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datazets

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  • datazets is Python package

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pip install datazets

Import datazets

# Import library
import datazets as dz
# Import data set
df = dz.get('titanic')

Data sets:

Dataset Name Shape Size Type Description
meta (1472, 20) Continuous, time Stock price of Meta
bitcoin (2522, 2) Continuous, time Bitcoin price history data for time series and price prediction
iris (150, 3) Continuous Classic flower classification dataset with iris species measurements with coordinates
gas_prices (6556, 2) Mixed, time Historical gas prices by region for trend analysis
ads (10000, 10) Discrete Data on online ads, covering click-through rates and targeting information
sprinkler (1000, 4) Discrete Synthetic dataset with binary variables for rain and sprinkler probability illustration
random_discrete (1000, 5) Discrete Synthetic dataset with random discrete variables, useful for probability modeling
malicious_urls (387588, 2) Text URLs labeled as malicious or benign, useful in cybersecurity
malicious_phish (651191, 4) Text URLs labeled as malicious or benign, defacement, phishing, malware (cybersecurity)
stormofswords (352, 3) Network Character data from A Storm of Swords, with relationships, traits, and alliance info
bigbang (9, 3) Network Data on The Big Bang Theory episodes and characters
energy (68, 3) Network Data on building energy consumption
auto_mpg (392, 8) Mixed Data on cars with features for predicting miles per gallon
breast_cancer (569, 30) Mixed Dataset for breast cancer diagnosis prediction using tumor cell features
cancer (4674, 9) Mixed Cancer patient data for classification and prediction of diagnosis outcome with Coordinates
census_income (32561, 15) Mixed US Census data with various demographic and economic factors for income prediction
elections_rus (94487, 23) Mixed Russian election data with demographic and political attributes
elections_usa (24611, 8) Mixed US election data with demographic and political attributes
fifa (128, 27) Mixed FIFA player stats including attributes like skill, position, country, and performance
marketing_retail (999, 8) Mixed Retail customer data for behavior and segmentation analysis
predictive_maintenance (10000, 14) Mixed Industrial equipment data for predictive maintenance
student (649, 33) Mixed Data on student performance with socio-demographic and academic factors
surfspots (9413, 4) Mixed, latlon Information on global surf spots, with details on location and wave characteristics
tips (244, 7) Mixed Restaurant tipping data with variables on meal size, day, and tip amount
titanic (891, 12) Mixed Titanic passenger data with demographic, class, and survival information
waterpump (59400, 41) Mixed Water pump data with features for predicting functionality and maintenance needs
cat_and_dog None Image Images of cats and dogs for classification and object recognition
digits (1083, 65) Image Handwritten digit images (8x8 pixels) for recognition and classification
faces (400, 4097) Image Images of faces used in facial recognition and feature analysis
flowers None Image Various flower images for classification and image recognition
img_peaks1 (930, 930, 3) Image Synthetic peak images for image processing and analysis
img_peaks2 (125, 496, 3) Image Additional synthetic peak images for image processing
mnist (1797, 65) Image MNIST handwritten digit images (28x28 pixels) for classification tasks
scenes None Image Scene images for scene classification tasks
southern_nebula None Image Images of the Southern Nebula, suitable for astronomical analysis
blobs Custom Continuous Synthetic data of datapoints in blob shape
moons Custom Continuous Synthetic data of datapoints in moon shape
circles Custom Continuous Synthetic data of datapoints in circle shape
anisotropic Custom Continuous Synthetic data of datapoints with anisotropic shape
globular Custom Continuous Synthetic data of datapoints with globular shape
uniform Custom Continuous Synthetic data with uniform shape
densities Custom Continuous Synthetic data with different densities

Example:

import datazets as dz
df = dz.get(data='titanic')
import datazets as dz

# Import from url
url='https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'
df = dz.get(url=url, sep=',')

Maintainer

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Licence

See LICENSE for details.

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