Python 如果条件满足，数据帧查找第一次出现

我有以下数据

        timestamp        bucket forward
   0    02/01/2012 08:00    1   2309.6
1156    02/01/2012 08:00    2   2305.9
2320    02/01/2012 08:00    3   2306
3481    02/01/2012 08:00    4   2240.9
4643    02/01/2012 08:00    5   2235.3
5807    02/01/2012 08:00    6   2224.1
6969    02/01/2012 08:00    7   2167.1
   1    02/01/2012 09:00    1   2327.3
1157    02/01/2012 09:00    2   2323.4
2321    02/01/2012 09:00    3   2323.5
3482    02/01/2012 09:00    4   2258.4
4644    02/01/2012 09:00    5   2252.8
5808    02/01/2012 09:00    6   2241.4
6970    02/01/2012 09:00    7   2183.2
   2    02/01/2012 10:00    1   2342.3

如果bucket>previou bucket，我需要找到具有相同时间戳的对应forward，即：

        timestamp        bucket forward   result
   0    02/01/2012 08:00    1   2309.6    2309.6
1156    02/01/2012 08:00    2   2305.9    2309.6
2320    02/01/2012 08:00    3   2306      2309.6
3481    02/01/2012 08:00    4   2240.9    2309.6
4643    02/01/2012 08:00    5   2235.3    2309.6
5807    02/01/2012 08:00    6   2224.1    2309.6
6969    02/01/2012 08:00    7   2167.1    2309.6
   1    02/01/2012 09:00    1   2327.3    2327.3
1157    02/01/2012 09:00    2   2323.4    2327.3
2321    02/01/2012 09:00    3   2323.5    2327.3
3482    02/01/2012 09:00    4   2258.4    2327.3
4644    02/01/2012 09:00    5   2252.8    2327.3
5808    02/01/2012 09:00    6   2241.4    2327.3
6970    02/01/2012 09:00    7   2183.2    2327.3
   2    02/01/2012 10:00    1   2342.3    2342.3

到目前为止，我已经：

df['result'] = np.where(df['bucket'].diff()>0, df['forward'].shift(1), df['forward']) 

---

不知道如何将第一次发生的情况合并到铲斗零件中。任何指针都是可取的

您可以使用

diff

和

cumsum

从bucket列创建一个组变量，然后使用transform从每个组中获取第一个正向值：

df['result']=df.groupby（by=（df.bucket.diff（）<0.cumsum（））['forward'].transform（'first'））
df

这里有一种方法

通过与上一个值进行比较来填充值，然后将

ffill

填充

NaN

值

In [1024]: df['result'] = df.loc[~(df.bucket > df.bucket.shift(1)), 'forward']

In [1025]: df
Out[1025]:
                 timestamp  bucket  forward  result
0    '02/01/2012    08:00'       1   2309.6  2309.6
1156 '02/01/2012    08:00'       2   2305.9     NaN
2320 '02/01/2012    08:00'       3   2306.0     NaN
3481 '02/01/2012    08:00'       4   2240.9     NaN
4643 '02/01/2012    08:00'       5   2235.3     NaN
5807 '02/01/2012    08:00'       6   2224.1     NaN
6969 '02/01/2012    08:00'       7   2167.1     NaN
1    '02/01/2012    09:00'       1   2327.3  2327.3
1157 '02/01/2012    09:00'       2   2323.4     NaN
2321 '02/01/2012    09:00'       3   2323.5     NaN
3482 '02/01/2012    09:00'       4   2258.4     NaN
4644 '02/01/2012    09:00'       5   2252.8     NaN
5808 '02/01/2012    09:00'       6   2241.4     NaN
6970 '02/01/2012    09:00'       7   2183.2     NaN
2    '02/01/2012    10:00'       1   2342.3  2342.3

正向填充

NaN

s

In [1026]: df.result = df.result.ffill()

In [1027]: df
Out[1027]:
                 timestamp  bucket  forward  result
0    '02/01/2012    08:00'       1   2309.6  2309.6
1156 '02/01/2012    08:00'       2   2305.9  2309.6
2320 '02/01/2012    08:00'       3   2306.0  2309.6
3481 '02/01/2012    08:00'       4   2240.9  2309.6
4643 '02/01/2012    08:00'       5   2235.3  2309.6
5807 '02/01/2012    08:00'       6   2224.1  2309.6
6969 '02/01/2012    08:00'       7   2167.1  2309.6
1    '02/01/2012    09:00'       1   2327.3  2327.3
1157 '02/01/2012    09:00'       2   2323.4  2327.3
2321 '02/01/2012    09:00'       3   2323.5  2327.3
3482 '02/01/2012    09:00'       4   2258.4  2327.3
4644 '02/01/2012    09:00'       5   2252.8  2327.3
5808 '02/01/2012    09:00'       6   2241.4  2327.3
6970 '02/01/2012    09:00'       7   2183.2  2327.3
2    '02/01/2012    10:00'       1   2342.3  2342.3

In [1026]: df.result = df.result.ffill()

In [1027]: df
Out[1027]:
                 timestamp  bucket  forward  result
0    '02/01/2012    08:00'       1   2309.6  2309.6
1156 '02/01/2012    08:00'       2   2305.9  2309.6
2320 '02/01/2012    08:00'       3   2306.0  2309.6
3481 '02/01/2012    08:00'       4   2240.9  2309.6
4643 '02/01/2012    08:00'       5   2235.3  2309.6
5807 '02/01/2012    08:00'       6   2224.1  2309.6
6969 '02/01/2012    08:00'       7   2167.1  2309.6
1    '02/01/2012    09:00'       1   2327.3  2327.3
1157 '02/01/2012    09:00'       2   2323.4  2327.3
2321 '02/01/2012    09:00'       3   2323.5  2327.3
3482 '02/01/2012    09:00'       4   2258.4  2327.3
4644 '02/01/2012    09:00'       5   2252.8  2327.3
5808 '02/01/2012    09:00'       6   2241.4  2327.3
6970 '02/01/2012    09:00'       7   2183.2  2327.3
2    '02/01/2012    10:00'       1   2342.3  2342.3