how to apply condition in pandas but only for finite number of rows?

I have a multi indexed pandas table as below.

I want to update Crop and Avl column, say with 'Tomato', and '0', but only for finite no of times (say, I need only 10 rows for Tomato, satisfying a condition). Currently via pandas I end up updating all rows that satisfy that condition.

col1 = ildf1.index.get_level_values(1) # https://stackoverflow.com/a/50608928/9148067
cond = col1.str.contains('DN_Mega') & (ildf1['Avl'] == 1)

ildf1.iloc[ cond , [0,2]] = ['Tomato', 0]

How do I restrict it to only say 10 rows of all rows that satisfy the condition?

PS: I used get_level_values as I have 4 columns (GR, PP+MT, Bay, Row) multi indexed in my df.

Solution

For df defined as below, you need to add additional index to numerate all rows with different number, then you can set new values based on slice. Here you go =^..^=

import pandas as pd


df = pd.DataFrame({'Crop': ['', '', '', '', ''], 'IPR': ['', '', '', '', ''], 'Avi': [1, 2, 3, 4, 5]}, index=[['0','0', '8', '8', '8'], ['6', '7', '7', '7', '7']])

# add additional index
df['id'] = [x for x in range(0, df.shape[0])]
df.set_index('id', append=True, inplace=True)

# select only two values based on condition
condition = df.index[df.index.get_level_values(0).str.contains('8')][0:2]
df.loc[condition, ['Crop', 'IPR']] = ['Tomato', 0]

Output:

          Crop IPR  Avi
    id                 
0 6 0                 1
  7 1                 2
8 7 2   Tomato   0    3
    3   Tomato   0    4
    4                 5