pandas 分组中连续日期之间的差异 [英] Difference between consecutive dates in pandas groupby

问题描述

我有一个如下数据框:

df_raw_dates = pd.DataFrame({"id": [102, 102, 102, 103, 103, 103, 104], "val": [9,2,4,7,6,3,2], "dates": [pd.Timestamp(2002, 1, 1), pd.Timestamp(2002, 3, 3), pd.Timestamp(2003, 4, 4), pd.Timestamp(2003, 8, 9), pd.Timestamp(2005, 2, 3), pd.Timestamp(2005, 2, 8), pd.Timestamp(2005, 2, 3)]})

    id  val      dates
0  102    9 2002-01-01
1  102    2 2002-03-03
2  102    4 2003-04-04
3  103    7 2003-08-09
4  103    6 2005-02-03
5  103    3 2005-02-08
6  104    2 2005-02-03

我要实现的是，而不是使 dates 列具有 diff_dates 列，该列将表示每个id的连续日期之间的差，其中每个id的第一个条目 diff_dates 列中的 id 将为 0 .如此说来，结果数据帧应该是:

What I want to achieve is instead of having the dates column to have a column diff_dates that will represent the difference between consecutive dates per id where the first entry for each id in the diff_dates column will be 0. With that said, the resulting data frame should be:

df_processed_dates = pd.DataFrame({"id": [102, 102, 102, 103, 103, 103, 104], "val": [9,2,4,7,6,3,2], "diff_dates": [0, 61, 397, 0, 544, 5, 0]})

    id  val  diff_dates
0  102    9           0
1  102    2          61
2  102    4         397
3  103    7           0
4  103    6         544
5  103    3           5
6  104    2           0

期待您的回答！

推荐答案

使用 groupby + fillna .

df_raw_dates.groupby('id').dates.diff().dt.days.fillna(0, downcast='infer')

0      0
1     61
2    397
3      0
4    544
5      5
6      0
Name: dates, dtype: int64

要将其重新分配为新列，请执行

To assign this back as a new column, do

df_raw_dates['date_diff'] = (
    df_raw_dates
       .pop('dates')
       .groupby(df_raw_dates['id'])
       .diff()
       .dt.days
       .fillna(0, downcast='infer'))


df_raw_dates

    id  val  date_diff
0  102    9          0
1  102    2         61
2  102    4        397
3  103    7          0
4  103    6        544
5  103    3          5
6  104    2          0

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