sample.md

Sample script

import pandas
import os
filepath = os.path.join(os.getcwd(), 'sample_wind_data.csv')

import pandas as pd 
# Read data from file 'filename.csv' 
# (in the same directory that your python process is based)
# Control delimiters, rows, column names with read_csv (see later) 
df = pd.read_csv(filepath) 
df1 = df.head(48)
result = [df1['AVGspeed'][i:i+3] for i in range(len(df['AVGspeed'])-2) if all(i > 8 for i in df['AVGspeed'][i:i+3])]
result

result is an array of numpy arrays that include a row's index and the corresponding AVGvalue for consecutive rows of 3 with avg values > 8. This is not deduped. For example, in the sample data, there are four 3 hour intervals when values are greater than 8. You can see that row 2 exists in results[0] and result[1], row 3 exists in result[1] and result[2], etc...

[0    12.960
 1    11.180
 2     9.835
 Name: AVGspeed, dtype: float64, 
 1    11.180
 2     9.835
 3     8.047
 Name: AVGspeed, dtype: float64, 
 2    9.835
 3    8.047
 4    9.388
 Name: AVGspeed, dtype: float64, 
 6    10.28
 7    10.28
 8    10.28
 Name: AVGspeed, dtype: float64]

Output ideas

This may not be what you want, so I included two other example output formats that might make sense? If you can provide markdown of the output format you want, that would be helpful to finish up the script. :)

Potential output examples

Array of booleans that are all true and contain start time - end time of date time hours that are > 8.

result = [ [start time, endtime, true], [start time, endtime, true] ] 

Array that contains start time, end time of date time hours that are > 8 and average of AVGspeed during that period.

result = [ [start time, endtime, avg(AVGspeed of time period)], [start time, endtime, avg(AVGspeed of time period)]]