Keep first occurrence row by id and first occurrence when value in column changes

Question

In the following example df, what is the best approach to keep:

• The first row when a Score appears for each id
• Then the first row when a value changes in Score for each id and drop duplicated rows until it changes

Example df

      date      id   Score
0   2001-09-06  1       3
1   2001-09-07  1       3
2   2001-09-08  1       4
3   2001-09-09  2       6
4   2001-09-10  2       6
5   2001-09-11  1       4
6   2001-09-12  2       5
7   2001-09-13  2       5
8   2001-09-14  1       3

Desired df

      date      id   Score
0   2001-09-06  1       3
1   2001-09-08  1       4
2   2001-09-09  2       6
3   2001-09-12  2       5
4   2001-09-14  1       3

Answer 1

Use groupby with diff:

print (df[df.groupby("id")["Score"].diff()!=0])

         date  id  Score
0  2001-09-06   1      3
2  2001-09-08   1      4
3  2001-09-09   2      6
6  2001-09-12   2      5
8  2001-09-14   1      3

The first appearance will always result in NaN which !=0.