Iris Dataset

A function that loads the iris dataset into NumPy arrays.

from mlxtend.data import iris_data

Overview

The Iris dataset for classification.

Features

1. Sepal length
2. Sepal width
3. Petal length

4. Petal width

5. Number of samples: 150

6. Target variable (discrete): {50x Setosa, 50x Versicolor, 50x Virginica}

References

• Source: https://archive.ics.uci.edu/ml/datasets/Iris
• Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

Example 1 - Dataset overview

from mlxtend.data import iris_data
X, y = iris_data()

print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))
print('\nHeader: %s' % ['sepal length', 'sepal width',
                        'petal length', 'petal width'])
print('1st row', X[0])

Dimensions: 150 x 4

Header: ['sepal length', 'sepal width', 'petal length', 'petal width']
1st row [ 5.1  3.5  1.4  0.2]

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

Classes: Setosa, Versicolor, Virginica
[0 1 2]
Class distribution: [50 50 50]

API

iris_data()

Iris flower dataset.

• Source : https://archive.ics.uci.edu/ml/datasets/Iris

• Number of samples : 150

• Class labels : {0, 1, 2}, distribution: [50, 50, 50]

  0 = setosa, 1 = versicolor, 2 = virginica.

  Dataset Attributes:

  • 1) sepal length [cm]
  • 2) sepal width [cm]
  • 3) petal length [cm]
  • 4) petal width [cm]

Returns

• X, y : [n_samples, n_features], [n_class_labels]

  X is the feature matrix with 150 flower samples as rows, and 4 feature columns sepal length, sepal width, petal length, and petal width. y is a 1-dimensional array of the class labels {0, 1, 2}

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/data/iris_data/