Get the value in a dataframe based on a value and a date in another dataframe - pandas

Question

I tried countless answers to similar problems here on SO but couldn't find anything that works for this scenario. It's driving me nuts.
I have these two Dataframes:
df_op:
index
Date
Close
Name
LogRet
0
2022-11-29 00:00:00
240.33
MSFT
-0.0059
1
2022-11-29 00:00:00
280.57
QQQ
-0.0076
2
2022-12-13 00:00:00
342.46
ADBE
0.0126
3
2022-12-13 00:00:00
256.92
MSFT
0.0173
df_quotes:
index
Date
Close
Name
72
2022-11-29 00:00:00
141.17
AAPL
196
2022-11-29 00:00:00
240.33
MSFT
73
2022-11-30 00:00:00
148.03
AAPL
197
2022-11-30 00:00:00
255.14
MSFT
11
2022-11-30 00:00:00
293.36
QQQ
136
2022-12-01 00:00:00
344.11
ADBE
198
2022-12-01 00:00:00
254.69
MSFT
12
2022-12-02 00:00:00
293.72
QQQ
I would like to add a column to df_op that indicates the close of the stock in df_quotes 2 days later. For example, the first row of df_op should become:
index
Date
Close
Name
LogRet
Next
0
2022-11-29 00:00:00
240.33
MSFT
-0.0059
254.69
In other words:
for each row in df_op, find the corresponding Name in df_quotes with Date of 2 days later and copy its Close to df_op in column 'Next'.
I tried tens of combinations like this without success:
df_quotes[df_quotes['Date'].isin(df_op['Date'] + pd.DateOffset(days=2)) & df_quotes['Name'].isin(df_op['Name'])]
How can I do this without recurring to loops?

Answer 1

Try this:
#first convert to datetime
df_op['Date'] = pd.to_datetime(df_op['Date'])
df_quotes['Date'] = pd.to_datetime(df_quotes['Date'])
#merge on Date and Name, but the date is offset 2 business days
(pd.merge(df_op,
df_quotes[['Date','Close','Name']].rename({'Close':'Next'},axis=1),
left_on=['Date','Name'],
right_on=[df_quotes['Date'] - pd.tseries.offsets.BDay(2),'Name'],
how = 'left')
.drop(['Date_x','Date_y'],axis=1))
Output:
Date index Close Name LogRet Next
0 2022-11-29 0 240.33 MSFT -0.0059 254.69
1 2022-11-29 1 280.57 QQQ -0.0076 NaN
2 2022-12-13 2 342.46 ADBE 0.0126 NaN
3 2022-12-13 3 256.92 MSFT 0.0173 NaN