Pandas split a column in a DF based on a condition and write back to the column

I need to split a column in a dataframe by '-', keep the last part if '-' exists or keep the original input including Nan and write back to the dataframe. This column may contain '-' and 'np.NaN', how do I achieve the goal?
code:

import pandas as pd
import numpy as np

#sample df
data = [[1, '1010'], [2, 'CORP-1030'], [3, 'LLC-1020'], [4, np.NaN],[5, '1040']]
df = pd.DataFrame(data, columns=['ID', 'Sector'])

   ID     Sector
0   1       1010
1   2  CORP-1030
2   3   LLC-1020
3   4        NaN
4   5       1040

This is what I came up with, NOT clean and readable, looking for a better solution.

df[['Sector Name', 'Sector Code']] = df['Sector'].apply(lambda x: pd.Series(str(x).split("-")))
df.drop(['Sector Name'], errors='ignore', inplace=True, axis=1)
df['Sector Code'].fillna(df['Sector'], inplace=True)
df.drop('Sector', inplace=True, axis=1)

Desired output:

   ID      Sector
0   1        1010
1   2        1030
2   3        1020
3   4         NaN
4   5        1040

That's fairly simple with str accessor

df['new'] = df['Sector'].str.split('-').str[-1]

Result

   ID     Sector   new
0   1       1010  1010
1   2  CORP-1030  1030
2   3   LLC-1020  1020
3   4        NaN   NaN
4   5       1040  1040

With simple regex replacement to substitute a leading part followed by -:

df['Sector'] = df['Sector'].str.replace(r'^[^-]+-', '', regex=True)

   ID Sector
0   1   1010
1   2   1030
2   3   1020
3   4    NaN
4   5   1040