How to use Pandas, to reassign a column value of one file according to the column name of another file?

now there are two tables (txt files). You need to change one column value of one file to the corresponding column value of the other file, as follows:
File 1
clipboard.png

2
clipboard.png

I need to reassign the value of file 1"s x and y column according to the column name t and userid to get the corresponding x, y value from file 2. As the use of Pandas is not familiar with, hope to know the guidance, thank you!

Mar.17,2021

import pandas as pd
import numpy as np
df1 = pd.DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]], columns=['t', 'user_id', 'x', 'y'])
>>> df1
   t  user_id  x  y
0  0        1  2  3
1  4        5  6  7
df2 = pd.DataFrame([[0, 1, 0, 0], [4, 5, 0, 0], [5, 6, 7, 8]], columns=['t', 'user_id', 'x', 'y'])
>>> df2
   t  user_id  x  y
0  0        1  0  0
1  4        5  0  0
2  5        6  7  8
def f(row):
    if np.isnan(row['x_y']):
        row['x'], row['y'] = row['x_x'], row['y_x']
    else:
        row['x'], row['y'] = row['x_y'], row['y_y']
    return row

res = pd.merge(df2, df1, how='left', on=['t', 'user_id']).apply(f, axis=1)[['t', 'user_id', 'x', 'y']]
>>> res
     t  user_id    x    y
0  0.0      1.0  2.0  3.0
1  4.0      5.0  6.0  7.0
2  5.0      6.0  7.0  8.0

to improve performance, you should reduce the line-by-line operation of pandas.DataFrame.apply () . In this case, you can use the numpy.where () binary operator, as follows

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: df1 = pd.DataFrame({'t': [1,2,3], 'user_id': [10,20,30], 'v': [1.1,2.2,3.3]})

In [4]: df1
Out[4]: 
   t  user_id    v
0  1       10  1.1
1  2       20  2.2
2  3       30  3.3

In [5]: df2 = pd.DataFrame({'t': [4,1,2], 'user_id': [40,10,20], 'v': [400,100,200]})

In [6]: df2
Out[6]: 
   t  user_id    v
0  4       40  400
1  1       10  100
2  2       20  200

In [7]: df3 = pd.merge(df1, df2, how='right', on=['t', 'user_id'])

In [8]: df3
Out[8]: 
   t  user_id  v_x  v_y
0  1       10  1.1  100
1  2       20  2.2  200
2  4       40  NaN  400

In [9]: df3['v'] = np.where(np.isnan(df3.v_x), df3.v_y, df3.v_x)

In [10]: df3
Out[10]: 
   t  user_id  v_x  v_y      v
0  1       10  1.1  100    1.1
1  2       20  2.2  200    2.2
2  4       40  NaN  400  400.0

In [11]: del df3['v_x']

In [12]: del df3['v_y']

In [13]: df3
Out[13]: 
   t  user_id      v
0  1       10    1.1
1  2       20    2.2
2  4       40  400.0
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