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

Image datasets:

vege_original : Images of vegetables captured manually in data acquisition stage

vege_cropped_renamed : Images in (1) cropped to remove background areas and image labels renamed

non-vege images : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods

food_image_dataset : Complete set of vege (2) and non-vege (3) images for architecture building.

food_image_dataset_split : Image dataset (4) split into train and test sets

process : Images created when cropping (pre-processing step) to create dataset (2).

Dataset Card for Dataset Name

Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small

  Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding. The dataset has two features - 'question' and 'code'. An example is: {'question': 'Create a function that takes in a string… See the full description on the dataset page: https://huggingface.co/datasets/Arjun-G-Ravi/Python-codes.

Context

This is the random date data-set generated by me using python script to create a Machine Learning model to tag the date in any given document.

Content

This data-set contains whether the given word of word are dates or not

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Implement Machine Learning model or Deep learning Model or train a custom spacy to tag the date and other POS.

Dataset to run Example.py script of the Valparaíso Stacking Analysis Tool (VSAT-2D). The Valparaíso Stacking Analysis Tool (VSAT-2D) provides a series of tools for selecting, stacking, and analyzing moment-0 intensity maps from interferometric datasets. It is intended for stacking samples of moment-0 extracted from interferometric datasets, belonging to large extragalactic catalogs by selecting subsamples of galaxies defined by their available properties (e.g. redshift, stellar mass, star formation rate) being possible to generate diverse (e.g. median, average, weighted average, histogram) composite spectra. However, it is possible to also use VSAT-2D on smaller datasets containing any type of astronomical object.

VSAT-2D can be downloaded from the github repository link.

example dataset with Python files

We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle.

The data is organized in a table structure. Code4ML includes several main objects: competitions information, raw code blocks collected form Kaggle and manually marked up snippets. Each table has a .csv format.

Each competition has the text description and metadata, reflecting competition and used dataset characteristics as well as evaluation metrics (competitions.csv). The corresponding datasets can be loaded using Kaggle API and data sources.

The code blocks themselves and their metadata are collected to the data frames concerning the publishing year of the initial kernels. The current version of the corpus includes two code blocks files: snippets from kernels up to the 2020 year (сode_blocks_upto_20.csv) and those from the 2021 year (сode_blocks_21.csv) with corresponding metadata. The corpus consists of 2 743 615 ML code blocks collected from 107 524 Jupyter notebooks.

Marked up code blocks have the following metadata: anonymized id, the format of the used data (for example, table or audio), the id of the semantic type, a flag for the code errors, the estimated relevance to the semantic class (from 1 to 5), the id of the parent notebook, and the name of the competition. The current version of the corpus has ~12 000 labeled snippets (markup_data_20220415.csv).

As marked up code blocks data contains the numeric id of the code block semantic type, we also provide a mapping from this number to semantic type and subclass (actual_graph_2022-06-01.csv).

The dataset can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.

This dataset is about books. It has 1 row and is filtered where the book is Natural language processing : Python and NLTK : learning path : learn to build expert NLP and machine learning projects using NLTK and other Python libraries. It features 7 columns including author, publication date, language, and book publisher.

License

MIT

  This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks





  Overview

The dataset has 4 key features (fields): instruction, input, output, and text.It's a rich source for Python codes, tasks, and extends into behavioral aspects.

  Dataset Statistics

Total Entries: 24,813 Unique Instructions: 24,580 Unique Inputs: 3,666 Unique Outputs: 24,581 Unique Texts: 24,813 Average Tokens per example: 508

  Features… See the full description on the dataset page: https://huggingface.co/datasets/flytech/python-codes-25k.

This resource contains a Jupyter notebook that demonstrates how someone can query the I-GUIDE data catalog, retrieve data, and execute a code workflow.

This dataset contains the metadata of the datasets published in 85 Dataverse installations and information about each installation's metadata blocks. It also includes the lists of pre-defined licenses or terms of use that dataset depositors can apply to the datasets they publish in the 58 installations that were running versions of the Dataverse software that include that feature. The data is useful for reporting on the quality of dataset and file-level metadata within and across Dataverse installations and improving understandings about how certain Dataverse features and metadata fields are used. Curators and other researchers can use this dataset to explore how well Dataverse software and the repositories using the software help depositors describe data. How the metadata was downloaded The dataset metadata and metadata block JSON files were downloaded from each installation between August 22 and August 28, 2023 using a Python script kept in a GitHub repo at https://github.com/jggautier/dataverse-scripts/blob/main/other_scripts/get_dataset_metadata_of_all_installations.py. In order to get the metadata from installations that require an installation account API token to use certain Dataverse software APIs, I created a CSV file with two columns: one column named "hostname" listing each installation URL in which I was able to create an account and another column named "apikey" listing my accounts' API tokens. The Python script expects the CSV file and the listed API tokens to get metadata and other information from installations that require API tokens. How the files are organized ├── csv_files_with_metadata_from_most_known_dataverse_installations │ ├── author(citation)_2023.08.22-2023.08.28.csv │ ├── contributor(citation)_2023.08.22-2023.08.28.csv │ ├── data_source(citation)_2023.08.22-2023.08.28.csv │ ├── ... │ └── topic_classification(citation)_2023.08.22-2023.08.28.csv ├── dataverse_json_metadata_from_each_known_dataverse_installation │ ├── Abacus_2023.08.27_12.59.59.zip │ ├── dataset_pids_Abacus_2023.08.27_12.59.59.csv │ ├── Dataverse_JSON_metadata_2023.08.27_12.59.59 │ ├── hdl_11272.1_AB2_0AQZNT_v1.0(latest_version).json │ ├── ... │ ├── metadatablocks_v5.6 │ ├── astrophysics_v5.6.json │ ├── biomedical_v5.6.json │ ├── citation_v5.6.json │ ├── ... │ ├── socialscience_v5.6.json │ ├── ACSS_Dataverse_2023.08.26_22.14.04.zip │ ├── ADA_Dataverse_2023.08.27_13.16.20.zip │ ├── Arca_Dados_2023.08.27_13.34.09.zip │ ├── ... │ └── World_Agroforestry_-_Research_Data_Repository_2023.08.27_19.24.15.zip └── dataverse_installations_summary_2023.08.28.csv └── dataset_pids_from_most_known_dataverse_installations_2023.08.csv └── license_options_for_each_dataverse_installation_2023.09.05.csv └── metadatablocks_from_most_known_dataverse_installations_2023.09.05.csv This dataset contains two directories and four CSV files not in a directory. One directory, "csv_files_with_metadata_from_most_known_dataverse_installations", contains 20 CSV files that list the values of many of the metadata fields in the citation metadata block and geospatial metadata block of datasets in the 85 Dataverse installations. For example, author(citation)_2023.08.22-2023.08.28.csv contains the "Author" metadata for the latest versions of all published, non-deaccessioned datasets in the 85 installations, where there's a row for author names, affiliations, identifier types and identifiers. The other directory, "dataverse_json_metadata_from_each_known_dataverse_installation", contains 85 zipped files, one for each of the 85 Dataverse installations whose dataset metadata I was able to download. Each zip file contains a CSV file and two sub-directories: The CSV file contains the persistent IDs and URLs of each published dataset in the Dataverse installation as well as a column to indicate if the Python script was able to download the Dataverse JSON metadata for each dataset. It also includes the alias/identifier and category of the Dataverse collection that the dataset is in. One sub-directory contains a JSON file for each of the installation's published, non-deaccessioned dataset versions. The JSON files contain the metadata in the "Dataverse JSON" metadata schema. The Dataverse JSON export of the latest version of each dataset includes "(latest_version)" in the file name. This should help those who are interested in the metadata of only the latest version of each dataset. The other sub-directory contains information about the metadata models (the "metadata blocks" in JSON files) that the installation was using when the dataset metadata was downloaded. I included them so that they can be used when extracting metadata from the dataset's Dataverse JSON exports. The dataverse_installations_summary_2023.08.28.csv file contains information about each installation, including its name, URL, Dataverse software version, and counts of dataset metadata...

Projet Python

## Overview

Projet Python is a dataset for classification tasks - it contains Generative Adversarial Network annotations for 620 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).

Example data included with the python package vreg (openmiblab.github.io/vreg).

The package includes an API for downloading and reading the data through the function vreg.fetch.

The database includes kidney MRI data of different types from a single subject of the iBEAt study:

This dataset is about book series. It has 1 row and is filtered where the books is Building machine learning systems with Python : master the art of machine learning with Python and build effective machine learning sytems with this intensive hands-on guide. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Dataset to run Example.py script of the Valparaíso Stacking Analysis Tool (VSAT-3D). The Valparaíso Stacking Analysis Tool (VSAT-3D) provides a series of tools for selecting, stacking, and analyzing 3D spectra. It is intended for stacking samples of datacubes extracted from interferometric datasets, belonging to large extragalactic catalogs by selecting subsamples of galaxies defined by their available properties (e.g. redshift, stellar mass, star formation rate) being possible to generate diverse (e.g. median, average, weighted average, histogram) composite spectra. However, it is possible to also use VSAT-3D on smaller datasets containing any type of astronomical object.

VSAT-3D can be downloaded from the github repository link.

The files in this repository can be used to generate the complete set of figures in the paper "An algorithm to identify vapor-liquid-liquid equilibria from vapor-liquid equilibria". The zip file, when expanded, includes a conda environment to populate the dependencies, and a set of python scripts. Running make_figures.py will regenerate all the figures, demonstrating how to use the algorithm.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Scientific computing with Python 3 : an example-rich, comprehensive guide for all of your Python computational needs. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Kivy : interactive applications in Python : create cross-platform UI/UX applications and games in Python. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

My Cool Dataset

This dataset is an example of how to create and upload a dataset card using Python. I use only to practice how to manipulate dataset iteslf, add new data remove them. Fix typos.

  Dataset Details

Language: English License: MIT Tags: text-classification, example

Python Test

## Overview

Python Test is a dataset for object detection tasks - it contains Atk annotations for 970 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).