AI News, Python For Loops Explained (Python for Data Science Basics #5)
- On 4. juni 2018
- By Read More
Python For Loops Explained (Python for Data Science Basics #5)
Remember, that I told last time that Python if statements are similar to how our brain processes conditions in our everyday life?
will be most of the time our well-known data types: lists, strings or dictionaries. Some times they can also be range() objects (I’ll get back to this at the end of the article.) Let’s take the simplest example first: a list! Do
Let’s create a list: dog = ['Freddie', 9, True, 1.1, 2001, ['bone', 'little ball']] Once it’s created, we can go through the elements of it and print them one by one –
The result is the elements of the list one by one, in separate lines: Wonderful!But how does this actually become useful?
= [1, 5, 12, 91, 102] Say that we want to square each of the numbers in this list!
Okay, now that you see that it’s useful, it’s time to understand the underlying logic of the Python for loops…
I see many people using simple loops like a piece of cake but struggling with more complex ones.
we have defined a list earlier: numbers = [1, 5, 12, 91, 102]).
variable and it’s only role is to store the given element of the list that we will work with in the given iteration of the loop.
Even if this variable is called i most of the times (in online tutorials or books for instance), it’s good to know that the naming is totally arbitrary.
It could not be just i (for i in numbers), but anything else, like x (for x in numbers) or hello (for hello in numbers) or whatever you prefer… The point is, set a variable and don’t forget that you have to refer to it when you want to use it inside the loop.
because of zero-based indexing – technically it’s the 0th element of the list). The first iteration of the loop will run!
7.) We take the next element and since there is an actual next element of the list, the second iteration of the loop will run!
In the future, you can just go ahead and use those 3 simple lines, because the underlying logic will be in the back of your mind!
I find it very important to write this down though, because many junior data professionals do not have this in their back of mind…
strings are basically handled as sequences of characters, thus the for loop will work with them pretty much as it did with lists.
print a pyramid of the string like in this example: my_string = 'python' OUTPUT: Write a script that does this for any my_string value! Okay!
I’ll show you here a relatively simple solution, but feel free to post your alternative solutions in the comment section below!
The syntax is simple but as you have seen, to fully understand the logic behind it needs a little bit of brainwork.
Control structures change the order that statements are executed or decide if a certain statement will be run.
As a side note, decision statements (e.g., if statements) also influence whether or not a certain statement will run.
The  != means does not equal so while a != 0 : means until a is zero run the tabbed in statements that are afterwards.
Note that this program could have been done in a shorter way: Here are some examples to show what happens with the range function: Another way to use the range() function in a for loop is to supply only one argument: The above code acts exactly the same as: with 0 implied as the starting point.
The output is The code would cycle through the for loop 10 times as expected, but starting with 0 instead of 1.
Here a quick way to add up all the elements: with the output simply being: Or you could write a program to find out if there are any duplicates in a list like this program does: and for good measure: How does it work?
Here is a special debugging version: with the output being: Note: The reason there are so many print statements is because print statements can show the value of each variable at different times, and help debug the program.
Here is some code to print out the first 9 numbers of the Fibonacci series: with the surprising output: Everything that can be done with for loops can also be done with while loops but for loops give an easy way to go through all the elements in a list or to do something a certain number of times.
For example, the following simple loop will print the million integers from 0 to 999999, but it will get them one at a time from the xrange call, instead of getting them all at once as a single list and going through that.
Here is one way to write such a loop: The trouble with this is the asymmetry between the two ways out of the loop: one through normal for-loop termination, the other through the break.
As a stylistic matter, it would be more consistent to follow this principle: In particular, this means that the loop construct itself becomes a “loop-forever” loop;
if not, it can be converted to one by wrapping it in a call to the iter() built-in function, and then using the next() built-in function to obtain successive values from this iterator.
18 Most Common Python List Questions
List comprehension is, basically speaking, a way of elegantly constructing your lists.
Now, in a more general sense, you can also use list comprehension to transform your lists into other lists, which sounds a bit more difficult, doesn’t it?
Consider the next example: In this example, you already have a list myList that you’re transforming through list comprehension: you see that again for x in myList, for every list element in myList, some operation will be done.
In other words, you could rewrite the whole second line of the above chunk of code as: In which you first define your lambda function that says that you want to multiply an element by itself, and then in the second line pass it your list element to make sure that each list element is multiplied by itself.
Keep on reading through the following frequently asked questions to learn more about nested list comprehension and to see more practical examples of what list comprehension can mean for your programming with Python!
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