in my csv data I have a column with a following data:
110.00
111.00
111.00 *
112.00
113.00
114.00
114.00 *
115.00
115.00 *
116.00
110.00
111.00
111.00 *
112.00
113.00
114.00
114.00 *
115.00
115.00 *
116.00
I read it in data frame and I'd like to delete on of the rows with the duplicating numbers but only if they are immedietely one after another. I marked the rows I's like to remove with an *. Thanks for any suggestions
I think you can do this using .shift(), which can shift a series forward or backward (defaulting to one forward.) You want to keep rows if they're not the same as the next ones, so something like:
df[df["A"] != df["A"].shift()]
For example:
>>> df = pd.DataFrame({"A": [1,2,1,2,2,3,3,3,1,2]})
>>> df["A"]
0 1
1 2
2 1
3 2
4 2
5 3
6 3
7 3
8 1
9 2
Name: A, dtype: int64
>>> df["A"].shift()
0 NaN
1 1
2 2
3 1
4 2
5 2
6 3
7 3
8 3
9 1
Name: A, dtype: float64
>>> df["A"] != df["A"].shift()
0 True
1 True
2 True
3 True
4 False
5 True
6 False
7 False
8 True
9 True
Name: A, dtype: bool
Leading up to:
>>> df[df["A"] != df["A"].shift()]
A
0 1
1 2
2 1
3 2
5 3
8 1
9 2
