Delete rows from bottom, from every group/id of a dataframe

I have a dataset as such:

#Load the required libraries
import pandas as pd
#Create dataset
data = {'id': [1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1,
2, 2,2,2,2,
3, 3, 3, 3, 3, 3,
4, 4,4,4,4,4,4,4,
5, 5, 5, 5, 5,5, 5, 5,5],
'cycle': [1,2, 3, 4, 5,6,7,8,9,10,11,
1,2, 3,4,5,
1,2, 3, 4, 5,6,
1,2,3,4,5,6,7,8,
1,2, 3, 4, 5,6,7,8,9,],
'Salary': [7, 7, 7,8,9,10,11,12,13,14,15,
4, 5,6,7,8,
8,9,10,11,12,13,
8,1,2,3,4,5,6,7,
7, 7,9,10,11,12,13,14,15,],
'Children': ['No', 'Yes', 'Yes', 'Yes', 'Yes', 'No','No', 'Yes', 'Yes', 'Yes', 'No',
'Yes', 'No', 'Yes', 'No', 'Yes',
'No','Yes', 'Yes', 'No','No', 'Yes',
'Yes','Yes', 'Yes', 'No','No', 'Yes', 'Yes', 'Yes',
'No',  'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No',],
'Days': [123, 128, 66, 66, 120, 141, 52,96, 120, 141, 52,
96, 120,128, 66, 120,
15,123, 128, 66, 120, 141,
141,128, 66, 123, 128, 66, 120,141, 
123, 128, 66, 123, 128, 66, 120, 141, 52,],
}
#Convert to dataframe
df = pd.DataFrame(data)
print("df = \n", df)

The dataframe looks as such:

Here, every id has different cycles as per the 'cycle' column. For example,

id-1 has maximum 11 cycles.

id-2 has maximum 5 cycles.

id-3 has maximum 6 cycles.

id-4 has maximum 8 cycles.

id-5 has maximum 9 cycles.

Now, for every id, I wish to delete rows from the bottom.

For example,

For id-1, delete last four rows.

For id-2, delete last two rows.

For id-3, delete last three rows.

For id-4, delete last five rows.

For id-5, delete last six rows.

The dataframe then looks as such:

Can somebody please let me know how do I achieve this task in Python?

Create dictionary for specify number of deleted rows and compare by counter from back by GroupBy.cumcount with ascending=False mapped id column by Series.map, so possible filter by boolean indexing:

d = {1:4,2:2,3:3,4:5,5:6}
df = df[df.groupby('id').cumcount(ascending=False).ge(df['id'].map(d))]
print (df)
id  cycle  Salary Children  Days
0    1      1       7       No   123
1    1      2       7      Yes   128
2    1      3       7      Yes    66
3    1      4       8      Yes    66
4    1      5       9      Yes   120
5    1      6      10       No   141
6    1      7      11       No    52
11   2      1       4      Yes    96
12   2      2       5       No   120
13   2      3       6      Yes   128
16   3      1       8       No    15
17   3      2       9      Yes   123
18   3      3      10      Yes   128
22   4      1       8      Yes   141
23   4      2       1      Yes   128
24   4      3       2      Yes    66
30   5      1       7       No   123
31   5      2       7      Yes   128
32   5      3       9       No    66

@jezrael 's method is a great catch, I'll give my try as below.
It simply scratches each sub-matrix, makes the deduction, and recombines them.
It seems wordy but it follows a clear pattern and you can make it a function for more generic use.

By using this function, the ascending index at the very left column will be reordered.

df1 = df[df['id'] == 1].iloc[:-4]
df2 = df[df['id'] == 2].iloc[:-2]
df3 = df[df['id'] == 3].iloc[:-3]
df4 = df[df['id'] == 4].iloc[:-5]
df5 = df[df['id'] == 5].iloc[:-6]
df = pd.concat([df1, df2, df3, df4, df5])
data = df.to_dict('list')
df = pd.DataFrame(data)
print("df = \n", df)

df = 
id  cycle  Salary Children  Days
0    1      1       7       No   123
1    1      2       7      Yes   128
2    1      3       7      Yes    66
3    1      4       8      Yes    66
4    1      5       9      Yes   120
5    1      6      10       No   141
6    1      7      11       No    52
7    2      1       4      Yes    96
8    2      2       5       No   120
9    2      3       6      Yes   128
10   3      1       8       No    15
11   3      2       9      Yes   123
12   3      3      10      Yes   128
13   4      1       8      Yes   141
14   4      2       1      Yes   128
15   4      3       2      Yes    66
16   5      1       7       No   123
17   5      2       7      Yes   128
18   5      3       9       No    66