如何在 pandas 中执行解释的操作？

Question

this is my df

idx = pd.date_range('2020-01-01',periods=26,freq='D')
vals = [0,0,0,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,0,0,1,0,0,0,1,1]
pd.DataFrame(vals,index=idx)

what I need to know is what periods the values turn 1. so this particular case it turns 1 for the following period (and the output I want to see)

2020-01-04:2020-01-07
2020-01-14:2020-01-18
2020-01-21:2020-01-21
2020-01-25:2020-01-26

thanks

Answer 1

We can group the index of the dataframe on the sequential blocks of 1's and aggreagte using first and last to calculate the periods where the value turns/stays 1 .

m = df[0].eq(1)
m[m].index.to_series().groupby((~m).cumsum()).agg(['first', 'last'])

---

        first       last
0                       
3  2020-01-04 2020-01-07
9  2020-01-14 2020-01-18
11 2020-01-21 2020-01-21
14 2020-01-25 2020-01-26

Answer 2

With itertools.groupby() .

import itertools
import operator

for k, g in itertools.groupby(enumerate(idx), lambda t: vals[t[0]]):
    if k == 1:
        days = list(map(operator.itemgetter(1), g))
        print(days[0], days[-1])

2020-01-04 00:00:00 2020-01-07 00:00:00
2020-01-14 00:00:00 2020-01-18 00:00:00
2020-01-21 00:00:00 2020-01-21 00:00:00
2020-01-25 00:00:00 2020-01-26 00:00:00