pandas :按另一列的值移动一列 [英] Pandas: Shift one column by other column value

问题描述

我正在尝试使用一列的值将另一列的值移动该数量.根据文档，Pandas shift()取一个整数，但是有办法代替Series吗?

I'm trying to use one column's values to shift another columns values by that amount. Pandas shift(), per the documentation, takes an integer, but is there a way to instead use a Series?

当前代码:

import pandas as pd

df = pd.DataFrame({ 'a':[1,2,3,4,5,6,7,8,9,10],
                    'b':[0,0,0,0,4,4,4,0,0,0]})

df['a'] = df['a'].shift(df['b'])

...这当然不起作用.

...which is of course not working.

所需的输出:

    a  b
0   1  0
1   2  0
2   3  0
3   4  0
4   1  4
5   2  4
6   3  4
7   8  0
8   9  0
9  10  0

如果更简单，则移位将始终相同，因此理论上'b'系列可以是True / False或其他某种二进制触发器，并且.shift()仍可以是整数.觉得这条路有点棘手，但可以完成工作.

If it makes it easier, the shift will always be the same, so theoretically the 'b' series could be True / False or some other binary trigger, and the .shift() could still be an integer. Feels a little hacky going that route, but it would get the job done.

推荐答案

我们可以使用 numba解决方案:

we can use numba solution:

from numba import jit

@jit
def dyn_shift(s, step):
    assert len(s) == len(step), "[s] and [step] should have the same length"
    assert isinstance(s, np.ndarray), "[s] should have [numpy.ndarray] dtype"
    assert isinstance(step, np.ndarray), "[step] should have [numpy.ndarray] dtype"
    N = len(s)
    res = np.empty(N, dtype=s.dtype)
    for i in range(N):
        res[i] = s[i-step[i]]
    return res

结果:

In [302]: df['new'] = dyn_shift(df['a'].values, df['b'].values)
# NOTE: we should pass Numpy arrays:   ^^^^^^^         ^^^^^^^

In [303]: df
Out[303]:
    a  b  new
0   1  0    1
1   2  0    2
2   3  0    3
3   4  0    4
4   5  4    1
5   6  4    2
6   7  4    3
7   8  0    8
8   9  0    9
9  10  0   10

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