Saravanan666/pypi-org huggingface dataset:
Package: data2tree
Tree BuilderInputlst=[{"id":1,"pid":0,"name":"Tree - 1"},{"id":2,"pid":1,"name":"Tree - 1 - 1"},{"id":3,"pid":1,"name":"Tree - 1 - 2"},{"id":4,"pid":2,"name":"Tree - 1 - 1- 1"},{"id":5,"pid":0,"name":"Tree - 2"}]Usagepipinstalldata2tree-ihttps://pypi.org/simple/fromTreeBuilder.builderimportBuildTreetree=BuildTree(record_key="id",parent_record_key="pid").build_tree(lst)print(tree)NotesParameterTyperequiredDescriptionrecord_keyStringTruerecord idparent_record_keyStringTrueparent_record idOutput[{"id":1,"pid":0,"name":"Tree - 1","child":[{"id":2,"pid":1,"name":"Tree - 1 - 1","child":[{"id":4,"pid":2,"name":"Tree - 1 - 1- 1"}]},{"id":3,"pid":1,"name":"Tree - 1 - 2"}]},{"id":5,"pid":0,"name":"Tree - 2"}]
Package: data4test
data4testyou can get useful data here for software testingInstallationThe last stable release is available on PyPI and can be installed with pip.
make sure that Chrome has been installed and match the selenium version$ pip install data4testBest PracticeFirstly, create a python file: c:\folder\mytest.py# c:\folder\mytest.py
from data4test import timestamp4test
print(timestamp4test.get_this_week_head_timestamp())
print(timestamp4test.get_this_week_end_timestamp())
print(timestamp4test.get_this_month_head_timestamp())
print(timestamp4test.get_this_month_end_timestamp())
print(timestamp4test.get_chinese_this_week_head_timestamp())
print(timestamp4test.get_chinese_this_week_end_timestamp())then use your IDE to run this script or$ python c:\folder\mytest.pyif successful, you will see many timestamps :)Contact meFor information and suggestions you can contact me at523314409@qq.com
Package: data-agent
Data AgentPython package for accessing real-time and historical data on industrial historians and control systems.
Different historian protocols and APIs are implemented through standalone plugins.THIS PACKAGE IS USELESS WITHOUT EXTERNAL PLUGINS IMPLEMENTING TARGET SYSTEM CUSTOM DATA ACCESS PROTOCOLSDescriptionThe package provides a unified data access API having several usage scenarios:As a Python packageAs a command line CLIAs a service (Windows or Linux) using AMQ protocolInstallationpip install data-agentInstall the plugins required for communicating with the target systemsPython Package Usagefromdata_agent.local_agentimportLocalAgentwithLocalAgent()asagent:agent.api.list_supported_connectors()agent.api.create_connection(...)Command Line Usagedagent--service.idmy_service--broker.uriamqp://guest:guest@192.168.4.23/
Package: data-agent-osisoft-pi
data-agent-osisoft-piData Agentplugin for accessing Osisoft PI Historian.CLI ExamplesExample of usingData AgentCLI to access PI tagsdagent exec create_connection --conn_name=pi --conn_type=osisoft-pi --enabled=True --server_name=DATA-SERVER
dagent exec list_connections
dagent exec connection_info --conn_name=pi
dagent exec list_tags --conn_name=pi
dagent exec read_tag_values_period --conn_name=pi --tags="['sinusoid', 'sinusoidu']" --first_timestamp=*-100h --last_timestamp=*
dagent exec copy_period --src_conn=pi --tags="['SINUSOID', 'sinusoidu']" --dest_conn=csv --dest_group='sinus.csv' --first_timestamp=*-100h --last_timestamp=*TroubleshootingOSIsoft.AF.PI.PITimeoutException when reading historical dataIncrease the SDK data access timeout settings on the client machine. There are two timeouts for the SDK, a connection timeout and a data access timeout. The connection timeout default is 10 seconds.  The data access timeout is 60 seconds. Data access timeouts are the most likely cause of the error.Launch AboutPI-SDK.exe.Navigate to the Connections tab.Select the PI Data Archive in question.Increase the Data Access Timeout and Connection Timeout to 120 seconds or more.
Package: data-alchemy
Data ProcessorDescriptionInstallationpipinstalldata-alchemyData Processing is a multi-step processA brief overview of the steps involved in data processing:Fetching InformationScrape data from a websiteFetch data from an APIFetch data from a databaseCleanTransformIndexSearch / AnalyzeVisualizeStoreThis packag aims to be a one-stop-shop for all your data processing needs. It provides a simple interface to perform all the above steps and more.DocumentationThe documentation for this package is hostedhere.
Package: data-alfred
Data Alfred: Structuring Data Projects in SecondsAbout the ProjectData Alfred is a tool designed to streamline the setup of new data projects by creating essential folders and files structure in seconds. Aimed at data teams and data analysis projects, Data Alfred establishes a solid and standardized foundation, allowing data scientists and analysts to focus on what truly matters: extracting valuable insights from data. Inpired byhttps://drivendata.github.io/cookiecutter-data-science/FeaturesCreates directories for raw, preprocessed, and mischaracterized data.Initializes directories for documentation, machine learning models, Jupyter notebooks, frontend for interactive visualizations, and references.Prepares a source code directory structure for visualization, data manipulation, feature engineering, models, and testing.Generates initial files, including.envfor environment variables,.gitignore,README.md,requirements.txtfor dependencies,setup.pyfor package installation,test_environment.pyfor testing, and aDockerfilefor containerization.PrerequisitesTo use Data Alfred, you will need to have Python installed on your system. The tool has been developed and tested in environments supporting Python 3.6 or newer.How to Installpipinstalldata-alfredHow to Useimportdata_alfreddata_alfred.create_project_structure()Data Alfred will take care of the rest, creating the necessary directory and file structure for your data project.Created Directory Structuredata/: Subdivided intopreprocessed,raw, andmischaracterizedfor different stages of data handling.docs/: Containsmkdocs.mdandconfig.ymlfor project documentation.models/: Intended to store trained machine learning models.notebooks/: For Jupyter notebooks of data analysis and exploration.frontend/: Includes__init__.pyandstreamlit_app.pyfor development of data visualization applications.references/: To store project references and resources.reports/: Intended for data analysis reports and visualizations.src/: Contains subdirectories forvisualization,data,features,models, andtests, along with an__init__.pyto treat the contents as a Python package.ContributingContributions to Data Alfred are welcome! If you have a suggestion to improve this tool, feel free to open an issue or pull request on the project repository. Let's work together to make starting data projects a quick and effortless task!LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactAlestan Alves -https://github.com/alestanalvesProject Link:https://github.com/TOTVS-Privacidade-de-Dados/data-alfred
Package: data-algebra
data_algebrais a piped data wrangling system
based on Codd's relational algebra and experience working with data manipulation languages at scale.The primary purpose of the package is to support an easy to
compose and maintain grammar of data processing steps that in turn can be used to generate
database specific SQL. The package also implements the same transforms for Pandas and Polars DataFrames.Currently the system is primarily adapted and testing for Pandas, Polars, Google BigQuery, PostgreSQL, SQLite, Spark, and
MySQL.Rversions of the system are available as
therqueryandrqdatatablepackages.
Package: data-analysis-task-pkg-macca2707
data-analyst-taskSimple data analyst program developed for Halfbrick studios as a mini assignment
Instructions are contained within CLI program but i will add them here aswellCommands:
json -- Creates a json file when provided an appropriate CSV file
data -- Makes a data summary of a provided CSV file
sql -- Creates an SQL file when provided an appropriate CSV file, also requires a table name
*quit -- exits the program*Note all commands except this one require a file name, file is contained within this package
Package: dataanalysistoolkit
Data Analysis ToolkitDataAnalysisToolkit is a comprehensive Python package offering a suite of tools designed for efficient data analysis. This toolkit simplifies tasks such as loading CSV data, performing statistical analysis, cleaning data, and visualizing results. It's an ideal tool for data analysts, scientists, and anyone looking to dive into data exploration and machine learning.FeaturesData Loading: Load data directly from CSV files into a Python environment.Statistical Analysis: Perform calculations like mean, median, mode, and trimmed mean.Outlier Detection: Identify outliers using the z-score method.Data Cleaning: Handle missing values, drop duplicates, and encode categorical data.Data Splitting: Easily split data into training and testing sets for machine learning models.Data Visualization: Create histograms and other plots to explore data visually.Data Export: Export cleaned and processed data back into CSV format.Enhanced FunctionalitiesAdvanced Visualization: Utilize a dedicated visualizer for creating a variety of insightful data plots.Feature Engineering: Enhance your data with new, informative features.Model Evaluation: Assess the performance of machine learning models.Report Generation: Automatically generate comprehensive HTML reports with summaries and visualizations.Data Imputation: Implement advanced imputation techniques to handle missing data.This toolkit is an asset for conducting preliminary data analysis, and it seamlessly integrates into larger data processing workflows.Getting StartedHere's how you can get started with DataAnalysisToolkit:fromdata_analysis_toolkitimportDataAnalysisToolkit# Initialize the analyzer with the path to a CSV fileanalyzer=DataAnalysisToolkit('../data/test.csv')# Calculate the mean, median, mode, and trimmed mean of a columnstatistics=analyzer.calculate_budget_statistics('column_name')print(statistics)# Detect outliers in a column using the z-score methodoutliers=analyzer.detect_outliers('column_name')print(outliers)# Handle missing values in a columnanalyzer.handle_missing_values('column_name',strategy='fill',fill_value=0)# Drop duplicate rows in the DataFrameanalyzer.drop_duplicates()# Encode categorical features in the DataFrameanalyzer.encode_categorical_features()# Split the data into training and testing setsX_train,X_test,y_train,y_test=analyzer.split_data('target_column')# Plot a histogram of a columnanalyzer.plot_data('column_name')# Export the data to a CSV fileanalyzer.export_data('new_file.csv')InstallationInstall DataAnalysisToolkit using pip:pipinstalldataanalysistoolkitDocumentationFor detailed documentation, examples, and usage guides, please visitDataAnalysisToolkit Documentation.ContributingContributions are welcome! For guidelines on how to contribute, please refer to ourContribution Guide.LicenseDataAnalysisToolkit is open-sourced under the MIT License. For more details, see theLICENSEfile.Developed with ❤ by the DataAnalysisToolkit Team.
Package: data-analyze
概述data analysis一、rules 规则系统会根据配置的规则，对原数据进行分析数据结构{"type":"system","rules":[{"id":1,"type":"log","condition":"xxxx"},{"id":2,"type":"assert","condition":"xxxx"},{"id":3,"type":"data","condition":"xxxx"}],"relation":"id1|id2|id3","result":{"type":"system","module":"bluetooth","desc":"蓝牙没打开"},"weights":1type 规则类型支持 coredump、system、testcoredump 数据分析system 系统级别日志分析test 测试业务数据分析rules 规则id 唯一标识type 匹配内容类型，log-行日志、assert-断言、data-直接数据condition 匹配条件，支持正则表达式，可配置多个条件relation 匹配条件关系多个条件id匹配关系，支持 与&、或|、非not、优先级()result 匹配结果type 结果类型：cloud云端问题，system系统端问题module 问题模块：bluetooth蓝牙模块，alarm闹钟模块等。。desc 问题描述weights 权重同时匹配多个规则时，权重越大概率越高打包上传1.pip install twine
2.python setup.py sdist bdist_wheel  --打包
3.twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*   --上传
Package: data-analyzer
Auto EDA packageAutomatically detects numeric and categorical featuresUser may manually assign numeric and categorical features using set_numeric_features()
and set_categorical_features() methods if the feature detection is incorrect (recommended).get_data_structure_summary() provides basic information like head, tail,
data types, missing value info, etc.get_categorical_features_summary() provides information like count of unique values,
unique values, data distribution, etc of categorical features.get_numeric_features_summary() provides summary of numeric features along with
distribution plots.plot_correlation_matrix() plots the Pearson and Spearman correlation matrices of
all  the numeric features.plot_chi_square_result() plots the p-values of the chi-square tests performed between
categorical features.plot_numeric_vs_numeric() plots scatter plots between the numeric features.plot_categorical_vs_categorical() plots stacked bar plots between the categorical features.a.plot_mutual_information(target) plots a bar plot showing the mutual information score
between the input features and target.a.get_vif() returns the VIF scores of all the numeric features.plot_categorical_vs_numeric() plots violin plots between all the categorical features
and numeric features.
Package: data-anonymizer-mapper
# Anonymizer_Anonymizer_ is a Python package that generates fake data for you. It internally makes use of the [Faker](https://github.com/joke2k/faker) package, and allows you to keep track of the mapping between your original and fake data. This will be especially useful when you are anonymizing data in pandas data frames.```_____                                           .__/  _  \    ____    ____    ____  ___.__.  _____  |__|________  ____ _______/  /_\  \  /    \  /  _ \  /    \<   |  | /     \ |  |\___   /_/ __ \\_  __ \/    |    \|   |  \(  <_> )|   |  \\___  ||  Y Y  \|  | /    / \  ___/ |  | \/\____|__  /|___|  / \____/ |___|  // ____||__|_|  /|__|/_____ \ \___  >|__|\/      \/              \/ \/           \/           \/     \/```## Basic Usage### Initialization```names = ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']anonymizer = Anonymizer()```### Get Anonymized Name```anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg')# 'Catherine Parker'```### Get Original Name```anonymizer.get_original_name('Catherine Parker')# 'Ghajinikanth Zuckerberg'```### Get Anonymized Name for Same Name```anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # First Call# 'Catherine Parker'anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # Second Call# 'Catherine Parker'```### Fetch list of Anonymized Names```anonymizer.get_anonymized_names(names)# ['Leslie Adams', 'Michelle Burke', 'Annette Maxwell']```### Fetch list of Original Names```anonymizer.get_original_names(anonymizedNames)# ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']```###  Get Anonymized Data for a different Faker Type```address_anonymizer = Anonymizer(faker_type=FakerType.ADDRESS)address_anonymizer.get_anonymized_name('74437 Alexandra Well\nSouth Jade, CT 40282')# 'USNS Hernandez\nFPO AA 32353'```## Acknowledgements- [Faker](https://github.com/joke2k/faker)
Package: data-art
DATA-ARTCreate easy to use, straight forward way of creating dynamic, interactive, responsive and beautiful chart-included reports by the help of html and css, pure OOP python package.It can simplify the dynamic report generating procedure.You can import the project as below:fromdata_art.html_visualizerimportPresentationMIT LicenseCopyright (c) 2023 erfanvaredi/data-artPermission 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.
Package: data-as-code
Data as CodeThis package provides tools which can be used to strictly define, version control,
and document data. To sum it up, this package treats Data as Code (DaC) in the
instances where that is possible.This framework is designed for tabular, file-based data sets, and works best with
fixed, unchanging files (although it can also be used for changing data, but some
of the tools in this framework are rendered moot by these changes).The product generated by this framework is a tabular data set which includes
complete documentation of the original source, intermediate steps of transformation,
and hash sums to guarantee that the data match the associated code.Stages:Get(Optional) unpack/inflateParseTransformPackageDistribute
Package: databand
DBNDDBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes.DBND simplifies the process of building and running data pipelines
from dbnd import taskfromdbndimporttask@taskdefsay_hello(name:str="databand.ai")->str:value="Hello%s!"%namereturnvalueAnd makes it easy to track your critical pipeline metadatafromdbndimportlog_metric,log_dataframelog_dataframe("my_dataset",my_dataset)log_metric("r2",r2)Getting StartedSee ourdocumentationwith examples and quickstart guides to get up and running with DBND.The Latest and GreatestFor using DBND, we recommend that you work with a virtual environment likeVirtualenvorConda. Update to the latest and greatest:pipinstalldbndIf you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch throughcontact@databand.ai
Package: databaseadmin
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 import DbAdministrator
DA = DbAdministrator()
Package: databaseauto
# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.# difference between requirements_dev.txt and requirements.txtrequirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.# tox.ini
We use if for the testing in the python package testing against different version of the python## how tox works tox enviornment creation
1. Install depedencies and packages
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec# Testing python applicationtypes of testing1. Automated testing
2. Manual testingMode of testing1. Unit testing
2. Integration testsTesting frameworkspytestunittestrobotframeworkseleniumbehavedoctest# check with the code style formatting and syntax(coding standard)pylintflake8(it is best because it containt 3 library pylint pycodestyle mccabe)pycodestyle
Package: database-auto-bulk-operation
1. pip database_auto_bulk_operation强大的自动批量聚合操作各种数据库，不需要再调用处手动去喂给批量调用方法一个个组装好了数组。
自动批量聚合操作数据库能减少客户端和数据库服务端的io往返次数，效率提升大。

包括支持mysql mongo redis elastic。

主要原理是批量操作对象内部有一个while 1的守护线程，不断的去自动组合列表任务，
然后调用各种数据库的的python包的批量操作方法。
同时加入一个atexit的钩子，防止守护线程随程序一起结束时候，掉一批还未批量插入的尾部任务。


各种数据库的更简单的批次操作，主要是不用再调用处手动切割分组来调用原生中间件操作类的批量操作方法
对于未知时间的离散任务能够自动批量聚合，这种情况下无法自己提前切割分组，使用此包的方式非常适合。
Package: databaseautomate
# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.# difference between requirements_dev.txt and requirements.txtrequirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.# tox.ini
We use if for the testing in the python package testing against different version of the python## how tox works tox enviornment creation
1. Install depedencies and packages
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec# Testing python applicationtypes of testing1. Automated testing
2. Manual testingMode of testing1. Unit testing
2. Integration testsTesting frameworkspytestunittestrobotframeworkseleniumbehavedoctest# check with the code style formatting and syntax(coding standard)pylintflake8(it is best because it containt 3 library pylint pycodestyle mccabe)pycodestyle
Package: databaseautomation
# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.# difference between requirements_dev.txt and requirements.txtrequirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.# tox.ini
We use if for the testing in the python package testing against different version of the python## how tox works tox enviornment creation
1. Install depedencies and packages
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec# Testing python applicationtypes of testing1. Automated testing
2. Manual testingMode of testing1. Unit testing
2. Integration testsTesting frameworkspytestunittestrobotframeworkseleniumbehavedoctest# check with the code style formatting and syntax(coding standard)pylintflake8(it is best because it containt 3 library pylint pycodestyle mccabe)pycodestyle