numpy数据集处理

import numpy
from sklearn.datasets import load_iris
data = load_iris()
#查看data类型，包含哪些数据

C:\Users\Administrator\PycharmProjects\untitled1\venv\Scripts\python.exe C:/Users/Administrator/PycharmProjects/untitled1/bybdfyf.py
C:\Users\Administrator\PycharmProjects\untitled1\venv\lib\site-packages\sklearn\externals\joblib\externals\cloudpickle\cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
数据类型: <class 'sklearn.utils.Bunch'>
数据内容: dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])
鸢尾花数据： (['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.1],
       [5.4, 3.7, 1.5, 0.2],
       [4.8, 3.4, 1.6, 0.2],
       [4.8, 3. , 1.4, 0.1],
       [4.3, 3. , 1.1, 0.1],
       [5.8, 4. , 1.2, 0.2],
       [5.7, 4.4, 1.5, 0.4],
       [5.4, 3.9, 1.3, 0.4],
       [5.1, 3.5, 1.4, 0.3],
       [5.7, 3.8, 1.7, 0.3],
       [5.1, 3.8, 1.5, 0.3],
       [5.4, 3.4, 1.7, 0.2],
       [5.1, 3.7, 1.5, 0.4],
       [4.6, 3.6, 1. , 0.2],
       [5.1, 3.3, 1.7, 0.5],
       [4.8, 3.4, 1.9, 0.2],
       [5. , 3. , 1.6, 0.2],
       [5. , 3.4, 1.6, 0.4],
       [5.2, 3.5, 1.5, 0.2],
       [5.2, 3.4, 1.4, 0.2],
       [4.7, 3.2, 1.6, 0.2],
       [4.8, 3.1, 1.6, 0.2],
       [5.4, 3.4, 1.5, 0.4],
       [5.2, 4.1, 1.5, 0.1],
       [5.5, 4.2, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.2],
       [5. , 3.2, 1.2, 0.2],
       [5.5, 3.5, 1.3, 0.2],
       [4.9, 3.6, 1.4, 0.1],
       [4.4, 3. , 1.3, 0.2],
       [5.1, 3.4, 1.5, 0.2],
       [5. , 3.5, 1.3, 0.3],
       [4.5, 2.3, 1.3, 0.3],
       [4.4, 3.2, 1.3, 0.2],
       [5. , 3.5, 1.6, 0.6],
       [5.1, 3.8, 1.9, 0.4],
       [4.8, 3. , 1.4, 0.3],
       [5.1, 3.8, 1.6, 0.2],
       [4.6, 3.2, 1.4, 0.2],
       [5.3, 3.7, 1.5, 0.2],
       [5. , 3.3, 1.4, 0.2],
       [7. , 3.2, 4.7, 1.4],
       [6.4, 3.2, 4.5, 1.5],
       [6.9, 3.1, 4.9, 1.5],
       [5.5, 2.3, 4. , 1.3],
       [6.5, 2.8, 4.6, 1.5],
       [5.7, 2.8, 4.5, 1.3],
       [6.3, 3.3, 4.7, 1.6],
       [4.9, 2.4, 3.3, 1. ],
       [6.6, 2.9, 4.6, 1.3],
       [5.2, 2.7, 3.9, 1.4],
       [5. , 2. , 3.5, 1. ],
       [5.9, 3. , 4.2, 1.5],
       [6. , 2.2, 4. , 1. ],
       [6.1, 2.9, 4.7, 1.4],
       [5.6, 2.9, 3.6, 1.3],
       [6.7, 3.1, 4.4, 1.4],
       [5.6, 3. , 4.5, 1.5],
       [5.8, 2.7, 4.1, 1. ],
       [6.2, 2.2, 4.5, 1.5],
       [5.6, 2.5, 3.9, 1.1],
       [5.9, 3.2, 4.8, 1.8],
       [6.1, 2.8, 4. , 1.3],
       [6.3, 2.5, 4.9, 1.5],
       [6.1, 2.8, 4.7, 1.2],
       [6.4, 2.9, 4.3, 1.3],
       [6.6, 3. , 4.4, 1.4],
       [6.8, 2.8, 4.8, 1.4],
       [6.7, 3. , 5. , 1.7],
       [6. , 2.9, 4.5, 1.5],
       [5.7, 2.6, 3.5, 1. ],
       [5.5, 2.4, 3.8, 1.1],
       [5.5, 2.4, 3.7, 1. ],
       [5.8, 2.7, 3.9, 1.2],
       [6. , 2.7, 5.1, 1.6],
       [5.4, 3. , 4.5, 1.5],
       [6. , 3.4, 4.5, 1.6],
       [6.7, 3.1, 4.7, 1.5],
       [6.3, 2.3, 4.4, 1.3],
       [5.6, 3. , 4.1, 1.3],
       [5.5, 2.5, 4. , 1.3],
       [5.5, 2.6, 4.4, 1.2],
       [6.1, 3. , 4.6, 1.4],
       [5.8, 2.6, 4. , 1.2],
       [5. , 2.3, 3.3, 1. ],
       [5.6, 2.7, 4.2, 1.3],
       [5.7, 3. , 4.2, 1.2],
       [5.7, 2.9, 4.2, 1.3],
       [6.2, 2.9, 4.3, 1.3],
       [5.1, 2.5, 3. , 1.1],
       [5.7, 2.8, 4.1, 1.3],
       [6.3, 3.3, 6. , 2.5],
       [5.8, 2.7, 5.1, 1.9],
       [7.1, 3. , 5.9, 2.1],
       [6.3, 2.9, 5.6, 1.8],
       [6.5, 3. , 5.8, 2.2],
       [7.6, 3. , 6.6, 2.1],
       [4.9, 2.5, 4.5, 1.7],
       [7.3, 2.9, 6.3, 1.8],
       [6.7, 2.5, 5.8, 1.8],
       [7.2, 3.6, 6.1, 2.5],
       [6.5, 3.2, 5.1, 2. ],
       [6.4, 2.7, 5.3, 1.9],
       [6.8, 3. , 5.5, 2.1],
       [5.7, 2.5, 5. , 2. ],
       [5.8, 2.8, 5.1, 2.4],
       [6.4, 3.2, 5.3, 2.3],
       [6.5, 3. , 5.5, 1.8],
       [7.7, 3.8, 6.7, 2.2],
       [7.7, 2.6, 6.9, 2.3],
       [6. , 2.2, 5. , 1.5],
       [6.9, 3.2, 5.7, 2.3],
       [5.6, 2.8, 4.9, 2. ],
       [7.7, 2.8, 6.7, 2. ],
       [6.3, 2.7, 4.9, 1.8],
       [6.7, 3.3, 5.7, 2.1],
       [7.2, 3.2, 6. , 1.8],
       [6.2, 2.8, 4.8, 1.8],
       [6.1, 3. , 4.9, 1.8],
       [6.4, 2.8, 5.6, 2.1],
       [7.2, 3. , 5.8, 1.6],
       [7.4, 2.8, 6.1, 1.9],
       [7.9, 3.8, 6.4, 2. ],
       [6.4, 2.8, 5.6, 2.2],
       [6.3, 2.8, 5.1, 1.5],
       [6.1, 2.6, 5.6, 1.4],
       [7.7, 3. , 6.1, 2.3],
       [6.3, 3.4, 5.6, 2.4],
       [6.4, 3.1, 5.5, 1.8],
       [6. , 3. , 4.8, 1.8],
       [6.9, 3.1, 5.4, 2.1],
       [6.7, 3.1, 5.6, 2.4],
       [6.9, 3.1, 5.1, 2.3],
       [5.8, 2.7, 5.1, 1.9],
       [6.8, 3.2, 5.9, 2.3],
       [6.7, 3.3, 5.7, 2.5],
       [6.7, 3. , 5.2, 2.3],
       [6.3, 2.5, 5. , 1.9],
       [6.5, 3. , 5.2, 2. ],
       [6.2, 3.4, 5.4, 2.3],
       [5.9, 3. , 5.1, 1.8]]))
鸢尾花形状类别： (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, 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, 2, 2]), array(['setosa', 'versicolor', 'virginica'], dtype='<U10'))
所有花萼长度： [5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.
 5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5.  7.  6.4 6.9 5.5
 6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
 6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.  6.7 6.3 5.6 5.5
 5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2
 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8
 6.7 6.7 6.3 6.5 6.2 5.9]
所有花瓣长宽： (array([[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4,
        1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1. , 1.7, 1.9, 1.6,
        1.6, 1.5, 1.4, 1.6],
       [1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3,
        1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4. , 4.6, 4.5,
        4.7, 3.3, 4.6, 3.9],
       [3.5, 4.2, 4. , 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4. , 4.9,
        4.7, 4.3, 4.4, 4.8, 5. , 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5,
        4.7, 4.4, 4.1, 4. ],
       [4.4, 4.6, 4. , 3.3, 4.2, 4.2, 4.2, 4.3, 3. , 4.1, 6. , 5.1, 5.9,
        5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5. , 5.1, 5.3,
        5.5, 6.7, 6.9, 5. ],
       [5.7, 4.9, 6.7, 4.9, 5.7, 6. , 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6,
        5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2,
        5. , 5.2, 5.4, 5.1]]), array([[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1,
        0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2,
        0.4, 0.2, 0.2, 0.2],
       [0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2,
        0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3,
        1.6, 1. , 1.3, 1.4],
       [1. , 1.5, 1. , 1.4, 1.3, 1.4, 1.5, 1. , 1.5, 1.1, 1.8, 1.3, 1.5,
        1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1. , 1.1, 1. , 1.2, 1.6, 1.5, 1.6,
        1.5, 1.3, 1.3, 1.3],
       [1.2, 1.4, 1.2, 1. , 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1,
        1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2. , 1.9, 2.1, 2. , 2.4, 2.3,
        1.8, 2.2, 2.3, 1.5],
       [2.3, 2. , 2. , 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2. , 2.2,
        1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3,
        1.9, 2. , 2.3, 1.8]]))
特征: [5.1 3.5 1.4 0.2]
类别: 0
新数组分类结果： ([[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, 0.2, 'setosa'], [5.0, 3.2, 1.2, 0.2, 'setosa'], [5.5, 3.5, 1.3, 0.2, 'setosa'], [4.9, 3.6, 1.4, 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']])

进程已结束,退出代码0

print('数据类型:',type(data))
print('数据内容:',data.keys())
#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['feature_names'],data['data']
print('鸢尾花数据：',iris_feature)
iris_target = data.target,data.target_names
print('鸢尾花形状类别：',iris_target)
#取出所有花的花萼长度(cm)的数据
sepal_length = numpy.array(list(len[0] for len in data['data']))
print('所有花萼长度：',sepal_length)
#取出所有花的花瓣长度(cm)+花瓣宽度(cm)的数据
petal_length = numpy.array(list(len[2] for len in data['data']))
petal_length.resize(5,30)
petal_width = numpy.array(list(len[3] for len in data['data']))
petal_width.resize(5,30)
iris_lens = (petal_length,petal_width)
print('所有花瓣长宽：',iris_lens)
#取出某朵花的四个特征及其类别
print('特征:',data['data'][0])
print('类别:',data['target'][0])
#将所有花的特征和类别分成三组，每组50个
# 建立每种花的相应列表，存放数据
iris_setosa = []
iris_versicolor = []
iris_virginica = []
# 用for循环分类,根据观察可知当target为0时对应setosa类型，1为versicolor，2为virginica
for i in range(0,150):
    if  data['target'][i] == 0:  # 类别为0的即为setosa，生成一条0为setosa类的鸢尾花花数据
        data1 = data['data'][i].tolist()
        data1.append('setosa')
        iris_setosa.append(data1)
    elif data['target'][i] == 1:  # 类别为1的即为versicolor，生成一条1为versicolor类的鸢尾花数据
        data1 = data['data'][i].tolist()
        data1.append('versicolor')
        iris_versicolor.append(data1)
    else:                          #剩下类别为2的归为virginica
        data1 = data['data'][i].tolist()
        data1.append('virginica')
        iris_virginica.append(data1)
#生成新的数组，每个元素包含四个特征+类别
datas = (iris_setosa,iris_versicolor,iris_virginica)
print('新数组分类结果：',datas)