#安装scrip numpy sklearn包 import numpy from sklearn.datasets import load_iris
#读出鸢尾花数据集data data=load_iris() print(data.data)
运行结果为:
print(type(data))#查看data类型 print(data.keys())#查看数据内容
运行结果为:
data_feature = data.feature_names iris_data = data.data print(data_feature) print(iris_data)#鸢尾花的特征
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data_target = data.target_names print(data_target) iris_data = data.target#鸢尾花的类别
运行结果为:
type(iris_data)#数据类型
运行结果为:
epal_length=numpy.array(list(len[0] for len in data['data'])) print(epal_length)#取出花萼长度的数据(cm)
运行结果为:
petal_length=numpy.array(list(len[2]for len in data['data'])) print(petal_length)#取出花瓣长度数据
运行结果为:
petal_width=numpy.array(list(len[3]for len in data['data'])) print(petal_width)#取出花瓣宽度数据(cm)
运行结果为:
print(data.data[0]) print(data.target_names[0])#取出其某朵花的四个特征及类别
运行结果为:
setosa_data = [] versicolor_data = [] virginica_data = []#.将所有花的特征和类别分成三组,每组50个
for i in range(0,150): if data.target[i] == 0: data1 = data.data[i].tolist() data1.append('setosa') setosa_data.append(data1)#生成setosa类的鸢尾花数据 elif data.target[i] == 1: data1 = data.data[i].tolist() data1.append('versicolor') versicolor_data.append(data1)#生成versicolor类的鸢尾花数据 else: data1 = data.data[i].tolist() data1.append('virginica') virginica_data.append(data1)#生成virginica类的鸢尾花数据类型
newdata=(setosa_data,versicolor_data,virginica_data) print(newdata)#生成新的数组,每个元素包含四个特征+类别
运行结果为: