Question

我需要计算delta列（如下所示）。但棘手的部分是下面提到的条件。我怎么能在熊猫中做到这一点？

speaker | video | frame | time |delta(expected)
--------|-------|-------|------|----------------
one     |1      | 0     |10    |0
one     |1      | 1     |15    |5
one     |2      | 0     |12    |0
one     |2      | 1     |16    |4
two     |2      | 0     |19    |0
two     |2      | 1     |22    |3
two     |2      | 2     |16    |-6

条件： Delta是具有相同视频的相同扬声器的帧之间的差异。换句话说，不应针对不同的扬声器或不同的视频在行上计算delta。对于这些情况，该值应初始化为零，如delta（预期）列中所示。

Answer 1

不要使用groupby，diff和fillna：

df['delta'] = df.groupby(['speaker','video'])['time'].diff().fillna(0)

输出：

    speaker  video  frame  time  delta(expected)  delta
0  one           1      0    10                0    0.0
1  one           1      1    15                5    5.0
2  one           2      0    12                0    0.0
3  one           2      1    16                4    4.0
4  two           2      0    19                0    0.0
5  two           2      1    22                3    3.0
6  two           2      2    16               -6   -6.0

Answer 2

选项1
假设df按['speaker', 'video']排序。如果没有，那就这样做。

delta = np.where(
    df.duplicated(['speaker', 'video']).values,
    np.append(0, np.diff(df.time.values)), 0
)

df.assign(delta=delta)

  speaker  video  frame  time  delta(expected)  delta
0     one      1      0    10                0      0
1     one      1      1    15                5      5
2     one      2      0    12                0      0
3     one      2      1    16                4      4
4     two      2      0    19                0      0
5     two      2      1    22                3      3
6     two      2      2    16               -6     -6

选项2

df.assign(
    delta=df.groupby(['speaker', 'video']).time.transform(
        lambda x: np.append(0, np.diff(x.values))
    )
)

  speaker  video  frame  time  delta(expected)  delta
0     one      1      0    10                0      0
1     one      1      1    15                5      5
2     one      2      0    12                0      0
3     one      2      1    16                4      4
4     two      2      0    19                0      0
5     two      2      1    22                3      3
6     two      2      2    16               -6     -6

如何在具有特定条件的pandas数据框行中查找delta

2 个答案: