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Pandas:将datetime时间线从一个数据帧强制到另一个数据帧

merge dataframe pandas python

Aquiles Páez · 技术社区 · 6 年前

主数据框:

    value    feed_id                created_at  
0     0.0  1010077.0 2019-03-06 07:38:18-05:00   
1     1.0  1010077.0 2019-03-06 07:39:26-05:00   
2     1.0  1010077.0 2019-03-06 07:40:33-05:00   
3     1.0  1010077.0 2019-03-06 07:41:41-05:00   
4     1.0  1010077.0 2019-03-06 07:42:49-05:00   
5     1.0  1010077.0 2019-03-06 07:43:56-05:00

辅助测向:

       value    feed_id                created_at
0  20.298492  1009408.0 2019-03-06 07:35:33-05:00
1  20.315002  1009408.0 2019-03-06 07:36:34-05:00
2  20.315002  1009408.0 2019-03-06 07:37:36-05:00
3  20.359650  1009408.0 2019-03-06 07:38:36-05:00
4  20.359650  1009408.0 2019-03-06 07:39:37-05:00
5  20.383179  1009408.0 2019-03-06 07:40:38-05:00
6  20.383179  1009408.0 2019-03-06 07:41:38-05:00
7  20.449524  1009408.0 2019-03-06 07:42:39-05:00
8  20.449524  1009408.0 2019-03-06 07:43:40-05:00
9  20.521912  1009408.0 2019-03-06 07:44:41-05:00

在这种情况下,我希望以下内容(最终的\u-df):我希望aux \u-df的'created \u at'列中描述的“timeline”完全合并到main \u-df中,不管它在两列中是否有公共值。对于普通的,我取整个时间戳,忽略以秒为单位的部分(注意 ).

       value    feed_id                created_at
0        nan        nan 2019-03-06 07:35:33-05:00
1        nan        nan 2019-03-06 07:36:34-05:00
2        nan        nan 2019-03-06 07:37:36-05:00
3        0.0  1010077.0 2019-03-06 07:38:36-05:00
4        1.0  1010077.0 2019-03-06 07:39:37-05:00
5        1.0  1010077.0 2019-03-06 07:40:38-05:00
6        1.0  1010077.0 2019-03-06 07:41:38-05:00
7        1.0  1010077.0 2019-03-06 07:42:39-05:00
8        1.0  1010077.0 2019-03-06 07:43:40-05:00
9        nan        nan 2019-03-06 07:44:41-05:00

在两个数据帧上创建一个名为“created_at_2”的新列,使用在每个时间戳上按分钟循环,这样我就可以丢弃
使用合并。

main_df['created_at_2']=main_数据框创建时间.dt.圆形('min') aux_df['created_at_2']=辅助_数据框创建时间.dt.圆形('min')

但是这种方法并不健壮,如本例所示。当你把时间戳(比如2019-03-06 07:40:33-05:00)四舍五入时,你将得到41分钟而不是40分钟。我需要一个按分钟连续的专栏。

我可以用这个重新格式化时间戳时间线:

main_df.created_at.map(lambda t: t.strftime('%Y-%m-%d %H:%M'))
aux_df.created_at.map(lambda t: t.strftime('%Y-%m-%d %H:%M'))
final_df = pd.merge(main_df, aux_df, on=['created_at_2'], how='inner')

提前谢谢!

1 回复 | 直到 6 年前

jezrael 6 年前

一个想法是使用 merge_asof ,但最后一行不同:

main_df['created_at'] = pd.to_datetime(main_df['created_at'])
aux_df['created_at'] = pd.to_datetime(aux_df['created_at'])

df = pd.merge_asof(aux_df[['created_at']], main_df, on=['created_at'])
print (df)
                 created_at  value    feed_id
0 2019-03-06 07:35:33-05:00    NaN        NaN
1 2019-03-06 07:36:34-05:00    NaN        NaN
2 2019-03-06 07:37:36-05:00    NaN        NaN
3 2019-03-06 07:38:36-05:00    0.0  1010077.0
4 2019-03-06 07:39:37-05:00    1.0  1010077.0
5 2019-03-06 07:40:38-05:00    1.0  1010077.0
6 2019-03-06 07:41:38-05:00    1.0  1010077.0
7 2019-03-06 07:42:39-05:00    1.0  1010077.0
8 2019-03-06 07:43:40-05:00    1.0  1010077.0
9 2019-03-06 07:44:41-05:00    1.0  1010077.0

另一个是使用 Series.dt.floor round

main_df['created_at'] = pd.to_datetime(main_df['created_at'])
aux_df['created_at'] = pd.to_datetime(aux_df['created_at'])
main_df['created_at_2'] = main_df.created_at.dt.floor('min') 
aux_df['created_at_2'] = aux_df.created_at.dt.floor('min') 

df = pd.merge(aux_df[['created_at_2']], main_df, on=['created_at_2'], how='left')
print (df)
               created_at_2  value    feed_id                created_at
0 2019-03-06 07:35:00-05:00    NaN        NaN                       NaT
1 2019-03-06 07:36:00-05:00    NaN        NaN                       NaT
2 2019-03-06 07:37:00-05:00    NaN        NaN                       NaT
3 2019-03-06 07:38:00-05:00    0.0  1010077.0 2019-03-06 07:38:18-05:00
4 2019-03-06 07:39:00-05:00    1.0  1010077.0 2019-03-06 07:39:26-05:00
5 2019-03-06 07:40:00-05:00    1.0  1010077.0 2019-03-06 07:40:33-05:00
6 2019-03-06 07:41:00-05:00    1.0  1010077.0 2019-03-06 07:41:41-05:00
7 2019-03-06 07:42:00-05:00    1.0  1010077.0 2019-03-06 07:42:49-05:00
8 2019-03-06 07:43:00-05:00    1.0  1010077.0 2019-03-06 07:43:56-05:00
9 2019-03-06 07:44:00-05:00    NaN        NaN                       NaT