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)