连接两个没有完全匹配值的数据框

Question

Ticker & Date are index

Dataframe-A

Ticker	Date	Renue
AAPL	2022-01-01	10M
AAPL	2022-04-01	10M
MSFT	2022-01-01	10M
MSFT	2022-04-01	10M

Dataframe-B

Ticker	Date	Price
AAPL	2022-01-02	11
AAPL	2022-01-03	12
AAPL	2022-04-01	15
AAPL	2022-04-02	16
MSFT	2022-01-01	20
MSFT	2022-01-02	21
MSFT	2022-01-03	22
MSFT	2022-04-02	26
MSFT	2022-04-03	26

Join the Dataframe by Ticker and Date

But AAPL without 2022-01-01 Price, use 2022-01-02

Result

Ticker	Date	Renue	Price
AAPL	2022-01-01	10M	11
AAPL	2022-04-01	10M	15
MSFT	2022-01-01	10M	20
MSFT	2022-04-01	10M	26

How to join the DatFrame to produce the result Dataframe?

Thanks a lot!

Answer 1

here is one way to do it

it is case of less than or equal join. We first merge the two DFs, on Ticker, and then filter(query) where the date among two matches or DF1 date is less than the second DF date. We then groupby Ticket, and date and take the first row for each group. Finally, reset index and drop the date from right column

df1.merge(df2, on='Ticker', 
          how='left', 
          suffixes=('','_y')
         ).query('Date<=Date_y').groupby(
                ['Ticker','Date']).first().reset_index().drop(columns='Date_y')

    Ticker  Date    Renue   Price
0   AAPL    2022-01-01  10M     11
1   AAPL    2022-04-01  10M     15
2   MSFT    2022-01-01  10M     20
3   MSFT    2022-04-01  10M     26

Answer 2

You can use merge as given below,

>>> data1 = pd.DataFrame({"col":range(11, 17),    # Create first 
pandas DataFrame
...                       "x1":range(12, 1, - 2),
...                       "x2":["a", "b", "c", "d", "e", "f"],
...                       "x3":range(17, 11, - 1)})
>>> print(data1)
col  x1 x2  x3
0   11  12  a  17
1   12  10  b  16
2   13   8  c  15
3   14   6  d  14
4   15   4  e  13
5   16   2  f  12
>>> data2 = pd.DataFrame({"col":range(15, 19),    # Create second 
pandas DataFrame
...                       "y1":["l", "k", "j", "h"],
...                       "y2":["x", "y", "y", "y"],
...                       "y3":range(18, 10, - 2)})
>>> print(data2)                                  # Print second 
pandas DataFrame
   col y1 y2  y3
0   15  l  x  18
1   16  k  y  16
2   17  j  y  14
3   18  h  y  12

>>> data1
  col  x1 x2  x3
0   11  12  a  17
1   12  10  b  16
2   13   8  c  15
3   14   6  d  14
4   15   4  e  13
5   16   2  f  12
>>> data2
  col y1 y2  y3
0   15  l  x  18
1   16  k  y  16
2   17  j  y  14
3   18  h  y  12
>>> data_join = pd.merge(data1,                   # Inner join
...                       data2,
...                       on = "col")
>>> data_join
   col  x1 x2  x3 y1 y2  y3
0   15   4  e  13  l  x  18
1   16   2  f  12  k  y  16

Taken from here .