If I Don't Lesnerize, The Feegs Can't Get Me

In SQL* you say select sum(sales) from accounts where state="NY".  In Pita, the way to do this is:

results = accounts.aggregate(state='NY')**

which will calculate for you the count, length, sum, minimum and maximum, as appropriate, for all the fields in the table at once, so the value you need is results.sales.sum.  Since the table scan is typically slower than any calculations you're likely to be doing, this seems a reasonable approach.

In addition, I've added a

results = accounts.stats()

which provides all those, plus mean,*** median, mode, standard deviation, and geometric and harmonic means.   Aaaaand standard error, coefficient of variation, sample and population variance, skewness and kurtosis.  I even sort of know what kurtosis is.

I'm working on two more functions now, group and break, though I may need to come up with another name for the latter because break is a Python keyword.  This:

for result in accounts.group('state', country='US'): ...

would give you the aggregate sales figures for each state in the US, sensibly enough.  And this:

for result in accounts.break('state', country='US'): ...

would give you the individual sales figures, and then automatically provide totals after the last sales record for each state.

As long as I don't come down with kurtosis...

Update: Kang and jag.  Or rather, agg and tab.  For aggregate and tabulate. 

for line in accounts.aggregate('state', country='US'): ...

will give you one summary line for each state, where

for line in accounts.tabulate('state', country='US'): ...

would give you both detail and summary lines.  I need to put subtotal and total flags on the records for tabulate.  Have to watch the keywords, there.  And keep my closet doors closed.

* Boo, hiss!

** Or indeed

results = accounts(state='NY').aggregate()

Either way should perform the same and produce the same results.  I think...

*** Which should come out the same as the average; just one I'm calculating myself and the other I'm pulling out of a stats module.

Posted by: Pixy Misa at 03:14 AM | Comments (2) | Add Comment | Trackbacks (Suck)
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