If you have coded in R or SPlus you should be familiar with avoiding loops and capitalizing on the
tappy function whenever you can. These rules of thumb will serve you well when coding in Python. Many python objects have built-in functions that can take iterators of pass parameters. A python also has functions such as map, reduce and filter that behavior similar to R’s
tappy. Below are two quick examples of how to use the profile module and performance hit from using loops in Python.
import profile mylist =  for i in range(10000): myList1.append(str(i)) def test_1( ): s_out1 = "" for val in myList1: s_out1 = s_out1 + val + ',' def test_2( ): s_out2= ",".join(myList1 ) profile.run('test_1()') profile.run('test_2()')
The join function allows you to quickly concatenate strings within an iterator object without the hassle of looping through it. Below is an example of transforming all the elements within an iterator object, first by looping through the object then by using the map function.
myList2= for i in range(26): myList2.append( chr(i+97)) myList2 = myList2*1000 def test_3( ): s_out1 = "" for val in myList2: s_out1 = val.upper( ) def test_4( ): s_out2= map(str.upper , myList2 ) profile.run('test_3()') profile.run('test_4()')
Although Python is quick to pick up there is, like with any language, good code and bad code.