Nested structures — lists of dicts, dicts of lists

List of dicts — the most common shape

orders = [
    {"id": 101, "customer": "Acme",  "items": ["A1", "B7"],         "total":  450},
    {"id": 102, "customer": "Beta",  "items": ["C3"],               "total":  120},
    {"id": 103, "customer": "Acme",  "items": ["A1", "A1", "B7"],   "total":  780},
]

Reading it

first = orders[0]
print(first["customer"])      # Acme
print(first["items"][0])      # A1
print(orders[2]["items"][-1]) # B7

Looping

for order in orders:
    print(f"Order {order['id']} for {order['customer']}: ${order['total']}")

Aggregating across a list of dicts

# Total revenue
total = sum(o["total"] for o in orders)        # 1350

# Revenue per customer
by_customer = {}
for o in orders:
    cust = o["customer"]
    by_customer[cust] = by_customer.get(cust, 0) + o["total"]

print(by_customer)   # {'Acme': 1230, 'Beta': 120}

And here's why DataFrames are so popular

That last calculation took five lines. With pandas it's one:

import pandas as pd
df = pd.DataFrame(orders)
df.groupby("customer")["total"].sum()

Same answer. Same data. Less code. That's why Module 9 is the turning point in this course.