Saravanan666/pypi-org huggingface dataset:
Package: tableau
Tableau is a collection of helper classes for building test fixtures and seed data.Model composition without any predefined schemas.  The model object relationships are automatically deduced through the value annotations:from tableau import Datum

foo = Datum(
  'Foo', ('id'), # schema name / identifiers
  id=1,
  field_a=1,
  field_b=2,
  collection=one_to_many([
    Datum(
      'Bar', auto('id'), # id will be automagically generated by the walker
      field_c=1
      ),
    Datum(
      'Bar', auto('id'),
      field_c=1
      ),
    ],
    referring_fields='foo_id'
    )
  )Export the model object graph to plain ANSI SQL statements, in the ORM-agnostic manner:import sys
from tableau import Datum, DataSuite, DataWalker
from tableau.sql import SQLGenerator

# ...

suite = DataSuite()
DataWalker(suite)(foo)
SQLGenerator(sys.stdout, encoding='utf-8')(suite)The above yields the following SQL statements:INSERT INTO `Foo` (`id`, `field_a`, `field_b`) VALUES
(1, 1, 2);
INSERT INTO `Bar` (`id`, `field_c`, `foo_id`) VALUES
(1, 1, 1),
(2, 1, 1);Automatically mapping the existing SQLAlchemy tables / declarative classes
to theData:from tableau.sqla import newSADatum

# metadata = ...
# Base = ...
# session = ...

class Foo(Base):
  __tablename__ = 'foos'
  id = Column(Integer, primary_key=True)
  field = Column(String)

  def some_model_specific_method(self):
    return self.field

Datum = newSADatum(metadata, Base)
datum = Datum(
  'foos', ('id'),
  field='test'
  )
print datum.some_model_specific_method() # 'test'
session.add(datum) # it can even be added to the session!Change history0.0.0Unreleased.0.0.1First release. No docs!0.0.2Support mapping to SQLAlchemy tables and declarative classses.
Still no docs!0.0.3Fix critical bugs in 0.0.2.
Package: tableau-client-talenttech-oss
Tableau dashboard clientInstallpiptableau-client-talenttech-ossfromtableau.clientimportClient#initial connection to server tableautableau_client=Client(url_server_tableau='https://example.org/',user_name_tableau='',password_tableau='',url_site='')# update extract refresh for tableau workbook# workbook_id - id published tableau workbook on server in urltableau_client.dashboard_refresh(workbook_id=1)# export sheet tableau workbook to csv file# sheet_name - name worksheet in tableau workbook (workbook_name)# csv_path - path with file name to save sheet_name to csvtableau_client.view_to_csv(workbook_name='',sheet_name='',csv_path='./view_data.csv')
Package: tableau-helpers
tableau_helpersA wrapper for the tableau-server-client and hyper-api which abstracts away
boilerplate and in some cases provides a CLI which works without admin privileges.tableau_helpers.hyperCreate hyperfiles from one or more csvs by providing a source, destination,
schema, and optional parameters.Code integrationSeetest_hyper.pyfor some examples in action.tableau_helpers.publishUpload datasources from your machine to the tableau server by providing a
hyperfile as a source and a project as a destination.Code integrationSeetest_publish.pyfor an example.DevelopmentThere are a few steps to set up the development environment.Make a copy of.env.sampleand save it as .envAdd yourPersonal Access Tokento .envSet the tableau-server url in .env ex:https://tableau-test.rki.localIf your SSL cert is self signed, download a copy of your pem and provide the path in .env, otherwise delete the cert entry.Installpre-commiteither in an environment or for your user.Setup pre-commit hookspre-commit install -t pre-commitpre-commit install -t pre-pushInstalltoxeither in an environment or for your user.Test run tox for unit teststox(note: some integration tests require a connection to your tableau server)Test run tox to reformat filestox -e lint
Package: tableauhyperapi
The Hyper API contains a set of functions you can use to automate your interactions with Tableau extract (.hyper) files. You can use the API to create new extract files, or to open existing files, and then insert, delete, update, or read data from those files.Using the Hyper API developers and administrators can:Create extract files for data sources not currently supported by Tableau.Automate custom extract, transform and load (ETL) processes (for example, implement rolling window updates or custom incremental updates).Retrieve data from an extract file.More information can be found here:https://tableau.github.io/hyper-db.
Package: tableauhyperio
#DEPRECATEDGo tohttps://github.com/innobi/pantabfor a maintained packageTableau Hyper IO: read and write Tableau hyper files using Pandas DataFramesWhat is it?A simple way to read Tableau hyper files into Pandas DataFrames
and write to Tableau hyper files from Pandas DataFrames.Why was this made?For a project I was working on I needed to read hyper files.
I searched if a package already existed and found only thepandleaupackage,
which only writes to hyper files but does not read them and also uses the older extract 2.0 API.
Since I couldn't find any other package that met my needs I decided to make one myself, which has been a good learning experience.InstallationYou can install tableauhyperio using pip:pipinstalltableauhyperioThis will also try downloading the Tableau hyper API, tqdm and pandas packages
if you don't have them already.Example usageimporttableauhyperioashio# Reading a regular hyper filedf=hio.read_hyper("example.hyper")# Reading a hyper file with a custom schemadf=hio.read_hyper("example.hyper","my_schema")# Writing a regular hyper filehio.to_hyper(df,"example_output.hyper")# Writing a hyper file with a custom schema and custom table namehio.to_hyper(df,"example_output.hyper","my_schema","my_table")DependenciesPandastableauhyperapitqdm
Package: tableau-prometheus-exporter
Tableau Prometheus ExporterTableau Server Manager Prometheus ExporterThis repository contains the codebase to expose the Tableau Server Manager metrics to prometheus and visualize them in grafana.Metrics Example# HELP tableau_service_status Tableau services statuses
# TYPE tableau_service_status gauge
tableau_service_status{instance_id="0",node_id="node2",service_name="filestore",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="authnservice",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="databasemaintenance",state="Disabled",status="Stopped"} 0.0
tableau_service_status{instance_id="0",node_id="node2",service_name="dataserver",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="1",node_id="node2",service_name="dataserver",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="cacheserver",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="1",node_id="node2",service_name="cacheserver",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="interactive",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="searchserver",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="statsservice",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="vizportal",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="1",node_id="node2",service_name="vizportal",state="Enabled",status="Active"} 1.0
tableau_service_status{instance_id="0",node_id="node2",service_name="backuprestore",state="Disabled",status="Stopped"} 0.0Install it from PyPI$pipinstalltableau-prometheus-exporterUsagePythonfromtableau_prometheus_exporter.serverimportServerServer(config).start_and_run_forever()Command Line$python-mtableau_prometheus_exporter--hDocker$dockerrun--rm-p8000:8000-v${PWD}/config.yaml:/app/config.yamltableau-prometheus-exporter--config-fileconfig.yamlGo tohttp://localhost:8000/to see the metrics.ConfigurationA sample configuration can be found atsamples/config.yaml.templateDashboardA sample Grafana Dashboard can be found atsamples/grafana.jsonAlertingA sample of prometheus alert can be found atsamples/prometheus.yamlContributingPlease refer to theCONTRIBUTINGfile for information about how to get involved. We welcome issues, questions, and pull requests.NoteThis project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: tableaurest
A Python based Tableau REST API interface.Warning: This package is still in a pre-v1 development phase. Breaking changes are likely to occur without warning.tableaurestA Python based Tableau REST API interface.Installationtableaurestis a Python 3.6+ based package, and can be installed throughpipusing the following command.$python-mpipinstalltableaurestExamplesPrint count of Workbooks Owned by Login User.>>>fromtableaurestimportTableauREST>>>>>>SERVER='YOUR_TABLEAU_URL'>>>USERNAME='YOUR_TABLEAU_USERNAME'>>>PASSWORD='YOUR_TABLEAU_PASSWORD'>>>>>>withTableauREST(SERVER,USERNAME,PASSWORD)asrestapi:>>>workbooks=restapi.queryWorkbooksforUser(owner=True)>>>>>>print(f'{USERNAME}owns{len(workbooks)}workbooks on{SERVER}.')'YOUR_TABLEAU_USERNAME owns 4 workbooks on YOUR_TABLEAU_URL.'ExtrasContributeCheck/Open Issue for related topics of changeFork/Clone/Branch repo and make discussed/desired changesAdd tests (¯\_(ツ)_/¯) and document code w/ numpy formattingOpen Pull Request and notify maintainerChangelogAll notable changes to this project will be documented in this file.DevelopUnreleasedv0.3.0AddedAll methods added for: Authentication, Sites, Projects, Subscriptions, ServerChangedRemove excessive (useless) dict.keys() usage from codebaseAllow user to specify ssl verify on TableauREST init (default=True)Remove auth token from session header on signOutUpdate self.site (contenturl) on signInAdd user impersonation to signInRemovedRemove custom logger(s) from submodulesv0.2.3ChangedCorrect queryWorkbookConnections to use GET methodCorrect queryDatasourceConnections to use GET methodAdd ‘connection’ dict wrapper updateWorkbookConnection detailsv0.2.2ChangedCorrect userid/siteid updating on switchSite methodv0.2.1ChangedSome light cleanup around formatting, logging, & bug fixes
Package: tableauserverclient
Tableau Server Client (Python)Use the Tableau Server Client (TSC) library to increase your productivity as you interact with the Tableau Server REST API. With the TSC library you can do almost everything that you can do with the REST API, including:Publish workbooks and data sources.Create users and groups.Query projects, sites, and more.This repository contains Python source code for the library and sample files showing how to use it. As of May 2022, Python versions 3.7 and up are supported.To see sample code that works directly with the REST API (in Java, Python, or Postman), visit theREST API Samplesrepo.For more information on installing and using TSC, see the documentation:https://tableau.github.io/server-client-python/docs/License
Package: tableau-sql-parser
tableau-sql-parserSQL parser for Tableau custom SQL requestsUsageGiven a .twb or a .twbx Tableau archive, you can extract these information:number of queries analyzedtables useda tree-like structure describingschema.tables.columnThe CLI command to use issqlparse:sqlparse -f file/to/parse.twb(x) -r custom_report_name -oTypesqlparse --helpfor more details.
Package: tableau-to-sqlite
tableau-to-sqliteFetch data from Tableau into a SQLite database. A wrapper aroundTableauScraper.InstallationInstall this tool usingpip:$ pip install tableau-to-sqliteUsageIf you have the URL to a Tableau dashboard like this:https://results.mo.gov/t/COVID19/views/VaccinationsDashboard/VaccinationsYou can pass that directly to the tool:tableau-to-sqlite tableau.db \
  https://results.mo.gov/t/COVID19/views/VaccinationsDashboard/VaccinationsThis will create a SQLite database calledtableau.dbcontaining one table for each of the worksheets in that dashboard.If the dashboard is hosted onhttps://public.tableau.com/you can instead provide the view name. This will be two strings separated by a/symbol - something like this:OregonCOVID-19VaccineProviderEnrollment/COVID-19VaccineProviderEnrollmentNow run the tool like this:tableau-to-sqlite tableau.db \
    OregonCOVID-19VaccineProviderEnrollment/COVID-19VaccineProviderEnrollmentGet the data as JSON or CSVIf you're building agit scraperyou may want to convert the data gathered by this tool to CSV or JSON to check into your repository.You can do that usingsqlite-utils. Install it usingpip:pip install sqlite-utilsYou can dump out a table as JSON like so:sqlite-utils rows tableau.db \
   'Admin Site and County Map Site No Info' > tableau.jsonOr as CSV like this:sqlite-utils rows tableau.db --csv \
   'Admin Site and County Map Site No Info' > tableau.csvDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd tableau-to-sqlite
python -mvenv venv
source venv/bin/activateOr if you are usingpipenv:pipenv shellNow install the dependencies and tests:pip install -e '.[test]'To run the tests:pytest
Package: tablebase
TablebaseIf you found a bug, need a feature, or have question please open anissue.All relesses starting at 1.4.2 can be foundhereWhat is Tablebase?Tablebase is a python package to make coding with tables
effortless.Install TablebaseTo install Tablebase use pypi (pip)pip install tablebaseMaking a blank tableTo make a blank table, use theTable()class:import tablebase

My_Table = tablebase.Table()

print(My_Table.table_content)For full documentation go tohttps://dev.centillionware.com/tablebase
Package: tablecalculation
Python library to calculate tables for football and handballGetting Startedpip install tablecalculationAlternatively: copy the tablecalculation package to your project.PrerequisitesShould work with Python 2.7 and up or 3. Possibly other versions
(untested).UsageIt’s best to seetablecalculationtest.pyfor reference.Basic usage is as follows:# Handball 3. Liga Herren, Saison 2015/16, 17. Spieltag
# Create table calculation oject with scope, sports type and mode
t1 = TableCalculation(TABLE_ALL, SPORTSTYPES.HANDBALL, CALCULATION_MODE_DIRECT_COMPARE)

# add your matches / fixtures (upto desired match day or all)
t1.add_match(_Match('DHK Flensborg', 35551, 'SC Magdeburg II', 35561, 41, 23))
t1.add_match(_Match('SV Meck.-Schwerin', 35651, 'TS Großburgwedel', 35661, 26, 17))
...

tb = t1.get_table()
# tb is now the sorted table_Match may be replaced with your own object but as a minimum requires a
team name and id for both participiants and goals for the home and away
team.The module supports two modes for calculation:
CALCULATION_MODE_GOAL_DIFFERENCE and CALCULATION_MODE_DIRECT_COMPARE.
The first one uses points, followed by goal difference and then goals
scored as sort order (as used in German Bundesliga for example).CALCULATION_MODE_DIRECT_COMPARE will first sort by points, then the
result of direct comparison between teams with equal points (often used
in handball or the Spanish La Liga).TestsSome tests are supplied and can be run like this:python -m unittest tablecalculation
python -m unittest tablecalculation.teampointAuthorsMarco Wilka-Initial work-marcowilkaLicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details
Package: table-compositor
The table-compositor library provides the API to render data stored in table-like data structures. Currently the library supports rendering data available in a Panda’s DataFrames. The DataFrame layout is used as the table layout(including single and multi hierarchical columns/indices) by the library. The table layout is rendered directly on to an XLSX sheet or to a HTML page. Styling and layout attributes can be used to render colorful XLSX or HTML reports. The library also supports rendering of multiple dataframes into a single XLSX sheet or HTML page. The objective of the library is to be able to use the DataFrame as the API to configure the style and layout properties of the report. Callback functions are provided to customize all styling properties. The nice thing about the callback functions are that the style properties are set on cells indexed with index/column values available in the original dataframe used during rendering.Code:https://github.com/InvestmentSystems/table-compositorDocs:http://table-compositor.readthedocs.ioPackages:https://pypi.python.org/pypi/table-compositor
Package: tablecsv
This module is made by Mainak Deb, this is a python module in which you can fetch CSV file data
and perform basic SQL operations in it.
Features:
1> Fetch csv
2> Show in command prompt
3> select
4> projection
5> update
6> delete
7> order by
8> group by
9> access
10> insert
11> count
12> sum
13> max
14> avarage
15> pie chart
16> histogram
17> self join
18> cross join
Package: tablecv
TableCVTableCVis a Python package designed to extract tables from images. It offers two approaches for extracting tables, allowing you to choose the one that best suits your needs.InstallationYou can easily installTableCVusing pip:pipinstalltablecvUsageApproach 1 (using PaddleOCR)TableCVoffers a straightforward method to extract tables using PaddleOCR. This approach returns a pandas DataFrame object:fromtablecvimportextract_table# Replace "image_path" with the path to your imageprint(extract_table(image_path="your_image.png"))Approach 2 (OCR with Your Preferred Tool)If you prefer using a different OCR tool like EasyOCR, KerasOCR, or any other OCR solution, you can still useTableCV. First, perform OCR on your image using your chosen tool. The OCR results should be structured as a list of tuples, each containing a bounding box and corresponding text:# List of tuples: (bounding box as (x, y, w, h), text)ocr_results=[((1,2,3,4),"a"),((4,5,6,7),"b"),# Add more tuples as needed]After obtaining your OCR results, you can extract tables from them usingTableCV:fromtablecvimportextract_table_from_ocr# Replace "ocr_results" with your OCR results listprint(extract_table_from_ocr(ocr_results))With these two approaches,TableCVprovides flexibility for table extraction from images, whether you prefer using PaddleOCR or another OCR tool of your choice.
Package: tabledata
tabledataSummaryInstallationInstall from PyPIInstall from PPA (for Ubuntu)DependenciesOptional Python packagesDocumentationSummarytabledatais a Python library to represent tabular data. Used for pytablewriter/pytablereader/SimpleSQLite/etc.InstallationInstall from PyPIpip install tabledataInstall from PPA (for Ubuntu)sudo add-apt-repository ppa:thombashi/ppa
sudo apt update
sudo apt install python3-tabledataDependenciesPython 3.7+Mandatory Python package dependencies (automatically installed)Optional Python packagesloguruUsed for logging if the package installedpandasrequired to get table data as a pandas data frameDocumentationhttps://tabledata.rtfd.io/
Package: tabledataextractor
TableDataExtractorExtracts data from tables with complicated structures, by standardizing the table.Documentationhttps://cambridgemolecularengineering-tabledataextractor.readthedocs-hosted.com/en/latest/LicenseThe MIT License (MIT)Copyright © 2019 Juraj Mavračić and contributorsPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the “Software”), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.AcknowledgementsCore algorithms used and modified inTableDataExtractorhave originally been developed by Embley et al.
This is the MIPS (Minimum Indexing Point Search) algorithm that is used to find the row/column headers
and the data region, as well as algorithms for prefixing header cells. Also, some of the examples in this documentation
are based on examples from Embley et al:Embley, D.W., Krishnamoorthy, M.S., Nagy, G., and Seth, S. (2016) Converting heterogeneous statistical tables on the web to searchable databases. *Int. J. Doc. Anal. Recognit.*, *19* (2), 119–138.Algorithms for duplicating spanning cells and extending headers, that are used inTableDataExtractor,
have been developed by Nagy and Seth:Nagy, G., and Seth, S. (2017) Table headers: An entrance to the data mine. *Proc. - Int. Conf. Pattern Recognit.*, 4065–4070.The algorithm for convertinghtmlfiles toNumpy arrayshas been modified from John Rico:John Ricco, (2017) Using Python to scrape HTML tables with merged cells, https://johnricco.github.io/2017/04/04/python-html/Please cite these works where appropriate.
Package: table-fifth
fifteen(DBAtable_fifth)This is a research fork of the original libraryExperimental library for quick quintet tallying, useful when you have a lot of quintets that somehow you don't want to count yourself.UsageBinary wheels are provided on PyPI for Python starting from 3.7, but note that PyPy is not supported (yet).python3 -m pip install table-fiveAPITreeSetA treeset is an efficient (i.e., fast parsing) list of tree topologies. The construction is $O(k n \lg n)$ where $k$ is the number of trees and $n$ the number of taxa. The log factor is due to the LCA data structure initialization.fromtable_fiveimportTreeSettrees=TreeSet("path_to_newline_delimited_newicks.tre")Quintet CountingThe major API istally_single_quintetreturning a list of length 15 containing the empirical
counts of the 15 ADR unrooted quintet topology among the tree-set in $O(k)$ time:# get counts of the ADR unrooted quintet topologies on taxa '1','2','3','4','5'. Taxa order matters.treeset.tally_single_quintet(('1','2','3','4','5'))# obviously you might want to convert it to numpy arrays# normalize by the number of genes in the tree-setnew_tree_dist=np.asarray(treeset.tally_single_quintet(q_taxa))/len(treeset)Development and BuildingAfter installing the Rust toolchain andMaturin, see the following commands:# build the librarymaturinbuild# installing it locallymaturindevelopSee the Maturin documentation for more details.