Saravanan666/pypi-org huggingface dataset:
Package: dffml-model-tensorflow
DFFML Models for Tensorflow LibraryDemoThis demo was taken before the below steps were updated, the actual accuracy
may vary as this video shows accuracy being assessed against the training
data. You should try it for yourself and see!DocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/dffml_model.html#dffml-model-tensorflowLicenseDFFML Tensorflow Models are distributed under the terms of theMIT License.LegalThis software is subject to the U.S. Export Administration Regulations and
other U.S. law, and may not be exported or re-exported to certain countries
(Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to
persons or entities prohibited from receiving U.S. exports (including
Denied Parties, Specially Designated Nationals, and entities on the Bureau
of Export Administration Entity List or involved with missile technology or
nuclear, chemical or biological weapons).
Package: dffml-model-tensorflow-hub
DFFML Tensorflow Hub ModelsAboutdffml_model_tensorflow_hub supports pretrained NLP models available onDocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/dffml_model.html#dffml-model-tensorflow-hubAcknowledgementFor tokenizatin needed by BERT models dffml_model_tensorflow_hub uses code from,Licensedffml_model_tensorflow_hub Models are distributed under the terms of theMIT License.
Package: dffml-model-transformers
DFFML Transformers ModelsAboutCurrently, dffml_model_transformers supports HuggingFace Transformers tensorflow based NER models.DocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/dffml_model.html#dffml-model-transformersAcknowledgementdffml_model_transformers derives code from,LicenseTransformers Models are distributed under the terms of theMIT License.
Package: dffml-model-vowpalWabbit
DFFML Vowpal Wabbit ModelsAboutVowpal Wabbit models.DocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/dffml_model.html#dffml-model-vowpalWabbitLicensedffml_model_vowpalWabbit Models are distributed under the terms of theMIT License.
Package: dffml-model-xgboost
DFFML XGBoost ModelsAboutdffml_model_xgboost supports XGBoost models.DocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/dffml_model.html#dffml-model-xgboostLicensedffml_model_xgboost Models are distributed under the terms of theMIT License.
Package: dffml-service-http
DFFML HTTP API ServiceProvide access to DFFML APIs via HTTP interface.DocumentationDocumentation is hosted athttps://intel.github.io/dffml/plugins/service/http/LicenseHTTP API Service is distributed under the terms of theMIT License.LegalThis software is subject to the U.S. Export Administration Regulations and
other U.S. law, and may not be exported or re-exported to certain countries
(Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to
persons or entities prohibited from receiving U.S. exports (including
Denied Parties, Specially Designated Nationals, and entities on the Bureau
of Export Administration Entity List or involved with missile technology or
nuclear, chemical or biological weapons).
Package: dff-node-stats
Statistics collection extension for Dialog Flow Frameworkdff_node_stats is package, that extends basicdialog_flow_engineby adding statistic collectionanddashboard for visualization.InstallationInstallation:# install dialog flow frameworkpipinstalldff# Install dff_node_statspipinstalldff-node-stats#basic# the default version is only capable of saving stats to a csv file.# However, you can use any combination of the listed extras that give you much more# freedom in saving and analyzing your data.# pip install dff-node-stats[api] # extra for rest-api interface# pip install dff-node-stats[streamlit] # extra for streamlit-based dashboard# pip install dff-node-stats[jupyter] # extra for jupyter-based dashboard# pip install dff-node-stats[pg] # extra for postgresql backend# pip install dff-node-stats[clickhouse] # extra for clickhouse backend# pip install dff-node-stats[all] # extra for all optionsCode snippetsInsert stats in your dff code:# import dependenciesfromdf_engine.core.plotimportPlotfromdf_engine.core.actorimportActorfromdff_node_statsimportStats,Saver# ....# Define a plot and an actorplot=Plot(foo)actor=Actor(bar,baz)# Define file for stats savingstats=Stats(saver=Saver("csv://examples/stats.csv"))# As an alternative, you can use a database. Currently, Clickhouse and Postgreql are supportedstats=Stats(saver=Saver("postgresql://user:password@localhost:5432/default"))# You can optionally add predefined Collectors to gather additional datafromdff_node_statsimportcollectorsasDSCfromdff_node_statsimportStats,Saverstats=Stats(saver=Saver("csv://examples/stats.csv"),collectors=[DSC.NodeLabelCollector()])# Or define your own Collector.# It should implement methods and properties, defined in the Collector protocol,# which will make it compatible with multiple databackends.# For more information see dff_node_stats.collectors# Add handlers to actorstats.update_actor_handlers(actor,auto_save=False)# ....# Handle user requests# ....Dashboard on stored data ([streamlit] extra required!):fromdff_node_statsimportStats,Saverfromdff_node_stats.widgets.streamlitimportStreamlitDashboardstats=Stats(saver=Saver("csv://examples/stats.csv"))streamlit_dashboard=StreamlitDashboard(df)streamlit_dashboard()http-api on stored data ([api] extra required!om dff_node_stats import Stats, Saverfromdff_node_stats.apiimportapi_runfromdff_node_statsimportStats,Saverstats=Stats(saver=Saver("csv://examples/stats.csv"),collectors=[DSC.NodeLabelCollector()])api_run(stats.dataframe)Run Examples:# run dff dialog bot and collect statspythonexamples/1.collect_stats.py# or this one, they have differences only in a dialog scriptspythonexamples/1.collect_stats_vscode_demo.py# run dashboard (make sure you installed the lib with [streamlit] extra)streamlitrunexamples/2.run_dashboard_for_stats.py# run api and follow to swagger by http://localhost:8000/docs# note that [api] install option is required.pythonexamples/2.get_stats_by_api.py# jupyter version of the dashboard can be launched by:jupyternotebookexamples/run_dashboard.ipynb# you need to have [jupyter] option installed.
Package: dffrntlab-thumbor
Thumbor is a smart imaging service. It enables on-demand crop, resizing and flipping of images.It also features a VERY smart detection of important points in the image for better cropping and
resizing, using state-of-the-art face and feature detection algorithms (more on that in Detection Algorithms).Using thumbor is very easy (after it is running). All you have to do is access it using an url for an image, like this:http://<thumbor-server>/300x200/smart/s.glbimg.com/et/bb/f/original/2011/03/24/VN0JiwzmOw0b0lg.jpg
Package: dff-sphinx-theme
Dialog Flow Framework Sphinx ThemeDialog Flow Framework Sphinx Theme is a sphinx theme for building DFF distributed websiteInstall project with demo module:make DEMO_BASE_URL="[DEMO_MODULE_BASE_PATH]" demo-buildNB! Requires NodeJS >= 8.4.0 and Python >= 3.7 (compatible withdff).Analyzed and updated source files (.tsand.scss) are marked with following header:/*** Final redaction**/WARNING!SASS styling is still under way.
By the time this inscription is being made, designing (mobile, middle-size) is still in process.
After website is finished andallrequired elements added, we should revisit SASS files once again.
We should:Restore everything that was useful and for any reason was deleted (regarding not-yet-included Sphinx addons and possibilities).Finish styling for website to match design.If needed, revisit TypeScript files too, some (most) of them were untouched since PyTorch import, only God knows if (and how) they work.Remove everything unused (RTD and PyTorch classes or Sphinx addons we don't need).Remove big files (_sphinx_base.scss), split them into smaller ones, specifying design for one particular element of website.I'm sorry to release the theme in this condition.
However, I don't have enough time to fix it and most importantly I don't know what website will look like yet, so it's impossible to check if most of the elements work, how they work and if they are needed at all.
Dear future programmer of DFF Sphinx theme, please accept my apologies and be strong.Global TODO:Addformatrules to MakefileAdd testingAdd docs (maybe insert demo module into docs)Local TODO:Replace.tsstring concatenation with `-wrapped stringsAdd pencil icon (if needed)_static/pencil-16.pngFix button fromsphinx_copybuttonpluginFinish color replacement with variables in.scssfiles + clean variablesCheck jinja nested blocks, flattenFix extra-long function definitions (in documented packages)Check top-bar height, adjust logo height if necessaryFix TODOs and FIXMEs (multiple)Transfer build process tocollect_documentation.pyExtracttheme_tutorial_namevariable to examples build (sphinx gallery)NOTES:Jupyter Notebooks:Use this:https://github.com/ngoldbaum/RunNotebookUse Google ColabExtra beautiful features:Use examples from here:https://pytorch.org/tutorials/
Package: df-fuzzy-merge
df_fuzzy_mergefuzzy merge for pandas dataframe packagelocal install:pip install git+https://github.com/mmcelhan/df_fuzzy_merge.git#egg=df_fuzzy_mergeto test:import df_fuzzy_merge as dfimport osimport pandas as pdfirst_df = pd.read_csv(os.path.join("testing_files", "data_1.csv"))
second_df = pd.read_csv(os.path.join("testing_files", "data_2.csv"))merged_df = df.df_fuzzy_merge(first_df, second_df, left_on=['last_name', 'first_name', 'school'], right_on=['first_name', 'school', 'last_name'])print(merged_df)source code available here:https://github.com/mmcelhan/dataframe_fuzzy_merge_source
Package: df-generics
Dialog Flow GenericsThereis an addon for theDialog Flow Engine, a minimalistic open-source engine for conversational services.Dialog Flow Genericsintroduces a GenericResponse class as well as generic classes for various media types. Using this API, you can create identical responses in DF adapters for Telegram, Yandex's Alice, or other services instead of learning service-specific classes.Quick StartInstallationpipinstallDF-generic-responseTo get more advanced examples, take a look atexampleson GitHub.Contributing to the Dialog Flow EnginePlease refer toCONTRIBUTING.md.
Package: dfgraph
DFGraphThedirected and flexible graphis part ofDigiForseffort
to contribute to the analysing and detection of content and context dependent information.
'DFGraph' is a simple, but powerful graph based database library based on thesqlalchemylibrary.
Its main purpose is to store data from large datasets and allows manipulate, connect and detect content.
This allows for more flexible conversion and detection processes.Installationpip install dfgraphUsageSee theexamples/files for some simple use-cases and implementations.Testingtillpip 21.3:pip install --use-feature=in-tree-build -e .pip 21.3+:pip install -e .coverage run --source=src -m pytest && coverage report -mUsagedfgraphcomes with two main classes:NodeandRelationship.
It was designed to represent Data from DatabasesCapabilitiesLegend:SymbolMeaning✅finished🔜working on implementation🟦plannedCapabilityStatusNodes and Relations✅Graph✅Database Storage and Loading✅Asynchronous Database Interaction🔜Visualisation🔜Support for Context Detection🔜Highperformance Queries🟦
Package: df-graph-construction
Dialog Flow Graph ConstructionDialog Flow Graph Constructionis python module add-on forDialog Flow Framework, a free and open-source software stack for creating chatbots, released under the terms of Apache License 2.0.Dialog Flow Graph Constructionallows you to automatically generate dff scripts from example dialogues.Quick StartInstallationpythonsetup.pyinstallUsagedf_gc--helpusage: df_gc [-h] (--pretrained-model PATH_TO_MODEL | --dataset-from-huggingface DATASET_NAME | --dataset-from-file DATASET_FILE) [--save-pretrained FILE_TO_SAVE_TO]
EXAMPLE_DIRECTORY OUTPUT_FILE

Use example dialogues to construct a dialog flow graph saved in output_file.

positional arguments:
EXAMPLE_DIRECTORY     directory containing example dialogues
OUTPUT_FILE           file with the resulting structure

optional arguments:
-h, --help            show this help message and exit
--pretrained-model PATH_TO_MODEL
file previously generated by --save_pretrained
--dataset-from-huggingface DATASET_NAME
train graph model on a dataset from huggingface
--dataset-from-file DATASET_FILE
train graph model on a dataset stored locally
--save-pretrained FILE_TO_SAVE_TO
file to save trained model todf_gc--dataset-from-fileexamples/example_dataset.dialoguesexamples/example_dialoguesexamples/output.jsonDialogue formatsDialogue files in theEXAMPLE_DIRECTORYfolder should have the following structure:{"dialogue_id":"unique identifier","turns":{"speaker":"array of 0 and 1 where 0 corresponds to a user utterance and 1 corresponds to a system utterance","utterance":"array of strings"}}Dialogue dataset is a list of such dictionaries.To get more advanced examples, take a look atexampleson GitHub.Contributing to the Dialog Flow Graph ConstructionPlease refer toCONTRIBUTING.md.
Package: dfhist
DFHistUtilities for histories and caches of pandas dataframesJ. M. F. Tsang (j.m.f.tsang@cantab.net)What is this?DFHist lets you cache the results of functions that produce pandas dataframes,
as well as maintaining a history of the dataframes on disk (as CSV files).This is useful for running functions that produce large dataframes that can
change over time, such as running SQL queries usingpd.read_sql_queryor
querying an API.
Package: dfiance
Dfiance is a tool for building serializers, deserializers, and validators for
python types.Dfiance does not fully serialize things, because you deserve control over the
format of the data you pass around. Instead, it serializes python objects into
‘dictified’ objects, which are either basic types - strings, ints, floats, and
bools - or lists/dictionaries whose keys and values are all dictified objects.The resulting objects are trivially serializable in JSON, YAML, XML, or pretty
much any other serialization protocol you like.At the moment the majority of the documentation is embedded in the code. You can
browse it on github at <https://github.com/dplepage/dfiance>.
Package: dfiibridge
DfiiBridgeThis tool provides a Python based server that enables you to connect to your Virtuoso sessions and control it remotely using SKILL and Python commands.
It is based on the public SKILL bridge (https://github.com/unihd-cag/skillbridge).
But there has been some major changes:Authentication addedImproved session management (multiple hosts, search and filtering options)Keep Cadence camel case namingIntroduced c++ SWIG to support other languages than Python (currently only perl is available as well)Improved loggingAdded reverse communication channel (calling Python methods from Skill)InstallationPrerequisits:cmake 3.14gcc >=10.3libzmqswigTo run the component tests, just call:toxThis command creates a new virtual python environment and installs the DfiiBdrige there.Getting startedStart Virtuoso.
Load thedfiiBridgeClient.il,dfiiBridgeServer.ilanddfiiBridgeUtils.ilin the CIW.
To start the Virtuoso server call:DfiiBridge_startServer()To connect to the Virtuoso session and get the workspace object, open a terminal inside of Virtuoso
and call the following python commands:from dfiibridge import VirtuosoSessionManager

vm = VirtuosoSessionManager()
ws = vm.get_workspace()
ws.skill.println("Hello World")
Package: dfilter
dfilter-python：机器学习一些统计量的分析工具===============================**注意: 本项目维护更新看作者心情！**.. contents::介绍----dfilter-python 采用 Python2.7 编写。import，很简单：.. code-block:: pythonimport dfilter快速开始---------准备~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~**Tips** :1.  确保你的系统里面已经安装了 `Python2.7 <https://www.python.org/>`_ ，不同作业系统如何安装不再赘述。2.  检查你系统中 `python` 和 `pip` 的版本, 如果不属于 `python2.7` , 请在执行代码范例时，自行将 `python` 和 `pip` 分别替换成 `python2.7` 和 `pip2` 。3.  确保你的系统中安装了 `git` 程序 以及 `python-pip` 。函数使用 ----在 pandas 后~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~**get_colume_name_as_list** (dataframe)得到数据的列名称。**basic_info** (dataframe，列名)得到数据列的基础信息。**get_coverage** (dataframe，列名)得到数据列元素的覆盖率。**get_pearson_similarity** (dataframe，列名1，列名2，是否均值填充空值)得到数据的皮尔逊相关系数。**get_fisher_score** (dataframe，列名1，y[列表]，是否均值填充空值)得到数据的Fisher得分。**get_fisher_score_with_list** (dataframe，[列名1,列名2，.....]，y[列表]，是否均值填充空值)输入列中元素的Fisher得分。**get_K_L_divergence** (dataframe，列名1，列名2)输入列中元素的KL散度**get_K_L_divergence_as_list** (dataframe，[列名1,列名2，.....])输入列中元素的KL散度。**get_K_L_divergence_as_list_with_y** (dataframe，[列名1,列名2，.....]，y[列表])输入列中元素的KL散度。**check_normal_cluster** (dataframe，列名，是否均值填充空值)输入列中元素的 正态检验**get_homogeneity_of_variance** (dataframe，列名1，列名2，是否均值填充空值)输入列中元素的 方差齐性检验**compare_between_two_couples** (dataframe，列名1，列名2，是否均值填充空值)输入列中元素的 两组数之间的比较
Package: dfindexeddb
dfIndexeddbdfindexeddb is an experimental Python tool for performing digital forensic
analysis of IndexedDB and leveldb files.It parses leveldb, IndexedDB and javascript structures from these files without
requiring native libraries.The content of IndexedDB files is dependent on what a web application stores
locally/offline using the web browser'sIndexedDB API.  Examples of content might
include:text from a text/source-code editor application,emails and contact information from an e-mail application,images and metadata from a photo gallery applicationInstallation$ pip install dfindexeddbInstallation from sourceLinuxInstall the snappy compression development package$ sudo apt install libsnappy-devClone or download the repository to your local machine.Create a virutal environemnt and install the package$ python3 -m venv .venv
    $ source .venv/bin/activate
    $ pip install .UsageA CLI tool is available after installation:$ dfindexeddb -h
usage: dfindexeddb [-h] -s SOURCE [--json] {log,ldb,indexeddb} ...

A cli tool for the dfindexeddb package

positional arguments:
  {log,ldb,indexeddb}

options:
  -s SOURCE, --source SOURCE
                        The source leveldb file
  --json                Output as JSONTo parse a LevelDB .log file:$ dfindexeddb -s <SOURCE> log -h
usage: dfindexeddb log [-h] {blocks,physical_records,write_batches,parsed_internal_key,records}

positional arguments:
  {blocks,physical_records,write_batches,parsed_internal_key,records}

options:
  -h, --help            show this help message and exitTo parse a LevelDB .ldb file:$ dfindexeddb -s <SOURCE> ldb -h
usage: dfindexeddb ldb [-h] {blocks,records}

positional arguments:
  {blocks,records}

options:
  -h, --help        show this help message and exitTo parse a LevelDB .ldb or .log file as IndexedDB:$ dfindexeddb -s <SOURCE> indexeddb -h
usage: dfindexeddb indexeddb [-h]

options:
  -h, --help  show this help message and exit
Package: df-io
df_ioPython helpers for doing IO with Pandas DataFramesAvailable methodsread_dfbzip2/gzip/zstandard compressionpassing parameters to Pandas' readersreading from anything, whichsmart_opensupports (local files, AWS S3 etc)most of the available formats, Pandas supportswrite_dfThis method supports:streaming writeschunked writesbzip2/gzip/zstandard compressionpassing parameters to Pandas' writerswriting to anything, whichsmart_opensupports (local files, AWS S3 etc)most of the available formats, Pandas supportsDocumentationAPI docExamplesWrite a Pandas DataFrame (df) to an S3 path in CSV format (the default):importdf_iodf_io.write_df(df,'s3://bucket/dir/mydata.csv')The same with gzip compression:df_io.write_df(df,'s3://bucket/dir/mydata.csv.gz')With zstandard compression using pickle:df_io.write_df(df,'s3://bucket/dir/mydata.pickle.zstd',fmt='pickle')Using JSON lines:df_io.write_df(df,'s3://bucket/dir/mydata.json.gz',fmt='json')Passing writer parameters:df_io.write_df(df,'s3://bucket/dir/mydata.json.gz',fmt='json',writer_options={'lines':False})Chunked write (splitting the df into equally sized parts and creating/writing outputs for them):df_io.write_df(df,'s3://bucket/dir/mydata.json.gz',fmt='json',chunksize=10000)
Package: dfipy
DFI API wrapperAboutPython library created byGeneral Systemand publicly available ongithubto query and experiment with the Data Flow Index (DFI) platform.DFI provides the user with an unparalled foundation and scalable solution for processing high velocity, high volume spatiotemporal workloads for any geospatial data science need.Read the code documentationRead the whitepaperQuickstartContact General System to obtain an API token.Install Python SDK:pipinstalldfipyStart querying withdfipy ExamplesLicenceCopyright (c) 2023, General System Group Limited.DFIPy is provided as it is and copyrighted underApache2 License.DFIPy is currently released on public pypi in beta, strictly for testing and evaluation purposes.
Package: dfir-iris-client
Python clientdfir_iris_clientoffers a Python interface to communicate with IRIS.It relies exclusively on the API, which means output of the methods are the same as specified in the API reference.VersionsThe Python client version follows the API versions (until the patch level). Meaning for API v2.0.1, one need to installdfir_iris_client-2.0.1.
Please refer to thedocumentationto check which version applies to your IRIS instance.InstallIRIS Client is now part of PyPI. You can simply install it with :pip3 install dfir-iris-clientBuildTo build a wheel from the sources:pip3 install wheelpython setup.py bdist_wheelpip3 install dist/XXX.whlExamplesSome examples are availablehere.DocumentationThe documentation is available in theIRIS documentation.
Package: df-jupyter-magic
df_jupyter_magic%dfmagic commands to get file usage within JupyterLab notebook. This package utilizes python functionshutil.disk_usage().RequirementsJupyterLab >= 3.0Python >= 3.6InstallTo install this package, execute:pip install df-jupyter-magicUninstallTo remove the package, execute:pip uninstall df-jupyter-magicHow to UseYou must first import the package by running following command:importdf_jupyter_magicThen, you may use one of the following commands:%dfreturns a human readable string in GB. Input is the path to the disk/partition.%df --rawreturns a raw data object.%df --onreturns rsults in a string in GB after every subsequent cell run.%df --offturns offon.%df -p /sets the path to the partition to check.%df -vprints off additional text for debugging.
Package: dflask
Direct-FlaskUse responses for Flask directlyInstallationFrom PyPIpip3installdflaskFrom GitHubpip3installgit+https://github.com/donno2048/Direct-FlaskUsagefromdflaskimportDirectFlask,Responseapp=DirectFlask(__name__)app.add_response("/",Response("<html><head><link href='/style.css' rel='stylesheet'></head><body></body></html>"))app.add_response("/style.css","body{background-color: red;}").run()
Package: df-lib-syren
What is this  about?Imagine you want to compare various iterations of a table or data file. You may need to ensure that your versions of a
list that is maintained by more than one person or system are same. When you have to solve this manually, it is
difficult. So, let's make use of this library right now!How does it work?The script takes the ID value of an entry from the first table, searches for it in the second table, and sees
if it can find it there. If it does not, it will list this entry as a table 1 entry that was not found in table 2.
This will occur in the opposite direction for table 2 versus table 1. Furthermore, the script compares the values of
entries with the same ID and, if they differ, lists the differences.What does it require?Each table entry must have a unique identifier, also known as a key value.
This could be an order number or an employee number, for example, but it could
also be any other characters as long as it is unique to an entry. This key value
must be located in your table's first column. The order of the other columns,
and also the overall order of the rows, is completely irrelevant.
we hope this library saves you some time!Version Release Beta0.0.3 (06/07/2022)UsageInstall the published library:pip install df_lib_syrenfrom df_lib_syren import syr_compareDF

source =  '/source file path/'
target =  '/target file path/'

compare_dfs = syr_compareDF(source_file = source, target_file= target)