation of data from Neptune projects and runs. It provides classes and methods for interacting with Neptune data in a read-only manner.Installationpipinstall--upgradeneptuneneptune-experimentalUsageImportingfromneptune_fetcherimportReadOnlyProjectOverview of ClassesReadOnlyProject: A lightweight, read-only class for handling basic project information.Constructor Parameters:project: Optional string specifying the project name.workspace: Optional string specifying the workspace.api_token: Optional string for the API token.proxies: Optional dictionary for proxy configuration.Methods:list_runs(): Yields dictionaries with basic information about each run, includingsys/idandsys/name.fetch_read_only_runs(with_ids: List[str]): Returns a generator forReadOnlyRuninstances for specified run IDs.fetch_runs(): Fetches runs as a DataFrame with default columns.fetch_runs_df(columns, with_ids, states, owners, tags, trashed): Fetches runs as a DataFrame based on specified filters.ReadOnlyProject.ReadOnlyRun: Represents a single Neptune run with read-only access.Methods:__getitem__(item): Accesses a field by its path.__delitem__(key): Removes a field from the local cache.field_names: Yields the names of all available fields in the run.prefetch(paths: List[str]): Loads values of specified fields into local cache.ExamplesFetching Project Metadatafromneptune_fetcherimportReadOnlyProjectproject=ReadOnlyProject(workspace="some",project="project")Listing Runs in a Projectfromneptune_fetcherimportReadOnlyProjectproject=ReadOnlyProject(workspace="some",project="project")ids=list(map(lambdarow:row["sys/id"],project.list_runs()))Filtering and Processing Runsfromneptune_fetcherimportReadOnlyProjectproject=ReadOnlyProject(workspace="some",project="project")df=project.fetch_runs_df()matches=df["sys/name"].str.match("metrics.*")ids=df[matches]["sys/id"]Iterating Over Runsfromneptune_fetcherimportReadOnlyProjectproject=ReadOnlyProject(workspace="some",project="project")forruninproject.fetch_read_only_runs(with_ids=["PROJ-2"]):forfieldinrun.field_names:iffield.startswith("param"):print(run[field].fetch())iffield.startswith("metric"):print(run[field].fetch_values())Prefetching Valuesrun.prefetch(["metric1","metric2"])print(run["metric1"].fetch(),run["metric2"].fetch())# This will use the local cachePurging Local Cachedelrun["metric1"]ExampleA full example can be found inexamples/fetch_api.py.LicenseThis project is licensed under the Apache License Version 2.0. For more details, seeApache License Version 2.0.
Package: neptune-fastai
Neptune + fastai integrationExperiment tracking, model registry, data versioning, and live model monitoring for fastai trained models.What will you get with this integration?Log, display, organize, and compare ML experiments in a single placeVersion, store, manage, and query trained models, and model building metadataRecord and monitor model training, evaluation, or production runs liveWhat will be logged to Neptune?HyperparametersLosses and metricsTraining code (Python scripts or Jupyter notebooks) and Git informationDataset versionModel configuration, architecture, and weightsOther metadataExample dashboard with train-valid metrics and selected parametersResourcesDocumentationCode example on GitHubExample dashboard in the Neptune appRun example in Google ColabExampleOn the command line:pip install neptune-fastaiIn Python:importneptune# Start a runrun=neptune.init_run(project="common/fastai-integration",api_token=neptune.ANONYMOUS_API_TOKEN,)# Log a single training phaselearn=learner(...)learn.fit(...,cbs=NeptuneCallback(run=run))# Log all training phases of the learnerlearn=cnn_learner(...,cbs=NeptuneCallback(run=run))learn.fit(...)learn.fit(...)# Stop the runrun.stop()SupportIf you got stuck or simply want to talk to us, here are your options:Check ourFAQ pageYou can submit bug reports, feature requests, or contributions directly to the repository.Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),You can just shoot us an email atsupport@neptune.ai
Package: neptune-lightgbm
Neptune + LightGBM IntegrationExperiment tracking, model registry, data versioning, and live model monitoring for LightGBM trained models.What will you get with this integration?Log, display, organize, and compare ML experiments in a single placeVersion, store, manage, and query trained models, and model building metadataRecord and monitor model training, evaluation, or production runs liveWhat will be logged to Neptune?training and validation metrics,parameters,feature names, num_features, and num_rows for the train set,hardware consumption (CPU, GPU, memory),stdout and stderr logs,training code and Git commit information,other metadataExample dashboard with train-valid metrics and selected parametersResourcesDocumentationCode example on GitHubExample of a run logged in the Neptune appRun example in Google ColabExample# On the command line:
pip install neptune-lightgbm# In Python:importlightgbmaslgbimportneptunefromneptune.integrations.lightgbmimportNeptuneCallback# Start a runrun=neptune.init_run(project="common/lightgbm-integration",api_token=neptune.ANONYMOUS_API_TOKEN,)# Create a NeptuneCallback instanceneptune_callback=NeptuneCallback(run=run)# Prepare datasets...lgb_train=lgb.Dataset(X_train,y_train)# Define model parametersparams={"boosting_type":"gbdt","objective":"multiclass","num_class":10,...}# Train the modelgbm=lgb.train(params,lgb_train,callbacks=[neptune_callback],)SupportIf you got stuck or simply want to talk to us, here are your options:Check ourFAQ pageYou can submit bug reports, feature requests, or contributions directly to the repository.Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),You can just shoot us an email atsupport@neptune.ai
Package: neptune-mlflow
Neptune + MLflow integrationNeptune is a lightweight experiment tracker that offers a single place to track, compare, store, and collaborate on experiments and models.This integration lets you enjoy tracking and reproducibility of MLflow with the organization and collaboration of Neptune. You can have your MLflow experiment runs hosted in a knowledge repo where you can invite and manage project contributors, while not having to change your MLflow logging code.Should you wish to switch to Neptune, you can migrate your MLflow data to Neptune with the exporter tool.What will you get with this integration?Apluginwhich you can use to send your MLflow-logged metadata to Neptune with the help of a tracking URI.Anexporterfor migrating existing MLflow experiments to your Neptune project.ResourcesDocumentationCode examples on GitHubRun logged in the Neptune appRun example in Google ColabExampleOn the command line:pip install neptune-mlflowSend your MLflow-logged metadata to Neptune (in Python):importmlflowfromneptune_mlflow_pluginimportcreate_neptune_tracking_uri# Create a Neptune tracking URIneptune_uri=create_neptune_tracking_uri(api_token=ANONYMOUS_API_TOKEN,# Set as environment variable or replace with your own tokenproject="common/mlflow-integration",# Set as environment variable or replace with your own projecttags=["mlflow","plugin"],# (optional) use your own)mlflow.set_tracking_uri(neptune_uri)withmlflow.start_run():...Export existing MLflow runs to Neptune:neptune mlflow --project your-neptune-workspace/your-neptune-projectSupportIf you got stuck or simply want to talk to us, here are your options:Check ourFAQ pageYou can submit bug reports, feature requests, or contributions directly to the repositoryChat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP)You can just shoot us an email atsupport@neptune.ai
Package: neptune-optuna
Neptune + Optuna integrationNeptune is a lightweight experiment tracker that offers a single place to track, compare, store, and collaborate on experiments and models.This integration lets you use it as an Optuna visualization dashboard to log and monitor hyperparameter sweeps live.What will you get with this integration?Log and monitor the Optuna hyperparameter sweep live:values and params for each Trialbest values and params for the Studyhardware consumption and console logsinteractive plots from the optuna.visualization moduleparameter distributions for each TrialStudy object itself for 'InMemoryStorage' or the database location for the Studies with database storageLoad the Study directly from the existing Neptune runResourcesDocumentationCode example on GitHubRun logged in the Neptune appRun example in Google ColabExampleOn the command line:pip install neptune-optunaIn Python:importneptuneimportneptune.integrations.optunaasnpt_utils# Start a runrun=neptune.init_run(api_token=neptune.ANONYMOUS_API_TOKEN,project="common/optuna-integration",)# Create a NeptuneCallback instanceneptune_callback=npt_utils.NeptuneCallback(run)# Pass the callback to study.optimize()study=optuna.create_study(direction="maximize")study.optimize(objective,n_trials=100,callbacks=[neptune_callback])# Watch the optimization live in NeptuneSupportIf you got stuck or simply want to talk to us, here are your options:Check ourFAQ pageYou can submit bug reports, feature requests, or contributions directly to the repository.Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),You can just shoot us an email atsupport@neptune.ai