How to join two dataframes for which column values

问题:

Given two dataframes df_1 and df_2, how to join them such that datetime column df_1 is in between start and end in dataframe df_2:

print df_1

  timestamp              A          B
0 2016-05-14 10:54:33    0.020228   0.026572
1 2016-05-14 10:54:34    0.057780   0.175499
2 2016-05-14 10:54:35    0.098808   0.620986
3 2016-05-14 10:54:36    0.158789   1.014819
4 2016-05-14 10:54:39    0.038129   2.384590


print df_2

  start                end                  event    
0 2016-05-14 10:54:31  2016-05-14 10:54:33  E1
1 2016-05-14 10:54:34  2016-05-14 10:54:37  E2
2 2016-05-14 10:54:38  2016-05-14 10:54:42  E3

Get corresponding event where df1.timestamp is between df_2.start and df2.end

  timestamp              A          B          event
0 2016-05-14 10:54:33    0.020228   0.026572   E1
1 2016-05-14 10:54:34    0.057780   0.175499   E2
2 2016-05-14 10:54:35    0.098808   0.620986   E2
3 2016-05-14 10:54:36    0.158789   1.014819   E2
4 2016-05-14 10:54:39    0.038129   2.384590   E3

回答1:

One simple solution is create interval index from start and end setting closed = both then use get_loc to get the event i.e (Hope all the date times are in timestamps dtype )

df_2.index = pd.IntervalIndex.from_arrays(df_2[\'start\'],df_2[\'end\'],closed=\'both\')
df_1[\'event\'] = df_1[\'timestamp\'].apply(lambda x : df_2.iloc[df_2.index.get_loc(x)][\'event\'])

Output :

            timestamp         A         B event
0 2016-05-14 10:54:33  0.020228  0.026572    E1
1 2016-05-14 10:54:34  0.057780  0.175499    E2
2 2016-05-14 10:54:35  0.098808  0.620986    E2
3 2016-05-14 10:54:36  0.158789  1.014819    E2
4 2016-05-14 10:54:39  0.038129  2.384590    E3

回答2:

A slight improvement to Dark\'s solution:

idx = pd.IntervalIndex.from_arrays(df_2[\'start\'], df_2[\'end\'], closed=\'both\')
event = df_2.loc[idx.get_indexer(df_1.timestamp), \'event\']

event
0    E1
1    E2
1    E2
1    E2
2    E3
Name: event, dtype: object

df_1[\'event\'] = event.values
df_1
            timestamp         A         B event
0 2016-05-14 10:54:33  0.020228  0.026572    E1
1 2016-05-14 10:54:34  0.057780  0.175499    E2
2 2016-05-14 10:54:35  0.098808  0.620986    E2
3 2016-05-14 10:54:36  0.158789  1.014819    E2
4 2016-05-14 10:54:39  0.038129  2.384590    E3

Reference: A question on IntervalIndex.get_indexer.

回答3:

Option 1

idx = pd.IntervalIndex.from_arrays(df_2[\'start\'], df_2[\'end\'], closed=\'both\')
df_2.index=idx
df_1[\'event\']=df_2.loc[df_1.timestamp,\'event\'].values

Option 2

df_2[\'timestamp\']=df_2[\'end\']
pd.merge_asof(df_1,df_2[[\'timestamp\',\'event\']],on=\'timestamp\',direction =\'forward\',allow_exact_matches =True)
Out[405]: 
            timestamp         A         B event
0 2016-05-14 10:54:33  0.020228  0.026572    E1
1 2016-05-14 10:54:34  0.057780  0.175499    E2
2 2016-05-14 10:54:35  0.098808  0.620986    E2
3 2016-05-14 10:54:36  0.158789  1.014819    E2
4 2016-05-14 10:54:39  0.038129  2.384590    E3

回答4:

You can use the module pandasql

import pandasql as ps

sqlcode = \'\'\'
select df_1.timestamp
,df_1.A
,df_1.B
,df_2.event
from df_1 
inner join df_2 
on d1.timestamp between df_2.start and df2.end
\'\'\'

newdf = ps.sqldf(sqlcode,locals())

回答5:

In this method, we assume TimeStamp objects are used.

df2  start                end                  event    
   0 2016-05-14 10:54:31  2016-05-14 10:54:33  E1
   1 2016-05-14 10:54:34  2016-05-14 10:54:37  E2
   2 2016-05-14 10:54:38  2016-05-14 10:54:42  E3

event_num = len(df2.event)

def get_event(t):    
    event_idx = ((t >= df2.start) & (t <= df2.end)).dot(np.arange(event_num))
    return df2.event[event_idx]

df1[\"event\"] = df1.timestamp.transform(get_event)

Explanation of get_event

For each timestamp in df1, say t0 = 2016-05-14 10:54:33,

(t0 >= df2.start) & (t0 <= df2.end) will contain 1 true. (See example 1). Then, take a dot product with np.arange(event_num) to get the index of the event that a t0 belongs to.

Examples:

Example 1

    t0 >= df2.start    t0 <= df2.end     After &     np.arange(3)    
0     True                True         ->  T              0        event_idx
1    False                True         ->  F              1     ->     0
2    False                True         ->  F              2

Take t2 = 2016-05-14 10:54:35 for another example

    t2 >= df2.start    t2 <= df2.end     After &     np.arange(3)    
0     True                False        ->  F              0        event_idx
1     True                True         ->  T              1     ->     1
2    False                True         ->  F              2

We finally use transform to transform each timestamp into an event.