pandas merge dataframes on closest timestamp

Question

I want to merge two dataframes on three columns: email, subject and timestamp. The timestamps between the dataframes differ and I therefore need to identify the closest matching timestamp for a group of email & subject.

Below is a reproducible example using a function for closest match suggested for this question.

import numpy as np
import pandas as pd
from pandas.io.parsers import StringIO

def find_closest_date(timepoint, time_series, add_time_delta_column=True):
   # takes a pd.Timestamp() instance and a pd.Series with dates in it
   # calcs the delta between `timepoint` and each date in `time_series`
   # returns the closest date and optionally the number of days in its time delta
   deltas = np.abs(time_series - timepoint)
   idx_closest_date = np.argmin(deltas)
   res = {"closest_date": time_series.ix[idx_closest_date]}
   idx = ['closest_date']
   if add_time_delta_column:
      res["closest_delta"] = deltas[idx_closest_date]
      idx.append('closest_delta')
   return pd.Series(res, index=idx)


a = """timestamp,email,subject
2016-07-01 10:17:00,a@gmail.com,subject3
2016-07-01 02:01:02,a@gmail.com,welcome
2016-07-01 14:45:04,a@gmail.com,subject3
2016-07-01 08:14:02,a@gmail.com,subject2
2016-07-01 16:26:35,a@gmail.com,subject4
2016-07-01 10:17:00,b@gmail.com,subject3
2016-07-01 02:01:02,b@gmail.com,welcome
2016-07-01 14:45:04,b@gmail.com,subject3
2016-07-01 08:14:02,b@gmail.com,subject2
2016-07-01 16:26:35,b@gmail.com,subject4
"""

b = """timestamp,email,subject,clicks,var1
2016-07-01 02:01:14,a@gmail.com,welcome,1,1
2016-07-01 08:15:48,a@gmail.com,subject2,2,2
2016-07-01 10:17:39,a@gmail.com,subject3,1,7
2016-07-01 14:46:01,a@gmail.com,subject3,1,2
2016-07-01 16:27:28,a@gmail.com,subject4,1,2
2016-07-01 10:17:05,b@gmail.com,subject3,0,0
2016-07-01 02:01:03,b@gmail.com,welcome,0,0
2016-07-01 14:45:05,b@gmail.com,subject3,0,0
2016-07-01 08:16:00,b@gmail.com,subject2,0,0
2016-07-01 17:00:00,b@gmail.com,subject4,0,0
"""

Notice that for a@gmail.com the closest matched timestamp is 10:17:39, whereas for b@gmail.com the closest match is 10:17:05.

a = """timestamp,email,subject
2016-07-01 10:17:00,a@gmail.com,subject3
2016-07-01 10:17:00,b@gmail.com,subject3
"""

b = """timestamp,email,subject,clicks,var1
2016-07-01 10:17:39,a@gmail.com,subject3,1,7
2016-07-01 10:17:05,b@gmail.com,subject3,0,0
"""
df1 = pd.read_csv(StringIO(a), parse_dates=['timestamp'])
df2 = pd.read_csv(StringIO(b), parse_dates=['timestamp'])

df1[['closest', 'time_bt_x_and_y']] = df1.timestamp.apply(find_closest_date, args=[df2.timestamp])
df1

df3 = pd.merge(df1, df2, left_on=['email','subject','closest'], right_on=['email','subject','timestamp'],how='left')

df3
timestamp_x        email   subject             closest  time_bt_x_and_y         timestamp_y  clicks  var1
  2016-07-01 10:17:00  a@gmail.com  subject3 2016-07-01 10:17:05         00:00:05                 NaT     NaN   NaN
  2016-07-01 02:01:02  a@gmail.com   welcome 2016-07-01 02:01:03         00:00:01                 NaT     NaN   NaN
  2016-07-01 14:45:04  a@gmail.com  subject3 2016-07-01 14:45:05         00:00:01                 NaT     NaN   NaN
  2016-07-01 08:14:02  a@gmail.com  subject2 2016-07-01 08:15:48         00:01:46 2016-07-01 08:15:48     2.0   2.0
  2016-07-01 16:26:35  a@gmail.com  subject4 2016-07-01 16:27:28         00:00:53 2016-07-01 16:27:28     1.0   2.0
  2016-07-01 10:17:00  b@gmail.com  subject3 2016-07-01 10:17:05         00:00:05 2016-07-01 10:17:05     0.0   0.0
  2016-07-01 02:01:02  b@gmail.com   welcome 2016-07-01 02:01:03         00:00:01 2016-07-01 02:01:03     0.0   0.0
  2016-07-01 14:45:04  b@gmail.com  subject3 2016-07-01 14:45:05         00:00:01 2016-07-01 14:45:05     0.0   0.0
  2016-07-01 08:14:02  b@gmail.com  subject2 2016-07-01 08:15:48         00:01:46                 NaT     NaN   NaN
  2016-07-01 16:26:35  b@gmail.com  subject4 2016-07-01 16:27:28         00:00:53                 NaT     NaN   NaN

The result is wrong, mainly because the closest date is incorrect since it does not take into account email & subject.

The expected result is

Amending the function to give the closest timesstamps for a given email and subject would be helpful.

df1.groupby(['email','subject'])['timestamp'].apply(find_closest_date, args=[df1.timestamp])

But that gives an error as the function is not defined for a group object. What's the best way of doing this?

Your expected output is text; add it to your post as text, not as an image. — Martijn Pieters, Aug 29 '16 at 07:06

unutbu · Accepted Answer · 2016-08-06T21:17:36.353

Notice that if you merge df1 and df2 on email and subject, then the result has all the possible relevant timestamp pairings:

In [108]: result = pd.merge(df1, df2, how='left', on=['email','subject'], suffixes=['', '_y']); result
Out[108]: 
             timestamp        email   subject         timestamp_y  clicks  var1
0  2016-07-01 10:17:00  a@gmail.com  subject3 2016-07-01 10:17:39       1     7
1  2016-07-01 10:17:00  a@gmail.com  subject3 2016-07-01 14:46:01       1     2
2  2016-07-01 02:01:02  a@gmail.com   welcome 2016-07-01 02:01:14       1     1
3  2016-07-01 14:45:04  a@gmail.com  subject3 2016-07-01 10:17:39       1     7
4  2016-07-01 14:45:04  a@gmail.com  subject3 2016-07-01 14:46:01       1     2
5  2016-07-01 08:14:02  a@gmail.com  subject2 2016-07-01 08:15:48       2     2
6  2016-07-01 16:26:35  a@gmail.com  subject4 2016-07-01 16:27:28       1     2
7  2016-07-01 10:17:00  b@gmail.com  subject3 2016-07-01 10:17:05       0     0
8  2016-07-01 10:17:00  b@gmail.com  subject3 2016-07-01 14:45:05       0     0
9  2016-07-01 02:01:02  b@gmail.com   welcome 2016-07-01 02:01:03       0     0
10 2016-07-01 14:45:04  b@gmail.com  subject3 2016-07-01 10:17:05       0     0
11 2016-07-01 14:45:04  b@gmail.com  subject3 2016-07-01 14:45:05       0     0
12 2016-07-01 08:14:02  b@gmail.com  subject2 2016-07-01 08:16:00       0     0
13 2016-07-01 16:26:35  b@gmail.com  subject4 2016-07-01 17:00:00       0     0

You could now take the absolute value of the difference in timestamps for each row:

result['diff'] = (result['timestamp_y'] - result['timestamp']).abs()

and then use

idx = result.groupby(['timestamp','email','subject'])['diff'].idxmin()
result = result.loc[idx]

to find the rows with the minimum difference for each group based on ['timestamp','email','subject'].

import numpy as np
import pandas as pd
from pandas.io.parsers import StringIO

a = """timestamp,email,subject
2016-07-01 10:17:00,a@gmail.com,subject3
2016-07-01 02:01:02,a@gmail.com,welcome
2016-07-01 14:45:04,a@gmail.com,subject3
2016-07-01 08:14:02,a@gmail.com,subject2
2016-07-01 16:26:35,a@gmail.com,subject4
2016-07-01 10:17:00,b@gmail.com,subject3
2016-07-01 02:01:02,b@gmail.com,welcome
2016-07-01 14:45:04,b@gmail.com,subject3
2016-07-01 08:14:02,b@gmail.com,subject2
2016-07-01 16:26:35,b@gmail.com,subject4
"""

b = """timestamp,email,subject,clicks,var1
2016-07-01 02:01:14,a@gmail.com,welcome,1,1
2016-07-01 08:15:48,a@gmail.com,subject2,2,2
2016-07-01 10:17:39,a@gmail.com,subject3,1,7
2016-07-01 14:46:01,a@gmail.com,subject3,1,2
2016-07-01 16:27:28,a@gmail.com,subject4,1,2
2016-07-01 10:17:05,b@gmail.com,subject3,0,0
2016-07-01 02:01:03,b@gmail.com,welcome,0,0
2016-07-01 14:45:05,b@gmail.com,subject3,0,0
2016-07-01 08:16:00,b@gmail.com,subject2,0,0
2016-07-01 17:00:00,b@gmail.com,subject4,0,0
"""

df1 = pd.read_csv(StringIO(a), parse_dates=['timestamp'])
df2 = pd.read_csv(StringIO(b), parse_dates=['timestamp'])

result = pd.merge(df1, df2, how='left', on=['email','subject'], suffixes=['', '_y'])
result['diff'] = (result['timestamp_y'] - result['timestamp']).abs()
idx = result.groupby(['timestamp','email','subject'])['diff'].idxmin()
result = result.loc[idx].drop(['timestamp_y','diff'], axis=1)
result = result.sort_index()
print(result)

yields

             timestamp        email   subject  clicks  var1
0  2016-07-01 10:17:00  a@gmail.com  subject3       1     7
2  2016-07-01 02:01:02  a@gmail.com   welcome       1     1
4  2016-07-01 14:45:04  a@gmail.com  subject3       1     2
5  2016-07-01 08:14:02  a@gmail.com  subject2       2     2
6  2016-07-01 16:26:35  a@gmail.com  subject4       1     2
7  2016-07-01 10:17:00  b@gmail.com  subject3       0     0
9  2016-07-01 02:01:02  b@gmail.com   welcome       0     0
11 2016-07-01 14:45:04  b@gmail.com  subject3       0     0
12 2016-07-01 08:14:02  b@gmail.com  subject2       0     0
13 2016-07-01 16:26:35  b@gmail.com  subject4       0     0

Kartik · Answer 2 · 2016-08-06T21:52:25.443

You want to apply the closest timestamp logic to each group of 'email' and 'subject'

a = """timestamp,email,subject
2016-07-01 10:17:00,a@gmail.com,subject3
2016-07-01 02:01:02,a@gmail.com,welcome
2016-07-01 14:45:04,a@gmail.com,subject3
2016-07-01 08:14:02,a@gmail.com,subject2
2016-07-01 16:26:35,a@gmail.com,subject4
2016-07-01 10:17:00,b@gmail.com,subject3
2016-07-01 02:01:02,b@gmail.com,welcome
2016-07-01 14:45:04,b@gmail.com,subject3
2016-07-01 08:14:02,b@gmail.com,subject2
2016-07-01 16:26:35,b@gmail.com,subject4
"""

b = """timestamp,email,subject,clicks,var1
2016-07-01 02:01:14,a@gmail.com,welcome,1,1
2016-07-01 08:15:48,a@gmail.com,subject2,2,2
2016-07-01 10:17:39,a@gmail.com,subject3,1,7
2016-07-01 14:46:01,a@gmail.com,subject3,1,2
2016-07-01 16:27:28,a@gmail.com,subject4,1,2
2016-07-01 10:17:05,b@gmail.com,subject3,0,0
2016-07-01 02:01:03,b@gmail.com,welcome,0,0
2016-07-01 14:45:05,b@gmail.com,subject3,0,0
2016-07-01 08:16:00,b@gmail.com,subject2,0,0
2016-07-01 17:00:00,b@gmail.com,subject4,0,0
"""

df1 = pd.read_csv(StringIO(a), parse_dates=['timestamp'])
df2 = pd.read_csv(StringIO(b), parse_dates=['timestamp'])
df2 = df2.set_index(['email', 'subject'])

def find_closest_date(timepoint, time_series, add_time_delta_column=True):
    # takes a pd.Timestamp() instance and a pd.Series with dates in it
    # calcs the delta between `timepoint` and each date in `time_series`
    # returns the closest date and optionally the number of days in its time delta
    time_series = time_series.values
    timepoint = np.datetime64(timepoint)
    deltas = np.abs(np.subtract(time_series, timepoint))
    idx_closest_date = np.argmin(deltas)
    res = {"closest_date": time_series[idx_closest_date]}
    idx = ['closest_date']
    if add_time_delta_column:
        res["closest_delta"] = deltas[idx_closest_date]
        idx.append('closest_delta')
    return pd.Series(res, index=idx)

# Then group df1 as needed
grouped = df1.groupby(['email', 'subject'])

# Finally loop over the group items, finding the closest timestamps
join_ts = pd.DataFrame()
for name, group in grouped:
    try:
        join_ts = pd.concat([join_ts, group['timestamp']\
                             .apply(find_closest_date, time_series=df2.loc[name, 'timestamp'])],
                            axis=0)
    except KeyError:
        pass

df3 = pd.merge(pd.concat([df1, join_ts], axis=1), df2, left_on=['closest_date'], right_on=['timestamp'])

So what does it give? An error, something else? Can you be a bit more specific? Please. — Kartik, Aug 06 '16 at 19:25
The picture in my post shows the expected result. The main problem is that the closest timestamp is wrong, as it does not take into account the 2 other dimensions, which are email and subject. If you look at the result of your inner join it contains only 5 e-mails, but it should show 10. (see the picture in my post). — TinaW, Aug 06 '16 at 19:29
Oh got it! You want the closest timesstamps for a given email and subject. I am editing my answer, it should work after the edits. — Kartik, Aug 06 '16 at 19:31
Thanks Kathik!! This is very helpful! The output is almost correct. Just note that b@gmail.com should have only 0 clicks & var1. Compare `df3.sort_values(['email_x'])` with the expected output. email B has zeros in all cases. — TinaW, Aug 06 '16 at 20:18
I have added a smaller example to the question, which shows the difference more clearly. The issue still comes from the find_closest_date function, which does not pick the correct date. — TinaW, Aug 06 '16 at 20:54
There were a couple of problems: 1. The `find_closest_date` function was not `MultiIndex` safe, and 2. The `append` was not working as I imagined it. I have edited my answer, and it should work as a drop-in replacement to your code. — Kartik, Aug 06 '16 at 21:50

pandas merge dataframes on closest timestamp

2 Answers2

Linked