Pandas: Filter dataframe by difference between adjacent rows

Question

I have the following data in a dataframe.

Timestamp	MeasureA	MeasureB	MeasureC	MeasureD
0.00	26.46	63.60	3.90	0.67
0.94	26.52	78.87	1.58	0.42
1.94	30.01	82.04	1.13	0.46
3.00	30.19	82.00	1.17	0.36
4.00	30.07	81.43	1.13	0.42
5.94	30.02	82.46	1.05	0.34
8.00	30.22	82.48	0.98	0.35
9.00	30.00	82.21	1.13	0.33
10.00	30.00	82.34	1.12	0.34

And I'd like to filter the entries using some non-uniform intervals. Let say that my intervals are [1.0, 1.5]

What I'm trying to achieve is that, we take the first row (row0), and to get the next valid row, we look at what is the next row whose Timestamp value is greater or equal to row0 + 1.0.

In this scenario, the next valid row will be the one with the 1.94 timestamp. Then, for the next valid row, we will use the next item in the intervals array. Which is 1.5. That will make the next row the one with a timestamp value of 4.00. Since 1.94 + 1.5 is equal to 3.44.

For the next row, we go back and start from the beginning of the array of intervals.

After going through all the data, the resulting dataframe should be:

Timestamp	MeasureA	MeasureB	MeasureC	MeasureD
0.00	26.46	63.60	3.90	0.67
1.94	30.01	82.04	1.13	0.46
4.00	30.07	81.43	1.13	0.42
5.94	30.02	82.33	1.11	0.35
8.00	30.22	82.48	0.98	0.35
9.00	30.00	82.21	1.13	0.33

Is there a way to achieve this with the existing filtering methods in pandas?

Answer 1

Try:

from itertools import cycle

# the interval:
A, B = 1.0, 1.5

comparing, out, last_t = cycle([B, A]), [], float("-inf")
j = next(comparing)

for i, t in zip(df.index, df.Timestamp):
    if t >= last_t + j:
        out.append(i)
        last_t = t
        j = next(comparing)

print(df.loc[out])

Prints:

   Timestamp  MeasureA  MeasureB  MeasureC  MeasureD
0       0.00     26.46     63.60      3.90      0.67
2       1.94     30.01     82.04      1.13      0.46
4       4.00     30.07     81.43      1.13      0.42
5       5.94     30.02     82.46      1.05      0.34
6       8.00     30.22     82.48      0.98      0.35
7       9.00     30.00     82.21      1.13      0.33