How to automate list separation. NOOB

[perfringo]

As always time is short at hand. What I accomplished:

- found a station list on page https://www.ndbc.noaa.gov/to_station.shtml
- wrote a script to read maximum value from column 'height' (ignoring 9999.0 values)
- wrote simple script to iterate over sample stations and years
- to-do (didn't have time): parse all station names from stations page; catch only HTTPError 404 Page Not Found (currently silences all errors and this is not good)

I used pandas for data parsing:

stations = [21413, 21414,   21415,   21416,   21417,   21418,   21419,   21420]

maximums = []

for station in stations:
  for year in range(2010, 2018):
    try:
      filename = f'https://www.ndbc.noaa.gov/view_text_file.php?filename={station}t{year}.txt.gz&dir=data/historical/dart/'
      df = pd.read_csv(filename, sep='\s+',header=None,skiprows=2, names=['height'],usecols=[7]).query('height != 9999.0').max()[0]
      maximums.append(df)
    except:
      print(f'Missing: station {station}, year: {year}')
      continue

It reported missing following datafiles:

Output:
Missing: station 21413, year: 2016
Missing: station 21417, year: 2010
Missing: station 21417, year: 2011
Missing: station 21417, year: 2012
Missing: station 21417, year: 2013
Missing: station 21417, year: 2014
Missing: station 21417, year: 2015
Missing: station 21417, year: 2016
Missing: station 21417, year: 2017
Missing: station 21420, year: 2010
Missing: station 21420, year: 2011
Missing: station 21420, year: 2012
Missing: station 21420, year: 2013
Missing: station 21420, year: 2014
Missing: station 21420, year: 2015
Missing: station 21420, year: 2016
Missing: station 21420, year: 2017

This code collected data from 47 files (took maybe 20-30 seconds) and maximum value among them was 5876.116.

Is this something you could take advantage of?