第六次作业--鸢尾花
from sklearn.datasets import load_iris import numpy as np import sklearn#从sklearn包自带的数据集中读出鸢尾花数据集data iris =load_iris() iris.keys()
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])
type(iris)#查看iris的类型
sklearn.utils.Bunch
data=iris['data'] data#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
Out[5]:
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, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 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]])
type(data)#查看data类型
numpy.ndarray
iris.target#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
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])
iris.target_names
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
iris.feature_names
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
import numpy iris.DESCR
'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\'s paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...\n'
iris.data
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, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 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]])
c=iris.data[:,0]#取出所有花的花萼长度(cm)的数据 print("花萼长度:",c)
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]
b=iris.data[:,1]#取出所有花的花瓣长度(cm) print("花瓣长度:",b)
花瓣长度: [3.5 3. 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3. 3. 4. 4.4 3.9 3.5 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3. 3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2 3.5 3.1 3. 3.4 3.5 2.3 3.2 3.5 3.8 3. 3.8 3.2 3.7 3.3 3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 2. 3. 2.2 2.9 2.9 3.1 3. 2.7 2.2 2.5 3.2 2.8 2.5 2.8 2.9 3. 2.8 3. 2.9 2.6 2.4 2.4 2.7 2.7 3. 3.4 3.1 2.3 3. 2.5 2.6 3. 2.6 2.3 2.7 3. 2.9 2.9 2.5 2.8 3.3 2.7 3. 2.9 3. 3. 2.5 2.9 2.5 3.6 3.2 2.7 3. 2.5 2.8 3.2 3. 3.8 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2 2.8 3. 2.8 3. 2.8 3.8 2.8 2.8 2.6 3. 3.4 3.1 3. 3.1 3.1 3.1 2.7 3.2 3.3 3. 2.5 3. 3.4 3. ]
a=iris.data[:,2]#花瓣宽度(cm)的数据 print("花瓣宽度:",a)
花瓣宽度: [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 1.5 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]
d=iris.data[0]#取出某朵花的四个特征及其类别 print(d)
[5.1 3.5 1.4 0.2]
Setasa=[] Versicolor=[] Virginica=[]#将所有花的特征和类别分成三组，每组50个 for a in range(0,150): if iris.target[a]==0: data1=iris.data[a].tolist() data1.append("Setosa") Setasa.append(data1) elif iris.target[a] ==1: data1=iris.data[a].tolist() data1.append("Versicolor") Versicolor.append(data1) else: data1=iris.data[a].tolist() data1.append("Virginica") Virginica.append(data1)
result=numpy.array([Setasa,Versicolor,Virginica]) result#生成新的数组，每个元素包含四个特征+类别
array([[['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']]], dtype='<U32')
print("最大值:",np.max(iris.data[:,1]),#计算鸢尾花花瓣长度的最大值，平均值，中值，均方差 "平均值:",np.mean(iris.data[:,1]), "中值:",np.median(iris.data[:,1]), "均方差:",np.std(iris.data[:,1]))
最大值: 4.4 平均值: 3.0540000000000003 中值: 3.0 均方差: 0.4321465800705435
import matplotlib.pyplot as plt#显示鸢尾花某一特征的曲线图，散点图 plt.plot(iris.data[:,0],iris.data[:,1]) plt.show()
plt.scatter(iris.data[:,0],iris.data[:,1])
plt.show()
