For Loops in Python#

🐍 Data Analysis Using Python 🔀 Control Flow Lesson 014

Important

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Repeating over a collection#

When you need to do something for each item in a collection, the for loop is the natural tool — it iterates over the items of a sequence, running its block once per item. This is the loop analysts use most, because data work is largely “do this to every row, value, or record.” This lesson covers the for loop, the workhorse of iteration.

The for loop#

A for loop iterates over the items of a collection, binding each to a variable in turn:

regions = ["North", "South", "East", "West"]
for region in regions:
    print(region)

Python takes each item of regions in order, assigns it to region, and runs the block — printing all four region names. The loop variable (region) holds the current item on each pass. Unlike the while loop, the for loop automatically stops when the collection is exhausted — no manual progress-tracking, and no risk of an infinite loop over a finite collection.

Iterating and accumulating#

A common pattern combines a for loop with a variable that accumulates a result across iterations:

sales = [100, 250, 175, 300]
total = 0
for amount in sales:
    total += amount        # accumulate
print(total)               # 825

The total starts at zero and grows by each amount as the loop visits it — computing a sum by iteration. This accumulate-across-a-loop pattern (summing, counting, collecting, building) is one of the most useful in programming, and it is how manual aggregation is expressed in code (though pandas, later, does it far more concisely).

For loops over different collections#

For loops iterate any iterable — lists, strings (character by character), dictionaries (the structures stage), and more:

for char in "data":        # iterates characters: d, a, t, a
    print(char)

This generality makes the for loop the standard way to process collections of any kind — whatever the data, “for each item, do something” is a for loop.

The caveat#

For loops are safer than while loops (they cannot loop infinitely over a finite collection), but they have their own pitfalls. Modifying a collection while iterating over it causes subtle bugs (the collection changes underfoot) and should be avoided — build a new collection instead. And a deeper point looms for data work: explicit Python for loops over large datasets are slow compared to the vectorised operations of numpy and pandas (the libraries stage), which do the same work far faster without an explicit loop. Loops are essential to understand and correct for general programming, but for large-scale data the idiom shifts to vectorised operations — a for loop over a million-row dataset is usually the wrong tool. Learn loops thoroughly, and later learn when not to loop. The next lesson covers generating sequences and controlling loops.

Hint