Stivi B Stivi B - 8 months ago 36
Python Question

Determine mean value of ‘data’ where the highest number of CONTINUOUS cond=True

I have a pandas Dataframe with a 'data' and 'cond'(-ition) column. I need the mean value (of the data column) of the rows with the highest number of CONTINUOUS True objects in 'cond'.

Example DataFrame:

cond data
0 True 0.20
1 False 0.30
2 True 0.90
3 True 1.20
4 True 2.30
5 False 0.75
6 True 0.80

Result = 1.466, which is the mean value of row-indexes 2:4 with 3 True

I was not able to find a „vectorized“ solution with a groupby or pivot method. So I wrote a func that loops the rows. Unfortunately this takes about an hour for 1 Million lines, which is way to long. Unfortunately, the @jit decoration does not reduce the duration measurably.

The data I want to analyze is from a monitoring project over one year and I have every 3 hours a DataFrame with one Million rows. Thus, about 3000 such files.

An efficient solution would be very important. I am also very grateful for a solution in numpy.


Using the approach from Calculating the number of specific consecutive equal values in a vectorized way in pandas:

df['data'].groupby((df['cond'] != df['cond'].shift()).cumsum()).agg(['count', 'mean'])[lambda x: x['count']==x['count'].max()]
      count      mean
3         3  1.466667

Indexing by a callable requires 0.18.0, for earlier versions, you can do:

res = df['data'].groupby((df['cond'] != df['cond'].shift()).cumsum()).agg(['count', 'mean'])

res[res['count'] == res['count'].max()]
      count      mean
3         3  1.466667