python读取鸢尾花数据集

可以使用以下代码读取鸢尾花数据集：

from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

这将返回一个包含鸢尾花数据集的NumPy数组，其中X包含特征，y包含目标变量。

相关问题

python导入鸢尾花数据集

你可以使用Scikit-learn库中的`load_iris()`函数来导入鸢尾花数据集，示例如下：

from sklearn.datasets import load_iris

# 导入鸢尾花数据集
iris = load_iris()

# 查看数据集的描述信息
print(iris.DESCR)

# 获取数据集的特征数据和标签数据
X = iris.data
y = iris.target

# 查看特征数据和标签数据的形状
print('特征数据的形状：', X.shape)
print('标签数据的形状：', y.shape)

输出结果如下：

.. _iris_dataset:
Iris plants dataset
--------------------

**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

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

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.

.. topic:: 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.
     
特征数据的形状： (150, 4)
标签数据的形状： (150,)

其中，`iris.data`为特征数据，`iris.target`为标签数据。

python鸢尾花数据集复现

这段代码是用Python复现了鸢尾花数据集，并使用DBSCAN算法进行聚类。首先，使用sklearn库中的datasets.load_iris()函数加载鸢尾花数据集，并取前两个特征作为样本点。然后，根据设定的阈值和最小对象数，生成核心对象。接着，将所有样本点和核心对象可视化展示。最后，利用生成的核心对象进行聚类，并将聚类结果可视化展示。需要注意的是，这段代码省略了一些简单函数，如distance函数，完整代码在jupyter上，需要私信或评论区回复获取。