在 pandas dataframe 上重叠滚动 windows 计数数据

Question

I need to count events on a pandas dataframe using rolling windows with overlaps.

In particular, I have a dataframe with discontinuous events in time, like this:

            Ma
2000-01-04  2.2 
2000-01-05  2.6
2000-01-06  3.1
2000-01-16  2.4
2000-01-22  2.1     
2000-01-27  2.5
2000-02-12  2.3
2000-02-19  3.5
2000-02-21  2.4
2000-02-27  2.4

and I want to count how many events occurred in time windows of 10 days with an overlap of 5 days. This is the result which i'm looking for:

                                Events
from 2000-01-04 to 2000-01-14   3
from 2000-01-09 to 2000-01-19   1
from 2000-01-14 to 2000-01-24   2
from 2000-01-19 to 2000-01-29   2

Have you got any suggestions? I tried to use groupby but I can only count data in non overlapping windows, using this line: df.groupby(pd.DatetimeIndex(df.Time).to_period("10d")).size()

I tried also Rolling.count from Pandas dataframe but again without success.

Answer 1

My approach is to create a data frame with a 5 day interval based on the minimum and maximum dates in the original data frame. Based on that, I extract the number of items above row i and below row i+2 and calculate the number of items. . and then list them in a loop process. Finally, we put it in a data frame. I'm sure there are better approaches.

import pandas as pd
import numpy as np
import io

data = '''
date Ma
2000-01-04  2.2 
2000-01-05  2.6
2000-01-06  3.1
2000-01-16  2.4
2000-01-22  2.1     
2000-01-27  2.5
2000-02-12  2.3
2000-02-19  3.5
2000-02-21  2.4
2000-02-27  2.4
'''
df = pd.read_csv(io.StringIO(data), sep='\s+')

df['date'] = pd.to_datetime(df['date'])
df2 = pd.DataFrame({'date':pd.date_range(df['date'].min(), df['date'].max(), freq='5D')})
tmp = []
for i in range(len(df)):
    try:
        cnt = df[(df['date'] >= df2.date[i])&(df['date'] <= df2.date[i+2])].count()
        fromto = 'from'+ str(df2.date[i].strftime('%Y-%m-%d'))+'to'+str(df2.date[i+2].strftime('%Y-%m-%d'))
        tmp.append([fromto, cnt[0]])
    except:
        break
df3 = pd.DataFrame(tmp, columns=['FromTo', 'Events'])

df3
    FromTo  Events
0   from2000-01-04to2000-01-14  3
1   from2000-01-09to2000-01-19  1
2   from2000-01-14to2000-01-24  2
3   from2000-01-19to2000-01-29  2
4   from2000-01-24to2000-02-03  1
5   from2000-01-29to2000-02-08  0
6   from2000-02-03to2000-02-13  1
7   from2000-02-08to2000-02-18  1
8   from2000-02-13to2000-02-23  2