Saravanan666/pypi-org huggingface dataset:
Package: cheesefactory
If you like using dataframes in Pandas and want to spit the results into an Excel or CSV and want to have the option to transfer the report to a remote SFTP server, then this package may be for you.If you want to export non-dataframe results into an Excel or CSV and want to have the option to transfer the report to a remote SFTP server, then this package may be for you.###BackgroundYes!  I know that Pandas has a lot of goodies built into it for doing the Excel and CSV thing, but thisI provide a number of daily reports to clients in the form of Excel and CSV files.  Sometimes, these clients need the information transferred to an SFTP server.As you may imagine, the first thing I did was create a framework for these reports so I wouldn’t have to repeat most of my work.  Thus, CheeseFactory was born.The idea here is that I would have Python wrappers for my most used packages:PostgreSQL (psycopg2)MSSQL (pyodbc)Email (builtin)Excel (xlsxwriter)CSV (builtin)And then I’d have a basic report class (cf_report.py) that brings everything together.  I never use the basic report class directly–I create create a child classes for each particular data source.This package was originally intended for me and the other data scientists I associate with.  I was very surprised when I saw that, according to PePy (http://pepy.tech/count/cheesefactory), it had been downloaded over 2500 times within its first 90 days of being in the wild!So, for the benefit of others, I’ve decided to pretty things up and describe how CheeseFactory may be able to help you.If you have any questions or constructive suggestions, please contact me.###What do I mean by “wrapper”?I’ve taken the packages that I use the most and pre-configured them with the configurations that I use 99% of the time.###What can CheeseFactory do?Keywords: reports development
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.6
Package: cheesefactory-database
cheesefactory-databaseA wrapper for psycopg2.Main FeaturesBuilt on psycopg2.Pandas dataframe support.Test table existence.Test field existence.Note:This package is still in beta status. As such, future versions may not be backwards compatible and features may change.InstallationThe source is hosted athttps://bitbucket.org/hellsgrannies/cheesefactory-databasepipinstallcheesefactory-databaseDependenciespsycopg2LicenseExamplesConnect to the remote PostgreSQL server and execute a queryfromcheesefactory_database.postgresqlimportCfPostgresqldb=CfPostgresql(host='myhostname',user='myusername',password='mypassword',database='mydatabase')Args:host(str): PostgreSQL server hostname/IP. Default: 127.0.0.1port(str): PostgreSQL server port. Default: 5432user(str): Username for authentication.password(str): Password for authentication.database(str): Database for connectionautocommit(bool): Use autocommit on changes? Default: Falsedictionary_cursor(bool): Return the results as a dictionary? Default: Falseencoding(str): Database client encoding ("utf8", "latin1", "usascii"). Default: utf8Returns:(CfPostgresql): An instance of the database connection.Execute a queryresults=db.execute("SELECT first_name, last_name FROM person WHERE last_name = 'Smith'")Args:query(str): SQL query to execute.dataframe(bool): Output the results to a pandas.Dataframe?fetchall(bool): Perform a fetchall() and return the results?Returns:Iffetchall=False, None is returned.Ifdataframe=True, a pandas.Dataframe is returned.Check connection statusstatus=db.connection_status()Returns:(str): Connection status: "OK" or "Error"Check database existencestatus=db.database_exists(database_name='my_database')Args:database_name(str): Name of the database to search forReturns:(bool): True, if database exists. False, if not.Check schema existencestatus=db.schema_exists(schema_name='my_schema')Args:schema_name(str): Name of the schema to search for.Returns:(bool): True, if schema exists. False, if not.Check table existencestatus=db.table_exists(table_path='my_schema.my_table')Args:table_path(str): Table name in the form <schema>.<table>Returns:(bool): True, if table exists. False, if not.Check field existencestatus=db.fields_exist(table_path='my_schema.my_table',table_fields=['first_name','last_name'])Args:table_path(str): Table name in the form <schema>.<table>table_fields(str): A list of fields to check.Returns:(bool): True, if all fields are present in the table. False, if not.Get a table's primary keysprimary_keys=db.get_primary_keys(table_path='my_schema.my_table')Args:table_path(str): Table name in the form <schema>.<table>Returns:(List): A list of primary keys.Quote PostgreSQL reserved words in a listquoted_word_list=CfPostgresql.quote_reserved_words(word_list)Args:word_list(List): A list of words to checkReturnes:(List):word_listwith all PostgreSQL reserved words surrounded in double-quotes.
Package: cheesefactory-docker
cheesefactory-dockerA toolset to create Docker containers and read the contents of secret and config files.Main FeaturesCreates containers using Docker's docker-py packageNote:This package is still in beta status. As such, future versions may not be backwards compatible and features may change.InstallationThe source is hosted athttps://bitbucket.org/hellsgrannies/cheesefactory-dockerpipinstallcheesefactory-dockerDependenciesdockerLicenseParametersExamples
Package: cheesefactory-email
cheesefactory-emailAn easier way to send an Email.FeaturesNote:This package is still in beta status. As such, future versions may not be backwards compatible and features may change. Parts of it may even be broken.TODOCreate an Email object and sendfromcheesefactory_emailimportCfEmailmail=CfEmail(host='localhost',port='25',username='emailuser',password='emailpassword',use_tls=False,header_tags={'X-Barracuda-Encrypted':'RJSE-RSA-AES256-SHA'},debug=False)transfer_log=mail.send(sender='tom@example.com',recipients=['bob@example.com','mary@example2.com'],cc_recipients=['carol@example.com','larry@example2.com'],subject='Fishing trip',body='Dear Bob,\nHow was the fishing trip?\n\nTom',attachments=['/tmp/file1.txt','/tmp/file2.txt'])
Package: cheesefactory-excel
###Features* Built around Xlsxwriter and Pandas.* Create Excel worksheets using either dictionaries or dataframes.* Worksheet columns are automatically sized.* Attractive default style.###Create an Excel object```pythonfrom cheesefactory-Excel import Excelexcel = Excel(output_file='my_excel.xlsx')````output_file` (str): The path and name of the Excel file to create.###Create a worksheet using a dictionary or Pandas Dataframe```pythonexcel.create_worksheet(worksheet_name='Sheet1',content='my_content',header='header_data')````worksheet_name` (str): The worksheet name.  It's limited to 31 characters.  Default = `'Worksheet'``content` (dict or pd.Dataframe): A dictionary or dataframe of the worksheet's content.`header` (List[str]):  The worksheet's header.  Default = `None`###Write the Excel file```pythonexcel.close()```Keywords: cheesefactory excel dataframe pandas developmentPlatform: UNKNOWNClassifier: Development Status :: 4 - BetaClassifier: Intended Audience :: DevelopersClassifier: License :: OSI Approved :: BSD LicenseClassifier: Programming Language :: Python :: 3.7
Package: cheesefactory-logger-postgres
cheesefactory-logger-postgresAn interface for logging to a Postgres database.Main FeaturesLog to a PostgreSQL databaseTable fields are customizable.Query the log table.Note: This package is still in beta status. As such, future versions may not be backwards compatible and features may change.Recent ChangesNoneInstallationThe source is hosted athttps://bitbucket.org/hellsgrannies/cheesefactory-logger-postgrespipinstallcheesefactory-logger-postgresDependenciesNoneBasic UsageComing SoonNote: This package is still in beta status. As such, future versions may not be backwards compatible and features may change.
Package: cheesefactory-logging
cheesefactory-loggingA wrapper for Python's logging.Main FeaturesBuilt on Python's logging package.Includes a handle for buffered logging to a PostgreSQL database.Includes a handle for muliprocessing logging.Note:This package is still in beta status. As such, future versions may not be backwards compatible and features may change.InstallationThe source is hosted athttps://bitbucket.org/hellsgrannies/cheesefactory-loggingpipinstallcheesefactory-loggingDependenciesPython's built-in logging package.LicenseParametersExamples
Package: cheesefactory-smb
cheesefactory-smbA simple wrapper for PySMB.Main FeaturesBuilt around PySMB.Push and pull files with ease.Note:This package is still in beta status. As such, future versions may not be backwards compatible and features may change.InstallationThe source is hosted athttps://bitbucket.org/hellsgrannies/cheesefactory-smbpipinstallcheesefactory-smbDependenciespysmbConnect to a remote SMB/CIFS serversmb=CfSmb(user='jdoe',password='superSecret',client_name='yoyo',server_name='windows_server1',server_ip='192.168.1.22',server_port='445',domain='mydomain',use_ntlm_v2=True,is_direct_tcp=True)user(str): SMB server username.password(str): SMB server password.client_name(str): Client machine name.server_name(str): SMB server name.server_ip(str): SMB server IP address or hostname.server_port(str): SMB server port.  Default ='445'domain(str): Domain used for authentication.use_ntlm_v2(bool): Use NTLMv1 (False) or NTLMv2 (True) for authentication. Default =Trueis_direct_tcp(bool): Use NetBIOS over TCP/IP (False) or direct TCP/IP (True) for communication. Default =TrueQuery the SMB server for a list of sharesshares=smb.get_shares()forshareinshares:print(share)Query the SMB server for a list of files in a directory on a sharefiles=smb.get_files(share='my_share',directory='/dir1/dir2/directory_of_stuff')share(str): Remote SMB share.directory(str): The remote directory to inspect.Download a file from the remote SMB serversmb.download(share='my_share',source_path='/remote_dir1/remote_dir2/cool_beans.pdf',destination_path='c:/local_dir1/cool_beans.pdf')share(str): Remote SMB share.source_path(str): Full path of remote filename to download.destination_path(str): Full path of destination filename on local machine.Upload a local file to the SMB serversmb.upload(share='my_share',source_path='c:/local_dir1/cool_beans.pdf',destination_path='/remote_dir1/remote_dir2/cool_beans.pdf')share(str): Remote SMB share.source_path(str): Full path of local filename to upload.destination_path(str): Full path of destination filename on Remote SMB server.Close the connection to a remote SMB serversmb.close()
Package: cheese-freezer
AboutCheese Freezer helps you freeze your project’s requirements by fetching them (through PIP)
and uploading back to a cheese shop of your choice (seehttp://github.com/disqus/chishop).Install$ pip install cheese-freezerUsageFirst you need to define an alternative shop in your .pypirc. For example:# ~/.pypirc
[distutils]
index-servers = myshop

[myshop]
repository = http://example.chishop.me/
username = my_username
passsword = my_passwordThen just run:$ cheese-freezer myshop requirements.pipand watch :)AuthorLukasz “lqc” Rekucki (<lrekucki+github@gmail.com>)
Package: cheesepi
Introduction to the internals of the CheesePi measurement platform.InstallTo obtain the CheesePi distribution, simply install the python module:$ sudo pip install cheesepiThis will download and install the modules and programs in your python module
directory.Starting CheesePiThere are three main components, the influxdb server, the measurement
dispatcher and the dashboard. The influxdb server stores measurement results,
the dispatcher regularly performs the measurement tasks and stores their
results in the influxdb. To view the results, the dashboard can optionally be
started.Start the storage server:$ cheesepi start storageThis will try to run the ARM version of influxdb from the CheesePi
distribution. Alternatively, you can run the system version via its normal
command.Start measuringTo start the measurement tasks being dipsatched run the following the command:$ cheesepi start dispatcherThe tasks specified in the schedule (described below), will be executing
according to their timing information. The InfluxDB needs to be running in
order to store the task results.Start the dashboard webserverTo start the host’s webserver running the dashboard use the following command:$ cheesepi start dashboardThis command starts a webserver on the localhost that will display the
collected data. Browse tohttp://HOSTNAME:8080to view this dashboard.Customising behaviourThere are two main files for customising the behaviour of CheesePi, a main
configuration file and a schedule of tasks to execute. The main configuration
filecheesepi.confcontrols the level of logging, whether to auto-upgrade the
code, which central sever to use, etc. The schedule fileschedule.datspecifies which tasks the system will perform. The location of both files from
your installation can be found with the following command:$ cheesepi statusLocal copies are these files are generated. Any change will stay local to your
installation and not be overwritten by software upgrades. If you delete your
local copy, a new default copy will be generated.ConfigurationThe configuration file format is rather simple, key value pairs of strings are
set with the format:key = valueLines can be commented out with a#.ScheduleThe schedule file format is a list of JSON strings (http://www.w3schools.com/json/).
Each JSON object represents ameasurement task, the only required
field istaskname, all others are option parameters to the task. Lines can
be commented out with a#.BUGS and problemsIf your install fails fails with –single-version-externally-managed* it may
be caused by problems with python version 2.7.11 To solve try the following:$ sudo conda install python=2.7.10If you find other issues, please emailcheesepi@sics.se
Package: cheesy
![](./cheese.gif)#cheesyCheesy pickup for pypy cheesecake factory. Gives all information you wanna know about today’s package.Customer: Now then, some cheese please, my good man.Owner: (lustily) Certainly, sir. What would you like?Customer: Well, eh, how about a little red Leicester.Owner: I’m, a-fraid we’re fresh out of red Leicester, <sir class=””></sir>## FeaturesWritten in simple Monty PythonEasy to [install](https://github.com/arindampradhan/cheesy#installation)Damn [easy to use](https://github.com/arindampradhan/cheesy#usage)Misogynistic tool to integrate with your Pip## Installation### Option 1: [Pip](https://pypi.python.org/pypi/cheesy)`bash $ pip install cheesy `### Option 2: From source`bash $ git clonehttps://github.com/arindampradhan/cheesy.git$ cd cheesy/ $ python setup.py install `## Usage[![asciicast](https://asciinema.org/a/44gy61ty9c9juzg50irg17re8.png)](https://asciinema.org/a/44gy61ty9c9juzg50irg17re8)### Basic usage`bash $ cheesy ls    # outputs all the packages content for today in pypy org `### Know what package you need for today`bash $ cheesy list    # adds description to the list `### Don’t miss a package`bash $ cheesy <package>   # outputs all the information about a package `### About the version`bash $ cheesy <package> <version>  # outputs the information about a package version `### And finally install what you like`bash $ pip install <pakage>    # Install the package you want `
Package: cheesyutils
cheesyutilsA number of miscelanious utilities, mostly for writing bots in discord.pyUsageYou can install the package by running the following command:pipinstall-UcheesyutilsYou can import the installed package by running the following python codefromcheesyutils.discord_botsimportDiscordBotCreating a simple Discord bot with only the builtin Meta commands:fromcheesyutils.discord_botsimportDiscordBotbot=DiscordBot(prefix=".",color="#a0365c")bot.run("YOUR TOKEN HERE")
Package: cheetah-lint
cheetah_lintLinting tools for theyelp_cheetahtemplating language.Installationpipinstallcheetah-lintConsole scripts$cheetah-reorder-imports--helpusage: cheetah-reorder-imports [-h] [filenames [filenames ...]]positional arguments:filenamesoptional arguments:-h, --help  show this help message and exit$cheetah-flake--helpusage: cheetah-flake [-h] [filenames [filenames ...]]positional arguments:filenames   Filenames to flake.optional arguments:-h, --help  show this help message and exitAs a pre-commit hookSeepre-commitfor instructionsHooks available:cheetah-reorder-imports- This hook reorders imports in cheetah files.cheetah-flake- Lint cheetah code using flake8 and some other checks.
Package: chef-cli
Chef-CliCodeChef command line interface. ChefCLI helps competitive coders to search, view, and submit problems in CodeChef.Table of ContentsIntroductionFeaturesInstallation InstructionScreenshotsFeedbackContributorsContributeAcknowledgementIntroductionChef-Cli is a command line interface which uses CodeChef’s official API. This application is for those who hate graphical application and love to do everything from terminal.  With features like sample submit and problem recommendation, It is designed to improve the overall productivity of the user.FeaturesView Problem Statement In TerminalGet complete profile information about a particluar user.Get a graph of submissions made by the user in past one year. (Similar to submission graph of SPOJ)Compare profiles of two users based on the ranking in various formats. Alos get a comparison graph right on the terminal for better visualizationSubmit a problem for evaluation against sample test cases and see if it passes or not.Problem RecommendationRecommends problem based on past solved problems by a user.Uses content based recommendation system for problem recommendation.Parameters - Difficulty , tags , No. of users who solved , Rating of user who solvedGet a ranking of all the institutes on CodeChef based on the performance of students in past contests.Submit problems to CodeChef IDE directly from the terminal and get the final result right on the terminal.Get list of both upcoming and currently running contests.Get all details about a contest.Get list of all the languages on codechef.Get list of problems with a particular tag.Search an institution from registered institution on Codechef.Installation InstructionChef-CLI is available as a python package.Open terminal and runpip install chef-cliThis installs the CLI app.Now run chef-cli --help to know the available commandsEnjoy !!ScreenshotsView Problem Statements In TerminalUser's Profile InformationUser's Submission GraphProfile ComparisonSample SubmitRecommend ProblemInstitute RankingFeedbackFeel free to send us feedback onEmailorfile an issue.ContributorsAbhey RanaMohit KhareContributeFeel free to report issues and bugs.It will be helpful for future launches of application.All Suggestions are welcome.Fork repository and Contribute.AcknowledgmentThanks toCodeCheffor providing CodeChef Api andAlibaba Cloudfor providing the platform.
Package: cheffy
Cheffy provides a method-based access to Chef server api. Typical usage often looks like this:# -*- coding: utf-8 -*-
from cheffy.client import Client
from cheffy.rsa import Key

chef_host = '123.123.123.123'
chef_user = 'john'

with open("private.pem", "r") as f:
    key = Key(f.read())

chef_client = Client(key, chef_host, chef_user)

# get clients list
# chef_request its a dict with the request data to the chef
# Its handy for logging.
chef_request, chef_response = chef_client.get('/clients')

clients = json.loads(chef_response.text)
Package: chefkoch
# chefkoch[![Version](https://img.shields.io/pypi/v/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Status](https://img.shields.io/pypi/status/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Python wheels](https://img.shields.io/pypi/wheel/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Documentation Status](https://readthedocs.org/projects/chefkoch/badge/?version=latest)](http://chefkoch.readthedocs.io/en/latest/?badge=latest)[![License](https://img.shields.io/pypi/l/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Python versions](https://img.shields.io/pypi/pyversions/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Implementation](https://img.shields.io/pypi/implementation/chefkoch.svg)](https://pypi.python.org/pypi/chefkoch)[![Coverage Status](https://coveralls.io/repos/github/EMS-TU-Ilmenau/chefkoch/badge.svg?branch=master)](https://coveralls.io/github/EMS-TU-Ilmenau/chefkoch?branch=master)[![GitHub issues](https://img.shields.io/github/issues/EMS-TU-Ilmenau/chefkoch.svg)](https://github.com/EMS-TU-Ilmenau/chefkoch/issues)**stable:** [![Build Status](https://www.travis-ci.org/EMS-TU-Ilmenau/chefkoch.svg?branch=stable)](https://www.travis-ci.org/EMS-TU-Ilmenau/chefkoch) **master:** [![Build Status](https://www.travis-ci.org/EMS-TU-Ilmenau/chefkoch.svg?branch=master)](https://www.travis-ci.org/EMS-TU-Ilmenau/chefkoch)## DescriptionA compute cluster cuisine for distributed scientific computing in python### Dependencies- Python >= 2.7 or >=3.5- dask### Authors & Contact Information- Christoph Wagner | christoph.wagner@tu-ilmenau.deTechnische Universität Ilmenau, Institute for Information Technology, EMS Group- **<https://www.tu-ilmenau.de/it-ems/>**## Documentation / HELP !Please have a look at the documentation, which is included in the sourcedistribution at github or may be built locally on your machine by running`make doc`If you experience any trouble please do not hesitate to contact us or to openan issue on our github projectpage: https://github.com/EMS-TU-Ilmenau/chefkoch### FAQPlease check out our project documentation at [readthedocs](https://chefkoch.readthedocs.io/).#### Issue not resolved yet?Please contact us or leave your bug report in the *issue* section. Thank You!
Package: chef-recipes
🍽️ ChefA simple, customizable recipe management app.A simple FastAPI + SQLAlchemy app + VueJS frontend.
Frontend app is built and bundled together with the python build downloadable from PyPI -> the entire app can be run with a single command.
It's also built as a single container and can be easily hosted (with little tweaks) on GCP Cloud Run.Installationcurlhttps://raw.githubusercontent.com/xyzjonas/chef/master/install.sh|bashor install the package yourselfpipinstallchef-recipes
chef--helpor run with docker:dockerrun-p8000:8000-v~/.chef:/chef/datascotch3840/chef:latestBuild & DevelopmentMigrationsmigrations are included in the build package, symbolic link to the alembic.ini is in the repository root# schema changesalembicrevision--autogenerate-m"<... message ...>"# upgrade the target database - accoring to $DATABASE_URIalembicupgradehead# ...or can be executed from the built script - i.e. without the sourceschefmigrate-dbBuildPoetry: build the standalone Python packagecd./src/js/chef
npmrunbuildcd-
poetrybuildpoetrypublishBuild the docker imagedockerbuild.-tscotch3840/chefdockerpushscotch3840/chef...or run on GCP usingscotch3840/misc:chef-gcpimage (PostgreSQL):! second generation execution environment required.BUCKET: name of your GCP/CloudStorage bucket.MNT_DIR: target where the bucket will be mounted.IMAGES_FOLDER: where the app will be looking for and storing uploaded images, has to be somewhere inside your bucket.DATABASE_URI: specify your posgres or possibly put an sqlite file in the bucket as well.
Package: chegg-marc-condon
USEpip install chegg-marc-condoncat input-file | chegg-marc-condonpip uninstall chegg-marc-condonAlgorithmic CostThis program has an O2 algorithmic cost, meaning there an N**2 steps for solution.  Where N is appoximately
(Number of Patterns + Number of Paths)/2. I optimized the solution by making sure all the patterns are unique
and immediately exclude patterns that cannot match because the of matching terms do not match.