####轉自:模式識別實驗室主任
#環境win64+anaconda+python3.6
list & array
(1)list不具有array的全部屬性(如維度、轉置等)
代碼1:
#eg1_1 import numpy as np a = np.array([[1,2,0,1],[1,6,9,55],[7,8,9,5]])#a為數組 print(a.T) #Result: [[ 1 1 7] [ 2 6 8] [ 0 9 9] [ 1 55 5]] #eg1_2 a = [[1,2,0,1],[1,6,9,55],[7,8,9,5]] #a為列表 print(a.T) #Result: 'list' object has no attribute 'T'
代碼2:
#eg1_3 import numpy as np a=np.array([[1,2,3],[1,1,4],[1,5,1]]) print(a.shape) #Result: (3, 3) #eg1_4 a=[[1,2,3],[1,1,4],[1,5,1]] print(a.shape) #Result 'list' object has no attribute 'shape'
(順帶一提,如何把一個數組轉化為列向量:↓)
import numpy as np a=np.array([[1,2,3],[1,1,4],[1,5,1]]) a=a.reshape(-1,1) print(a) #Result: [[1] [2] [3] [1] [1] [4] [1] [5] [1]]
(2)a[:m]的含義,a可以是列表或者數組,但是無論是哪種情況,a[:0]為空
#eg2_1 import numpy as np a=np.array([[4,1,2], [7,4,10], [12,17,88]]) #a=np.array([(4,1,2), # (7,4,10), # (12,17,88)]) 這兩個a中[和(不一樣,其實它們完全一樣 print(a[:0]) print(a[:1]) print(a[:2]) #Result: [] [[4 1 2]] [[ 4 1 2] [ 7 4 10]] #eg2_1 a=[(4,1,2),(7,4,10),(12,17,88)] print(a[:0]) print(a[:1]) print(a[:2]) #Result: [] [(4, 1, 2)] [(4, 1, 2), (7, 4, 10)]
(3)array和list關於“==”的計算
#eg3_1 import numpy as np a=np.array(['dog','cat','car']) b=np.array(['dog','cat','trunk']) acc = (np.mean(a == b)) print(acc) #Result 0.6666666666666666 #eg3_2 import numpy as np a=['dog','cat','car'] b=['dog','cat','trunk'] acc = (np.mean(a == b)) print(acc) #Result 0.0
(4)array和list關於“*”的計算
from numpy import * #a為數組 a=array([[1,2,3], [4,5,6]]) b=4*a print(b) [[ 4 8 12] [16 20 24]] from numpy import * #a為列表 a=([[1,2,3], [4,5,6]]) b=4*a print(b) [[1, 2, 3], [4, 5, 6], [1, 2, 3], [4, 5, 6], [1, 2, 3], [4, 5, 6], [1, 2, 3], [4, 5, 6]]