利用Numpy进行鸢尾花数据集分析
Numpy进行鸢尾花数据集分析
使用鸢尾花数据集“iris_data”
鸢尾花数据集是sklearn中自带的一个经典数据集，在这个数据集中，包括了三类不同的鸢尾属植物：Iris Setosa,Iris Versicolour,Iris Virginica。每类收集50个样本，共150个样本。
数据集的特征：
sepallength：萼片长度sepalwidth：萼片宽度petallength：花瓣长度petalwidth：花瓣宽度以上四个特征的单位都是厘米（cm） 1. 导入鸢尾花数据集，保持文本不变
import numpy as np from sklearn.datasets import load_iris iris=load_iris() iris_data=np.c_[iris['data'],iris['target']] 1234
iris_data[:10,:]#查看前十个数据 1
array([[5.1, 3.5, 1.4, 0.2, 0. ], [4.9, 3. , 1.4, 0.2, 0. ], [4.7, 3.2, 1.3, 0.2, 0. ], [4.6, 3.1, 1.5, 0.2, 0. ], [5. , 3.6, 1.4, 0.2, 0. ], [5.4, 3.9, 1.7, 0.4, 0. ], [4.6, 3.4, 1.4, 0.3, 0. ], [5. , 3.4, 1.5, 0.2, 0. ], [4.4, 2.9, 1.4, 0.2, 0. ], [4.9, 3.1, 1.5, 0.1, 0. ]]) 12345678910
target_map_dict={0:'Iris-setosa',1:'Iris-versicolor',2:'Iris-virginica'} 1'
def target_map(x,map_dict): return map_dict[x] if x in map_dict else x target_map_vector=np.vectorize(target_map) iris_target_maped=target_map_vector(iris['target'],target_map_dict) 1234
1'
array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica'], dtype='<U15')
12345678910111213141516171819202122232425262728293031323334353637383940414243444546iris_data_map=np.c_[iris['data'],iris_target_maped]#合并后会统一数据类型 1
iris_data_map[0:10] 1
array([['5.1', '3.5', '1.4', '0.2', 'Iris-setosa'], ['4.9', '3.0', '1.4', '0.2', 'Iris-setosa'], ['4.7', '3.2', '1.3', '0.2', 'Iris-setosa'], ['4.6', '3.1', '1.5', '0.2', 'Iris-setosa'], ['5.0', '3.6', '1.4', '0.2', 'Iris-setosa'], ['5.4', '3.9', '1.7', '0.4', 'Iris-setosa'], ['4.6', '3.4', '1.4', '0.3', 'Iris-setosa'], ['5.0', '3.4', '1.5', '0.2', 'Iris-setosa'], ['4.4', '2.9', '1.4', '0.2', 'Iris-setosa'], ['4.9', '3.1', '1.5', '0.1', 'Iris-setosa']], dtype='<U32') 12345678910
np.savetxt('iris.data',iris_data_map,fmt='%s',delimiter=',') 1
iris_data = np.loadtxt('iris.data', dtype=object, delimiter=',', skiprows=1) print(iris_data[0:10]) 123
[['4.9' '3.0' '1.4' '0.2' 'Iris-setosa'] ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa'] ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa'] ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa'] ['5.4' '3.9' '1.7' '0.4' 'Iris-setosa'] ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa'] ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa'] ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa'] ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa'] ['5.4' '3.7' '1.5' '0.2' 'Iris-setosa']] 12345678910 2求出鸢尾属植物萼片的平均值，中位数和标准差（第一列，sepallenth）
outfile='iris.data' sepallength = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0]) print(sepallength[0:10]) 123
[5.1 4.9 4.7 4.6 5. 5.4 4.6 5. 4.4 4.9] 1
print(np.mean(sepallength)) 1
5.843333333333334 1
print(np.median(sepallength)) 1
5.8 1
print(np.std(sepallength)) 1
0.8253012917851409 1 3.创建一种标准化形式的鸢尾属植物萼片长度,其值正好介于0和1之间,这样最小值为0,最大值为1(第1列,sepallength)。
#方法一 aMax = np.amax(sepallength) aMin = np.amin(sepallength) x = (sepallength-aMin)/(aMax - aMin) print(x[0:10]) #方法二 x = (sepallength-aMin)/np.ptp(sepallength) print(x[:10]) 123456789
[0.22222222 0.16666667 0.11111111 0.08333333 0.19444444 0.30555556 0.08333333 0.19444444 0.02777778 0.16666667] [0.22222222 0.16666667 0.11111111 0.08333333 0.19444444 0.30555556 0.08333333 0.19444444 0.02777778 0.16666667] 1234 4. 找到鸢尾属植物萼片长度的第5和第95百分位数(第1列,sepallength)。
print(np.percentile(sepallength,[5,95])) 1
[4.6 7.255] 1 5. 把iris_data数据集中的20个随机位置修改为np.nan值。
i, j = iris_data.shape np.random.seed(20200621) iris_data[np.random.randint(i, size=20), np.random.randint(j, size=20)] = np.nan print(iris_data[0:10]) 12345
[['4.9' '3.0' '1.4' '0.2' 'Iris-setosa'] ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa'] ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa'] ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa'] ['5.4' '3.9' '1.7' '0.4' 'Iris-setosa'] ['4.6' nan '1.4' '0.3' 'Iris-setosa'] ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa'] ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa'] ['4.9' '3.1' '1.5' '0.1' nan] ['5.4' '3.7' '1.5' '0.2' 'Iris-setosa']] 12345678910
i, j = iris_data.shape np.random.seed(20200620) iris_data[np.random.choice(i, size=20), np.random.choice(j, size=20)] = np.nan print(iris_data[0:10]) 1234
[['4.9' '3.0' '1.4' '0.2' 'Iris-setosa'] ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa'] ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa'] ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa'] ['5.4' '3.9' '1.7' nan 'Iris-setosa'] ['4.6' nan '1.4' '0.3' 'Iris-setosa'] ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa'] ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa'] ['4.9' '3.1' '1.5' '0.1' nan] [nan '3.7' '1.5' '0.2' 'Iris-setosa']] 12345678910 6. 在iris_data的sepallength中查找缺失值的个数和位置(第1列)。
outfile = r'iris.data' iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[0, 1, 2,3]) i, j = iris_data.shape np.random.seed(20200621) iris_data[np.random.randint(i, size=20), np.random.randint(j, size=20)] = np.nan sepallength = iris_data[:, 0] x = np.isnan(sepallength) print(sum(x)) # 6 print(np.where(x))#返回空值的位置 # (array([ 26, 44, 55, 63, 90, 115], dtype=int64),) 1234567891011
6 (array([ 26, 44, 55, 63, 90, 115]),) 12 7. 筛选具有 sepallength(第1列)< 5.0 并且 petallength(第3列)> 1.5 的 iris_data行。
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) sepallength = iris_data[:,0] petallength = iris_data[:,2] index = np.where(np.logical_and(petallength > 1.5,sepallength < 5.0)) print(index) print(iris_data[index]) 1234567
(array([ 11, 24, 29, 30, 57, 106]),) [[4.8 3.4 1.6 0.2] [4.8 3.4 1.9 0.2] [4.7 3.2 1.6 0.2] [4.8 3.1 1.6 0.2] [4.9 2.4 3.3 1. ] [4.9 2.5 4.5 1.7]] 1234567 8.选择没有任何nan值的iris_data行
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) i,j = iris_data.shape np.random.seed(20201201) iris_data[np.random.randint(i,size=20),np.random.randint(j,size=20)]=np.nan x = iris_data[np.sum(np.isnan(iris_data),axis=1)==0] #print(x) print(x[0:10]) 12345678
[[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 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]] 12345678910 计算iris_data中sepallengrh（第一列）和petallength（第三列）之间的相关系数
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) sepalLength = iris_data[:, 0] petalLength = iris_data[:, 2] #方法一 m1=np.mean(sepalLength) m2=np.mean(petalLength) cov = np.dot(sepalLength - m1 ,petalLength - m2) std1 = np.sqrt(np.dot(sepalLength - m1,sepalLength - m1)) std2 = np.sqrt(np.dot(petalLength - m2,petalLength - m2)) print(cov/(std1*std2)) #方法二 x = np.mean((sepalLength - m1)*(petalLength - m2)) y = np.std(sepalLength)*np.std(petalLength) print(x/y) #方法三 x = np.cov(sepalLength,petalLength,ddof=False) y = np.std(sepalLength)*np.std(petalLength) print(x[0,1]/y) #方法四 x = np.corrcoef(sepalLength,petalLength) print(x)
123456789101112131415161718192021220.8717537758865833 0.8717537758865831 0.8717537758865835 [[1. 0.87175378] [0.87175378 1. ]] 12345 10.找出iris_data是否有任何缺失值
x = np.isnan(iris_data) print(np.any(x)) 12
False 1 11. 在numpy数组中将所有出现的nan替换为0。
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) i,j = iris_data.shape np.random.seed(20201201) iris_data[np.random.randint(i,size=20),np.random.randint(j,size=20)] = np.nan iris_data[np.isnan(iris_data)] = 0 print(iris_data[0:10]) 1234567
[[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 0. 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]] 12345678910 12. 找出鸢尾属植物物种中的唯一值和唯一值出现的数量。
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=object,delimiter=',',usecols=[4]) x = np.unique(iris_data,return_counts=True) print(x) 1234
(array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object), array([50, 50, 50])) 1 13. 将 iris_data 的花瓣长度(第3列)以形成分类变量的形式显示。定义:Less than 3 -->‘small’;3-5 --> ‘medium’;’>=5 --> ‘large’。
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) petal_length_bin = np.digitize(iris_data[:,2],[0,3,5,10]) label_map = {1:'small',2:'median',3:'large',4:np.nan} petal_length_cat = [label_map[x] for x in petal_length_bin] print(petal_length_cat[0:10]) 123456
['small', 'small', 'small', 'small', 'small', 'small', 'small', 'small', 'small', 'small'] 1
petal_length_bin 1
array([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, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) 1234567 14. 在 iris_data 中创建一个新列,其中 volume 是 (pi x petallength x sepallength ^ 2)/ 3 。
outfile = 'iris.data' iris_data = np.loadtxt(outfile,dtype=object,delimiter=',') sepalLength = iris_data[:,0].astype(float) petalLength = iris_data[:,2].astype(float) volume = (np.pi *petalLength*sepalLength**2 )/3 volume=volume[:,np.newaxis] iris_data = np.concatenate([iris_data,volume],axis=1) print(iris_data[0:10]) 12345678
[['5.1' '3.5' '1.4' '0.2' 'Iris-setosa' 38.13265162927291] ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa' 35.200498485922445] ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa' 30.0723720777127] ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa' 33.238050274980004] ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa' 36.65191429188092] ['5.4' '3.9' '1.7' '0.4' 'Iris-setosa' 51.911677007917746] ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa' 31.022180256648003] ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa' 39.269908169872416] ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa' 28.38324242763259] ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa' 37.714819806345474]] 12345678910 15. 随机抽鸢尾属植物的种类,使得Iris-setosa的数量是Iris-versicolor和Iris-virginica数量的两倍。
species = np.array(['Iris-setosa','Iris-versicolor','Iris-virginica']) species_out = np.random.choice(species,1000,p=[0.5,0.25,0.25]) print(np.unique(species_out,return_counts=True)) 123
(array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype='<U15'), array([508, 256, 236])) 1 16. 根据 sepallength 列对数据集进行排序。
outfilr ='iris.data' iris_data = np.loadtxt(outfile,dtype=object,delimiter=',') sepalLength = iris_data[:,0] index = np.argsort(sepalLength) print(iris_data[index][0:10]) 12345
[['4.3' '3.0' '1.1' '0.1' 'Iris-setosa'] ['4.4' '3.2' '1.3' '0.2' 'Iris-setosa'] ['4.4' '3.0' '1.3' '0.2' 'Iris-setosa'] ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa'] ['4.5' '2.3' '1.3' '0.3' 'Iris-setosa'] ['4.6' '3.6' '1.0' '0.2' 'Iris-setosa'] ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa'] ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa'] ['4.6' '3.2' '1.4' '0.2' 'Iris-setosa'] ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa']] 12345678910 17. 在鸢尾属植物数据集中找到最常见的花瓣长度值(第3列)。
outfile = 'iris.data' iris_data=np.loadtxt(outfile,dtype=object,delimiter=',') petalLength = iris_data[:,2] vals,counts=np.unique(petalLength,return_counts=True) print(vals[np.argmax(counts)]) print(np.amax(counts)) 123456
1.4 13 12 18. 在鸢尾花数据集的 petalwidth(第4列)中查找第一次出现的值大于1.0的位置。
outfile ='iris.data' iris_data = np.loadtxt(outfile,dtype=float,delimiter=',',usecols=[0,1,2,3]) petalWidth = iris_data[:,3] index = np.where(petalWidth > 1.0) print(index) print(index[0][0]) 123456
(array([ 50, 51, 52, 53, 54, 55, 56, 58, 59, 61, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]),) 50 123456789
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