How to check if id in DF1 appeared within the past 30 minutes in DF2? using Pandas

Question

I have DF1 with customer_id, datetime and fruites purchases, and DF2 with customer_id, datetime and Veggies purchases, how to check if within the past 30 minutes of the fruit purchases a customer did a Veggies puchase?

df1.head()

customer_id	puchase_date	fruit_item
1	2019-08-01 23:55:55	Apples
2	2019-08-01 23:58:32	Bananas

df2.head()

customer_id	puchase_date	veggies_item
1	2019-08-01 23:44:55	Eggplants
2	2019-08-01 22:00:32	Carrots

#after writing the required code and adding a new column to df1
df1.head()

customer_id	puchase_date	fruit_item	baught_veggies_last_30_minutes?
1	2019-08-01 23:55:55	Apples	Yes
2	2019-08-01 23:58:32	Bananas	No

Answer 1

You can use merge_asof. You want to merge within 30 mins of the purchase date, so you set that using the tolerance parameter. Note that you misspelt purchase as puchase. I spelt it the same way so that you can run it without error.

out = (pd.merge_asof(df1.assign(puchase_date=pd.to_datetime(df1['puchase_date'])).sort_values(by='puchase_date'),
                    df2.assign(puchase_date=pd.to_datetime(df2['puchase_date'])).sort_values(by='puchase_date'), 
                    on='puchase_date', 
                    by='customer_id', 
                    tolerance=pd.Timedelta('30 minute'))
       .rename(columns={'veggies_item':'bought_veggies_last_30_minutes'})
       .assign(bought_veggies_last_30_minutes=lambda x: x['bought_veggies_last_30_minutes']
               .notna().replace({True: 'Yes', False:'No'})))

Output:

   customer_id        puchase_date fruit_item bought_veggies_last_30_minutes
0            1 2019-08-01 23:55:55     Apples                            Yes
1            2 2019-08-01 23:58:32    Bananas                             No