用shift()获取熊猫数据中的下一个元素

Question

在this question上，我有一个类似于这样的数据，我想创建一个新的列next_domain和next_next_domain。

它是通过根据时间戳查找IP的下一个域(和下一个域)来计算的。我怎样才能在熊猫身上做到这一点？

输入：

    domain      ip      timestamp
0   Google      101     2020-04-01 23:01:41
1   Google      101     2020-04-01 23:01:59
2   Google      101     2020-04-02 12:01:41
3   Facebook    101     2020-04-02 13:11:33
4   Facebook    101     2020-04-02 13:11:35
5   Youtube     103     2020-04-21 13:01:41
6   Youtube     103     2020-04-21 13:11:46
7   Youtube     103     2020-04-22 01:01:01
8   Google      103     2020-04-22 02:11:23
9   Facebook    103     2020-04-23 14:11:13
10  Youtube     103     2020-04-23 14:11:55

预期输出：

    domain      ip      timestamp              next_domain    next_next_domain
0   Google      101     2020-04-01 23:01:41    Facebook       N/A
1   Google      101     2020-04-01 23:01:59    Facebook       N/A
2   Google      101     2020-04-02 12:01:41    Facebook       N/A
3   Facebook    101     2020-04-02 13:11:33    N/A            N/A
4   Facebook    101     2020-04-02 13:11:35    N/A            N/A
5   Youtube     103     2020-04-21 13:01:41    Google         Facebook
6   Youtube     103     2020-04-21 13:11:46    Google         Facebook
7   Youtube     103     2020-04-22 01:01:01    Google         Facebook
8   Google      103     2020-04-22 02:11:23    Facebook       Youtube
9   Facebook    103     2020-04-23 14:11:13    Youtube        N/A
10  Youtube     103     2020-04-23 14:11:55    N/A            N/A

Answer 1

在前面问题的my answer基础上，您可以创建一个函数并迭代该过程：

def next_domain(df, col='domain', group='ip'):
    s = df[col]
    return (s.where(s.ne(s.shift())) # keep only first domain of each stretch
                      .groupby(df[group])                   # per group
                      .apply(lambda s: s.bfill().shift(-1)) # bfill and shift up
                    )

df['next_domain'] = next_domain(df, 'domain')
df['next_next_domain'] = next_domain(df, 'next_domain')

产出：

      domain   ip            timestamp next_domain next_next_domain
0     Google  101  2020-04-01 23:01:41    Facebook              NaN
1     Google  101  2020-04-01 23:01:59    Facebook              NaN
2     Google  101  2020-04-02 12:01:41    Facebook              NaN
3   Facebook  101  2020-04-02 13:11:33         NaN              NaN
4   Facebook  101  2020-04-02 13:11:35         NaN              NaN
5    Youtube  103  2020-04-21 13:01:41      Google         Facebook
6    Youtube  103  2020-04-21 13:11:46      Google         Facebook
7    Youtube  103  2020-04-22 01:01:01      Google         Facebook
8     Google  103  2020-04-22 02:11:23    Facebook          Youtube
9   Facebook  103  2020-04-23 14:11:13     Youtube              NaN
10   Youtube  103  2020-04-23 14:11:55         NaN              NaN