optimizing large dictionaries

pruebauno at latinmail.com pruebauno at latinmail.com
Fri Jan 16 17:54:22 CET 2009

On Jan 15, 4:39 pm, Per Freem <perfr... at yahoo.com> wrote:
> hello
> i have an optimization questions about python. i am iterating through
> a file and counting the number of repeated elements. the file has on
> the order
> of tens of millions elements...
> i create a dictionary that maps elements of the file that i want to
> count
> to their number of occurs. so i iterate through the file and for each
> line
> extract the elements (simple text operation) and see if it has an
> entry in the dict:
> for line in file:
>   try:
>     elt = MyClass(line)# extract elt from line...
>     my_dict[elt] += 1
>   except KeyError:
>     my_dict[elt] = 1
> i am using try/except since it is supposedly faster (though i am not
> sure
> about this? is this really true in Python 2.5?).
> the only 'twist' is that my elt is an instance of a class (MyClass)
> with 3 fields, all numeric. the class is hashable, and so my_dict[elt]
> works well.
> the __repr__ and __hash__ methods of my class simply return str()
> representation
> of self, while __str__ just makes everything numeric field into a
> concatenated string:
> class MyClass
>   def __str__(self):
>     return "%s-%s-%s" %(self.field1, self.field2, self.field3)
>   def __repr__(self):
>     return str(self)
>   def __hash__(self):
>     return hash(str(self))
> is there anything that can be done to speed up this simply code? right
> now it is taking well over 15 minutes to process, on a 3 Ghz machine
> with lots of RAM (though this is all taking CPU power, not RAM at this
> point.)
> any general advice on how to optimize large dicts would be great too
> thanks for your help.

I am willing to bet a beer that the slow performance has nothing to do
with the dict but is either because of MyClass or the read from disk.

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