Pandas - Merge two DataFrame with partial match

What about this :

matches = [['A', 'B', 'C'], ['X', 'Y', 'Z']]
df = df1.copy()
for k in range(len(matches[0])):

    #Get your left/right keys right at each iteration :
    left, right = matches
    left = left if k==0 else left[:-k]
    right = right if k==0 else right[:-k]

    #Make sure columns from df2 exist in df
    for col in df2.columns.tolist():
        try:
            df[col]
        except Exception:
            df[col] = np.nan

    #Merge dataframes
    df = df.merge(df2, left_on=left, right_on=right, how='left')

    #Find which row of df's "left" columns (previously initialised) are empty
    ix_left_part = np.all([df[x + "_x"].isnull() for x in right], axis=0)

    #Find which row of df's "right" columns are not empty
    ix_right_part = np.all([df[x + "_y"].notnull() for x in right], axis=0)

    #Combine both to get indexes
    ix = df[ix_left_part & ix_right_part].index

    #Complete values on "left" with those from "right"
    for x in df2.columns.tolist():
        df.loc[ix, x+"_x"] = df.loc[ix, x+'_y']

    #Drop values from "right"
    df.drop([x+"_y" for x  in df2.columns.tolist()], axis=1, inplace=True)

    #Rename "left" columns to stick with original names from df2
    df.rename({x+"_x":x for x  in df2.columns.tolist()}, axis=1, inplace=True)

#drop eventual duplicates
df.drop_duplicates(keep="first", inplace=True)
print(df)

EDIT

I corrected the loop ; this should be easier on the memory :

import pandas as pd
import numpy as np

df1 = pd.DataFrame({"A":['a','b','c'],"B":['d','e','f'],"C":['d','e','f']})
df2 = pd.DataFrame({"X":['a','b','a','c'],"Y":['d','e','y','z'],"Z":['d','x','y','z'],"V":['v1','v2','v3','v4']})

matches = [['A', 'B', 'C'], ['X', 'Y', 'Z']]
df = df1.copy()

#Make sure columns of df2 exist in df
for col in df2.columns.tolist():
    df[col] = np.nan

for k in range(len(matches[0])):

    #Get your left/right keys right at each iteration :
    left, right = matches
    left = left if k==0 else left[:-k]
    right = right if k==0 else right[:-k]
    
    #recreate dataframe of (potential) usable datas in df2:
    ix = df[df.V.isnull()].index
    temp = (
            df.loc[ix, left]
            .rename(dict(zip(left, right)), axis=1)
            )
    
    temp=temp.merge(df2, on=right, how="inner")
    
    #Merge dataframes
    df = df.merge(temp, left_on=left, right_on=right, how='left')
    
    
    #Combine both to get indexes
    ix = df[(df['V_x'].isnull()) & (df['V_y'].notnull())].index
    

    #Complete values on "left" with those from "right"
    cols_left = [x+'_x' for x in df2.columns.tolist()]
    cols_right = [x+'_y' for x in df2.columns.tolist()]    
    df.loc[ix, cols_left] = df.loc[ix, cols_right].values.tolist()
        
    #Drop values from "right"
    df.drop(cols_right, axis=1, inplace=True)
    
    #Rename "left" columns to stick with original names from df2
    rename = {x+"_x":x for x  in df2.columns.tolist()}
    df.rename(rename, axis=1, inplace=True)

print(df)