Control flow

Perform an operation for every element in a collection.

Data processing: iterating over a list of records to apply a transformation, File handling: processing each filename in a directory to read its contents, Data aggregation: summing values from a dictionary of counts

General‑purpose Python code, data‑processing scripts, web back‑ends, scientific notebooks.

for element in iterable:

for ... in ...:

loop_var: identifier, iterable: expression

• if
• break
• continue
• else
• enumerate
• zip

• list comprehensions
• map/filter
• while loops with manual indexing

Modifying the iterable while looping (misconception: assuming safe mutation) → can skip elements or raise RuntimeError; Forgetting the colon after the for statement (syntax error) → SyntaxError: invalid syntax; Using the wrong variable name inside the loop body (typo/misunderstanding) → NameError or using stale/incorrect data

while loop (explicit condition), list comprehension (expression‑level iteration).

Coming from C/Java: expect braces; Python relies on indentation and the colon.

• iteration patterns
• looping constructs

Iterating over a dict yields only keys; use .items() for key/value pairs when both are needed. Large iterables may benefit from generators to save memory. Iterating over a mutable sequence while changing its length can skip elements or raise IndexError.

Enables repetitive processing of collections, enabling batch operations and data transformation.

Think of a conveyor belt: each item rides by, you inspect it, then it moves on.

Be careful not to modify the iterable while iterating; if you need to modify, iterate over a copy or collect changes separately.

Consider using list comprehensions, generator expressions, or itertools for more concise or memory‑efficient iteration patterns.