How do you work with nested data structures such as a list of dictionaries?

A list of dictionaries is the most common Python pattern for representing structured data from APIs, CSV rows, database query results, and JSON payloads. Each dictionary is one record; the list is the collection.

employees = [
    {'name': 'Alice', 'dept': 'Engineering', 'salary': 95000},
    {'name': 'Bob',   'dept': 'Marketing',   'salary': 72000},
    {'name': 'Carol', 'dept': 'Engineering', 'salary': 105000},
    {'name': 'Dave',  'dept': 'Marketing',   'salary': 68000},
]

# Filter: Engineering employees
eng = [e for e in employees if e['dept'] == 'Engineering']

# Map: extract names only
names = [e['name'] for e in employees]

# Sort by salary descending
ranked = sorted(employees, key=lambda e: e['salary'], reverse=True)

# Group by department using defaultdict
from collections import defaultdict
by_dept = defaultdict(list)
for emp in employees:
    by_dept[emp['dept']].append(emp['name'])
# {'Engineering': ['Alice', 'Carol'], 'Marketing': ['Bob', 'Dave']}

# Average salary per department
dept_salary = {}
for dept, members in by_dept.items():
    salaries = [e['salary'] for e in employees if e['name'] in members]
    dept_salary[dept] = sum(salaries) / len(salaries)

When accessing nested values that may not exist, chain .get() calls or use a library like glom for deeply nested paths. Safe access pattern: record.get('address', {}).get('city', 'Unknown').

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