how to handle this in dataframe with python programing

If you wanted to fill CityName based on Acc numb you can try the following:

import pandas as pd
import numpy as np

df = df.replace('',np.nan)

df['CityName'] = df.groupby('Acc numb',group_keys=False).apply(lambda group: group['CityName'].ffill().bfill())

Explanation:

This code groups by Acc numb and fills the missing values in CityName using ffill() and bfill() in order to use the CityName found in that group. If there is no CityName for certain Acc numb the rows will stay as NaN

Create consecutive groups by compare shifted values with cumulative sum by Series.cumsum and get first non missing value per groups in new column by GroupBy.transform with GroupBy.first:

Notice: 910234 is different like 910324, so not filled by Bangalore.

g = df['Acc numb'].ne(df['Acc numb'].shift()).cumsum()
df['CityName'] = df.groupby(g)['CityName'].transform('first')
print (df)
    Acc numb   CityName
0     123456      Delhi
1     123456      Delhi
2     123456      Delhi
3     123456      Delhi
4     123456      Delhi
5     910234       None
6     910234       None
7     910234       None
8     910324  Bangalore
9     910324  Bangalore
10    910324  Bangalore
11    910324  Bangalore
12    360825     Mumbai
13    360825     Mumbai
14    360825     Mumbai
15    360825     Mumbai
16    360825     Mumbai
17    123456       None
18    123456       None
19    123456       None
20    123456       None
21    123456       None