In [1]: data = np.arange(-50,50,10)
要重复每个元素5次,请使用np.repeat:In [3]: np.repeat(data, 5)
Out[3]:
array([-50, -50, -50, -50, -50, -40, -40, -40, -40, -40, -30, -30, -30,
-30, -30, -20, -20, -20, -20, -20, -10, -10, -10, -10, -10, 0,
0, 0, 0, 0, 10, 10, 10, 10, 10, 20, 20, 20, 20,
20, 30, 30, 30, 30, 30, 40, 40, 40, 40, 40])
要重复数组5次,请使用np.tile:In [2]: np.tile(data, 5)
Out[2]:
array([-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, -50, -40, -30,
-20, -10, 0, 10, 20, 30, 40, -50, -40, -30, -20, -10, 0,
10, 20, 30, 40, -50, -40, -30, -20, -10, 0, 10, 20, 30,
40, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40])
但是,请注意,有时您可以利用NumPy broadcasting而不是使用重复的元素创建更大的数组。
例如,如果z = np.array([1, 2])
v = np.array([[3], [4], [5]])
然后将这些数组添加到[[4 5]
[5 6]
[6 7]]
不需要使用平铺:In [12]: np.tile(z, (3,1))
Out[12]:
array([[1, 2],
[1, 2],
[1, 2]])
In [13]: np.tile(v, (1,2))
Out[13]:
array([[3, 3],
[4, 4],
[5, 5]])
In [14]: np.tile(z, (3,1)) + np.tile(v, (1,2))
Out[14]:
array([[4, 5],
[5, 6],
[6, 7]])
相反,NumPy将为您广播阵列:In [15]: z + v
Out[15]:
array([[4, 5],
[5, 6],
[6, 7]])