pandas 按时间顺序重复行 [英] Pandas duplicate rows with time sequence

问题描述

我正在尝试复制熊猫数据框的行，并且还为在列FROM和TO之间的分钟数内的时间序列添加了另一列.

I am trying to duplicate my pandas' data frame's rows and also adding an additional column for a time sequence in minutes between column FROM and TO.

例如，我有这个数据框.

For example, I have this data frame.

ID  FROM    TO
A   15:30   15:33
B   16:40   16:44
C   15:20   15:22

我想要的输出是

ID  FROM    TO  time
A   15:30   15:33   15:30
A   15:30   15:33   15:31
A   15:30   15:33   15:32
A   15:30   15:33   15:33
B   16:40   16:41   16:40
B   16:40   16:41   16:41
C   15:20   15:22   15:20
C   15:20   15:22   15:21
C   15:20   15:22   15:22

在 R 中，我可以这样做:new_df = setDT(df)[, .(ID, FROM, TO, time=seq(FROM,TO,by="mins")), by=1:nrow(df)]，但是我很难找到与之等效的Python.

In R, I could do this: new_df = setDT(df)[, .(ID, FROM, TO, time=seq(FROM,TO,by="mins")), by=1:nrow(df)], but I am having trouble finding the Python equivalent of this.

提前谢谢！

推荐答案

解决问题的两个步骤:

pd.date_range 和 apply 和 strftime

pd.date_range with apply and strftime

df['duration'] = df.apply(
    lambda row: [
    i.strftime('%H:%M')
    for i in pd.date_range(
        row['FROM'], row['TO'], freq='60s'
        )
    ], 
    axis=1)

  ID   FROM     TO                             duration
0  A  15:30  15:33         [15:30, 15:31, 15:32, 15:33]
1  B  16:40  16:44  [16:40, 16:41, 16:42, 16:43, 16:44]
2  C  15:20  15:22                [15:20, 15:21, 15:22]

apply 和 stack

apply with stack

df.set_index(['ID', 'FROM', 'TO']) \
    .duration.apply(pd.Series) \
    .stack().reset_index(level=3, drop=True) \
    .reset_index() \
    .set_index('ID')

# Result

     FROM     TO      0
ID
A   15:30  15:33  15:30
A   15:30  15:33  15:31
A   15:30  15:33  15:32
A   15:30  15:33  15:33
B   16:40  16:44  16:40
B   16:40  16:44  16:41
B   16:40  16:44  16:42
B   16:40  16:44  16:43
B   16:40  16:44  16:44
C   15:20  15:22  15:20
C   15:20  15:22  15:21
C   15:20  15:22  15:22

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