Wine Dataset

A function that loads the Wine dataset into NumPy arrays.

from mlxtend.data import wine_data

Overview

The Wine dataset for classification.

Samples	178
Features	13
Classes	3
Data Set Characteristics:	Multivariate
Attribute Characteristics:	Integer, Real
Associated Tasks:	Classification
Missing Values	None

column	attribute
1)	Class Label
2)	Alcohol
3)	Malic acid
4)	Ash
5)	Alcalinity of ash
6)	Magnesium
7)	Total phenols
8)	Flavanoids
9)	Nonflavanoid phenols
10)	Proanthocyanins
11)	Color intensity
12)	Hue
13)	OD280/OD315 of diluted wines
14)	Proline

class	samples
0	59
1	71
2	48

References

• Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.
• Source: https://archive.ics.uci.edu/ml/datasets/Wine
• Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Example 1 - Dataset overview

from mlearner.data import wine_data
X, y = wine_data()

print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))

print('\n1st row')
print(X.iloc[0])

Dimensions: 178 x 12

1st row
alcohol                            1.71
malic acid                         2.43
ash                               15.60
ash alcalinity                   127.00
magnesium                          2.80
total phenols                      3.06
flavanoids                         0.28
nonflavanoid phenols               2.29
proanthocyanins                    5.64
color intensity                    1.04
hue                                3.92
OD280/OD315 of diluted wines    1065.00
Name: 14.23, dtype: float64

X

import numpy as np
print('Classes: %s' % np.unique(y))
print('Class distribution: %s' % np.bincount(y))

API