Saravanan666/pypi-org huggingface dataset:
Package: db0905
BriefA simple xml parse and build library.Installationpipinstalldb0905Examplesxml to python object>>>importdb0905
>>>xml='<demo><foo>foo</foo><bar>bar</bar></demo>'>>>db0905.loads(xml){'bar':'bar','foo':'foo'}>>>db0905.loads(xml,strip_root=False){'demo':{'bar':'bar','foo':'foo'}}>>>xml='<demo><foo>foo</foo><bar>1</bar><bar>2</bar></demo>'>>>db0905.loads(xml){'bar':['1','2'],'foo':'foo'}python object to xml>>>data={'demo':{'bar':['1','2'],'foo':'<foo>'}}>>>db0905.dumps(data)'<?xml version="1.0" encoding="utf-8"?><demo><foo><![CDATA[<foo>]]></foo><bar><![CDATA[1]]></bar><bar><![CDATA[2]]></bar></demo>'>>>db0905.dumps(data,cdata=False)'<?xml version="1.0" encoding="utf-8"?><demo><foo>&lt;foo&gt;</foo><bar>1</bar><bar>2</bar></demo>'>>>printdb0905.dumps(data,indent=' '*4)<?xmlversion="1.0"encoding="utf-8"?>
<demo><foo><![CDATA[<foo>]]></foo><bar><![CDATA[1]]></bar><bar><![CDATA[2]]></bar>
</demo>DocumentationSeedocs in readthedocsChangelogSeechangelog
Package: db1
DB1Store one item in the cloud with one line. No login. Share, visualize and embed live view in Notion.Getting startedDB1 is like a global python dict. Upload anything, access anywhere. For Javascript client, CLI and more examples seedocumentation.1. Installpipinstalldb12. Upload and retrieve dataChoose a key and replace 'example_key' with your own.from db1 import DB1
import numpy as np

# upload anything
DB1['example_key'] = {
    "message" : "Hey, I'm using DB1! 🤙",
    "temperature" : 21.2421,
    "numbers" : [1, 2, 3, "four"],
    "matrix" : np.eye(5)
}

# retrieve anywhere
my_value = DB1['example_key']3. View your item in the browserGo tohttps://db1.io/and enter your key in the text field.4. Embed item in NotionTo embed the item above in your Notion page, simply paste the URLhttps://db1.io/?key=example_keyin any page and click “Create embed”.Notionexamples.
Package: db1-test-package
db1-pyDB1 is a simple to use cloud key-value database for storing, sharing and visualizing data items. All items are stored in the same global address space making them easily shareable with co-workers  and friends.Getting Started1. Install the python clientpipinstallgit+https://github.com/db1-io/db1-py.git2. Store an item to the cloudimportdb1item=db1.Item("my_key")item.val={"test_list":[1,2,3,"four"],"test_array":np.ones((3,3)),}3. View your item in the browser on any deviceClick the link:https://db1.io/?key=my_key. Or go tohttps://db1.ioand enter your key in the search bar.4. Share your item or retrieve it at a later timeimportdb1item=db1.Item("my_key")print(item.val)CommunityJoin ourdiscord serverto discuss new features and help shape the future of DB1! You can also drop us an email athello@db1.io.FAQWhat data types are supported?The currently supported data types are the built-instr,int,float,bytes,bool,dict,listas well asnumpy arrayandpandas dataframe.
Package: db2pq
Library to convert PostgreSQL data to parquet filesThis package was created to convert PostgreSQL data to parquet format.
This package has three major functions, one for each of three popular data formats.wrds_pg_to_pq(): Exports a WRDS PostgreSQL table to a parquet file.db_to_pq(): Exports a PostgreSQL table to a parquet file.db_schema_to_pq(): Exports a PostgreSQL schema to parquet files.Report bugsAuthor: Ian Gow,iandgow@gmail.com
Package: db2py
Usage:>>> import db2py
    >>> db = db2py.createdb("mydatabase", {"user1": "password1"})
    >>> db = updatedb(db, {"user2": "PWD2"})
    >>> meth = getmeth()
    >>> for username, pwd in getdb(db):
    ...    db = updatedb(db, {username: hashpwd(pwd, meth)})
    >>> savedb(*getdb(db), "database1.db")
    >>> db = opendb("database1.db")
    >>> deldb("database1.db")
Package: db2qthelp
db2qthelp — a DocBook book to QtHelp project converterIntroductiondb2qthelpconverts a DocBook book to a QtHelp project.db2qthelpis in an early stage of "development". It works well for me but it may work with my setup only.Any feedback is appreciated.BackgroundI usually write my user documentation using DocBook. For my recent applications built on top of Qt, I needed something
that generates in-app help pages.db2qthelpdoes this.Download and InstallationThecurrent versionisdb2qthelp-0.2.You mayinstall db2qthelpusingpython -m pip install db2qthelpYou maydownload a copy or fork the codeatdb2qthelp's github page.Besides, you maydownload the current releasehere:db2qthelp-0.2.zipdb2qthelp-0.2.tar.gzLicensedb2qthelpis licensed under theBSD license.DocumentationUsagedb2qthelpis implemented inPython. It is started on the command line.db2qthelpparses a single-file HTML representation of a DocBook book. If you have a DocBook book you have
to convert it to a single-file HTML document. The images you use within the book should be located in folders.As soon as you have converted your DocBook book into a single-file HTML page, you may rundb2qthelpto
convert it into a QtHelp project.The optionOptionsThe script has the following options:--input/-i<PATH>: the file or the folder to process--help: Prints the help screenExamplesdb2qthelp -i my_page.html -a quotes.germanReplaces !!!db2qthelp -i my_folder -r --no-backupApplies !!!Further DocumentationThe PyPI page is located at:https://pypi.org/project/db2qthelp/The github repository is located at:https://github.com/dkrajzew/db2qthelpThe issue tracker is located at:https://github.com/dkrajzew/db2qthelp/issuesExamples / UsersPaletteWB— a sophisticated palette editor for MS Windows.Change LogVersion 0.2Initial checkinAdding configuration optionsSummaryWell, have fun. If you have any comments / ideas / issues, please submit them todb2qthelp's issue trackeron github.
Package: db2xlsx
db-to-xlsxДанный скрипт позволяет конвертировать файлы *.db в файлы *.xlsxКоманда скрипта:
python converter.py -i db.db -o result.xlsx -t table_name
-i или --input файл базы данных-o или --output выходной xlsx файл-t или --table название таблицы для сбора данных
Package: db4me
db4meBasic sqlalchemy helpersInstallationpython-mpipinstalldb4meUsageInclude the database settings into your model and, at application
start-up time, create the connection based on these settings.frompydantic_settingsimportBaseSettingsfromdb4meimportAllDatabaseSettings,get_engineclassAppSettings(BaseSettings):# ...database:AllDatabaseSettings=Field(description="Database settings.",)# ...settings=AppSettings()engine=get_engine(settings.database)DevelopmentStart by creating a virtual environment and installing the dependencies.
If you have amakecommand available, you can runmake initafter
the virtual environment is created and activated. Otherwise, you can run
the following commands:python-mvenvvenvsourcevenv/bin/activate
python-mpipinstall--upgradepip
python-mpipinstall-e.[dev]On Windows, to be able to serve the documentation, you may also need to
install thecairo2package:pipinstallpipwin
pipwininstallcairocffi
Package: db4sphinx
This project provides a simple Docbook converter for use with docutils and
sphinx.  When used with Sphinx, it can read docbook assembly files to produce
toctrees automatically, or it can be used to incorporate individual docbook
files in documentation (together with rST toctree).Assembly handling is designed to work with thetopic-maker-chunk.xslstyle
from the Docbook distribution:xsltproc --xinclude \
   --stringparam chunk.section.depth 1  \
   --stringparam assembly.filename index.xml \
   --stringparam base.dir topics/ \
   /usr/share/sgml/docbook/xsl-ns-stylesheets-1.79.1/assembly/topic-maker-chunk.xsl -
Package: dbaas-base-provider
[![Build Status](https://www.travis-ci.com/bento-dbaas/dbaas-base-provider.svg?branch=master)](https://www.travis-ci.com/bento-dbaas/dbaas-base-provider) [![codecov](https://codecov.io/gh/bento-dbaas/dbaas-base-provider/branch/master/graph/badge.svg?token=QNH0Q4UVGQ)](https://codecov.io/gh/bento-dbaas/dbaas-base-provider)
Package: dbaas_cloudstack
A cloudstack adapter to DBaaSDocumentationThe full documentation is athttps://dbaas-cloudstack.readthedocs.org.QuickstartInstall dbaas-cloudstack:pip install dbaas-cloudstackThen use it in a project:import dbaas-cloudstackFeaturesTODOHistory0.0.1 (2014-01-01)First release on PyPI.
Package: dbaas_networkapi
DBaaS NetworkApiDBaaS NetworkApi is a simple network api wrapper for DBaaSFree software: MIT licenseDocumentation:https://dbaas-networkapi.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2016-06-24)First release on PyPI.
Package: db-access
Easy Database Access through SqlAlchemyPurpose of this simplify access process to different types of databases through SQLAlchemyInstallationCopy the repo and run apip install db-accessin the root directory of the repo.You may also need to install other applications on your computer for these connections to work (i.e. drivers). You can find the links to each of the driver installations below.Databases SupportSo far the package supports the following databases:OracleMS SQLMySQLImpalaFeel free to let me know if there is a need for additional database coverageUseImport the corresponding class for the database type you are connecting to and add the required login info. From the object, you can create a SQLAlchemy engine and get that engine. You can also get specific sqlalchemy.Table objects from that connection.
Package: db-accounting
django_dbaccountingA Python based accounting core built with Django==============
DB AccountingDB Accounting is a Django app that provides a core for accounting and account management.
Although developed primarily for accounting purposes, the flexible nature of DB Accounting,
allows it to be configured for anything that represents the need for categories, accounts, and transactions.Detailed documentation is in the "docs" directory.Quick startIf you are only looking to install the python package, use:pip install db-accounting (under maintenance, resolving with Python Packaging Authority)download the .tar file from Githubinstall it from DjangoPackages.org (pending PyPi issue mentioned above)To configure:Add "dbaccounting" to your INSTALLED_APPS setting like this::INSTALLED_APPS  [
...
'dbaccounting',
]Include the dbaccounting URLconf in your project urls.py like this::path('fin/',include('dbaccounting.urls')),Runpython manage.py migrateto create the dbaccounting models.Start the development server and visithttp://127.0.0.1:8000/adminto create Account Types and Accounts (you'll need the Admin app enabled).Visithttp://127.0.0.1:8000/fin/to manage your accounts.NotesThis is the first version so far, and some of the side links are not properly configured yet
(e.g. Balance Sheet rendering, etc). The application thus far provides core functionality for
transactions in general , which will be extended in future versions.Special thanks to those using this package and the feedback given. What started out as a side project
to help a few startups has now increased to include the open-source community. The package is coupled with
over 100 tests just to test the core functionality itself, and I have done my best to optimize for performance
and memory.If you liked this package, leave a star, and feel free to join my mailing list on robmel.com, where I occasionally
send out insights and goodies from tech, finance, AI and link them to the individual level.Best,
Robert M.Reach me on instagram: @officialrobmel or @quantrobbie
Package: dbadmin
Disclaimer: This was written as part of the wnframework, while working there as an employee.http://code.google.com/p/wnframework/Just a basic python wrapper for most of the Mysql Admin commands. Got tired of having to exit python and going to mysql shell for the same. Been using it in production on mysql Ver 14.12 Distrib 5.0.77, for some time(occasionally tried with percona too).though no proper test cases/testing has been done….TO INSTALL :
Just clone the repo, cd to the directory and run sudo python setup.py install.Or just use sudo easy_install DbAdminUsage:
from DbAdmin.DbAdmin import DbAdministrator
DA = DbAdministrator()
Package: dbam
Aboutdbam(short for DataBamboo) is the command line tool for services provided by DataBamboo.com.InstallationYou can installdbamusingpip:pip install dbamdbamis supported on Python 3.6 and above.How to userundbam initto setup your local environment with API token from DataBamboo.com.Testpipinstallpytest
python-mpytestPackaging and Uploadpython setup.py sdist
twine upload dist/*
Package: dbami
dbami: The database friend you didn't know you neededA lightweight, Python-based, framework-agnostic, async-compatible PostgreSQL
migration tool.What doesdbamimean?dbamiwas initially inspired by the tooldbmate, and therefore takes inspiration
fromdbmatewhen it comes to name. That is, "ami" is French for "friend," a
synonym of "mate."Other languages may have yielded suitable words for friend, but "ami" was
chosen due to its short length (easier to type) and the allowance for a future
golang-based implementation, which, most serendipitously, could be nameddbamigo.
Package: dbapi
dbapi豆瓣命令行客户端安装pip3installdbapi使用dbapi--help
Usage:dbapi<api>[options...]dbapi<module><api>[options...]AvailableAPI:-ck-expire-flush-json<url><method><params><data>-load-login<username><password>-logout-persist-req<url><method><params><data><auth>-toJSON-to_xml<content><kwargs>-use<cookies>-xml<url><method><params><data>-group-groupadd_comment<topic_id><content><reply_id>-groupadd_group<kwargs>-groupadd_topic<group_alias><title><content>-groupget_topic<topic_id>-groupjoin_group<group_alias><message>-groupleave_group<group_alias>-grouplike_topic<topic_id>-grouplist_commented_topics<start>-grouplist_comments<topic_id><start>-grouplist_joined_groups<user_alias>-grouplist_joined_topics<start>-grouplist_liked_topics<user_alias><start>-grouplist_reced_topics<user_alias><start>-grouplist_topics<group_alias><_type><start>-grouplist_user_comments<topic_id><user_alias>-grouplist_user_topics<start>-grouprec_topic<topic_id>-groupremove_comment<topic_id><comment_id><reason><other>-groupremove_commented_topic<topic_id>-groupremove_group<group_id>-groupremove_topic<topic_id>-groupsearch_groups<keyword><start>-groupsearch_topics<keyword><sort><start>-grouptoJSON-groupundo_like_topic<topic_id>-groupundo_rec_topic<rec_id>-groupupdate_topic<topic_id><title><content>-people-peopleadd_album<kwargs>-peopleadd_album_comment<kwargs>-peopleadd_photo<kwargs>-peopleadd_photo_comment<photo_id><content>-peopleadd_status<kwargs>-peopleget_album<album_id>-peopleget_people<user_alias>-peopleget_photo<photo_id>-peoplelike_photo<photo_id>-peoplelike_status<status_id>-peoplelist_albums<user_alias>-peoplelist_contacts<user_alias><start>-peoplelist_photo_comments<photo_id><start>-peoplelist_photo_likes<photo_id><start>-peoplelist_photo_recs<photo_id><start>-peoplelist_photos<album_id>-peoplelist_rev_contacts<user_alias><start>-peoplelist_status_comments<user_alias><start>-peoplelist_statuses<user_alias><start>-peoplerec_photo<photo_id>-peopleremove_album<album_id>-peopleremove_album_comment<kwargs>-peopleremove_photo<photo_id>-peopleremove_photo_comment<comment_id>-peopleremove_status<status_id>-peopletoJSON-peopleundo_like_photo<photo_id>-peopleundo_like_status<status_id>-peopleundo_rec_photo<photo_id>-peopleupdate_album<kwargs>LicenseMIT
Package: dbapi2abc
dbapi2abcThis package provides abstract classes to present an interface toPEP249compliant database Connection and Cursor objects.This allows you to use type hints on functions handling database
connections and cursors without needing to specify the specific
backend database engine being used.from dbapi2abc import Connection, Cursor

class MyObject():
    def __init__(self, db: Connection):
        self.db = db

    def run_some_query(self) -> Cursor:
        cur = self.db.cursor()
        cur.execute("SELECT * FROM Table")
        return cur
Package: dbapi-compliance
A unit test suite to help confirm that Python database drivers conform
to the PythonDB-APIspecification v2.0.Licensedbapi-compliance is released in the public domain.ChangelogNext version - unreleased1.15.0 - 2015-07-04Apply fix to no-result statement check in test_rowcount being too
strict.Fix parameter substitution being mixed.Fix for non idempotent close.Fix psycopg2 test not being Python 3 compatible.Make it possible to customize insert statements (thanks
@lalinsky).Support databases that require primary keys being non null (thanks
@lalinsky).Build on travis.Provide PyPI package (thanks @lalinsky for the setup.py fixes).Use bumpversion.1.14.3 - 2013-06-05 and earlierInitial implementation by Stuart Bishop.Python 3 compatibility by Vernon Cole.
Package: dbarray
Array stored in database========================`DBArray` is an 2D array stored in database.The purpose of this class is to provide a way to store and access largearray which can not be loaded into memory.## Example```pythonimport numpy as npfrom dbarray import DBArray# Create random arraynrows = 5ncols = 8dtype = np.float32arr = np.random.random((nrows, ncols))arr = np.require(arr, dtype)print arr""" `DBArray` from scratch"""# Create `DBArray`dba1 = DBArray('test1.db')# Initializedba1.set_shape((nrows, ncols))dba1.set_dtype(dtype)# Set rowsfor i in range(nrows):dba1[i] = arr[i]# Convert to ndarrayprint dba1.tondarray()""" `DBArray` from `ndarray`"""# Create `DBArray`dba2 = DBArray.fromndarray(arr, 'test2.db')# Check attributesprint dba2.nrows, dba2.ncols, dba2.dtype# Convert to ndarrayprint dba2.tondarray()```
Package: dbase32
The Dbase32 encoding is a base32 variant designed for document-oriented
databases, specifically for encoding document IDs.It uses an alphabet whose symbols are in ASCII/UTF-8 sorted order:3456789ABCDEFGHIJKLMNOPQRSTUVWXYThis means that unlikeRFC-3548 Base32encoding, the sort-order of the
encoded data will match the sort-order of the binary data.Thedbase32 packageprovides a high-performanceC implementationof the
encoding, plus a pure-Python fallback.Dbase32 is licensedLGPLv3+, requiresPython 3.4or newer, and fully
supportsPython 3.5.ExamplesEncoding and decoding:>>> from dbase32 import db32enc, db32dec
>>> db32enc(b'binary foo')
'FCNPVRELI7J9FUUI'
>>> db32dec('FCNPVRELI7J9FUUI')
b'binary foo'Validation:>>> from dbase32 import isdb32, check_db32
>>> isdb32('../very/naughty/')
False
>>> check_db32('../very/naughty/')
Traceback (most recent call last):
  ...
ValueError: invalid Dbase32: '../very/naughty/'Generating 120-bit Dbase32-encoded random IDs:>>> from dbase32 import random_id
>>> random_id()
'FOLQCPOFDVADYMVGQDI49Y7F'
>>> random_id()
'IFL63RNSQ9NN5ANMUQD6BQSJ'Dbase32 resourcesDocumentationReport a bugBrowse the sourceLaunchpad projectA Novacut componentDbase32 is being developed as part of theNovacutproject. Packages are
available forUbuntuin theNovacut Stable Releases PPAand theNovacut Daily Builds PPA.If you have questions or need help getting started with Dbase32, please stop
by the#novacutIRC channel on freenode.
Package: dbastable
DBasTableA simplier way to access SQLite tables, just like Numpy structured arrarys or Pandas dataframes.>>> from dbastable import SQLDatabase
>>> db = SQLDatabase('test.db', autocommit=True)
>>> db.add_table('table1')
>>> db['table1']['col1'] = [1, 2, 3, 4]
>>> db['table1']['col2'] = ['a', 'b', 'c', 'd']
>>> print(db['table1'][2].values)
(3, 'c')Its design is based mainly in the way you interact withNumpy structured arrays,Pandas dataframesandAstropy Tables, while keep the possibility of non-volatile and in-disk data storage.What is it for?The design of this package was made forvery simple interactionswith Python's sqlite implementation, so you can interact with sqlite databases without know SQL commands. So, if you need to work with simple tables, composed by columns of data in standard python formats and don't want to perform SQL queries manually, may be this package is for you.What is itNOTfor?As menioned above, we intended to perform only simple operations with this package. So,we intentionally limited the functionality. Do not expect perform complex queries here. This package is for simplicity. There are several alternatives that are more feature-complete.Do not use in large productionI'm not a SQL master, nor a digital security guru. I'm an astrophysicist that do some python. So, if you want to use it, use with care.Install and DocumentationInstallationThe easiest way to install dbastable is viapippip install dbastableAlternatively, you can clone the repository and install it manually:git clone
cd dbastable
pip install -U .orpip install -U git+https://github.com/juliotux/dbastableDevelopment version is also available in pip:pip install -U --pre dbastableDocumentationThe documentation is available athttps://dbastable.readthedocs.io/en/latest/Licensedbastableis licensed under the terms of theMIT license. See the fileLICENSEfor information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.
Package: db-az-sync-provider
databricks-azure-ad-sync-providerPrerequisites: AuthenticationThere are multiple options to authenticate to Microsoft Entra ID and Azure Databricks, we used the following two and you could choose in between.Option 1: If you have Azure CLI installedRun 'az login' on your machine to authenticate yourself as an Azure user.Setup environment variables for DATABRICKS_HOST and DATABRICKS_ACCOUNT_ID, or create a .databrickscfg file (~ for Linux or macOS, and %USERPROFILE% for Windows) containing the following info:[DEFAULT]
host = https://accounts.azuredatabricks.net/
account_id = <Databricks account id>Option 2: Use Microsoft Entra ID service principal:Create a service principal in Microsoft Entra ID and add it to Azure Databricks and grant it target permissions (seereference documentation)Add the following environment varialbes:For Azure: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET (seeMicrosoft Entra ID authentication)For Databricks: DATABRICKS_HOST, DATABRICKS_ACCOUNT_ID, DATABRICKS_CLIENT_ID, DATABRICKS_CLIENT_SECRET (seeDatabricks authentication)Alternatively, you can setup databricks environment varialbes in a .databrickscfg file (~ for Linux or macOS, and %USERPROFILE% for Windows) containing the following info:[DEFAULT]
host = https://accounts.azuredatabricks.net/
account_id = <Databricks account id>
azure_tenant_id = <Azure tenant id>
azure_client_id = <Azure service principal application ID>
azure_client_secret = <Azure service principal secret>InstallRun the following to install this package:pip install db_az_sync_providerUsageYaml fileTo use the package, it's required to provide:Object ID(s) of Azure groups and (optionally) exclude object ID(s) in a yaml file (see syncgroups.yaml for example).Existing relations between Azure and Databricks objects in a json file (see syncstates.json from example).ExecutionsThe following two arguments are asked when you execute the sync:-f/--file-j/--json-d/--delete (if this option provided, you enable to delete identities in Databricks, it's recommended to not use this option though)
Package: dbb
# Distribution-based Binner (DBB)Cluster scaffolds from a single metagenomic sample into population genomes using a distribution-based binning approach.## InstallThe simplest way to install this package is through pip:
> sudo pip install dbbDBB relies on numpy, scipy, matplotlib, and pysam v0.7.x.## CiteIf you find this package useful, please cite this git repository (https://github.com/dparks1134/dbb)## CopyrightCopyright © 2015 Donovan Parks. See LICENSE for further details.
Package: dbbackup
dbbackup to manage server databases backups:Mysql: this releasePostgreSQL: next releaseOther: futures releases (need some help...)Installation:pip install dbbackupdbbackup-tools is instaled with dbbackup dependenciesRun it:pythonimport dbbackup_tools.utilEnter your folder name to hold dbbackup configurationcd to your new dbbackup folderConfiguration:ajust the file .env to match your local configrationTo logging on smtp, rename and adjust:loggin_config_example.yaml to loggin_config.yamlTo backup Mysql databases, rename and adjust:mysql-backup-script_example.config to mysql-backup-script.configWork with it:pythonimport dbbackupfrom dbbackup import check_dbbackup