Controlling Loops

Stopping early and skipping work

So far, loops have followed a strict and predictable pattern: start, repeat, and stop only when the main loop condition is met or the data runs out.

Now we add a powerful new capability:

You can control the loop’s flow from inside the loop body.

This is incredibly useful for data checking, quality control, and making your processing more efficient.

1. Placeholders with `pass`¶

Why pass exists¶

Sometimes you want to sketch out the structure of your code before you actually know what the logic should be.

However, Python relies on indentation. If you create a for loop or an if statement and leave the indented block completely empty, Python will throw an error.

pass is a placeholder statement that literally does nothing. It simply tells Python, “I know a block of code belongs here, just ignore it for now.”

for i in range(5):
    pass  # Keeps the code valid while you are drafting

This loop runs perfectly, but performs no action.

When to use pass¶

Use pass when you:

sketch out a loop you will complete later
create a temporary placeholder while debugging
need a code block that is syntactically required but intentionally empty

2. Exiting a loop with `break`¶

Stopping early¶

The break statement is an emergency exit. It ends the loop immediately. Execution jumps completely out of the loop and continues with the first line of code after the loop block.

This is useful when your goal has been reached and continuing the loop would just be wasted work.

Example: Stop when an outlier is found¶

temperatures = [3, 5, 7, 6, 8, 9, 10, 111, 11]
min_temp = -20
max_temp = 40

for day, temp in enumerate(temperatures, start=1):
    print(f"Day {day}: {temp} °C")

    if temp < min_temp or temp > max_temp:
        print(f"⚠️ Outlier detected on day {day}: {temp} °C. Stopping analysis!")
        break

print("Finished checking the record.")

As soon as the break statement is triggered on the 111 °C outlier, the loop is abandoned. The remaining temperature (11 °C) is never checked.

Why use break?¶

Without break, the loop would uselessly keep checking values even after the problem was found. With break, the loop stops early, saving time and processing power.

This is a common pattern when:

searching for the very first occurrence of a feature
detecting the onset of an environmental event
safely stopping a process when an error condition is met

3. Skipping an iteration with `continue`¶

Skip the rest of this round¶

While break stops the entire loop, continue only stops the current iteration. It skips the remaining code in the loop body and immediately jumps back to the top to start the next iteration.

This is perfect when some values should be ignored, but you still want to process the rest of the dataset.

Side-by-side flowcharts showing 'break' completely exiting a loop boundary, while 'continue' skips the rest of the current block and loops back to the top. — Notice the difference in the escape paths: `break` shatters the loop entirely and moves on. `continue` acts like a reset button for the current iteration, sending Python straight back to the top to grab the next value.

Example: Ignore missing values¶

temperatures = [3, None, 7, 6, None, 9]

for temp in temperatures:
    if temp is None:
        continue  # Skip to the next temperature

    print(f"Valid value: {temp} °C")

Here, the loop skips the None entries without breaking the loop, ensuring the valid temperatures (7, 6, 9) are still printed.

What is `None`?¶

In Python, None represents the absence of a value. It is commonly used to indicate:

missing data
unavailable measurements
placeholders where no valid value exists

None is not zero, not an empty string, and not False. It is a distinct data type with a special meaning. Because of this, it is usually checked explicitly using the is operator.

A common coding pattern¶

Often, continue is used as a “guard” at the very top of a loop:

for value in dataset:
    if value is None:
        continue

    # main, complex processing happens down here

This acts as a filter, keeping the main logic less indented and much easier to read.

4. `continue` and `while` loops¶

Why continue can be risky¶

In a for loop, Python automatically fetches the next value from the list when continue is called. So, continue is very safe to use.

In a while loop, you are responsible for updating the control variable (like index += 1). If continue skips the line of code that updates your variable, the loop will evaluate the exact same condition again and again, freezing your program.

Example of the risk (Do not run this!)¶

values = [3, None, 7]
index = 0

while index < len(values):
    value = values[index]

    if value is None:
        continue  # DANGER: We skip the index update!

    print(value)
    index += 1  # This line is never reached for None

This code prints 3, and then gets permanently stuck on the None value because index remains 1 forever.

The safe pattern¶

If you use continue in a while loop, make sure your control variable is updated before the continue statement is reached.

values = [3, None, 7]
index = 0

while index < len(values):
    value = values[index]
    index += 1  # Update happens FIRST

    if value is None:
        continue

    print(value)

Now the loop safely progresses to the end of the list.

5. Exercises¶

Exercise 1: Drafting a data check with pass¶

You are planning to write a quality check for a dataset, but you are not yet sure what the check should do.

Create a loop that iterates over the indices of a dataset and prints the index number.

Leave the body of the loop empty for now using pass.

data = [12, 15, 18, 21, 24]

Why might this be useful while developing code?

Sample solution

data = [12, 15, 18, 21, 24]

for i in range(len(data)):
    pass

Explanation:

the loop structure is correct
the body is intentionally empty
logic can be filled in later without breaking the code

Exercise 2: Stop at the first invalid measurement¶

You are checking daily temperature measurements. Values below −20 °C or above 40 °C are invalid.

temperatures = [5, 7, 9, 11, 13, 45, 14, 16]

Write a loop that:

Iterates over the list using an index
Prints each day and temperature
Stops immediately when an invalid value is found
Prints a clear warning using an f-string

Sample solution

temperatures = [5, 7, 9, 11, 13, 45, 14, 16]
min_temp = -20
max_temp = 40

for i in range(len(temperatures)):
    temp = temperatures[i]
    print(f"Day {i + 1}: {temp} °C")

    if temp < min_temp or temp > max_temp:
        print(f"⚠️ Invalid measurement on day {i + 1}")
        break

Explanation:

indexing makes the day number explicit
break stops the loop as soon as the decision is made
the loop’s intent is detecting the first error

Exercise 3: Skip missing values but keep counting¶

You are processing sensor data where some measurements are missing (None).

temperatures = [3, None, 6, 7, None, 9]

Write a loop that:

Iterates over the list
Skips missing values
Prints only valid temperatures
Includes the position in the output

Sample solution

temperatures = [3, None, 6, 7, None, 9]

for i in range(len(temperatures)):
    temp = temperatures[i]

    if temp is None:
        continue

    print(f"Valid value at position {i}: {temp} °C")

Explanation:

continue ignores invalid entries
indexing preserves positional information
output stays readable and informative

Exercise 4: Decide whether to break or continue¶

Consider the following dataset:

values = [10, 12, None, 15, -999, 18]

Assume:

None means missing and should be skipped
-999 means corrupted and should stop processing

Write a loop that:

skips None values
stops entirely when -999 is encountered
prints all valid values before stopping

Think carefully about where to use continue and where to use break.

Sample solution

values = [10, 12, None, 15, -999, 18]

for i in range(len(values)):
    value = values[i]

    if value is None:
        continue

    if value == -999:
        print(f"⚠️ Corrupted value at position {i}")
        break

    print(f"Value at position {i}: {value}")

Explanation:

continue skips missing values
break stops processing when data is corrupted
different control statements express different intent

6. Summary¶

pass is a placeholder that does nothing.
break exits a loop entirely and immediately.
continue skips the remainder of the current iteration and moves to the next one.

break and continue are most useful when processing real-world data, because they allow your program to react dynamically to missing, corrupted, or target values.

What comes next¶

Next, you will combine everything you have learned:

repetition with for and while
decisions with if, elif, else
loop control with pass, break, and continue

This gives you a complete toolkit for writing programs that can automate spatial logic and react to real data in a controlled, intelligent way.

1. Placeholders with pass¶

Why pass exists¶

When to use pass¶

2. Exiting a loop with break¶

Stopping early¶

Example: Stop when an outlier is found¶

Why use break?¶

3. Skipping an iteration with continue¶

Skip the rest of this round¶

Example: Ignore missing values¶

What is None?¶

A common coding pattern¶

4. continue and while loops¶

Why continue can be risky¶

Example of the risk (Do not run this!)¶

The safe pattern¶

5. Exercises¶

Exercise 1: Drafting a data check with pass¶

Exercise 2: Stop at the first invalid measurement¶

Exercise 3: Skip missing values but keep counting¶

Exercise 4: Decide whether to break or continue¶

6. Summary¶

What comes next¶

1. Placeholders with `pass`¶

2. Exiting a loop with `break`¶

3. Skipping an iteration with `continue`¶

What is `None`?¶

4. `continue` and `while` loops¶