Why Mock Data Matters

Layer 4 begins. Real teams build and test an application's logic long before a real data source is ready. We formalize working against fake data on purpose.

1. Learning Objective

2. Pre-Coding Quiz

Answer these before reading the concept explanation or writing any code. It is fine to get these wrong — that is the point. You need 4/5 to proceed to the lab.

3. The Concept — Why Mock Data Matters

Building against fake data on purpose

Every session so far has technically used mock data — hand-typed Country instances. This session makes that a deliberate practice: build and test your application's real logic against a fake data source that mirrors what a real one will eventually look like.

# mock_countries.py — deliberately fake, but shaped like the real thing will be
MOCK_COUNTRIES = [
    {"name": "Kenya", "region": "Africa", "population": 54000000},
    {"name": "Ghana", "region": "Africa", "population": 31000000},
    {"name": "Peru", "region": "Americas", "population": 33000000},
    {"name": "Japan", "region": "Asia", "population": 125000000},
    {"name": "Norway", "region": "Europe", "population": 5400000},
]

A contract is a shape everyone agrees to honor

The "contract" is simply: every country record has a name (str), a region (str), and a population (int). As long as both the mock data and a future real API honor this same contract, code written against one will keep working against the other.

The independence this buys us

With a mock data source in place, every remaining feature — search, filtering, the whole CountryExplorer built in Layer 2 and 3 — can be developed and fully exercised without a real API existing yet, or even being reachable over the network at all.

from mock_countries import MOCK_COUNTRIES

countries = [Country.from_dict(r) for r in MOCK_COUNTRIES]
explorer = CountryExplorer(countries=countries)
print(explorer.total_population)   # works completely offline, no network needed

The risk: silent divergence from the real shape

Mock data is only useful if it honestly represents what real data will look like. If the mock and the eventual real source disagree on key names or types, everything built "safely" against the mock will break the moment real data arrives — an important risk to keep in mind as we build Session 30's formal data contracts.

4. Lab

What you will build

A file called mock_countries.py and a small script that uses it.

Step-by-step instructions

# mock_countries.py
MOCK_COUNTRIES = [
    {"name": "Kenya", "region": "Africa", "population": 54000000},
    {"name": "Ghana", "region": "Africa", "population": 31000000},
    {"name": "Nigeria", "region": "Africa", "population": 223000000},
    {"name": "Peru", "region": "Americas", "population": 33000000},
    {"name": "Canada", "region": "Americas", "population": 38000000},
    {"name": "Brazil", "region": "Americas", "population": 216000000},
    {"name": "Japan", "region": "Asia", "population": 125000000},
    {"name": "India", "region": "Asia", "population": 1428000000},
    {"name": "Norway", "region": "Europe", "population": 5400000},
    {"name": "Germany", "region": "Europe", "population": 84000000},
]

# explore_offline.py
from mock_countries import MOCK_COUNTRIES

class Country:
    def __init__(self, name, region, population):
        self.name = name
        self.region = region
        self.population = population

    def summary(self):
        return f"{self.name} ({self.region}): pop. {self.population:,}"

    @classmethod
    def from_dict(cls, data):
        return cls(**data)

countries = [Country.from_dict(r) for r in MOCK_COUNTRIES]
print(len(countries))

class CountryExplorer:
    def __init__(self, countries):
        self.countries = countries

    @property
    def country_count(self):
        return len(self.countries)

    @property
    def total_population(self):
        return sum(c.population for c in self.countries)

explorer = CountryExplorer(countries=countries)
print(explorer.country_count)
print(explorer.total_population)

for c in explorer.countries:
    print(c.summary())

5. Expected Files Changed

6. Commit Checkpoint

Once the lab is complete and you can explain every line, make this exact commit:

git add mock_countries.py explore_offline.py docs/sessions/session-27/index.html
git commit -m "session-27: build a mock dataset and run the explorer entirely offline"

7. Code Review Checklist

Go through your code line by line and check each item:

8. Post-Coding Quiz

Same 5 questions. Take it again now that you have written and run the code. You need 4/5 to mark this session complete.

9. Reflection Questions

Think through these after the post-quiz. No right answer — they are for discussion.

10. What Breaks If This Knowledge Is Missing?

11. What We Learned