Sort Pandas DataFrame based on previous row in another column

I have the following DataFrame in my Python porject:

df1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})

I wish to order it in this kind of way:

df2 = pd.DataFrame({"Col A":[1,3,2],"Col B":[3,2,2]})

My goal is that each value in Col A matches the previous' value in Col B.

Do you have any idea of how to make this work properly and as little effort as possible?

I tried to work with .sort_values(by=) but that's also where my current knowledge stops.

If need roll one value per Col B use lambda function:

df1 = pd.DataFrame({"Col A":[1,2,3,7,4,8],"Col B":[3,2,2,1,1,1]})
print (df1)
   Col A  Col B
0      1      3
1      2      2
2      3      2
3      7      1
4      4      1
5      8      1

df1['Col A'] = df1.groupby('Col B')['Col A'].transform(lambda x: np.roll(x, -1))
print (df1)
   Col A  Col B
0      1      3
1      3      2
2      2      2
3      4      1
4      8      1
5      7      1

Yes, you can achieve the desired output by using sort_values() and by creating a mapping dictionary so:

import pandas as pd

df1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})

# mapping_dict for ordering
mapping_dict = {1:3, 3:2, 2:2}


df1["sort_order"] = df1["Col A"].map(mapping_dict)

df2 = df1.sort_values(by="sort_order").drop(columns=["sort_order"])

print(df2)

Output:

   Col A  Col B
0      1      3
2      3      2
1      2      2