On Mon, 2012-05-14 at 23:38 -0400, bob gailer wrote: [...] > I would set up a SQLite database with a table of 4 numeric columns: > year, month, rainfall, firearea > Use SQL to select the desired date range and do the max and avg > calculations: > select year, avg(firearea), max(rainfall) from table where year = 1973 > and month between 6 and 8) > > you can use dictionaries but that will be harder. Here a start > (untested). Assumes data are correct.
Clearly if the data is to be stored for a long time and have various
(currently unknown) queries passed over it then year a database it the
right thing -- though I would probably choose a non-SQL database.
If the issues is to just do quick calculations over the data in the file
format then nothing wrong with using dictionaries or parallel arrays à
la:
with open ( 'yearmonthrainfire.txt' ) as infile :
climateindexname = infile.readline ( ).split ( )
data = [ line.split ( ) for line in infile.readlines ( ) ]
years = sorted ( { item[0] for item in data } )
months = [ 'Jan' , 'Feb' , 'Mar' , 'Apr' , 'May' , 'Jun' , 'Jul' ,
'Aug' , 'Sep' , 'Oct' , 'Nov' , 'Dec' ]
dataByYear = { year : [ ( float ( item[2] ) , float ( item[3] ) ) for
item in data if item[0] == year ] for year in years }
dataByMonth = { month : [ ( float ( item[2] ) , float ( item[3] ) ) for
item in data if item[1] == month ] for month in months }
averagesByYear = { year : ( sum ( dataByYear[year][0] ) / len (
dataByYear[year][0] ) , sum ( dataByYear[year][1] ) / len ( dataByYear[year][1]
) ) for year in years }
averagesByMonth = { month : ( sum ( dataByMonth[month][0] ) / len (
dataByMonth[month][0] ) , sum ( dataByMonth[month][1] ) / len (
dataByMonth[month][1] ) ) for month in months }
for year in years :
print ( year , averagesByYear[year][0] , averagesByYear[year][1] )
for month in months :
print ( month , averagesByMonth[month][0] ,
averagesByMonth[month][1] )
The cost of the repetition in the code here is probably minimal compared
to the disc access costs. On the other hand this is a small data set so
time is probably not a big issue.
--
Russel.
=============================================================================
Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net
41 Buckmaster Road m: +44 7770 465 077 xmpp: rus...@winder.org.uk
London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
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