zoukankan      html  css  js  c++  java
  • numpy数据集练习

    import numpy as np
    l=list(range(10))
    a=np.arange(10)
    b=np.array([a,2*a])
    
    print(type(l),type(a),type(b),type(l[0]),type(a[0]),a.dtype,b.dtype)


    l.append("xiaodudu")
    print(l)
    

      

    a.reshape(2,5)
    

      

    a[3:7]
    

      

    a[:7:2]
    

      

    b.reshape(5,4,1)
    

      

    b.ravel()
    

      

    b.flatten()
    

      

    b.shape=(4,5)
    

      

    b.transpose()
    

      

    c=list(range(10))
    d=np.arange(5)
    e=np.array([c,d])
    f=np.arange(0,60,5).reshape(3,4)
    g=np.linspace(0,20)
    h=np.random.random(10)
    i=np.random.randint(1,100,[5,5])#范围和矩阵形状
    j=np.random.rand(2,3)#均匀分布随机数组
    k=np.random.randn(3,3)#正态分布随机数组
    

      

    np.max(c)
    np.mean(i)
    np.std(k)
    np.median(k)
    

      

      




    import numpy as np # 从sklearn包自带的数据集中读出鸢尾花数据集data from sklearn.datasets import load_iris data = load_iris() # 查看data类型,包含哪些数据 print("数据类型:",type(data)) print("数据类目:",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("鸢尾花特征:",iris_feature) print("iris_feature数据类型",type(iris_feature)) iris_target = data.target print("鸢尾花数据类别:",iris_target) print("iris_target数据类型:",type(iris_target)) # 取出所有花的花萼长度(cm)的数据 sepal_len = np.array(list(len[0] for len in data.data)) print("花萼长度:",sepal_len) # 取出所有花的花瓣长度(cm)+花瓣宽度(cm)的数据 pental_len = np.array(list(len[2] for len in data.data)) pental_len.resize(3,50) #重新分配花瓣长度内存 pental_wid = np.array(list(len[3] for len in data.data)) pental_wid.resize(3,50) #重新分配花瓣宽度内存 iris_lens = (pental_len,pental_wid) print("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[1]) print("类别:",data.target[1]) # 将所有花的特征和类别分成三组,每组50个 #建立3个相应列表存放数据 iris_set = [] iris_ver = [] iris_vir = [] for i in range(0,150): if data.target[i] == 0: Data = data.data[i].tolist() Data.append('setosa') iris_set.append(Data) elif data.target[i] ==1: Data = data.data[i].tolist() Data.append('versicolor') iris_ver.append(Data) else: Data = data.data[i].tolist() Data.append('virginica') iris_vir.append(Data) # 生成新的数组,每个元素包含四个特征+类别 datas = (iris_set,iris_ver,iris_vir) print("新的数组:",datas)

      

    data=iris['data']
    X=data[:,3]
    X
    import numpy as np
    import matplotlib.pyplot as plt
    x=np.linspace(0,150,num=150)
    plt.plot(x,X)
    plt.show()
    plt.scatter(x,X)
    plt.show()
    

      

    from sklearn.datasets import load_iris
    iris=load_iris()
    iris.keys() #查看data类型,包含哪些数据
    Out[3]:
    dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])
    In [34]:
    data1=iris['data']
    data1#"鸢尾花特征
    #type(data1)#鸢尾花特征数据类型
    data1.transpose()#转置形状矩阵
    Out[34]:
    array([[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],
           [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. ],
           [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],
           [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, 0.1, 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]])
    In [17]:
    data2=iris['target']
    data2
    Out[17]:
    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])
    In [28]:
    import numpy as np
    data=iris['data']
    a=np.array(data[:,2])
    a
    Out[28]:
    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, 1.5, 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])
    In [37]:
    data[1]#谋朵花的的四种特征
    Out[37]:
    array([4.9, 3. , 1.4, 0.2])
    In [38]:
    data2[1]#某朵花的类别
    Out[38]:
    0
    

      

  • 相关阅读:
    LeetCode对撞指针汇总
    167. Two Sum II
    215. Kth Largest Element in an Array
    2018Action Recognition from Skeleton Data via Analogical Generalization over Qualitative Representations
    题解 Educational Codeforces Round 84 (Rated for Div. 2) (CF1327)
    题解 JZPKIL
    题解 八省联考2018 / 九省联考2018
    题解 六省联考2017
    题解 Codeforces Round #621 (Div. 1 + Div. 2) (CF1307)
    题解Codeforces Round #620 (Div. 2)
  • 原文地址:https://www.cnblogs.com/cc013/p/9787881.html
Copyright © 2011-2022 走看看