How to save rows when value change in column python - python-3.x

Question

I have DataFrame with two columns ID and Value1, I want to select rows when the value of column value1 column changes. I want to save rows 3 before change and 3 after the change and also change point row.
df=pd.DataFrame({'ID':[1,3,4,6,7,8,90,23,56,78,90,34,56,78,89,34,56],'Value1':[0,0,0,0,0,2,2,2,2,0,0,0,1,1,1,1,1]})
ID Value1
0 1 0
1 3 0
2 4 0
3 6 0
4 7 0
5 8 2
6 90 2
7 23 2
8 56 2
9 78 0
10 90 0
11 34 0
12 56 1
13 78 1
14 89 1
15 34 1
16 56 1
output:
ID Value1
0 4 0
1 6 0
2 7 0
3 8 2
4 90 2
5 23 2
6 90 2
7 23 2
8 56 2
9 78 0
10 90 0
11 34 0

Answer 1

IIUC,
import numpy as np
df=pd.DataFrame({'ID':[1,3,4,6,7,8,90,23,56,78,90,34,56,78,89,34,56],'Value1':[0,0,0,0,0,2,2,2,2,0,0,0,1,1,1,1,1]})
df.reset_index(drop=True) #index needs to start from zero for solution
ind = list(set([val for i in df[df['Value1'].diff()!=0].index for val in range(i-3, i+4) if i>0 and val>=0]))
# diff gives column wise differencing. combined it with nested list and
# finally, list(set()) to drop any duplicates in index values
df[df.index.isin(ind)]
ID Value1
2 4 0
3 6 0
4 7 0
5 8 2
6 90 2
7 23 2
8 56 2
9 78 0
10 90 0
11 34 0
12 56 1
13 78 1
14 89 1
15 34 1
If you want to retain occurrences of duplicates, drop the list(set()) function over the list