Pandas - join by time proximity

Question

I have 2 dataframes, left_df and right_df , each of which has a column corresponding to datetime. I want to join them in such a way, that for every row R in left_df, I find the row in right_df that is closest in time to R out of all rows in right_df, and put them together. I don't have about whether the row from left_df or from right_df came first.

An example is given below:

left_df = 
              left_dt           left_flag
0  2014-08-23 07:57:03.827516   True
1  2014-08-23 09:27:12.831126  False
2  2014-08-23 11:55:27.551029   True
3  2014-08-23 16:11:33.511049   True


right_df =
    right dt                   right_flag 
0   2014-08-23 07:12:52.80587    True
1   2014-08-23 15:12:34.815087   True




desired output_df =

              left_dt           left_flag        right dt               right_flag 
0  2014-08-23 07:57:03.827516   True        2015-08-23 07:12:52.80587      True
1  2014-08-23 09:27:12.831126  False        2015-08-23 07:12:52.80587      True
2  2014-08-23 11:55:27.551029   True        2015-08-23 15:12:34.815087     True
3  2014-08-23 16:11:33.511049   True        2015-08-23 15:12:34.815087     True

I haven't had a chance to try it yet. I will get back to you as soon as I do. — Baron Yugovich, Sep 27 '15 at 20:49

score 0 · Answer 1 · answered Sep 24 '15 at 22:32

I'm not sure it will work in all cases. But I think it can be a solution.

# Test data
left_df = pd.DataFrame({'left_dt': ['2014-08-23 07:57:03.827516',
  '2014-08-23 09:27:12.831126',
  '2014-08-23 11:55:27.551029',
  '2014-08-23 16:11:33.511049'],
 'left_flag': [True, False, True, True]})
left_df['left_dt'] = pd.to_datetime(left_df['left_dt'])


right_df = pd.DataFrame(
{'right_dt': ['2014-08-23 07:12:52.80587', '2014-08-23 15:12:34.815087'],
 'right_flag': [True, True]})
right_df['right_dt'] = pd.to_datetime(right_df['right_dt'])


# Setting the date as the index for each DataFrame
left_df.set_index('left_dt', drop=False, inplace=True)
right_df.set_index('right_dt', drop=False, inplace=True)

# Merging them and filling the gaps
output_df = left_df.join(right_df, how='outer').sort_index()
output_df.fillna(method='ffill', inplace=True)
# Droping unwanted values from the left
output_df.dropna(subset=['left_dt'], inplace=True)
# Computing a difference to select the right duplicated row to drop (the one with the greates diff)
output_df['diff'] = abs(output_df['left_dt'] - output_df['right_dt'])
output_df.sort(columns='diff', inplace=True)
output_df.drop_duplicates(subset=['left_dt'], inplace=True)
# Bringing back the index
output_df.sort_index(inplace=True)
output_df = output_df.reset_index(drop=True)
# Droping unwanted column
output_df.drop('diff', axis=1, inplace=True)
output_df

                     left_dt left_flag                   right_dt right_flag
0 2014-08-23 07:57:03.827516      True 2014-08-23 07:12:52.805870       True
1 2014-08-23 09:27:12.831126     False 2014-08-23 07:12:52.805870       True
2 2014-08-23 11:55:27.551029      True 2014-08-23 15:12:34.815087       True
3 2014-08-23 16:11:33.511049      True 2014-08-23 15:12:34.815087       True

Pandas - join by time proximity

1 Answers1