Variable naming means choosing identifiers that show what values or roles they represent. Prefer names such as total_score, subject_count, and average_score over vague names like x or temp_var; readable names help others understand and maintain the code. section 1.1.1 section 2.1.3
Use variable names that make each step and its data easy to follow:
- Name values according to their meaning or role, not their temporary storage location. section 1.1.1
- Split complicated expressions into named intermediate values when the names clarify the computation. section 2.1.3
- Use sensible identifiers consistently because clear names reduce the likelihood of handling the wrong data. section 2.5.3
Naming a calculation
Instead of embedding the entire calculation in formatted output, give each result a role-based name: scores = [95, 98, 97, 96, 97, 93] total_score = sum(scores) subject_count = len(scores) average_score = total_score / subject_count summary_text = f"Your Average Score: {average_score}." Each identifier tells the reader what the value represents. section 2.1.3
Listing 2.9 Using named groups to extract data
regex = re.compile(r"(?P<task_id>\d{3}), (?P<task_title>\w+); (?P<task_desc>.+)")
tasks = []
for line in text_data.split("\n"):
match = regex.match(line)
if match:
task = (match.group('task_id'), match.group('task_title'),
match.group('task_desc'))
tasks.append(task)
Named groups extend the same practice beyond ordinary variables: task_id, task_title, and task_desc identify extracted data more clearly than numeric group positions. section 2.5.3
