Saravanan666/pypi-org huggingface dataset:
Package: tableread
TableRead is a script designed to read reStructredText (reST)simple tablesfrom a file and convert them into Python objects.QuickstartSay you have a simple table like this located in a./example.rst:++++++++++++
Damage Doers
++++++++++++

======  ===  ==============
Name    Age  Favorite Color
======  ===  ==============
Mookie  26   Red
Andrew  24   Red
JD      31   Red
Xander  26   Red
======  ===  ==============Here are a few useful things you can do with that table:>>> from tableread import SimpleRSTReader
>>>
>>> reader = SimpleRSTReader("./example.rst")
>>> reader.tables
['Damage Doers']
>>>
>>> table = reader["Damage Doers"]
>>> table.fields
['name', 'age', 'favorite_color']
>>>
>>> for row in table:
...     print(row.favorite_color)
...
Red
Red
Red
Red
>>>
>>> for row in table.matches_all(age="26"):
...     print(row.name)
...
Mookie
Xander
>>>
>>> for row in table.exclude_by(age="26"):
...     print(row.name)
...
Andrew
JDUsageclass tableread.SimpleRSTReader(file_path)Parse a reStructredText file,file_path, and convert any simple tables intoSimpleRSTTableobjects.
Individual tables can be accessed using the table name as the key (SimpleRSTReader['table_name'])dataAn OrderedDict of the table(s) found in the reST file. The key is either the
section header before the table name from the file, orDefaultfor tables not under a header.
For multiple tables in a section (or multipleDefaulttables),
subsequent tables will have a incrementing number appended to the key:Default,Default_2, etc.
The value is aSimpleRSTTableobject.tablesA list of the table names; an alias forlist(data.keys())firstA helper to get the first table found in the file; an alias forlist(self.data.values())[0]class tableread.SimpleRSTTable(header, rows, column_spans)A representation of an individual table. In addition to the methods below,
you may iterate over the table itself as a shortcut (for entry in table:),
which will yield fromtable.data.len(table)will also return the number of entries intable.data.dataA list of namedtuples withfieldsas the names.fieldsA tuple of the table fields, as used in thedatanamedtuple.
Field names are adapted from table columns by lower-casing,
and replacing spaces and periods with underscores.from_data(data)A helper function to create an object with. Expects a prepared list of namedtuples.matches_all(**kwargs)Given a set of key/value filters, returns a new TableRead object with only
the filtered data, that can be iterated over.
Values may be either a simple value (str, int) or a function that returns a boolean.
SeeQuickstartfor an example.Note: When filtering both keys and values arenotcase sensitive.exclude_by(**kwargs)Given a set of key/value filters, returns a new TableRead object without the
matching data, that can be iterated over.
Values may be either a simple value (str, int) or a function that returns a boolean.
SeeQuickstartfor an example.Note: When filtering both keys and values arenotcase sensitive.get_fields(*fields)Given a list of fields, return a list of only the values associated with those fields.
A single field returns a list of values, multiple fields returns a list of value tuples.
Package: tablereader
# TableReaderTableReader is an unified abstraction for handling xls, xlsx and CSV files in Python. It also reads csv tables as unicode on request.If you are familiar with csv.DictReader from the standard library, think of TableReader as DictReader on steroids.fromtablereaderimportTableReaderreader=TableReader("some_input.xlsx",sheet="The Data")forrowinreader:printrow['valuecolumn']In case you have csv and need unicode support:reader=TableReader("unicode_input.csv",force_type="unicodecsv")forrowinreader:printrow['valuecolumn']If you want to strip out leading and trailing spaces while reading rows, you can:reader=TableReader("input_with_whitespaces.csv",strip_whitespaces=True)forrowinreader:printrow['valuecolumn']And if for some reason you have a file with wrong type by line-ending (commonly found when sharing xls), override auto-detection:reader=TableReader("wrong_named.xls",force_type="xlsx")forrowinreader:printrow['valuecolumn']Sometimes headers are not in the first line. So specify some header row search text and the entire row will be used as column name and all rows after are returned:fromtablereaderimportOffsetTableReaderreader=OffsetTableReader("wrong_named.xls","BEGIN_DATA")forrowinreader:printrow['valuecolumn']The library has been tested on CPython 2.6, 2.7 and 3.4 as well as PyPy 2.4.1.
Package: table-reconstruction
Table Reconstructiontable-reconstructionis a tool used to detect table spaces and reconstruct the information in them using DL models.To provide the above feature, Table reconstruction works based on several components as follows:A table detection model is developed based on Yolov5A line segmentation model is built based on UnetAdditional modules are used in the information extraction process, especially a directed graph is used to extract information related to the merged cells.Before startDue to the requirements of the used libraries, table-reconstruction requires version 3.7 or higher.Currently, this package works well with most popular operating systems including Windows, Linux/GNU and MacOS. its system requirements will be mainly based on the requirements of Pytorch version 1.9.1, please check morehereNote that although not exactly measured, the processing of this library uses a RAM amount of about 235.9 MiB (for the example providedhere) when using the CPU device and about 1000MiB VRAM when used with GPU. In general, the amount of resources used is still quite large and they will be gradually reduced by optimizing the models used in the next versions.Finally, because it does not require too much computing power, this library is only too demanding on CPU when most devices can use this package without any problems. The processing time with measured in the example provided above has a value of 13.4 s . wall timeInstallationTable Reconstruction is published onPyPIand can be installed from there:pipinstalltable-reconstructionYou can also install this package manually with the following command:pythonsetup.pyinstallBasic usageYou can easily use this library by using the following statements:importtorchfromtable_reconstructionimportTableExtractiondevice=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")extraction=TableExtraction(device=device)image=...# Accept Numpy ndarray and PIL imagetables=extraction.extract(image)We also provide a simple Jupyter notebook which can be used to illustrate the results obtained after processing, please check it outhereDocumentationDocumentation will be available soon.Get in touchReport bugs, suggest features or view the source code onGitHub.
Package: table-rl
table-rlThis repository contains a set of implementations and functionality of purely online tabular reinforcement learning algorithms.Currently we support Python 3.8.InstallationYou should be able to installtable-rlfrom PyPi.pip3 install table-rlYou can also installtable-rlfrom source.git clone https://github.com/prabhatnagarajan/table-rl.git
cd table-rl
pip3 install -e .How the library worksReward functions, value functions, transition functions are allnumpyarrays and matrices. We follow the conventions from Sutton and Barto'sRL Book.We focus only on online algorithms that interact with and get experience from the environment in a purely online way.ContributingUsepyteston the test directories.python -m pytest tests/LicenseMIT License.
Package: tableschema-bigquery
tableschema-bigquery-pyGenerate and load BigQuery tables based onTable Schemadescriptors.Featuresimplementstableschema.StorageinterfaceContentsGetting StartedInstallationPrepare BigQueryDocumentationAPI ReferenceStorageContributingChangelogGetting StartedInstallationThe package use semantic versioning. It means that major versions  could include breaking changes. It's highly recommended to specifypackageversion range in yoursetup/requirementsfile e.g.package>=1.0,<2.0.pipinstalltableschema-bigqueryPrepare BigQueryTo start using Google BigQuery service:Create a new project -linkCreate a service key -linkDownload json credentials and setGOOGLE_APPLICATION_CREDENTIALSenvironment variableDocumentationimportioimportosimportjsonfromdatapackageimportPackagefromapiclient.discoveryimportbuildfromoauth2client.clientimportGoogleCredentials# Prepare BigQuery credentialsos.environ['GOOGLE_APPLICATION_CREDENTIALS']='.credentials.json'credentials=GoogleCredentials.get_application_default()service=build('bigquery','v2',credentials=credentials)project=json.load(io.open('.credentials.json',encoding='utf-8'))['project_id']# Save package to BigQuerypackage=Package('datapackage.json')package.save(storage='bigquery',service=service,project=project,dataset='dataset')# Load package from BigQuerypackage=Package(storage='bigquery',service=service,project=project,dataset='dataset')package.resourcesAPI ReferenceStorageStorage(self,service,project,dataset,prefix='')BigQuery storagePackage implementsTabular Storageinterface (see full documentation on the link):Only additional API is documentedArgumentsservice (object): BigQueryServiceobjectproject (str): BigQuery project namedataset (str): BigQuery dataset nameprefix (str): prefix for all bucketsContributingThe project follows theOpen Knowledge International coding standards.Recommended way to get started is to create and activate a project virtual environment.
To install package and development dependencies into active environment:$makeinstallTo run tests with linting and coverage:$maketestChangelogHere described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formattedcommit history.v1.0Initial driver realease
Package: tableschema-ckan-datastore
tableschema-ckan-datastore-pyGenerate and load CKAN DataStore tables based onTable Schemadescriptors.Featuresimplementstableschema.StorageinterfaceContentsGetting StartedInstallationDocumentationAPI ReferenceStorageContributingChangelogGetting StartedInstallationThe package use semantic versioning. It means that major versions  could include breaking changes. It's highly recommended to specifypackageversion range in yoursetup/requirementsfile e.g.package>=1.0,<2.0.pipinstalltableschema-ckan-datastoreDocumentationWhen writing data, tableschema-ckan-datastore uses theCKAN APIdatastore_upsertendpointwith theupsertmethod. This requires a unique key in the data defined by aTable Schema primary key property. If your data has a primary key, you can use thetable.savemethod:fromtableschemaimportTable# Load and save CKAN DataStore recordresource_id='bd79c992-40f0-454a-a0ff-887f84a792fb'base_url='https://demo.ckan.org'dataset_id='test-dataset-010203'api_key='my-ckan-user-api-key'table=Table('data.csv',schema='schema.json')# data.csv has primary keystable.save(resource_id,storage='ckan_datastore',base_url=base_url,dataset_id=dataset_id,api_key=api_key)If you need to define the method used to save data to the DataStore, you can create thetableschema.Storageobject directly and specify which method parameter to use:importioimportjsonfromtabulatorimportStreamfromtableschemaimportStorage# Load and save CKAN DataStore recordresource_id='bd79c992-40f0-454a-a0ff-887f84a792fb'base_url='https://demo.ckan.org'dataset_id='test-dataset-010203'api_key='my-ckan-user-api-key'schema=json.load(io.open('schema.json',encoding='utf-8'))data=Stream('data.csv',headers=1).open()storage=Storage.connect('ckan_datastore',base_url=base_url,dataset_id=dataset_id,api_key=api_key)storage.create(resource_id,schema,force=True))storage.write(resource_id,data,method='insert')# specify the datastore_upsert methodAPI ReferenceStorageStorage(self,base_url,dataset_id=None,api_key=None)Ckan Datastore storagePackage implementsTabular Storageinterface (see full documentation on the link):Only additional API is documentedArgumentsbase_url (str):the base url (and scheme) for the CKAN instance (e.g. http://demo.ckan.org).dataset_id (str):
id or name of the CKAN dataset we wish to use as the bucket source.
If missing, all tables in the DataStore are used.api_key (str):
either a CKAN user api key or, if in the formatenv:CKAN_API_KEY_NAME,
an env var that defines an api key.ContributingThe project follows theOpen Knowledge International coding standards.Recommended way to get started is to create and activate a project virtual environment.
To install package and development dependencies into active environment:$makeinstallTo run tests with linting and coverage:$maketestChangelogHere described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formattedcommit history.v1.1Increase max size of a bucket to 100 resourcesv1.0Initial driver implementation
Package: table-schema-resource-template
Table Schema resource templateGenerate an empty resource file template from aTable SchemaJSON file.Supported output formats:Microsoft Excel XLSXInstallpipinstalltable-schema-resource-templateUsageTable Schema JSON can be given as file or URL:table-schema-resource-template--formatxlsxschema.jsontemplate.xlsx
table-schema-resource-template--formatxlsxhttps://git.opendatafrance.net/scdl/subventions/raw/master/schema.jsontemplate.xlsx
Package: tableschema-sql
tableschema-sql-pyGenerate and load SQL tables based onTable Schemadescriptors.Featuresimplementstableschema.Storageinterfaceprovides additional features like indexes and updatingContentsGetting StartedInstallationDocumentationAPI ReferenceStorageContributingChangelogGetting StartedInstallationThe package use semantic versioning. It means that major versions  could include breaking changes. It's highly recommended to specifypackageversion range in yoursetup/requirementsfile e.g.package>=1.0,<2.0.pipinstalltableschema-sqlDocumentationfromdatapackageimportPackagefromtableschemaimportTablefromsqlalchemyimportcreate_engine# Create sqlalchemy engineengine=create_engine('sqlite://')# Save package to SQLpackage=Package('datapackage.json')package.save(storage='sql',engine=engine)# Load package from SQLpackage=Package(storage='sql',engine=engine)package.resourcesAPI ReferenceStorageStorage(self,engine,dbschema=None,prefix='',reflect_only=None,autoincrement=None)SQL storagePackage implementsTabular Storageinterface (see full documentation on the link):Only additional API is documentedArgumentsengine (object):sqlalchemyenginedbschema (str): name of database schemaprefix (str): prefix for all bucketsreflect_only (callable):
a boolean predicate to filter the list of table names when reflectingautoincrement (str/dict):
add autoincrement column at the beginning.
- if a string it's an autoincrement column name
- if a dict it's an autoincrements mapping with column
names indexed by bucket names, for example,{'bucket1': 'id', 'bucket2': 'other_id}storage.createstorage.create(self,bucket,descriptor,force=False,indexes_fields=None)Create bucketArgumentsindexes_fields (str[]):
list of tuples containing field names, or list of such listsstorage.writestorage.write(self,bucket,rows,keyed=False,as_generator=False,update_keys=None,buffer_size=1000,use_bloom_filter=True)Write to bucketArgumentskeyed (bool):
accept keyed rowsas_generator (bool):
returns generator to provide writing control to the clientupdate_keys (str[]):
update instead of inserting if key values match existent rowsbuffer_size (int=1000):
maximum number of rows to try and write to the db in one batchuse_bloom_filter (bool=True):
should we use a bloom filter to optimize DB update performance
(in exchange for some setup time)ContributingThe project follows theOpen Knowledge International coding standards.Recommended way to get started is to create and activate a project virtual environment.
To install package and development dependencies into active environment:$makeinstallTo run tests with linting and coverage:$maketestChangelogHere described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formattedcommit history.v1.3Implemented constraints loading to a databasev1.2Add option to configure buffer size, bloom filter use (#77)v1.1Added support for theautoincrementparameter to be a mappingFixed autoincrement support for SQLite and MySQLv1.0Initial driver implementation.
Package: table-schema-to-markdown
Table Schema to MarkdownCreate a Markdown documentation file from aTable Schema file.The original scripts are coming fromdoc-generatorfor theValidataproject, which are now available asan npm package.Installationpip install table-schema-to-markdownUsageCommand line toolThe package provides a command line tool.$ table-schema-to-md -h
usage: table-schema-to-md [-h] [-o OUTPUT] [--log LOG] table_schema

positional arguments:
  table_schema          path or URL of table schema file

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output file name
  --log LOG             level of logging messagesExample:# Print generated Markdowntable-schema-to-mdschema.json# Save to Markdown filetable-schema-to-mdschema.json-odocumentation.mdIn Pythonfromtable_schema_to_markdownimportconvert_source# Generate a Markdown documentation from a JSON filetable_schema='/tmp/schema.json'withopen('/tmp/doc.md','a')asout:convert_source(table_schema,out)# Generate Markdown documentation in a stringimportiowithio.StringIO()asbuff:convert_source(table_schema,buff)documentation=buff.getvalue()Generated documentation exampleThis table schema fileis rendered in Markdown inthis file.Find a generated documentation file below.Métadonnées d'un répertoire GitAuteur : Antoine Augustiantoine.augusti@example.comSchéma créé le : 12/31/18Site web :https://github.com/AntoineAugusti/data-codes-sources-frClé primaire :repertoire_urlModèle de donnéesNomTypeDescriptionExemplePropriétésnomchaîne de caractèresLe nom du répertoirenom-repertoireValeur obligatoireorganisation_nomchaîne de caractèresLe nom de l'organisationetalabValeur obligatoireplateformechaîne de caractèresLa plateforme de dépôt de codeGitHubValeur obligatoire, Valeurs autorisées : GitHubrepertoire_urlchaîne de caractères (formaturi)L'URL vers le répertoirehttps://github.com/etalab/nom-repertoireValeur obligatoiredescriptionchaîne de caractèresLa description du répertoireCe répertoire est utileValeur optionnelleest_forkbooléenIndique si le répertoire est un forkfalseValeur obligatoiredate_creationdate et heureLa date de création du répertoire2018-12-01T20:00:55ZValeur obligatoirederniere_mise_a_jourdate et heureLa date de dernière mise à jour du répertoire2018-12-01T20:00:55ZValeur obligatoirepage_accueilchaîne de caractèresURL vers la page d'accueil du projethttps://etalab.gouv.frValeur optionnellenombre_starsnombre entierLe nombre de fois où le répertoire a été ajouté aux favoris42Valeur obligatoire, Valeur minimale : 0nombre_forksnombre entierLe nombre de fois où le répertoire a été forké13Valeur obligatoire, Valeur minimale : 0licencechaîne de caractèresLa licence du répertoire, telle que détectée par la plateformeMITValeur optionnellenombre_issues_ouvertesnombre entierLe nombre d'issues actuellement ouvertes0Valeur obligatoire, Valeur minimale : 0langagechaîne de caractèresLe langage principal du répertoire, tel que détecté par la plateformePythonValeur optionnelletopicschaîne de caractèresLes tags du répertoireutile,france,opendataValeur optionnelleNoticeThis software is available under the MIT license.
Package: tableschema-to-template
tableschema-to-templateGiven aFrictionless Table Schema(likethis),
generate an Excel template with input validation
(likethis).UsageDownload asampleschema.yaml, and then:pipinstalltableschema-to-template
ts2xl.pyschema.yamltemplate.xlsx# Open with Excel:opentemplate.xlsxOr to use inside Python:fromtableschema_to_templateimportcreate_xlsxschema={'fields':[{'name':'a_number','description':'A number!','type':'number'}]}create_xlsx(schema,'/tmp/template.xlsx')Additional docs:For CLI usersFor Python usersFor project developersFeaturesEnum constraints transformed into pull-downs.Field descriptions transformed into comments in header.Float, integer, and boolean type validation, with range checks on numbers.More details in thechangelog.Related workIf you want to construct Excel files programmatically,XlsxWriteris great!For validated data entry, from the Frictionless community:table-schema-resource-template: Generates templates, but doesn't go beyond row headers.data-curator: Desktop application for data entry based on Table Schema.csv-gg: Web app which serves data entry form, and usesValidata APIfor validation.From the biomedical ontologies community:CEDAR: Data entry tool based on ontologies.Webulous: Google sheets plugin that adds pulldowns based on ontology terms.
Package: table-step
SEAMM Table Plug-inA SEAMM plug-in for data tables in a flowchart.Free software: BSD-3-ClauseDocumentation:https://molssi-seamm.github.io/table_step/index.htmlCode:https://github.com/molssi-seamm/table_stepFeaturesCreation of datatables with named, typed columns.Reading and writing data to the table.Printing and saving to CSV files.Accessible incustomscripts.Canloopover the rows in a flowchart.AcknowledgementsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award OAC-1547580 and CHE-2136142.History2023.11.10 – Bugfix: title of edit dialog was wrong2023.10.30 – Cleaned up outputNothing large, just made the output properly indented, etc.2023.7.25 – Bug fix and EnhancementsFixed bug with reading table using a variable for the filename, but asking for the
type from the extension.Add ability to save tables with a frequency of other than ever call.2023.2.15 – Bugs fixes and documentationRestructured documentation and moved to new themeFixed bug with access rows of tables with non-integer indexes as well as “current”
indexAdded support for lists of tables in pulldowns in the GUI2021.12.22 – Improved the handling of index columns, added formats.Improved the handling of the index columnAdded Save asAdded Excel and JSON formats.2021.10.14 – Updated for PythonNow supporting Python 3.8 and 3.92021.2.12 (12 February 2021)Updated the README file to give a better description.Updated the short description in setup.py to work with the new installer.Added keywords for better searchability.2020.12.5 (5 December 2020)Internal: switching CI from TravisCI to GitHub Actions, and in the
process moving documentation from ReadTheDocs to GitHub Pages where
it is consolidated with the main SEAMM documentation.Updated to be compatible with the new command-line argument
handling.0.9 (15 April 2020)General bug fixing and code cleanup.Part of release of all modules.0.7.0 (17 December 2019)General clean-up of code and output.Part of release of all modules.0.3.0 (20 August 2019)First release on PyPI.
Package: tablestore
概述此Python SDK基于阿里云表格存储服务API 构建。阿里云表格存储是构建在阿里云飞天分布式系统之上的 NoSQL 数据存储服务，提供海量结构化数据的存储和实时访问。运行环境安装Python即可运行，支持python2.6、Python2.7、python3.3、python3.4、python3.5 和 python3.6。安装方法PIP安装$pipinstalltablestoreGithub安装下载源码$gitclonehttps://github.com/aliyun/aliyun-tablestore-python-sdk.git安装$pythonsetup.pyinstall源码安装下载SDK发布包并解压安装$pythonsetup.pyinstall示例代码表操作（表的创建、获取、更新和删除）单行写（向表内写入一行数据）单行读（从表内读出一样数据）更新单行（更新某一行的部分字段）删除某行（从表内删除某一行数据）批量写（向多张表，一次性写入多行数据）批量读（从多张表，一次性读出多行数据）范围扫描（给定一个范围，扫描出该范围内的所有数据）主键自增列（主键自动生成一个递增ID）全局二级索引局部事务（提交事务）局部事务（舍弃事务）SearchParallel ScanMax/Min/Sum/Avg/Count/DistinctCount/PercentilesGroupBy/HistogramSQL执行测试注意：测试case中会有清理某个实例下所有表的动作，所以请使用专门的测试实例来测试。安装nosetests$pipinstallnose设置执行Case的配置$exportOTS_TEST_ACCESS_KEY_ID=<youraccessid>$exportOTS_TEST_ACCESS_KEY_SECRET=<youraccesskey>$exportOTS_TEST_ENDPOINT=<tablestoreserviceendpoint>$exportOTS_TEST_INSTANCE=<yourinstancename>运行case$noseteststests/贡献代码我们非常欢迎大家为Tablestore Python SDK以及其他Tablestore SDK贡献代码。非常感谢@Wall-ee对4.3.0版本的贡献。联系我们阿里云Tablestore官方网站阿里云官网联系方式