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Let's compare how long Selection Sort, Quicksort, and Merge Sort take to run on different data sets.

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I've removed the call to print that
displays the sorted list at the end of our
0:00

selection_sort, quicksort,
and merge_sort scripts.
0:04

That way, it'll still run the sort, but
0:07

the output won't get in the way
of our comparing run times.
0:09

Let's try running each of these scripts,
and see how long it takes.
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time python selection_sort, we'll do that
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one first, numbers/10,000.txt.
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We combine the user and sys results,
and that gives us about 6 seconds.
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Now let's try quicksort, time python
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quicksort.py numbers/10000.txt.
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Much faster, less than a second.
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And finally, time python
merge_sort.py numbers/10000.txt.
0:48

A little longer, but
far less than a second.
0:58

So even on a list with
just 10,000 numbers,
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selection_sort takes many times as
long as quicksort and merge_sort.
1:04

And remember, I ran the selection_sort
script on a file with a million numbers.
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And it took so long that my workspace
timed out before it completed.
1:13

It looks like selection_sort is out of
the running as a viable sorting algorithm.
1:18

It may be easy to understand and
implement, but
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it's just too slow to handle the huge
data sets that are out in the real world.
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Now let's try quicksort and merge_sort
on our file with a million numbers, and
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see how they compare there.
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time python quicksort.py
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numbers/1000000.txt.
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Looks like it took about
11 seconds of CPU time.
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Now let's try merge_sort, time python
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merge_sort.py numbers/1000000.txt.
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That took about 15 seconds of CPU time.
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It looks like quicksort is marginally
faster than merge_sort on this
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sample data.
2:12

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