NumPy是否可以注意数组(非限制性地)沿一个轴增加? [英] Can NumPy take care that an array is (nonstrictly) increasing along one axis?

问题描述

在numpy中是否存在一个函数来保证或更确切地说确定一个数组，使其沿一个特定轴(非严格地)增加? 例如，我有以下2D数组:

Is there a function in numpy to guarantee or rather fix an array such that it is (nonstrictly) increasing along one particular axis? For example, I have the following 2D array:

X = array([[1, 2, 1, 4, 5],
           [0, 3, 1, 5, 4]])

np.foobar(X)的输出应返回

array([[1, 2, 2, 4, 5],
       [0, 3, 3, 5, 5]])

foobar是否存在，或者我需要使用np.diff之类的方法和一些智能索引手动进行操作吗?

Does foobar exist or do I need to do that manually by using something like np.diff and some smart indexing?

推荐答案

使用

Use np.maximum.accumulate for a running (accumulated) max value along that axis to ensure the strictly increasing criteria -

np.maximum.accumulate(X,axis=1)

样品运行-

In [233]: X
Out[233]: 
array([[1, 2, 1, 4, 5],
       [0, 3, 1, 5, 4]])

In [234]: np.maximum.accumulate(X,axis=1)
Out[234]: 
array([[1, 2, 2, 4, 5],
       [0, 3, 3, 5, 5]])

为了提高内存效率，我们可以使用其out参数将其分配回输入以进行原位更改.

For memory efficiency, we can assign it back to the input for in-situ changes with its out argument.

运行时测试

案例1:数组作为输入

In [254]: X = np.random.rand(1000,1000)

In [255]: %timeit np.maximum.accumulate(X,axis=1)
1000 loops, best of 3: 1.69 ms per loop

# @cᴏʟᴅsᴘᴇᴇᴅ's pandas soln using df.cummax
In [256]: %timeit pd.DataFrame(X).cummax(axis=1).values
100 loops, best of 3: 4.81 ms per loop

案例2:数据帧作为输入

Case #2 : Dataframe as input

In [257]: df = pd.DataFrame(np.random.rand(1000,1000))

In [258]: %timeit np.maximum.accumulate(df.values,axis=1)
1000 loops, best of 3: 1.68 ms per loop

# @cᴏʟᴅsᴘᴇᴇᴅ's pandas soln using df.cummax
In [259]: %timeit df.cummax(axis=1)
100 loops, best of 3: 4.68 ms per loop

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