Saravanan666/pypi-org huggingface dataset:
Package: pydatamail-ml
Manage your emails with PythonThepydatamail_mlpython module extends the functionality ofpydatamailby adding support for machine learning. This
includes but is not limited to text preprocessing utilities as well as basic machine learning models based on decision
trees.InstallationInstall thepydatamail_mlpackage usingpip:pip install pydatamail_mlOr alternatively usingconda:conda install -c conda-forge pydatamail_mlPython interfaceImport thepydatamail_mlmodulefrom pydatamail_ml import one_hot_encoding, get_machine_learning_database, gather_data_for_machine_learning, train_model, get_machine_learning_recommendations, text_pipeline, detect_languageThe individual components are briefly explained below:one_hot_encoding()- Encoding the email headers fields (to,from,cc) in binary descriptors.get_machine_learning_database()- Get a database to store machine learning models in.get_gather_data_for_machine_learning()- Clean up encoded data by separating input and output.train_model()- Train a randomforest machine learning model.get_machine_learning_recommendations()- Get recommendations from the machine learning model.text_pipeline()- Convert text from the email to machine readable text by removing HTML design elements.detect_language()- Detect the language of the email.
Package: pydata-master
I. INTRODUCTIONpydata_masteris an all-in-one package for daily data operation that contains the most frequently used functions I need.II. REFERENCES2.1. How to use this package?Using pip to install the pre-built package on Pypippip install pydata_masterIf you want to use the latest pydata_master version instead of the stable one, you can install it from the source with the following command:pip install git+https://github.com/thinh-vu/pydata_master.git@mainImport the whole package to your project:from pydata_master import *Import a specific part of the package:from pydata_master.util import *2.2. Install dependenciesIf you install the package from the Github source, you must install dependencies separately. You can run this command to install all of them at once.!pip install trafilatura
!pip install df2gspread
!pip install pytube
!pip install nltk
!pip install trafilatura
!pip install google-auth-httplib2Google servicescreds, gc = google_auth('YOUR_PATH_TO_JSON')III. APENDICESIV. 🙋‍♂️ CONTACT INFORMATIONIf you want to support my open-source projects, you can "buy me a coffee" viaPatreonor Momo e-wallet (VN). Your support will help to maintain my blog hosting fee & to develop high-quality content.
Package: pydatamocker
PydatamockerCreate rich mock data for testing, learning and proofs of concepts!AboutPydatamocker can generate tabular data of various data types and distributions using random generation and sampling. It is also possible to create a lookups from one table to another. Sampling is very fast even when generating 1'000'000s of records with ~10 fields. The tables are presented in the form ofpandasDataFrames.The API for writing the tables to files is easy to use. Just specify the file path and the formatting is inferred from the file extension, be it csv, tsv, json or a simple text file.DatasetsSome datasets are included with the package. They can be sampled for fields. Datasets included are:~ 20'000 names of various origins~ 20'000 surnames of various originsGet startedProductionInstall the prod version by runningpython3-mpipinstallpydatamockerTestingInstall the latest testing version by runningpython3-mpipinstall--index-urlhttps://test.pypi.org/simple/pydatamockerCode exampleYou can generate mock tables with# Create data tableacc=MockTable('Accounts')# Create an integer field with binomial(10, 0.4) distributionacc.add_field('YearsOfExperience','integer',distr='binomial',n=10,p=0.4)# Field with real randomized first namesacc.add_field('FirstName','first_name')# Field with real randomized surnamesacc.add_field('LastName','last_name')# Date fieldacc.add_field('DateHired','date',distr='uniform',start='2016-10-25',end='2020-02-10')# Integer fieldacc.add_field('Id','integer',distr='range',start=100000,end=90000000)# Create sampleacc.sample(100_000)# Create another tableaudit=MockTable('Audits')# Add a lookupaudit.add_lookup(acc,['Id'])# Add an enum fieldaudit.add_field('Subject','enum',values=['PPI Access','Administrative Reconfiguration','Phone contact'],weights=[5,1,2])audit.add_field('ContactedAt','datetime',distr='range',start='2016-10-25',end='2019-03-15')audit.sample(1_000_000)# Get pandas dataframeacc_df=acc.get_dataframe()audit_df=audit.get_dataframe()# Dump into csv (other formats also supported)acc.dump('accounts.csv')audit.dump('audits.csv')
Package: pydatamodel
pydatamodelpydatamodel程序包，包括以下功能：（1）开发信用评分卡，（2）机器学习模型，（3）数据库建模，（4）数据分析而专门撰写的程序包。安装包pipinstallpydatamodel使用包frompydatamodelimportcreditScoreascsfrompydatamodelimportmechineLearningasmclfrompydatamodelimportdatabaseModelasdmfrompydatamodelimportdataAnalysisasda更多信息creditScore:信用评分卡模块
mechineLearning:机器学习模型模块
databaseModel:数据库建模模块
dataAnalysis:数据分析模块
Package: pyDataMover
Move Data fast, perhaps even at warp speed.pyDataMover is a wrapper and server around the DataMover library, which is a hevaly modified fork of the Facebook WDT library.The DataMover library is an embeddedable library aiming to
transfer data as fast as possible.The pyDataMover module aims to provide the following…Retain all DataMover performance, minimal Python -> C++ or C++ -> Python calls..Low level access to functions and Class’.High level helper classes with reasonable default options, with the ability to override all options.Optional progress callback.Optional logging callback so python handles all logging (Currently in progress)InstallationComming soonDocumentationLicensepyDataMover is provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.Testing`python import python_example python_example.add(1, 2) `
Package: pydatanet
pydatanet, a lightweight easy-to-use yet intuitive networking library, built on top of plain-vanilla sockets.PyDataNet does most of the tedious hard labour for you and removes the pain out of socket programming. It supports TCP and UDP (UDP WIP).It features:Object serialization into bytes and de-serialization into the literal object through pickle so you don't have to worry about converting your objects into sendable bytes.An easy to understand client/server concept.Even a beginner can start learning it and get a small hang of how sockets work, hence, great if you don't want to directly dip your toes into sockets; but I would still recommend learning sockets as it can be crucial whilst debugging errors.Some boilerplateBASIC SERVER:importpydatanetfrompydatanet.serverimportServer,ServerTypedefonPacketSend(sender,data):print(f"{sender.getIp()}sent me this!:{data}")myServer=Server('localhost',5050,ServerType.TCP)myServer.on_recv_bind(onPacketSend)myServer.connect()BASIC CLIENT:importpydatanetfrompydatanet.clientimportClient,ClientType# do not mistake for pydatanet.server.client!defmyFuncOnReceiveTCP(data_decoded):print(f"I got some data:{data_decoded}")classmyFancyClass():def__init__(self,doLikePizza:bool=False):self.doLikePizza=doLikePizzac=Client(ClientType.TCP)c.hook_tcp_recv(myFuncOnReceiveTCP)c.connect("localhost",5050)c.send({"foo":"bar","math":55,"myNumbers":[0,1,2,3,4,5],"myClass":myFancyClass(True)})and the best part, your data will reach the other side just in the same shape! Now, isn't that convenient?EXTRA FEATURESIncredibly foolproof; sockets created auto-close upon the program stopping so you don't shoot yourself in the foot. Even if THAT fails, it will auto-reuse the socket.You can supply the extra argumentautoPollto False and hook upClient._connectto your task managing system if you want to (if you want to control the frequency of heartbeats/polls).CLOSING NOTESPlease report vulnerabilites, bugs, suggestions and performance issues in the issues tab. That would be greatly appreciated.This isNOTa full time project; this is just something I made because PyEnet was extremely imperformant and other libraries did not satisfy my needs. I will not dedicate my entire time to this.Check out TUO; a game I am working on, that was the drive to make this library. (https://discord.gg/2ZycjAA)I do not appreciate any illegal tomfoolery, better known asfeloniesto be committed using my library. I am not responsible for any damage conducted using this library.I swear I shall not add any protestware or malware. If you see someone adding such things, my account will have been likely hijacked. I do not like mixing politics into programming. (cough cough, @RIAEvangelist)
Package: pydatapipes
Python data pipelinesFeaturesThis package implements the basics for building pipelines similar to magrittr in R. Pipelines are
created using>>. Internally it usessingledispatchto provide a way for a unified API
for different kinds of inputs (SQL databases, HDF, simple dicts, …).Basic example what can be build with this package:>>>frommy_libraryimportappend_col>>>importpandasaspd>>>pd.DataFrame({"a":[1,2,3]})>>append_col(x=3)aX013123233In the future, this package might also implement the verbs from the R packagesdplyrandtidyrforpandas.DataFrameand  or I will fold this into one of the other available
implementation ofdplyrstyle pipelines/verbs for pandas.DocumentationThe documentaiton can be found onReadTheDocs:https://pydatapipes.readthedocs.ioLicenseFree software: MIT licenseCreditsmagrittrand it’s usage indplyr/tidyrfor the idea of using pipelines in that wayslots of python implementations of dplyr style pipelines:dplython,pandas_ply,dfplyThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2016-10-22)First release on PyPI.
Package: pydataportability.model.resource
IntroductionThe pydataportability.model.resource package defines models for resources.The model entities are modelled after an XRD document, where a resource
contains certain information and a list of links to other resources which have
certain properties themselves.Check out the documentation athttp://pydataportability.netfor more information.ContributorsChristian Scholz, COM.lounge GmbH,cs@comlounge.net,http://mrtopf.de/blogLicenseThis project is released under a BSD license:Copyright (c) 2009, Christian Scholz
All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following
disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided with the distribution.Neither the name of the author nor the names of its contributors may be used to endorse or promote products derived from
this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Changelog0.4 - 12 Oct 2009Initial release
Package: pydataportability.xrd
IntroductionThis is an XRD parser for pydataportability. For more information
check outhttp://pydataportability.netFor an example you might look at pydataportability.discovery which you can also
find on PyPI.ContributorsChristian Scholz, COM.lounge GmbH,cs@comlounge.net,http://mrtopf.de/blogLicenseThis project is released under a BSD license:Copyright (c) 2009, Christian Scholz
All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following
disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided with the distribution.Neither the name of the author nor the names of its contributors may be used to endorse or promote products derived from
this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Changelog0.4 - 12 Oct 2009Initial release
Package: pydataportability.xrds
pydataportability.xrdspydataportability.xrdsis a parser forXRDS-SimpleFor more information about pydataportability check outthis blog postInstallationInstall using setuptools, e.g. (within a virtualenv):$ easy_install pydataportability.xrdsSource Code and reporting bugsVisithttp://code.google.com/p/pydataportability/0.2 (2009-09-08)DocumentationCHANGES.txtandREADME.txthave been addedFeaturesSome basic tests using py.test have been addedBugfixesupper- and lowercase namespaces are not recognized, see issue 3:issue 3; thanks to scherpenisse for the patchservice priorities and URI priorities have been mixed previously resulting in the service priority always being
the last URI priority to be found, this is fixed now.ElementTree import is made more stable to check for alternative locations (seeissue 4)Backwards Incompatibilitieslocalidcan occur multiple times with different priorities. We just checked for a single element without
a priority. A newLocalIDclass which holds the id and the priority has been added and will be returned aslocalidsattribute on theServiceclass.priorities will now be always converted to integers or toNonein case the value isnull.theprioattribute of theURIclass has been renamed toprioritythe default value forpriorityparameters in constructors has been changed from0toNone.0.1 (2008-04-19)initial release
Package: pydataproject
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: pydatascraper
pydatascraperpydatascraper is a Python application that provides web scraping capabilities, including fetching Google and Yelp reviews. The application has a user-friendly graphical user interface (GUI) for easy interaction.FeaturesWeb Scraping:Extract information from web pages based on user-provided URLs.Google Reviews:Fetch reviews for a given business or location using Google Maps API.Yelp Reviews:Retrieve reviews for a business using the Yelp API.OpenStreetMap Data:Extract latitude, longitude, and additional information from OpenStreetMap.RequirementsPython 3.xRequired Python packages (install usingpip install -r requirements.txt):requestsbeautifulsoup4pandasopenpyxlnltk(for text processing)tkinter(GUI toolkit)UsageClone the repository:gitclonehttps://github.com/arjunlimat/pydatascraper.gitInstall the package directly:pipinstallpydatascraperimport the webscraper model:frompydatascraper.modelsimportmainRun the application:main()The GUI will appear, allowing you to choose different services and perform web scraping tasks.Services
Web ScrapingEnter a URL and click "Search" to explore available data types.Choose the desired data type, enter a file name, and click "Download" to save the data.Google Reviews
Select "Google reviews" from the services dropdown.Enter the business or location name and address.
Provide a file name and click "Download" to fetch and save Google reviews.Yelp Reviews
Select "Yelp reviews" from the services dropdown.
Enter the business name and address.
Provide a file name and click "Download" to fetch and save Yelp reviews.OpenStreetMap
Select "Open Street Map" from the services dropdown.
Enter the map URL, provide a file name, and click "Download" to extract map data.ContributingContributions are welcome! If you encounter issues or have ideas for improvement, please open an issue or submit a pull request.License:This project is licensed under the MIT License - see the LICENSE file for details.
Package: pydatasentry
The combination of datasets, questions, and nature of analysis is
growing everyday. Data scientists find it hard to keep track of all
the different datasets they dealt with, what they did with those
datasets, and what they presented to the model-audience (business etc)pydatasentry package allows auditability of modeling code and data by
logging all relevant information for every single model run (e.g., a
regression) You could use this for audit past results for correctness,
share models and results with peers, search past results to avoid
repition of work.Note that code is very alpha. Expect it to break often. Please try it
out and give me feedback/create issues.Please seedocsfor detailed documentation.
Package: pydata-sphinx-theme
The PyData Sphinx ThemeA clean, three-column, Bootstrap-based Sphinx theme by and for thePyData community.:books: Documentation:https://pydata-sphinx-theme.readthedocs.io/en/stable:bulb: Examples:https://pydata-sphinx-theme.readthedocs.io/en/stable/examples:raised_hands: Contribute:https://pydata-sphinx-theme.readthedocs.io/en/stable/communityInstallation and usageThe theme is available on PyPI and conda-forge. You can install
and use as follows:Install thepydata-sphinx-themein your doc build environment:pipinstallpydata-sphinx-theme# orcondainstallpydata-sphinx-theme--channelconda-forgeThen, in theconf.pyof your sphinx docs, you update thehtml_themeconfiguration option:html_theme="pydata_sphinx_theme"And that's it!NoteThis theme may not work with the latest major versions of Sphinx, especially
if they have only recently been released. Please give us a few months of
time to work out any bugs and changes when new releases are made.
Seeour contributing documentationfor more information.Contribute to and develop the themeContributions are very welcome! Installing the development version, building
the example docs and developing theCSS/JSof the theme, etc., is explained in
more detail in the contributing section of the documentation:Community and contributing documentation
Package: pyDataStore
pyDataStorePersistent and portable serialized data store.A Pythondictobjectthat has beendump-ed can beload-ed or retrieved in the future.To secure thedata, it is using thebase64package when encoding it inBase64, which is the default encoding, andpycryptodomepackage when encoding it inAES. Then,pickleto serialize thedictobject.RequirementsThis requiresPython>=3.5,<3.10.Python Packagespycryptodome>= 3.10.1InstallationInstall the package through PyPI:pipinstallpyDataStoreUsageAs this will be usingPython 3, we have to set theshebangto the appropriate executable.#!/usr/bin/env python3To usepyDataStore, we have to import it first.frompyDataStoreimportCipher,DataStoreThen, we can use this sampledictobject.# Sample dict object.objDict={"object":"test_datastore","data":{"user":"dummy-user","pass":"123qwe456asd789zxc"}}There is arequired formatfor thedictobject.{"object":"OBJECT_NAME","data":{"DATA_KEY":"DATA_VALUE","DATA_KEY":"DATA_VALUE",...}}Otherwise, this will throw anException.Exception: This dict object has an invalid format.Base64 EncodingUsing the default data store file.print(f"Raw:{objDict}")# Create an instance that will store the data in a file named data.store (default).# Default encoding is set to Base64.ds64=pyDataStore()# This is the same as the above.# ds64 = pyDataStore(fileDataStore="data.store", cipher=Cipher.Base64)# Dump or store the dictionary object.ds64.dump(objDict)# At this point, the objDict dict object is already encoded.# Based64 Encoded.print(f"Base64:{objDict}")# Retrieve the stored encoded dict object.data64=ds64.load("test_datastore")print(f"Decoded:{data64}")AES EncodingUsing a custom data store file.print(f"Raw:{objDict}")# Create an instance that will store the data in a file named aes.store.# Encoding is set to AES.dsAES=pyDataStore(fileDataStore="aes.store",cipher=Cipher.AES,aesKey="SPECIFY_AN_AES_KEY_HERE")# Dump or store the dictionary object.dsAES.dump(objDict)# At this point, the objDict dict object is already encoded.# AES Encoded.print(f"AES:{objDict}")# Retrieve the stored encoded dict object.dataAES=dsAES.load("test_datastore")print(f"Decoded:{dataAES}")ContributeCommunity contributions are encouraged! Feel free to report bugs and feature requests to theissue trackerprovided byGitHub.LicensepyDataStoreis an Open-Source Software(OSS)and is available for use under theGNU GPL v3license.
Package: py-data-structure
Py Data StructurePy data stucture makes it easier to use data structures with python. While you can perform these operations in different ways, py data structure makes it much easier to use many data structures which we can use in C++ but not in Python. You can use Stacks, and Queues in this module, however we will be adding more data structures like heaps.Pypi Page:https://pypi.org/project/py-data-structure/Github Page:https://github.com/tinyCodersDen/py-data-structureSee documentation below for the functions and how it works.Installation:pip install py-data-structureStacks:from py_data_structure import StackCreate a stack:stack = Stack()Push an element into a stack:stack.push(element)Pop/remove the top element from stack:stack.pop()Get the top element:print(stack.top())Get bottom element:print(stack.bottom())Get size of stack:print(stack.size())Check if stack is empty:print(stack.isEmpty())Get a list of stack values:print(stack.get_stack())Queues:from py_data_structure import QueueCreate a queue:queue = Queue()Enqueue to a queue:queue.enqueue(element)Dequeue to a queue:queue.dequeue()Get the front element of a queue:print(queue.front())Get the back element of a queue:print(queue.back())Check if a queue is empty:print(queue.isEmpty())Get the size of a queue:print(queue.size())Get the whole queue in a list format:print(queue.get_queue())