鸢尾花数据散点图分析

分析：

引入warnings库

import warnings
warnings.filterwarnings('ignore')    # 将警告设置为忽略

从sklearn机器学习库中引入datasets数据集

from sklearn import datasets

从matlotlib库引入pyplot作为plt

from matplotlib import pyplot as plt

调用函数

iris = datasets.load_iris()

查看数据集中load_iris的数据类型

type(iris)
——sklearn.utils.Bunch

获取iris所有的键

iris.keys()
——dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

打印iris数据集中DESCR的内容

print(iris.DESCR)
# iris.DESCR    # 相当于iris['DESCR']

Iris Plants Database
====================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
[http://archive.ics.uci.edu/ml/datasets/Iris](http://archive.ics.uci.edu/ml/datasets/Iris)

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

References
----------
   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1\.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64\.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...</pre>

输出iris数据集中data的内容

X = iris.data
X
——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],
         ...
         [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]])

data的维度

X.ndim
——2

data的型状

X.shape
——(150, 4)

data的元素个数

X.size
——600

iris数据集中特征列的名字

iris.feature_names
——['sepal length (cm)',
   'sepal width (cm)',
   'petal length (cm)',
   'petal width (cm)']

确认散点图中每个点的y坐标，

鸢尾花分为三类：山鸢尾、变色鸢尾和维吉尼亚鸢尾， iris.target获取iris鸢尾花数据集中的target数据，其中0代表山鸢尾，1代表变色鸢尾，2代表维吉尼亚鸢尾

y = iris.target    # 只有一列（与data一一对应）
y
——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])

提取萼片维度

X = X[: , :2]
X
——array([[5.1, 3.5],
         [4.9, 3. ],
         [4.7, 3.2],
         [4.6, 3.1],
         [5. , 3.6],
         ...
         [6.7, 3. ],
         [6.3, 2.5],
         [6.5, 3. ],
         [6.2, 3.4],
         [5.9, 3. ]])