El dataset wine — 2:59 min

• 2:59 min | Ultima modificación: Septiembre 28, 2021 | YouTube

https://scikit-learn.org/stable/datasets/toy_dataset.html

Este dataset contiene los resultados del análisis químico de vinos provenientes de la misma región de Italia provenientes de tres cultivadores diferentes. El dataset tiene 178 patrones y está conformado por las mediciones de 13 componentes que están en los tres tipos de vinos. Hay:

• 59 muestras para el primer vino.

• 71 muestras para el segundo vino.

• 48 muestras para el tercer vino.

from sklearn.datasets import load_wine

bunch = load_wine(
    # -----------------------------------------------------
    # If True, returns (data, target) instead of a Bunch
    # object.
    return_X_y=False,
)

bunch.keys()

dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

bunch.target

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2])

bunch.target_names

array(['class_0', 'class_1', 'class_2'], dtype='<U7')

bunch.feature_names

['alcohol',
 'malic_acid',
 'ash',
 'alcalinity_of_ash',
 'magnesium',
 'total_phenols',
 'flavanoids',
 'nonflavanoid_phenols',
 'proanthocyanins',
 'color_intensity',
 'hue',
 'od280/od315_of_diluted_wines',
 'proline']

X, y = load_wine(
    # -----------------------------------------------------
    # If True, returns (data, target) instead of a Bunch
    # object.
    return_X_y=True,
)

display(
    X[:5, :],
    y[:5],
)

array([[1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,
        3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,
        1.065e+03],
       [1.320e+01, 1.780e+00, 2.140e+00, 1.120e+01, 1.000e+02, 2.650e+00,
        2.760e+00, 2.600e-01, 1.280e+00, 4.380e+00, 1.050e+00, 3.400e+00,
        1.050e+03],
       [1.316e+01, 2.360e+00, 2.670e+00, 1.860e+01, 1.010e+02, 2.800e+00,
        3.240e+00, 3.000e-01, 2.810e+00, 5.680e+00, 1.030e+00, 3.170e+00,
        1.185e+03],
       [1.437e+01, 1.950e+00, 2.500e+00, 1.680e+01, 1.130e+02, 3.850e+00,
        3.490e+00, 2.400e-01, 2.180e+00, 7.800e+00, 8.600e-01, 3.450e+00,
        1.480e+03],
       [1.324e+01, 2.590e+00, 2.870e+00, 2.100e+01, 1.180e+02, 2.800e+00,
        2.690e+00, 3.900e-01, 1.820e+00, 4.320e+00, 1.040e+00, 2.930e+00,
        7.350e+02]])

array([0, 0, 0, 0, 0])

#
# Carga a un dataframe de pandas
#
import pandas as pd

wine = pd.DataFrame(
    bunch.data,
    columns=bunch.feature_names,
)

wine["class"] = bunch.target_names[bunch.target]

wine.head()

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	class
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	class_0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	class_0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	class_0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	class_0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	class_0