Pandas - merge multiple dataframes into main df

Question

I have several (side) dataframes that each only have one column and one (main) df that has stacked, unique values of the dfs.

Code for 'main' df:

stacked_df = pd.concat([df_1,df_2,df_3,df_n], axis=0)
uniques = stacked_df.drop_duplicates(subset='key', keep='first').reset_index(drop=True)

Goal is to add the 'side dfs' into 'uniques', ie match values in df_1 to df_n against values in uniques and create a new column with values/ nans per df_n. As follows:

result = pd.DataFrame()
result['uniques'] = uniques
result['df1'] = result.merge(df1, on='key', how='inner')

That works fine for the first df, but I get an error when trying to expand it as follows:

result = pd.DataFrame()
result['uniques'] = uniques
result['df1'] = result.merge(df1, on='key', how='inner')
result['df2'] = result.merge(df2, on='key', how='inner')

Error: ValueError: Wrong number of items passed 2, placement implies 1