Description: - This dataset includes all 22 built-in datasets from the Seaborn library, a widely used Python data visualization tool. Seaborn's built-in datasets are essential resources for anyone interested in practicing data analysis, visualization, and machine learning. They span a wide range of topics, from classic datasets like the Iris flower classification to real-world data such as Titanic survival records and diamond characteristics.

• Included Datasets:
  • Anagrams: Analysis of word anagram patterns.
  • Anscombe: Anscombe's quartet demonstrating the importance of data visualization.
  • Attention: Data on attention span variations in different scenarios.
  • Brain Networks: Connectivity data within brain networks.
  • Car Crashes: US car crash statistics.
  • Diamonds: Data on diamond properties including price, cut, and clarity.
  • Dots: Randomly generated data for scatter plot visualization.
  • Dow Jones: Historical records of the Dow Jones Industrial Average.
  • Exercise: The relationship between exercise and health metrics.
  • Flights: Monthly passenger numbers on flights.
  • FMRI: Functional MRI data capturing brain activity.
  • Geyser: Eruption times of the Old Faithful geyser.
  • Glue: Strength of glue under different conditions.
  • Health Expenditure: Health expenditure statistics across countries.
  • Iris: Famous dataset for classifying Iris species.
  • MPG: Miles per gallon for various vehicles.
  • Penguins: Data on penguin species and their features.
  • Planets: Characteristics of discovered exoplanets.
  • Sea Ice: Measurements of sea ice extent.
  • Taxis: Taxi trips data in a city.
  • Tips: Tipping data collected from a restaurant.
  • Titanic: Survival data from the Titanic disaster.

This complete collection serves as an excellent starting point for anyone looking to improve their data science skills, offering a wide array of datasets suitable for both beginners and advanced users.

Attention: This dataset is a summary and reformat pulled from github code.

You should make your own assumptions based on this. In fact, there is another dataset I formed through parsing that addresses several points:

out of 500k python related items, most of them are python-ish, not pythonic the majority of the items here contain excessive licensing inclusion of original code the items here are sometimes not even python but have references There's a whole lot of gpl summaries… See the full description on the dataset page: https://huggingface.co/datasets/jtatman/python-code-dataset-500k.

This dataset is about Python programming. Question and answers are generated using Gemma. There are more than four hundred questions and their corresponding answers about Python programming.

Questions are ranging from concepts like data-types, variables and keywords to regular-expression and threading.

I have used this dataset here

The code used for dataset generated is available here

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

Dataset

This dataset was created by Kolluri Nithin

Contents

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).

What is Pandas?

Pandas is a Python library used for working with data sets.

It has functions for analyzing, cleaning, exploring, and manipulating data.

The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

Why Use Pandas?

Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.

Relevant data is very important in data science.

What Can Pandas Do?

Pandas gives you answers about the data. Like:

Is there a correlation between two or more columns?

What is average value?

Max value?

Min value?

Automatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce.

PraCegoVer arose on the Internet, stimulating users from social media to publish images, tag #PraCegoVer and add a short description of their content. Inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images.

PraCegoVer has 533,523 pairs with images and captions described in Portuguese collected from more than 14 thousand different profiles. Also, the average caption length in #PraCegoVer is 39.3 words and the standard deviation is 29.7.

Dataset Structure

PraCegoVer dataset is composed of the main file dataset.json and a collection of compressed files named images.tar.gz.partX

containing the images. The file dataset.json comprehends a list of json objects with the attributes:

user: anonymized user that made the post;

filename: image file name;

raw_caption: raw caption;

caption: clean caption;

date: post date.

Each instance in dataset.json is associated with exactly one image in the images directory whose filename is pointed by the attribute filename. Also, we provide a sample with five instances, so the users can download the sample to get an overview of the dataset before downloading it completely.

Download Instructions

If you just want to have an overview of the dataset structure, you can download sample.tar.gz. But, if you want to use the dataset, or any of its subsets (63k and 173k), you must download all the files and run the following commands to uncompress and join the files:

cat images.tar.gz.part* > images.tar.gz tar -xzvf images.tar.gz

Alternatively, you can download the entire dataset from the terminal using the python script download_dataset.py available in PraCegoVer repository. In this case, first, you have to download the script and create an access token here. Then, you can run the following command to download and uncompress the image files:

python download_dataset.py --access_token=

CodeParrot 🦜 Dataset

  What is it?

This is the full CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

  Creation

It was created with the GitHub dataset available via Google's BigQuery. It contains approximately 22 million Python files and is 180 GB (50 GB compressed) big. The… See the full description on the dataset page: https://huggingface.co/datasets/transformersbook/codeparrot.

Dataset Card for "code-search-net-python"

  Dataset Description

Homepage: None Repository: https://huggingface.co/datasets/Nan-Do/code-search-net-python Paper: None Leaderboard: None Point of Contact: @Nan-Do

  Dataset Summary

This dataset is the Python portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-python.

Datasets for practising in class

Dataset

This dataset was created by JABERI Mohamed Habib

Contents

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

Dataset

This dataset was created by Ada Luo daa

Released under Apache 2.0

Contents

Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

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).

Images sous forme de fichiers CSV pour une application de méthodes de machine learning "classiques" Ces datasets sont utilisés pour le cours de Centrale Lille sur le Machine Learning de Pascal Yim

"mnist_big.csv"

Reconnaissance d'images de chiffres manuscrits

Version "mnist_small.csv" avec moins de données pouvant servir aussi d'ensemble de test

Source : https://www.kaggle.com/datasets/oddrationale/mnist-in-csv

"sign_mnist_big.csv"

Reconnaissance d'images de gestes de la langue des signes

Version "sign_mnist_small.csv" avec moins de données pouvant servir aussi d'ensemble de test

Source : https://www.kaggle.com/datasets/datamunge/sign-language-mnist

"zalando_small.csv"

Reconnaissance de vêtements et chaussures (Zalando)

Source : https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000

"hmnist_8_8_RGB.csv"

Reconnaissance de tumeurs de la peau (images en couleurs, trois valeurs R,G,B par pixel)

Autres versions avec des images plus petites et/ou en niveaux de gris

Source : https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000

"cifar10_small.csv"

Reconnaissance de petites images en couleurs dans 10 catégories Version en CSV du dataset CIFAR10

Source : https://www.kaggle.com/datasets/fedesoriano/cifar10-python-in-csv?select=train.csv

The MNIST database of handwritten digits.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/mnist-3.0.1.png" alt="Visualization" width="500px">

This dataset is joint work with Gang Wang.

smoker.csv is a simple simulated dataset on treatment results for patients, who may or may not be smokers.

• smoker: 1 yes, 0 no
• treatment: 1 treated, 0 not treated (control)
• outcome: 1 dead, 0 not dead

groupon.csv is a dataset of Groupon deals collected by Gang and used in his research paper.

• deal_id: the ID of the deal
• start_date: the starting date of the deal
• min_req: minimal number of orders for the deal to work
• treatment: 1 if has min_req, 0 otherwise
• prom_length: the length of the deal
• price: unit price of the item
• discount_pct: discount percentage
• coupon_duration: coupon duration
• featured: whether the deal is featured or not
• limited_supply: whether the supply of the item is limited or not
• fb_likes: Facebook likes received
• quantity_sold: quantity sold
• revenue: revenue of the deal

employment.csv is adapted from the dataset in Card and Krueger (1994), which estimates the causal effect of an increase in the state minimum wage on the employment.

• On April 1, 1992, New Jersey raised the state minimum wage from $4.25 to $5.05 while the minimum wage in Pennsylvania stays the same at $4.25.
• data about the employment in the fast food restaurants in NJ (0) and PA (1) were collected in February 1992 and in November 1992.
• total 384 restaurants after removing null values

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.