Shifted Series Join to Dataframe with Concat

There is a DataFrame z with

['title','module_number','beginners','finishers'] 

columns. I need to shift z.beginners Series as every last module_number should have NaN there, and join it to DataFrame as new column z.shift.

I've tried to do it with pd.concat. My code:

def exp2(df):
    
    fx = df.join(df.beginners[1:].rename('shifted').reset_index()).drop('index', axis =1)
    return fx

def exp3(z,lst):
    
    common = list()
    
    for title in lst:

        df = z[z['title'] == title]
        example = exp2(df)
        common.append(example)
        
    return pd.concat(common)

exp3(z, z.title.unique())

That works fine only for the first title in z and incorrectly for the second one. However, it doesn't work with any other title:

        title module_number beginners finishers shift
    0   Uu    1             518       458       434
    1   Uu    2             434       406       NaN
    3   Kk    1             627       563       27
    4   Kk    2             521       427       NaN
    5   Rr    1             158       142       NaN
    6   Rr    2             136       106       NaN

Does anyone know an alternative solution or how I can enhance my code?