range() Function and Loop Control in Python#

🐍 Data Analysis Using Python 🔀 Control Flow Lesson 015

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Sequences and finer loop control#

Two tools refine looping: the range() function, which generates sequences of numbers to loop over, and loop control statements (break and continue), which give finer control over how a loop proceeds. This lesson covers both, completing the control stage’s treatment of iteration.

The range() function#

range() generates a sequence of numbers, most often used to repeat something a set number of times or to loop over numeric indices:

for i in range(5):         # 0, 1, 2, 3, 4
    print(i)

for i in range(1, 6):      # 1, 2, 3, 4, 5 (start, stop)
    print(i)

for i in range(0, 10, 2):  # 0, 2, 4, 6, 8 (start, stop, step)
    print(i)

range(n) produces 0 up to (but not including) n; range(start, stop) starts elsewhere; range(start, stop, step) steps by an interval. The exclusive upper bound — range(5) stops at 4, not 5 — is the detail to remember, echoing zero-based indexing. range() is how a for loop repeats a fixed number of times or walks a numeric sequence.

Loop control: break and continue#

Two statements alter a loop’s normal flow:

for amount in sales:
    if amount < 0:
        continue           # skip this iteration, go to the next
    if amount > 10000:
        break              # exit the loop entirely
    process(amount)

break — exits the loop immediately, skipping any remaining iterations. Useful for stopping once a condition is met (found what you sought, hit a limit).
continue — skips the rest of the current iteration and moves to the next. Useful for skipping items that should not be processed (invalid values, ones to ignore).

These give a loop finer control than “process every item to the end” — stop early, or skip selectively.

Why these matter#

range() and loop control complete the looping toolkit. range() handles count-based and index-based repetition (repeat n times, iterate positions), complementing the for loop’s collection iteration. break and continue let a loop respond to conditions — stopping when done, skipping what should be skipped — making loops precise rather than all-or-nothing. Together with the while and for loops, they cover the iteration patterns programming requires.

The caveat#

Each tool has a trap. range()’s exclusive upper bound is a persistent off-by-one source — range(1, 5) gives 1,2,3,4, not 1–5 — so getting the bounds right requires care (the edge-case discipline). break and continue used heavily can make a loop’s flow hard to follow — a loop with several breaks and continues scattered through it can be as tangled as deeply nested branches, so use them where they clarify (a clean early exit) rather than as a substitute for well-structured loop logic. And the earlier caution stands: for large data, range()-driven index loops are usually slower and less clear than the vectorised operations of pandas/numpy. Clear, correct loops first; know their limits for scale. The next stage turns to Python’s data structures, starting with strings.

Hint