Question

我发现了一些意外的结果。我想做的是创建一个查看ID号和日期的列，并将计算过去7天该ID号出现的次数（我也想将其动态化x倍）天，但只需尝试7天即可。

因此，给出此数据框：

import pandas as pd



df = pd.DataFrame(
        [['A', '2020-02-02 20:31:00'],
        ['A', '2020-02-03 00:52:00'],
        ['A', '2020-02-07 23:45:00'],
        ['A', '2020-02-08 13:19:00'],
        ['A', '2020-02-18 13:16:00'],
        ['A', '2020-02-27 12:16:00'],
        ['A', '2020-02-28 12:16:00'],
        ['B', '2020-02-07 18:57:00'],
        ['B', '2020-02-07 21:50:00'],
        ['B', '2020-02-12 19:03:00'],
        ['C', '2020-02-01 13:50:00'],
        ['C', '2020-02-11 15:50:00'],
        ['C', '2020-02-21 10:50:00']],
        columns = ['ID', 'Date'])

用于计算每个实例在最近7天内发生的代码：

df['Date'] = pd.to_datetime(df['Date'])

delta = 7
df['count_in_last_%s_days' %(delta)] = df.groupby(['ID', pd.Grouper(freq='%sD' %delta, key='Date')]).cumcount()

输出：

   ID                Date  count_in_last_7_days
0   A 2020-02-02 20:31:00                     0
1   A 2020-02-03 00:52:00                     1
2   A 2020-02-07 23:45:00                     2
3   A 2020-02-08 13:19:00                     0 #<---- This should output 3
4   A 2020-02-18 13:16:00                     0
5   A 2020-02-27 12:16:00                     0
6   A 2020-02-28 12:16:00                     1
7   B 2020-02-07 18:57:00                     0
8   B 2020-02-07 21:50:00                     1
9   B 2020-02-12 19:03:00                     0 #<---- THIS SHOULD OUTPUT 2
10  C 2020-02-01 13:50:00                     0
11  C 2020-02-11 15:50:00                     0
12  C 2020-02-21 10:50:00                     0

Answer 1

您不想在Grouper上使用Date，而是使用rolling窗口。分组程序会将数据帧分割为所需持续时间的单独连续块。您希望从每个日期起7天，这是rolling的工作：

delta = 7
df['count_in_last_%s_days' %(delta)] = df.assign(count=1).groupby(
    ['ID']).apply(lambda x: x.rolling('%sD' %delta, on='Date').sum(
        ))['count'].astype(int) - 1

它给出了预期的结果：

   ID                Date  count_in_last_7_days
0   A 2020-02-02 20:31:00                     0
1   A 2020-02-03 00:52:00                     1
2   A 2020-02-07 23:45:00                     2
3   A 2020-02-08 13:19:00                     3
4   A 2020-02-18 13:16:00                     0
5   A 2020-02-27 12:16:00                     0
6   A 2020-02-28 12:16:00                     1
7   B 2020-02-07 18:57:00                     0
8   B 2020-02-07 21:50:00                     1
9   B 2020-02-12 19:03:00                     2
10  C 2020-02-01 13:50:00                     0
11  C 2020-02-11 15:50:00                     0
12  C 2020-02-21 10:50:00                     0

Answer 2

看起来像在Date上滚动并具有正确的窗口可以做到：

(df.set_index('Date')
   .assign(count_last=1)
   .groupby('ID')
   .rolling(f'{delta}D')
   .sum() - 1
)

输出：

                        count_last
ID Date                           
A  2020-02-02 20:31:00         0.0
   2020-02-03 00:52:00         1.0
   2020-02-07 23:45:00         2.0
   2020-02-08 13:19:00         3.0
   2020-02-18 13:16:00         0.0
   2020-02-27 12:16:00         0.0
   2020-02-28 12:16:00         1.0
B  2020-02-07 18:57:00         0.0
   2020-02-07 21:50:00         1.0
   2020-02-12 19:03:00         2.0
C  2020-02-01 13:50:00         0.0
   2020-02-11 15:50:00         0.0
   2020-02-21 10:50:00         0.0

熊猫-最近x天的价值计数频率

2 个答案: