NumPy追加vs串联 [英] NumPy append vs concatenate
本文介绍了NumPy追加vs串联的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
NumPy append和concatenate有什么区别?
What is the difference between NumPy append and concatenate?
我的观察是，如果未指定轴，则concatenate会快一点，而append会使数组变平.
My observation is that concatenate is a bit faster and append flattens the array if axis is not specified.
In [52]: print a
[[1 2]
[3 4]
[5 6]
[5 6]
[1 2]
[3 4]
[5 6]
[5 6]
[1 2]
[3 4]
[5 6]
[5 6]
[5 6]]
In [53]: print b
[[1 2]
[3 4]
[5 6]
[5 6]
[1 2]
[3 4]
[5 6]
[5 6]
[5 6]]
In [54]: timeit -n 10000 -r 5 np.concatenate((a, b))
10000 loops, best of 5: 2.05 µs per loop
In [55]: timeit -n 10000 -r 5 np.append(a, b, axis = 0)
10000 loops, best of 5: 2.41 µs per loop
In [58]: np.concatenate((a, b))
Out[58]:
array([[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[5, 6]])
In [59]: np.append(a, b, axis = 0)
Out[59]:
array([[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[1, 2],
[3, 4],
[5, 6],
[5, 6],
[5, 6]])
In [60]: np.append(a, b)
Out[60]:
array([1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5,
6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 5, 6])
推荐答案
np.append使用np.concatenate:
def append(arr, values, axis=None):
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
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