# 3.6. Common Loop Patterns¶

We often use a `for` or `while` loop to iterate over a list of items or the contents of a file looking to compute something from that data. Commonly, we might want to compute a count, a sum, the largest or smallest value, or some other result that can be found by looking at each value in the list.

These loops are generally constructed with a few common pieces:

1. Initialize an accumulator variable before the loop starts that will hold a partial result as the loop executes and the final result when it finishes.

2. Iterate over every value using a loop.

3. In the loop body, perform some computation on each value, possibly changing the accumulator variable based on that computation.

4. When the loop completes, the accumulator variable holds the value you wanted to compute.

We will use a list of numbers to demonstrate the concepts and construction of these loop patterns.

## 3.6.1. Counting and Summing Loops¶

For example, to count the number of items in a list, we would write the following `for` loop:

Here, the accumulator variable is `count`. We set `count` to zero before the loop starts, and then we write a `for` loop to run through the list of numbers. Our iteration variable is named `itervar`, and while we do not use `itervar` in the loop, it does control the loop and cause the loop body to be executed once for each of the values in the list.

In the body of the loop, we update the accumulator variable by adding 1 to the current value of `count` for each of the values in the list. While the loop is executing, the value of `count` is the number of values we have seen “so far.”

Once the loop completes, the value of `count` is the total number of items. We constructed the loop so that we have what we want when the loop finishes.

Another similar loop that computes the sum or total of a set of numbers is as follows:

In this loop we do use the iteration variable. Instead of simply adding one to the `count` as in the previous loop, we add the actual number (3, 41, 12, etc.) to the running total during each loop iteration. If you think about the variable `total`, it contains the “running total of the values so far.” Before the loop starts, `total` is zero because we have not yet seen any values; during the loop `total` is the running total; and at the end of the loop `total` is the overall total of all the values in the list.

As the loop executes, `total` accumulates the sum of the elements; it is the accumulator variable in this example.

Note

Counting items and summing values are common enough operations that Python has built-in functions to perform them. We’ve already seen `len()`, which returns the number of items in a sequence. Computing the sum of the numbers in a sequence can be done with the unsurprisingly-named `sum()` function.

Nevertheless, loop patterns like those presented above are used frequently. The `len()` and `sum()` functions can handle simple cases of counting or summing, but more complex cases, like counting or summing only certain values from a sequence for example, may not be achievable using the built-in functions.

## 3.6.2. Minimum and Maximum Loops¶

Another common use of loops is to find a value in a sequence with a particular property. For example, we may need to find the largest or smallest number in a sequence.

Think about how you would find the largest number in a sequence if someone were to give you a long list of numbers one at a time. As you went through the numbers, you would probably keep track of the largest number you had seen so far at any point in time. If you ever got a larger number, you would make that the one you were remembering.

We can follow those same basic steps in code using a variable to remember the largest value we’ve seen so far, a loop to go through the sequence of numbers, and an if statement to check whether each new number is larger than the one we’re remembering:

We have added some print statements to display the state of the variables as the program runs. You can also explore the execution of the code with CodeLens to see more detail.

When the program executes, the output is as follows:

```(Before)  largest = None
(In loop)  itervar = 3  largest = 3
(In loop)  itervar = 41  largest = 41
(In loop)  itervar = 12  largest = 41
(In loop)  itervar = 9  largest = 41
(In loop)  itervar = 74  largest = 74
(In loop)  itervar = 15  largest = 74
(After)  largest = 74
```

The variable `largest` is best thought of as the “largest value we have seen so far.” Before the loop, we set `largest` to the constant `None`. `None` is a special constant value which we can store in a variable to mark the variable as “empty.”

Before the loop starts, the largest value we have seen so far is `None` since we have not yet seen any values. While the loop is executing, if `largest` is `None` then we take the first value we see as the largest so far. You can see in the first iteration when the value of `itervar` is 3, since `largest` is `None`, the condition of the if statement evaluates to `True`, and we immediately set `largest` to be 3.

After the first iteration, `largest` is no longer `None`, so the second part of the compound logical expression that checks `itervar > largest` evaluates to `True` only when we see a value that is larger than the “largest so far.” When we see a new “even larger” value we take that new value for `largest`. You can see in the program output that `largest` progresses from 3 to 41 to 74.

At the end of the loop, we have scanned all of the values and the variable `largest` now does contain the largest value in the list.

Again, `smallest` is the “smallest value seen so far” before, during, and after the loop executes. When the loop has completed, `smallest` contains the minimum value in the list.
Finding the minimum and maximum values in a sequence are also common operations, and again Python includes built-in functions to perform them. The functions are named `min()` and `max()` respectively.
Again, though, you will often find yourself needing to write loop patterns like those above instead of using `min()` or `max()` directly, because you may need to solve a problem that is similar but not exactly solved by them.