numpy的Fancy Indexing和array比较详解

yuhuicheng

一：Fancy Indexing

1. import numpy as np #Fancy Indexingx = np.arange(16)np.random.shuffle(x)print(x) #打印所有的元素  print(x[2])#获取某个元素的值print(x[1:3])#切片print(x[3:9:2])#指定间距切片 index = [2,4,7,9] #索引数组print(x[index])#获取索引数组中的元素的值 ind = np.array([[0,2],[1,4]]) #索引二维数组print(x[ind])##获取索引二维数组中的元素的值 print("---------------------") X = x.reshape(4,-1)print(X) ind1 = np.array([1,3]) #行的索引ind2 = np.array([2,0]) #列的索引print(X[ind1,ind2]) print(X[:-2,ind2]) bool_index = [True,False,True,False] #True就取当前列，False就不取print(X[:-1,bool_index])

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Fancy Indexing 应用在一维数组 

1. x = np.arange(16) x[3] # 3x[3:9] # array([3, 4, 5, 6, 7, 8])x[3:9:2] # array([3, 5, 7])[x[3], x[5], x[7]] # [3, 5, 7]ind = [3, 5, 7] x[ind]  # array([3, 5, 7])ind = np.array([[0, 2], [1, 3]]) x[ind] """array([[0, 2],    [1, 3]])"""

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Fancy Indexing 应用在二维数组 

1. X = x.reshape(4, -1) """array([[ 0, 1, 2, 3],    [ 4, 5, 6, 7],    [ 8, 9, 10, 11],    [12, 13, 14, 15]])"""row = np.array([0, 1, 2]) col = np.array([1, 2, 3])# 1行2列，2行3列，3行4列X[row, col]  # array([ 1, 6, 11])# 前2行 2,3,4列X[:2, col] """array([[1, 2, 3],    [5, 6, 7]])"""col = [True, False, True, True] X[0, col]  # array([0, 2, 3])

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二：array比力
[code]import numpy as np x = np.arange(16)print(x) print(x < 3) #返回的是bool数组 print(x == 3) print(x != 3) print(x * 4 == 24 - 4 * x)   print(x + 1) print(x * 2) print(x / 4) print(x - 10) print(np.sum(x