datazets 1.1.3

Datazets is a python package to import well known example data sets.

datazets

• 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

• Erdogan Taskesen, github: erdogant

Contribute

• All kinds of contributions are welcome!
• If you wish to buy me a Coffee for this work, it is very appreciated :)

Licence

See LICENSE for details.