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1.7.花式索引 2018/11/11==================================================================1.说明# 1）NumPy数组可用切片进行索引# 2）可用布尔或整数数组（掩码）进行索引.这种方法称为花式索引.# .3）花式索引跟切片不一样，它创建副本而不是视图。用法：a[ [bool or int]]==================================================================# 1.1.使用布尔掩码np.random.seed(3)np.random.randint(0, 10, 8) # array([8, 9, 3, 8, 8, 0, 5, 3])mask = (a % 2 == 0) # array([True, False, False,True,True,True,False,False])b= a[mask] # 等价a[a%2==0]b # array([8, 8, 8, 0])#1.2.为子数组分配新值：a[a % 2 == 0] = -1a # array([-1, 9, 3, -1, -1, -1, 5, 3])===================================================================# 2.1.使用整数数组进行索引a = np.arange(0, 100, 10)a # array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])a[[2, 3, 2, 4, 2]] # note: [2, 3, 2, 4, 2] is a Python list# array([20, 30, 20, 40, 20])# 用整数数组进行索引来创建新数组，新数组形状与整数数组形状相同：----------------------------------------------a = np.arange(10)idx = np.array([[3, 4], [9, 7]])idx.shape # (2, 2)a[idx] #array([[3, 4],[9, 7]])----------------------------------------------# 一次传入多个索引数组：arr= np.arange(32).reshape((8, 4))arr #array( [[ 0, 1, 2, 3], [ 4, 5, 6, 7],[ 8, 9, 10, 11], [12, 13, 14, 15],[16, 17, 18, 19],[20, 21, 22, 23], [24, 25, 26, 27],[28, 29, 30, 31]])arr[[1, 5, 7, 2], [0, 3, 1, 2]]#array([ 4, 23, 29, 10])终选元素(1,0),(5,3),(7,1),(2,2)-----------------------------------------------# ix＿函数将两个一维数组转换为一个用于选取方形区域的索引器：arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]arr[[1, 5, 7, 2]][:,[0, 3, 1, 2]] # 等价于上面# array( [[ 4, 7, 5, 6],[20, 23, 21, 22],[28, 31, 29, 30],[ 8, 11, 9, 10]])------------------------------------------------# 2.2.可以使用此类索引分配新值：a[[9, 7]] = -100a # array([0,10,20,30,40,50,60,-100,80,-100])=====================================================================

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