Loops in Python for Business Analyst
Choosing the Right Loop Construct
Python offers a variety of constructs to do loops. This article presents them and gives advice on their specific usage. Furthermore, we will also have a look at the performance of each looping construct in your Python code. It might be surprising for you.
Loops, Loops, Loops
A programming language typically consists of several types of basic elements, such as assignments, statements, and loops. The idea behind a loop is to repeat single actions that are stated in the body of the loop. Different kinds of loops are common:
- as long as a specified condition is true (while condition do sth.)
- until a certain condition is met (do sth. until condition)
- for a fixed number of steps (iterations) (for/from ‘x’ to ‘y’ do sth.)
- endless loop and exit/break on condition (while condition1 do sth. and exit on condition2)
Loop Constructs Supported by Python
Python supports a partial number of the constructs named above, plus it offers unique extensions to the types we have mentioned.
while condition: statements
As long as the “condition” is complied with all the statements in the body of the
while loop are executed at least once. After each time the statements are executed, the condition is re-evaluated. Writing a loop looks like this:
fruits = ["banana", "apple", "orange", "kiwi"] position = 0 while position < len(fruits): print(fruits[position]) position = position + 1 print("reached end of list")
This code will output one list element after the next:
banana apple orange kiwi reached end of list
while Loops with
This construct is specific to the Python language, but quite helpful:
while condition: statements else: statements
while loop acts similar to the regular
while loop as introduced before. The statements in the
else part are executed as soon as the condition is no longer true. For example, in case the end of a list is reached, as in our previous example. You may interpret it as
then if the condition of the loop is no longer met.
fruits = ["banana", "apple", "orange", "kiwi"] position = 0 while position < len(fruits): print(fruits[position]) position = position + 1 else: print("reached end of list")
This will output one list element after the next, plus the additional text from the
banana apple orange kiwi reached end of list
This kind of loop with an
else clause is handy in order to output messages or execute statements in case your condition fails.
One important thing to note is that the
else clause is not executed if you
break out of the
while loop or if an error is thrown from within the
Infinite loops are always taught as being critical components and to be avoided if the break condition is a complicated matter. Although there are cases in which infinite loops help you to write code in an elegant way.
Here are just a few use-cases of infinite loops:
- devices that try to keep network connections active like wireless access points
- clients that try to constantly exchange data with a host system, like a network-based file system (NFS or Samba/CIFS)
- game loops for drawing and updating your game state
while True: if condition: break statements
Keep in mind that the statements in the body of an endless loop are run at least once. That’s why I recommend to write the break condition as the very first statement after the head of the loop. Following our example code, an infinite loop looks as follows:
fruits = ["banana", "apple", "orange", "kiwi"] position = 0 while True: if position >= len(fruits): break print(fruits[position]) position = position + 1 print("reached end of list")
for Loops with an Iterator
Working with lists is described as using the keyword
for in combination with an iterator. The pseudocode looks as follows:
for temp_var in sequence: statements
This simplifies the Python code for processing our list as follows:
fruits = ["banana", "apple", "orange", "kiwi"] for food in fruits: print(food) print("reached end of list")
In this type of looping construct the Python interpreter handles iterating over the list and takes care that the loop does not run outside the range of the list. Keep in mind that the statements in the body of the loop are run once for every element in the list – no matter if it is just a single one, or twenty thousand.
In case the list is empty, the statements in the body of the loop are not executed. Changing the list in terms of adding or removing elements within the
for loop may confuse the Python interpreter and cause problems, so be careful.
for Loops with Iterator and
Similar to the
while loop, Python also offers an
else statement for the
for loop. It works similar and can be interpreted as
then, just as before. The pseudocode looks as follows:
for temp_var in sequence: statements else: statements
Using this keyword our code changes as follows:
fruits = ["banana", "apple", "orange", "kiwi"] for food in fruits: print(food) else: print("reached end of list")
Unsupported Loop Constructs
As stated at the beginning, there are many different loop styles. However, Python does not support them all. Python does not support a
do-until loop or a
foreach loop, as possibly known from PHP. Such cases are solved using Python’s
in operator that creates quite sexy code if you got familiar with it. See the alternative ways of writing a loop from above.
Which Loop to Choose?
In general the
while condition loops require a condition to be specified before the loop’s statements. This may lead to the case that the statements in the body of the loop are never executed. Also, it is not always clear how many times the loop will execute for
while loops. Instead,
for loops focus on the iterator that specifies how often the statements in the body of the loop are run.
It is recommended to use a
for loop if you know exactly the number of elements to be iterated over. In contrast, a
while loop is better for when you have a boolean expression to evalutate, and not a list of elements to loop over.
Improving the Quality of your Code
Many younger programmers don’t always care about the quality of their code, largely because they’ve grow up in a time in which nobody has to think about memory and CPU power – we just have plenty of it available in modern computers. Instead, more experienced (aka “older”) developers are more prone to optimize their code as much as possible and may remember counting CPU instructions and the number of memory cells that are in use.
So what does quality mean today? In terms of effectivity it covers writing the least amount of code as possible, and effectively executing code – only as many processor instructions as needed. Firstly, with today’s interpreters, run-times, and frameworks it is quite difficult to calculate that properly, and secondly it is always a trade-off between these two measures. The key questions are, how often will this code be in use and how much time shall we spent on optimizing it to win a few microseconds of CPU time.
As an example we’ll have a look at a
for loop iterating over a list. Usually, we write it as follows:
for entry in range(0, 3): print(entry)
This outputs the values 0, 1 and 2. The
range() method creates the iterable
[0, 1, 2] every time the head of the loop is evaluated. Therefore it is better to write it as follows:
entryRange = range(0, 3) for entry in entryRange: print(entry)
While this may not seem like much optimization for the given example, consider if the range was from 0 to 1,000,000 or more. As our list grows larger, we save more time and our code executes faster.
Furthermore, these statements can be expressed as a
entryRange = range(0, 3) index = 0 while index < len(entryRange): print(entryRange[index]) index = index + 1
And by this point it seems a bit pointless to even use the
range() function. Instead we might as well just use a constant for the conditional and
index as a counter for the conditional and printing:
index = 0 while index < 3: print(index) index = index + 1
Small optimizations like these can provide small performance improvements for your loops, especially as the number of iterations become very large.
So far we spoke about loop code and how to write it properly. A performance test may help to bring in some light. The idea is kindly borrowed from an interesting blog article by Ned Batchelder .
In use is the
perf tool that does performance tests for program code that is executed . The basic call is
perf stat program whereas
stat abbreviates statistics and program is the call we would like to evaluate. To test our loop variants these calls were done:
perf stat python3 while-1.py perf stat python3 while-2.py perf stat python3 while-3.py perf stat python3 for-4.py perf stat python3 for-5.py perf stat python3 for-6.py perf stat python3 for-7.py perf stat python3 while-8.py
This results are the average based on 10 runs due to load differences in the Linux kernel. The following table shows the results:
|Topic||Listing 1||Listing 2||Listing 3||Listing 4||Listing 5|
|task clock (msec)||20.160077||18.535264||15.975387||15.427334||15.503672|
For the Listings 6-8 it looks as follows:
|Topic||Listing 6||Listing 7||Listing 8|
|task clock (msec)||16.480322||18.193437||15.734627|
Python offers different ways to repeat actions and to write write loops. There are variants per specific use case. Our tests have shown that the loops are in the same dimension with little differences, and the optimization of the Python interpreter is quite good.
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Recipes in Project-Based Learning:
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.