In this post, we demonstrate how to use the merge_asof() function in the Pandas library to merge two dataframes, df1 and df2, where df1 contains names and IDs for certain individuals and df2 contains historical addresses for those same individuals. The goal is to match each person in df1 with a corresponding address in df2 that is closest to a specified cutoff date.

The cutoff date can be any date, such as today or last year, and is used to determine which address from the historical data in df2 is the most relevant to use in the merge. For example, if the cutoff date is January 1, 2022, and an individual has three historical addresses, the address that is closest in time (and not exceeding) the cutoff date will be used in the merge.

To begin, the code imports the Pandas library and creates two dataframes: df1, which contains names and IDs for individuals, and df2, which contains historical addresses for those same individuals.

Next, the code converts the ‘Effective Date’ column in df2 to a datetime format using the to_datetime() function.

Then, the code sorts the rows of df2 by the ‘Effective Date’ column in ascending order using the sort_values() function. This is important because the merge_asof() function expects the data to be sorted in order to properly match rows based on the cutoff date.

Finally, the code uses the merge_asof() function to merge df1 and df2 based on the ‘ID’ column and the cutoff date. The merge_asof() function returns a new dataframe that includes the matching rows from both df1 and df2, along with any additional columns that are specified in the function.

Overall, this code demonstrates how to use the merge_asof() function in Pandas to merge two dataframes based on the closest date in time. [Further explained below]

import pandas as pd

df1
Name ID
0 Jane Smith 2
1 John Doe 1
2 Johnathan Adams 5
3 Kimberly Smith 8
4 Mary Brown 4
5 Mike Jones 3
6 Sara Davis 6
7 Will Johnson 7 
df2
ID Name Address 1 Address 2 City State Country Zip Effective Date
0 1 John Doe 123 Main St Suite 100 San Francisco CA USA 94105 2016-05-02
1 1 John Doe 222 Post St, NaN San Francisco CA USA 94108 2018-01-01
2 1 John Doe 456 Market St, NaN San Francisco CA USA 94103 2021-03-22
3 2 Jane Smith 456 Elm St Apt 4 New York NY USA 10012 2021-09-05
4 2 Jane Smith 620 Reiss Pl Apt 2D Bronx NY USA 12345 2001-10-23
5 2 Jane Smith 3484 Fort Independence St NaN Glen Oaks NY USA 11004 2008-11-15
6 3 Mike Jones 789 Pine St Floor 2 Seattle WA USA 98101 2015-10-09
7 3 Mike Jones 26142A Langston Ave NaN New York NY USA 59712 2020-01-28
8 3 Mike Jones 620 Reiss Pl NaN Staten Island NY USA 56712 2020-02-09
9 3 Mike Jones 379 Amboy St NaN Rockville Centre NY USA 56921 2020-01-24
10 4 Mary Brown 111 1st Ave Apt 5 Los Angeles CA USA 90012 2019-08-06
11 4 Mary Brown 5530 99th St NaN White Plains NY USA 15637 2019-01-23
12 5 Johnathan Adams 222 2nd St Ste 200 Chicago IL USA 60606 2015-06-07
13 5 Johnathan Adams 477 De Mott Ave NaN New Hyde Park NY USA 10023 2019-01-13
14 6 Sara Davis 333 3rd Ave Floor 4 Houston TX USA 77001 2020-08-03
15 6 Sara Davis 76 Shotwell Ave NaN Bronx NY USA 15893 2019-01-05
16 7 Will Johnson 444 4th St Apt 6 Philadelphia PA USA 19106 2015-05-09
17 8 Kimberly Smith 555 5th Ave Apt 7 Phoenix AZ USA 85001 2017-12-06

So for presentation purposes, let’s say the cutoff date is 1/1/2022. Notice John Doe (ID1) has three historical addresses. The one where he resided that is closest in time (and not to exceed) the cutoff date is the “456 Market St” address with and effective date of “2021-03-22”.

First, we ensure that the “Effective Date” column is in a datetime format.

df2['Effective Date']=pd.to_datetime(df2['Effective Date'])

Next, we need to sort by the Effective Date in an ascending order.

df2.sort_values(by='Effective Date',inplace=True)

df2
ID Name Address 1 Address 2 City State Country Zip Effective Date
4 2 Jane Smith 620 Reiss Pl Apt 2D Bronx NY USA 12345 2001-10-23
5 2 Jane Smith 3484 Fort Independence St NaN Glen Oaks NY USA 11004 2008-11-15
16 7 Will Johnson 444 4th St Apt 6 Philadelphia PA USA 19106 2015-05-09
12 5 Johnathan Adams 222 2nd St Ste 200 Chicago IL USA 60606 2015-06-07
6 3 Mike Jones 789 Pine St Floor 2 Seattle WA USA 98101 2015-10-09
0 1 John Doe 123 Main St Suite 100 San Francisco CA USA 94105 2016-05-02
17 8 Kimberly Smith 555 5th Ave Apt 7 Phoenix AZ USA 85001 2017-12-06
1 1 John Doe 222 Post St, NaN San Francisco CA USA 94108 2018-01-01
15 6 Sara Davis 76 Shotwell Ave NaN Bronx NY USA 15893 2019-01-05
13 5 Johnathan Adams 477 De Mott Ave NaN New Hyde Park NY USA 10023 2019-01-13
11 4 Mary Brown 5530 99th St NaN White Plains NY USA 15637 2019-01-23
10 4 Mary Brown 111 1st Ave Apt 5 Los Angeles CA USA 90012 2019-08-06
9 3 Mike Jones 379 Amboy St NaN Rockville Centre NY USA 56921 2020-01-24
7 3 Mike Jones 26142A Langston Ave NaN New York NY USA 59712 2020-01-28
8 3 Mike Jones 620 Reiss Pl NaN Staten Island NY USA 56712 2020-02-09
14 6 Sara Davis 333 3rd Ave Floor 4 Houston TX USA 77001 2020-08-03
2 1 John Doe 456 Market St, NaN San Francisco CA USA 94103 2021-03-22
3 2 Jane Smith 456 Elm St Apt 4 New York NY USA 10012 2021-09-05

Also, we need to add the cutoff date to the first dataframe (df1) as a separate column (name it “Effective Date” to be consistent with df2

df1['Effective Date']=pd.to_datetime('1/1/2022')

df1.sort_values(by='Effective Date',inplace=True)

df1
Name ID Effective Date
0 Jane Smith 2 2022-01-01
1 John Doe 1 2022-01-01
2 Johnathan Adams 5 2022-01-01
3 Kimberly Smith 8 2022-01-01
4 Mary Brown 4 2022-01-01
5 Mike Jones 3 2022-01-01
6 Sara Davis 6 2022-01-01
7 Will Johnson 7 2022-01-01

And now merge

merged_df=pd.merge_asof(df1,df2, on = 'Effective Date', by='ID',direction='backward')

merged_df
Name_x ID Effective Date Name_y Address 1 Address 2 City State Country Zip
0 Jane Smith 2 2022-01-01 Jane Smith 456 Elm St Apt 4 New York NY USA 10012
1 John Doe 1 2022-01-01 John Doe 456 Market St, NaN San Francisco CA USA 94103
2 Johnathan Adams 5 2022-01-01 Johnathan Adams 477 De Mott Ave NaN New Hyde Park NY USA 10023
3 Kimberly Smith 8 2022-01-01 Kimberly Smith 555 5th Ave Apt 7 Phoenix AZ USA 85001
4 Mary Brown 4 2022-01-01 Mary Brown 111 1st Ave Apt 5 Los Angeles CA USA 90012
5 Mike Jones 3 2022-01-01 Mike Jones 620 Reiss Pl NaN Staten Island NY USA 56712
6 Sara Davis 6 2022-01-01 Sara Davis 333 3rd Ave Floor 4 Houston TX USA 77001
7 Will Johnson 7 2022-01-01 Will Johnson 444 4th St Apt 6 Philadelphia PA USA 19106

And we get the expected results. Note: this merge can also be performed by finding the nearest match in the future, so the closest one past the cutoff date, and similarly on the nearest. This is accomplished by changing the direction parameter to forward or nearest. Additionally documentation on this function can be accessed via the link below.

pandas.merge_asof

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