掌握sklearn系列——1 学会加载数据

我们直接看代码：

from sklearn import datasets

#读取三组数据，前两个用于分类，第三个用于回归
iris = datasets.load_iris()
digits = datasets.load_digits()
boston = datasets.load_boston()

#打印datasets放在哪里了
print(datasets.get_data_home())

print('-'*30)

#查看加载的数据，都有几行，几列，如果是分类的话，都有哪几个类别,特征都是什么。如果是回归的话，特征都是什么
#先看iris
print(iris.data.shape)
print(iris.target.shape)
print(iris.target_names)
print(iris.feature_names)

print('-'*30)

#再看digits
print(digits.data.shape)
print(digits.images.shape)
print(digits.target.shape)
print(digits.target_names)

print('-'*30)

#最后看boston (回归)
print(boston.data.shape)
print(boston.target.shape)
print(boston.feature_names)

print('-'*30)

#将load的数据，画在图上看一下
import matplotlib.pyplot as plt
image0 = digits.images[0]
plt.imshow(image0)
plt.show()

我们的代码中，加载了三组数据，分别为iris, digits和boston

其中，前两个是用于分类的数据，你可以看到数据对应的target_names, 由于boston是用来回归的，

回归的y一般都是一个数字，而且没法一一列举，所以，你只能看到每一维特征都代表了什么。 

 

代码执行结果：

/Users/chenkuo/scikit_learn_data
------------------------------
(150, 4)
(150,)
['setosa' 'versicolor' 'virginica']
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
------------------------------
(1797, 64)
(1797, 8, 8)
(1797,)
[0 1 2 3 4 5 6 7 8 9]
------------------------------
(506, 13)
(506,)
['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
 'B' 'LSTAT']
------------------------------

 

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vJ8MRfGgwcPqr29XVVVVaqvr1dJSYnKy8uDXkvbtm1TTU2N+vTpE/QqD9TU1CgpKUkbN27UzZs3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%0AIwAYhBEADMIIAAZhBACDMAKA8V+WXFXj2uEDFgAAAABJRU5ErkJggg==" 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