Editing.
Suppose I have the following series in pandas:
>>>p
0 0.0
1 0.0
2 0.0
3 0.3
4 0.3
5 0.3
6 0.3
7 0.3
8 1.0
9 1.0
10 1.0
11 0.2
12 0.2
13 0.3
14 0.3
15 0.3
I need to identify each sequence of consecutive duplicates - its first and last index. Using the above example, I need to identify the first sequence of 0.3 (from index 3 to 7) independently from the last sequence of 0.3 (from index 13 to 15).
Using Series.duplicated is insufficient because:
*using keep='first' marks all first instances of duplicates False, but will leave index 13 as True because it is not the first appearance of 0.3.
*Same goes for keep='last'
*keep=False just marks all of the entries as True.
Thank you!
I believe need trick with compare shifted values for not equal by ne with cumsum and last drop_duplicates:
s = df['a'].ne(df['a'].shift()).cumsum()
a = s.drop_duplicates().index
b = s.drop_duplicates(keep='last').index
df = pd.DataFrame({'first':a, 'last':b})
print (df)
first last
0 0 2
1 3 7
2 8 10
3 11 12
4 13 15
If want also duplicated value to new column a bit change solution with duplicated:
s = df['a'].ne(df['a'].shift()).cumsum()
a = df.loc[~s.duplicated(), 'a']
b = s.drop_duplicates(keep='last')
df = pd.DataFrame({'first':a.index, 'last':b.index, 'val':a})
print (df)
first last val
0 0 2 0.0
3 3 7 0.3
8 8 10 1.0
11 11 12 0.2
13 13 15 0.3
If need new column:
df['count'] = df['a'].ne(df['a'].shift()).cumsum()
print (df)
a count
0 0.0 1
1 0.0 1
2 0.0 1
3 0.3 2
4 0.3 2
5 0.3 2
6 0.3 2
7 0.3 2
8 1.0 3
9 1.0 3
10 1.0 3
11 0.2 4
12 0.2 4
13 0.3 5
14 0.3 5
15 0.3 5
Collected from the Internet
Please contact [email protected] to delete if infringement.
Comments