scikit-learn Toy Datasets in Python:
Iris plants dataset

For more info, see here:
https://scikit-learn.org/stable/datasets/toy_dataset.html#iris-plants-dataset

The iris plants dataset contains measurements of the features of flowers:

• 3 species of Iris were analysed (3 classes)
• 150 flowers were analysed (150 instances)
  • Specifically, 50 specimens of each species were analysed
• 4 measurements were taken from each flower (4 attributes)

The dataset can be loaded using load_iris() or load_iris(as_frame=True). Both return a ‘Bunch’ object which can be indexed as if it were a dictionary with the following being the most important keys:

Key	Value
DESCR	Description of the dataset
filename	Location of the CSV file containing the data being imported
feature_names	Names of the 4 attributes (the measurements taken)
data	The 150 data points, formatted as a 150x4 array by default or a 150x4 Pandas data frame if as_frame=True was used
target_names	Names of the classes (ie the names of the 3 species of Iris)
target	Which class each data point is in (ie which species of Iris each flower was): 0, 1 or 2. These correspond to the indexes of the names in target_names. This is an array of length 150 by default, or a Pandas series if as_frame=True was used.

Example usage:

from sklearn import datasets
from matplotlib import pyplot as plt
import itertools

# Load the dataset
dataset = datasets.load_iris(as_frame=True)

# Separate out the data
X = dataset['data']
y = dataset['target']

# Translate the target
y = y.apply(lambda x: dataset['target_names'][x])

# Plot
fig, ax = plt.subplots(3, 2, figsize=(8, 10))
ax = ax.flatten()
fig.suptitle('Iris plants dataset')
for i, combination in enumerate(itertools.combinations(list(X), 2)):
    col1, col2 = combination
    for species in dataset['target_names']:
        df = X[y == species]
        ax[i].scatter(df[col1], df[col2], label=species)
    ax[i].set_xlabel(col1)
    ax[i].set_ylabel(col2)
    ax[i].legend()
plt.tight_layout()
plt.show()