Pandas - Resample/GroupBy DateTime Index and perform calculations

Question

I will try my best to explain what I need help with. I have the following df (thousands if not millions of rows) with a datetime index like the sample below:

INDEX                   COL A       COL B
2018-05-07 21:53:13.731 0.365127    9391.800000
2018-05-07 21:53:16.201 0.666127    9391.800000
2018-05-07 21:53:18.038 0.143104    9391.800000
2018-05-07 21:53:18.243 0.025643    9391.800000
2018-05-07 21:53:18.265 0.640484    9391.800000
2018-05-07 21:53:18.906 -0.100000   9391.793421
2018-05-07 21:53:19.829 0.559516    9391.800000
2018-05-07 21:53:19.846 0.100000    9391.800000
2018-05-07 21:53:19.870 0.006560    9391.800000
2018-05-07 21:53:20.734 0.666076    9391.800000
2018-05-07 21:53:20.775 0.666076    9391.800000
2018-05-07 21:53:28.607 0.100000    9391.800000
2018-05-07 21:53:28.610 0.041991    9391.800000
2018-05-07 21:53:29.283 -0.053518   9391.793421
2018-05-07 21:53:47.322 -0.046302   9391.793421
2018-05-07 21:53:49.182 0.100000    9391.800000

What I would like to do is group the rows in 5 second intervals and perform (sometimes complex) calculations on each 5 second interval/subset.

Let's say for example I want to calculate the percentage of positive vs negative values in column A within each 5 second block.

2018-05-07 21:53:10 to 2018-05-07 21:53:15 only contains one row and column A is a positive so I would create a new column C with 100% .

Similarly 2018-05-07 21:53:15 to 2018-05-07 21:53:20 has 8 rows in column A, 7 which are positive and 1 of which is negative. So column C would be 87.5% .

I would post sample code but I'm really unsure the best way to do this. A sample output (new df) may be something like the below with COL D being simply the minimum number in COL B for that 5 second grouping:

INDEX               COL C     COL D (MIN)
2018-05-07 21:53:10 100%     9391.800000
2018-05-07 21:53:15 12.5%    9391.793421
2018-05-07 21:53:20 100%     9391.800000
2018-05-07 21:53:25 66.7%    9391.793421
2018-05-07 21:53:30 nan      nan
2018-05-07 21:53:35 nan      nan
2018-05-07 21:53:40 nan      nan
2018-05-07 21:53:45 100%     9391.793421

Please keep in mind I want to do many different calculations over each grouping. So using built-in .sum() , .mean() , .agg() etc will not suffice for more complex calculations.

Appreciate any help and am happy to clarify the question if needed.

Answer 1

I believe need for percentage of positive values need mean of values >0 :

df = df.resample('5S').agg({'COL A': lambda x: (x > 0).mean() * 100, 'COL B': 'min'})
print (df)
                          COL A        COL B
INDEX                                       
2018-05-07 21:53:10  100.000000  9391.800000
2018-05-07 21:53:15   87.500000  9391.793421
2018-05-07 21:53:20  100.000000  9391.800000
2018-05-07 21:53:25   66.666667  9391.793421
2018-05-07 21:53:30         NaN          NaN
2018-05-07 21:53:35         NaN          NaN
2018-05-07 21:53:40         NaN          NaN
2018-05-07 21:53:45   50.000000  9391.793421

and for percentage of negative values need mean of <0 :

df = df.resample('5S').agg({'COL A': lambda x: (x < 0).mean() * 100, 'COL B': 'min'})
print (df)
                         COL A        COL B
INDEX                                      
2018-05-07 21:53:10   0.000000  9391.800000
2018-05-07 21:53:15  12.500000  9391.793421
2018-05-07 21:53:20   0.000000  9391.800000
2018-05-07 21:53:25  33.333333  9391.793421
2018-05-07 21:53:30        NaN          NaN
2018-05-07 21:53:35        NaN          NaN
2018-05-07 21:53:40        NaN          NaN
2018-05-07 21:53:45  50.000000  9391.793421

As @Alexander pointed 0 is neither positive nor negative. So the best is remove it before count:

df = df.resample('5S').agg({'COL A': lambda x: (x[x.ne(0)] > 0).mean() * 100, 'COL B': 'min'})