Issues with categorical binning

I am trying to get top values for an unordinal categorical column which accounts for 50% or more of the total counts, so all other occurences are replaced with 'others'.

values_df = df['column'].value_counts(normalize = True)

total = 0
for i, row in enumerate(values_df.values):
      row = round(row,2)
      if total <= 0.5:
           total+=row
      else:
           df['column'][i] = 'others'

but then when I print(df['column'].value_counts()) I don't see values below to be changed to others.

IIUC, you can use cumsum to compute the cumulated total, then boolean indexing with isin:

values_df = (df['column']
             .value_counts(normalize = True)
             .round(2)
            )
m = values_df.cumsum().gt(0.5)
df.loc[df['column'].isin(values_df.index[m]), 'column'] = 'others'

Example output:

    column
0        4
1   others
2   others
3        3
4        3
5        3
6   others
7        3
8   others
9   others
10       4
11  others
12  others
13       4
14  others

Used input:

import pandas as pd
import numpy as np

np.random.seed(0)
df = pd.DataFrame({'column': np.random.randint(0, 6, size=15)})

    column
0        4
1        5
2        0
3        3
4        3
5        3
6        1
7        3
8        5
9        2
10       4
11       0
12       0
13       4
14       2