• numpy


    c = np.array([a,b])#建立二维数组
    c
    type(c)
    type(c[0])
    c.dtype
    c[1].dtype
    结果:
    array([[10, 11, 12, 13, 14],
           [ 0,  1,  2,  3,  4]])
    numpy.ndarray
    dtype('int32')

    1. 安装scipy,numpy,sklearn包
    import numpy as np
    a = list(range(10,15))
    b = np.arange(5)
    a
    b
    a*2
    b*2
    a+b
    b.shape
    b.dtype

    结果:
    [10, 11, 12, 13, 14]
    array([0, 1, 2, 3, 4])
    [10, 11, 12, 13, 14, 10, 11, 12, 13, 14]
    array([0, 2, 4, 6, 8])
    array([10, 12, 14, 16, 18])
    (5,)
    dtype('int32')

    2. 从sklearn包自带的数据集中读出鸢尾花数据集data
    from sklearn.datasets import load_iris
    data = load_iris()


    3.查看data类型,包含哪些数据
    print(type(data))
    print(data.keys())
    结果:

    4.取出鸢尾花特征和鸢尾花类别数据,查看其形状及数据类型
    iris_feature=data['data'],data['feature_names']  
    print("形状:",iris_feature)

    iris_target=data.target  
    print("类型:",iris_target)
    type(iris_feature)
    type(iris_target)
    结果:

    5.取出所有花的花萼长度(cm)的数据

    iris_len=np.array(list(len[0] for len in data['data']))
    print("花萼长度:",iris_len)

    结果:

    6.取出所有花的花瓣长度(cm)+花瓣宽度(cm)的数据

    iris1_len=np.array(list(len[2] for len in data['data']))  
    iris1_len.resize((10,15))
    print("花瓣长:",iris1_len)
    结果:

    iris1_wid=np.array(list(len[3] for len in data['data']))
    iris1_wid.resize((10,15))
    print("花瓣宽:",iris1_wid)

    结果:

    7.取出某朵花的四个特征及其类别。

    print("第三朵花数据:",data['data'][2],data['target'][2])

    结果:
    第三朵花数据: [4.7 3.2 1.3 0.2] 0

    8.将所有花的特征和类别分成三组,每组50个
    iris_setosa=[]      #定义三个新列表用于存放数据
    iris_versicolor=[]
    iris_virginica=[]

    for i in range(0,150): 
        if data['target'][i]==0:
            data1=data['data'][i].tolist()
            data1.append('setosa')
            iris_setosa.append(data1)
        elif data['target'][i]==1:
            data1=data['data'][i].tolist()
            data1.append('versicolor')
            iris_versicolor.append(data1)
        else:
            data1=data['data'][i].tolist()
            data1.append('virginica')
            iris_virginica.append(data1)
    
    

    9.生成新的数组,每个元素包含四个特征+类别
    data2=np.array([iris_setosa,iris_versicolor,iris_virginica])
    print(data2)

    结果:
    [[['5.1' '3.5' '1.4' '0.2' 'setosa']
      ['4.9' '3.0' '1.4' '0.2' 'setosa']
      ['4.7' '3.2' '1.3' '0.2' 'setosa']
      ['4.6' '3.1' '1.5' '0.2' 'setosa']
      ['5.0' '3.6' '1.4' '0.2' 'setosa']
      ['5.4' '3.9' '1.7' '0.4' 'setosa']
      ['4.6' '3.4' '1.4' '0.3' 'setosa']
      ['5.0' '3.4' '1.5' '0.2' 'setosa']
      ['4.4' '2.9' '1.4' '0.2' 'setosa']
      ['4.9' '3.1' '1.5' '0.1' 'setosa']
      ['5.4' '3.7' '1.5' '0.2' 'setosa']
      ['4.8' '3.4' '1.6' '0.2' 'setosa']
      ['4.8' '3.0' '1.4' '0.1' 'setosa']
      ['4.3' '3.0' '1.1' '0.1' 'setosa']
      ['5.8' '4.0' '1.2' '0.2' 'setosa']
      ['5.7' '4.4' '1.5' '0.4' 'setosa']
      ['5.4' '3.9' '1.3' '0.4' 'setosa']
      ['5.1' '3.5' '1.4' '0.3' 'setosa']
      ['5.7' '3.8' '1.7' '0.3' 'setosa']
      ['5.1' '3.8' '1.5' '0.3' 'setosa']
      ['5.4' '3.4' '1.7' '0.2' 'setosa']
      ['5.1' '3.7' '1.5' '0.4' 'setosa']
      ['4.6' '3.6' '1.0' '0.2' 'setosa']
      ['5.1' '3.3' '1.7' '0.5' 'setosa']
      ['4.8' '3.4' '1.9' '0.2' 'setosa']
      ['5.0' '3.0' '1.6' '0.2' 'setosa']
      ['5.0' '3.4' '1.6' '0.4' 'setosa']
      ['5.2' '3.5' '1.5' '0.2' 'setosa']
      ['5.2' '3.4' '1.4' '0.2' 'setosa']
      ['4.7' '3.2' '1.6' '0.2' 'setosa']
      ['4.8' '3.1' '1.6' '0.2' 'setosa']
      ['5.4' '3.4' '1.5' '0.4' 'setosa']
      ['5.2' '4.1' '1.5' '0.1' 'setosa']
      ['5.5' '4.2' '1.4' '0.2' 'setosa']
      ['4.9' '3.1' '1.5' '0.1' 'setosa']
      ['5.0' '3.2' '1.2' '0.2' 'setosa']
      ['5.5' '3.5' '1.3' '0.2' 'setosa']
      ['4.9' '3.1' '1.5' '0.1' 'setosa']
      ['4.4' '3.0' '1.3' '0.2' 'setosa']
      ['5.1' '3.4' '1.5' '0.2' 'setosa']
      ['5.0' '3.5' '1.3' '0.3' 'setosa']
      ['4.5' '2.3' '1.3' '0.3' 'setosa']
      ['4.4' '3.2' '1.3' '0.2' 'setosa']
      ['5.0' '3.5' '1.6' '0.6' 'setosa']
      ['5.1' '3.8' '1.9' '0.4' 'setosa']
      ['4.8' '3.0' '1.4' '0.3' 'setosa']
      ['5.1' '3.8' '1.6' '0.2' 'setosa']
      ['4.6' '3.2' '1.4' '0.2' 'setosa']
      ['5.3' '3.7' '1.5' '0.2' 'setosa']
      ['5.0' '3.3' '1.4' '0.2' 'setosa']]
    
     [['7.0' '3.2' '4.7' '1.4' 'versicolor']
      ['6.4' '3.2' '4.5' '1.5' 'versicolor']
      ['6.9' '3.1' '4.9' '1.5' 'versicolor']
      ['5.5' '2.3' '4.0' '1.3' 'versicolor']
      ['6.5' '2.8' '4.6' '1.5' 'versicolor']
      ['5.7' '2.8' '4.5' '1.3' 'versicolor']
      ['6.3' '3.3' '4.7' '1.6' 'versicolor']
      ['4.9' '2.4' '3.3' '1.0' 'versicolor']
      ['6.6' '2.9' '4.6' '1.3' 'versicolor']
      ['5.2' '2.7' '3.9' '1.4' 'versicolor']
      ['5.0' '2.0' '3.5' '1.0' 'versicolor']
      ['5.9' '3.0' '4.2' '1.5' 'versicolor']
      ['6.0' '2.2' '4.0' '1.0' 'versicolor']
      ['6.1' '2.9' '4.7' '1.4' 'versicolor']
      ['5.6' '2.9' '3.6' '1.3' 'versicolor']
      ['6.7' '3.1' '4.4' '1.4' 'versicolor']
      ['5.6' '3.0' '4.5' '1.5' 'versicolor']
      ['5.8' '2.7' '4.1' '1.0' 'versicolor']
      ['6.2' '2.2' '4.5' '1.5' 'versicolor']
      ['5.6' '2.5' '3.9' '1.1' 'versicolor']
      ['5.9' '3.2' '4.8' '1.8' 'versicolor']
      ['6.1' '2.8' '4.0' '1.3' 'versicolor']
      ['6.3' '2.5' '4.9' '1.5' 'versicolor']
      ['6.1' '2.8' '4.7' '1.2' 'versicolor']
      ['6.4' '2.9' '4.3' '1.3' 'versicolor']
      ['6.6' '3.0' '4.4' '1.4' 'versicolor']
      ['6.8' '2.8' '4.8' '1.4' 'versicolor']
      ['6.7' '3.0' '5.0' '1.7' 'versicolor']
      ['6.0' '2.9' '4.5' '1.5' 'versicolor']
      ['5.7' '2.6' '3.5' '1.0' 'versicolor']
      ['5.5' '2.4' '3.8' '1.1' 'versicolor']
      ['5.5' '2.4' '3.7' '1.0' 'versicolor']
      ['5.8' '2.7' '3.9' '1.2' 'versicolor']
      ['6.0' '2.7' '5.1' '1.6' 'versicolor']
      ['5.4' '3.0' '4.5' '1.5' 'versicolor']
      ['6.0' '3.4' '4.5' '1.6' 'versicolor']
      ['6.7' '3.1' '4.7' '1.5' 'versicolor']
      ['6.3' '2.3' '4.4' '1.3' 'versicolor']
      ['5.6' '3.0' '4.1' '1.3' 'versicolor']
      ['5.5' '2.5' '4.0' '1.3' 'versicolor']
      ['5.5' '2.6' '4.4' '1.2' 'versicolor']
      ['6.1' '3.0' '4.6' '1.4' 'versicolor']
      ['5.8' '2.6' '4.0' '1.2' 'versicolor']
      ['5.0' '2.3' '3.3' '1.0' 'versicolor']
      ['5.6' '2.7' '4.2' '1.3' 'versicolor']
      ['5.7' '3.0' '4.2' '1.2' 'versicolor']
      ['5.7' '2.9' '4.2' '1.3' 'versicolor']
      ['6.2' '2.9' '4.3' '1.3' 'versicolor']
      ['5.1' '2.5' '3.0' '1.1' 'versicolor']
      ['5.7' '2.8' '4.1' '1.3' 'versicolor']]
    
     [['6.3' '3.3' '6.0' '2.5' 'virginica']
      ['5.8' '2.7' '5.1' '1.9' 'virginica']
      ['7.1' '3.0' '5.9' '2.1' 'virginica']
      ['6.3' '2.9' '5.6' '1.8' 'virginica']
      ['6.5' '3.0' '5.8' '2.2' 'virginica']
      ['7.6' '3.0' '6.6' '2.1' 'virginica']
      ['4.9' '2.5' '4.5' '1.7' 'virginica']
      ['7.3' '2.9' '6.3' '1.8' 'virginica']
      ['6.7' '2.5' '5.8' '1.8' 'virginica']
      ['7.2' '3.6' '6.1' '2.5' 'virginica']
      ['6.5' '3.2' '5.1' '2.0' 'virginica']
      ['6.4' '2.7' '5.3' '1.9' 'virginica']
      ['6.8' '3.0' '5.5' '2.1' 'virginica']
      ['5.7' '2.5' '5.0' '2.0' 'virginica']
      ['5.8' '2.8' '5.1' '2.4' 'virginica']
      ['6.4' '3.2' '5.3' '2.3' 'virginica']
      ['6.5' '3.0' '5.5' '1.8' 'virginica']
      ['7.7' '3.8' '6.7' '2.2' 'virginica']
      ['7.7' '2.6' '6.9' '2.3' 'virginica']
      ['6.0' '2.2' '5.0' '1.5' 'virginica']
      ['6.9' '3.2' '5.7' '2.3' 'virginica']
      ['5.6' '2.8' '4.9' '2.0' 'virginica']
      ['7.7' '2.8' '6.7' '2.0' 'virginica']
      ['6.3' '2.7' '4.9' '1.8' 'virginica']
      ['6.7' '3.3' '5.7' '2.1' 'virginica']
      ['7.2' '3.2' '6.0' '1.8' 'virginica']
      ['6.2' '2.8' '4.8' '1.8' 'virginica']
      ['6.1' '3.0' '4.9' '1.8' 'virginica']
      ['6.4' '2.8' '5.6' '2.1' 'virginica']
      ['7.2' '3.0' '5.8' '1.6' 'virginica']
      ['7.4' '2.8' '6.1' '1.9' 'virginica']
      ['7.9' '3.8' '6.4' '2.0' 'virginica']
      ['6.4' '2.8' '5.6' '2.2' 'virginica']
      ['6.3' '2.8' '5.1' '1.5' 'virginica']
      ['6.1' '2.6' '5.6' '1.4' 'virginica']
      ['7.7' '3.0' '6.1' '2.3' 'virginica']
      ['6.3' '3.4' '5.6' '2.4' 'virginica']
      ['6.4' '3.1' '5.5' '1.8' 'virginica']
      ['6.0' '3.0' '4.8' '1.8' 'virginica']
      ['6.9' '3.1' '5.4' '2.1' 'virginica']
      ['6.7' '3.1' '5.6' '2.4' 'virginica']
      ['6.9' '3.1' '5.1' '2.3' 'virginica']
      ['5.8' '2.7' '5.1' '1.9' 'virginica']
      ['6.8' '3.2' '5.9' '2.3' 'virginica']
      ['6.7' '3.3' '5.7' '2.5' 'virginica']
      ['6.7' '3.0' '5.2' '2.3' 'virginica']
      ['6.3' '2.5' '5.0' '1.9' 'virginica']
      ['6.5' '3.0' '5.2' '2.0' 'virginica']
      ['6.2' '3.4' '5.4' '2.3' 'virginica']
      ['5.9' '3.0' '5.1' '1.8' 'virginica']]]



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  • 原文地址:https://www.cnblogs.com/SJMHJ/p/9771509.html
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