Pandas dataframe, in a row, to find the max in selected column, and find value of another column based on that

Question

I have a dataframe like this:

>>>import pandas as pd
>>>df = pd.DataFrame({'x1':[20,25],'y1':[5,8],'x2':[22,27],'y2':[10,2]})

>>>df
      x1  y1  x2  y2
   0  20   5  22  10
   1  25   8  27   2
>>>

X and Y pair together. I need to compare y1 and y2 and get the max in every row. And find the corresponding x. Hence the max of row [0] is y2 (=10), and the corresponding x is x2 (=22). The second row will be y1 (=8) and x1(=25). Expected result, new columns x and y:

   x1  y1  x2  y2   x   y
0  20   5  22  10  22  10
1  25   8  27   2  25   8

This is a simple dataframe I made to elaborate on the question. X and Y pairs, in my case, can be 30 pairs.

Answer 1

# get a hold on "y*" columns
y_cols = df.filter(like="y")

# get the maximal y-values' suffixes, and then add from front "x" to them
max_x_vals = y_cols.idxmax(axis=1).str.extract(r"(\d+)$", expand=False).radd("x")
# get the locations of those x* values
max_x_ids = df.columns.get_indexer(max_x_vals)

# now we have the indexes of x*'s in the columns; NumPy's indexing
# helps to get a cross section
df["max_xs"] = df.to_numpy()[np.arange(len(df)), max_x_ids]

# for y*'s, it's directly the maximum per row
df["max_ys"] = y_cols.max(axis=1)

to get

>>> df

   x1  y1  x2  y2  max_xs  max_ys
0  20   5  22  10      22      10
1  25   8  27   2      25       8