iamketan25/python-qa-instructions-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for my-distiset-986461

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-986461/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/sdiazlor/python-reasoning-dataset.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

This dataset contains morphometric information from Burmese pythons collected from an invasive population in southern Florida between 1995-2021. Scientists from the U.S. Geological Survey and the National Park Service curated this dataset as a repository for records of Burmese pythons found on or nearby federal lands in southern Florida, including Everglades National Park, Big Cypress National Preserve, Biscayne National Park, and Crocodile Lake National Wildlife Refuge. As such, numerous entities actively or incidentally involved in python research or management activities contributed specimens and/or data to this dataset, including but not limited to the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, University of Florida, Conservancy of Southwest Florida, Florida Fish and Wildlife Conservation Commission, South Florida Water Management District, volunteers, and members of the public. The dataset includes python identification information, capture information, morphometric data, and necropsy data. The structure of the dataset is such that every row pertains to a single date that data were collected from a single python so that serial captures and morphological data collected from unique individuals can be tracked across time via different rows.

Applied Data Science With Python

## Overview

Applied Data Science With Python is a dataset for classification tasks - it contains Fruits annotations for 327 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset of Python projects used for the study of code change patterns and their automation. The dataset lists 120 projects, divided into four domains — Web, Media, Data, and ML+DL.

Upload From Python

## Overview

Upload From Python is a dataset for object detection tasks - it contains Cars WyfR annotations for 3,002 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input features in the public supply delivery and water use machine learning models. This page includes the following file: census_data_collector.zip - a zip file containing the census data collector Python code used to retrieve data from the U.S. Census Bureau and a README file.

This dataset is randomly generated using the built-in function from python random.randint(). This csv file contains 2 columns, index and value. Index represents the unique row id and value represents the randomly generated value at each row.

This child item describes Python code used to retrieve gridMET climate data for a specific area and time period. Climate data were retrieved for public-supply water service areas, but the climate data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the climate data collector code were used as input feature variables in the public supply delivery and water use machine learning models. This page includes the following file: climate_data_collector.zip - a zip file containing the climate data collector Python code used to retrieve climate data and a README file.

This folder contains datasets to be downloaded from students for their practices with R and Python

Sample data set used in an introductory course on Programming in Python

This dataset is about book subjects. It has 2 rows and is filtered where the books is Python data science handbook : essential tools for working with data. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

KGTorrent is a dataset of Python Jupyter notebooks from the Kaggle platform.

The dataset is accompanied by a MySQL database containing metadata about the notebooks and the activity of Kaggle users on the platform. The information to build the MySQL database has been derived from Meta Kaggle, a publicly available dataset containing Kaggle metadata.

In this package, we share the complete KGTorrent dataset (consisting of the dataset itself plus its companion database), as well as the specific version of Meta Kaggle used to build the database.

More specifically, the package comprises the following three compressed archives:

1. KGT_dataset.tar.bz2, the dataset of Jupyter notebooks;

2. KGTorrent_dump_10-2020.sql.tar.bz2, the dump of the MySQL companion database;

3. MetaKaggle27Oct2020.tar.bz2, a copy of the Meta Kaggle version used to build the database.

Moreover, we include KGTorrent_logical_schema.pdf, the logical schema of the KGTorrent MySQL database.

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

These data were collected from Burmese pythons removed from the Florida everglades as part of invasive-species management. After euthanasia, we sexed (male or female) and measured the snout-vent length (SVL; cm) and total body mass (g) for each python. We also measured total fat mass (g) by removing all visible fat bodies from the coelomic cavity and weighing this mass. For a subset of specimens, we recorded whether the pythons were put on ice after euthanasia and measured within 24 hours ('fresh') or whether the pythons were frozen after euthanasia, thawed, and then measured ('frozen'). These data were used to validate several body condition indices in Burmese pythons.

The EPA GitHub repository PAU4ChemAs as described in the README.md file, contains Python scripts written to build the PAU dataset modules (technologies, capital and operating costs, and chemical prices) for tracking chemical flows transfers, releases estimation, and identification of potential occupation exposure scenarios in pollution abatement units (PAUs). These PAUs are employed for on-site chemical end-of-life management. The folder datasets contains the outputs for each framework step. The Chemicals_in_categories.csv contains the chemicals for the TRI chemical categories. The EPA GitHub repository PAU_case_study as described in its readme.md entry, contains the Python scripts to run the manuscript case study for designing the PAUs, the data-driven models, and the decision-making module for chemicals of concern and tracking flow transfers at the end-of-life stage. The data was obtained by means of data engineering using different publicly-available databases. The properties of chemicals were obtained using the GitHub repository Properties_Scraper, while the PAU dataset using the repository PAU4Chem. Finally, the EPA GitHub repository Properties_Scraper contains a Python script to massively gather information about exposure limits and physical properties from different publicly-available sources: EPA, NOAA, OSHA, and the institute for Occupational Safety and Health of the German Social Accident Insurance (IFA). Also, all GitHub repositories describe the Python libraries required for running their code, how to use them, the obtained outputs files after running the Python script modules, and the corresponding EPA Disclaimer. This dataset is associated with the following publication: Hernandez-Betancur, J.D., M. Martin, and G.J. Ruiz-Mercado. A data engineering framework for on-site end-of-life industrial operations. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 327: 129514, (2021).

Compilation of python codes for data preprocessing and VegeNet building, as well as image datasets (zip files).

Image datasets:

1. vege_original : Images of vegetables captured manually in data acquisition stage
2. vege_cropped_renamed : Images in (1) cropped to remove background areas and image labels renamed
3. non-vege images : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods
4. food_image_dataset : Complete set of vege (2) and non-vege (3) images for architecture building.
5. food_image_dataset_split : Image dataset (4) split into train and test sets
6. process : Images created when cropping (pre-processing step) to create dataset (2).

Script we use to test the python ETL update process on milo. Keep it private, but please do not delete.