Populate a list with a for loop

Populating a list with a for loop is one of the most common and readable ways to build dynamic collections in Python. You start with an empty list (or sometimes pre-allocate with a comprehension), then use a for loop to compute or fetch values and append them one by one. In 2026, while list comprehensions and generators are often preferred for conciseness and performance, the classic for + append() pattern remains essential — especially when logic inside the loop is complex, conditional, involves I/O, error handling, or accumulates results incrementally (e.g., processing large files, APIs, or user input).

Here’s a complete, practical guide to populating lists with for loops: basic appending, real-world patterns (file reading, API results, filtering), performance considerations, and modern best practices with type hints, alternatives, and safety.

The simplest form: loop over a range or existing data, compute something, and append the result to an empty list.


squares = []                  # Start empty

for i in range(10):
    squares.append(i ** 2)    # Compute and append

print(squares)                # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Real-world pattern: reading and processing lines from a large file — append cleaned or parsed data while keeping memory low.


valid_lines = []

with open("log.txt", "r", encoding="utf-8") as f:
    for line_num, line in enumerate(f, start=1):
        clean = line.strip()
        if clean and "ERROR" in clean.upper():
            valid_lines.append((line_num, clean))  # Store tuple: (line number, content)

print(f"Found {len(valid_lines)} error lines:")
for num, msg in valid_lines:
    print(f"Line {num}: {msg}")

Another everyday use: collecting filtered or transformed results from an API or database query — append only what matches criteria.


active_users = []

# Simulated API response (list of dicts)
users = [
    {"id": 1, "name": "Alice", "active": True},
    {"id": 2, "name": "Bob", "active": False},
    {"id": 3, "name": "Charlie", "active": True}
]

for user in users:
    if user["active"]:
        active_users.append(user["name"])

print("Active users:", active_users)   # ['Alice', 'Charlie']

Best practices make list population safe, efficient, and maintainable. Prefer list comprehensions for simple transformations — [i**2 for i in range(10)] is faster and clearer than for + append(). Use for + append() when logic is complex — conditionals, try/except, file I/O, API calls, or early breaks. Pre-allocate when possible — results = [None] * expected_size — but only if size is known and fixed. Avoid appending inside deep nests — extract helpers or use comprehensions. Modern tip: use type hints for the list — valid_lines: list[tuple[int, str]] = [] — improves readability and IDE/mypy support. For very large results, prefer generators (yield) over lists — avoids memory explosion; convert to list only when needed (list(gen)). In production, add error handling per item — skip bad data instead of crashing, and log skips. Use extend() instead of multiple append() when adding batches — faster for large chunks.

Populating a list with a for loop is flexible and intuitive — ideal for incremental building, filtering, or processing streams. In 2026, use it for complex logic, prefer comprehensions for simple cases, and lean on generators for huge data. Master this pattern, and you’ll build dynamic collections cleanly and efficiently — from small scripts to big data pipelines.

Next time you need to collect results — start with an empty list and a for loop (or comprehension). It’s Python’s go-to way to grow data one piece at a time.

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