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Week 4 Checkpoint: Data Wrangling and the Capstone — Python For Professionals

Day 29 · ~12 min●

Twenty-five functions. That's what this track added to your toolkit — format_kpi_line, is_over_budget, group_by_channel, load_campaigns_from_csv, campaigns_to_json, and twenty more. Which one from this week surprised you most?

The try/except realisation on Day 27. I'd been writing if "spend" in campaign and campaign["spend"] != 0 before every division — this defensive if-chain that grew longer with every new failure mode I'd seen in production. The tuple catch was one line: except (KeyError, ValueError, ZeroDivisionError). Same coverage, no ceremony.

That's the pattern click. You stopped guarding the inputs and started handling the failures. And the list comprehension on Day 24?

That one landed differently. I knew what it did — I'd used them in snippets before — but writing [{**c, "cpl": round(c["spend"] / c["leads"], 2)} for c in valid] myself, after having done it the long way in Week 2, made the trade-off concrete. Four lines of loop-and-append collapsed into one. I actually went back and rewrote my own spreadsheet script that evening.

The capstone on Day 28 was designed for exactly that moment — all five concepts in a single function call chain. extract_campaign_names feeds rank_channels_by_cpl which feeds the quarterly report. You wrote it in one sitting.

I did. And I understood every line. Six questions — let's close it out.

Four from this week, two from earlier to keep those patterns sharp. The track is behind you now.

Quiz

0/6

6 questions80% to passretry anytime

1.What does top_campaigns_by_cpl(campaigns, n=2) return for the input below?

campaigns = [
    {"name": "Email", "spend": 1250.0, "leads": 50},
    {"name": "Social", "spend": 900.0, "leads": 0},
    {"name": "Search", "spend": 12400.0, "leads": 52}
]

def top_campaigns_by_cpl(campaigns: list, n: int = 5) -> list:
    valid = [c for c in campaigns if c.get("leads", 0) > 0]
    with_cpl = [{**c, "cpl": round(c["spend"] / c["leads"], 2)} for c in valid]
    top = sorted(with_cpl, key=lambda c: c["cpl"])[:n]
    return top

[{"name": "Email", "spend": 1250.0, "leads": 50, "cpl": 25.0}, {"name": "Search", "spend": 12400.0, "leads": 52, "cpl": 238.46}][{"name": "Social", "spend": 900.0, "leads": 0, "cpl": 0.0}, {"name": "Email", "spend": 1250.0, "leads": 50, "cpl": 25.0}][{"name": "Email", "spend": 1250.0, "leads": 50, "cpl": 25.0}, {"name": "Social", "spend": 900.0, "leads": 0}][{"name": "Search", "spend": 12400.0, "leads": 52, "cpl": 238.46}, {"name": "Email", "spend": 1250.0, "leads": 50, "cpl": 25.0}]

2.What does extract_campaign_names(raw_text) return for the input below?

raw_text = '"name": "PAID_SEARCH", "spend": 8100\n"name":"Brand Awareness Q2", "cpl": 310'

import re

def clean_campaign_name(raw: str) -> str:
    return raw.strip().lower().replace("_", " ")

def extract_campaign_names(raw_text: str) -> list:
    names = re.findall(r'"name":\s*"([^"]+)"', raw_text)
    return [clean_campaign_name(n.strip()) for n in names]

["paid search", "brand awareness q2"]["PAID_SEARCH", "Brand Awareness Q2"]["paid search"][]

3.What does rank_channels_by_cpl(campaigns) return for the input below?

campaigns = [
    {"channel": "social", "spend": 5000.0, "leads": 250},
    {"channel": "email", "spend": 2000.0, "leads": 80},
    {"channel": "social", "spend": 1000.0, "leads": 50}
]

def rank_channels_by_cpl(campaigns: list) -> list:
    grouped = {}
    for c in campaigns:
        grouped.setdefault(c["channel"], []).append(c)
    channels = []
    for channel, items in grouped.items():
        total_spend = sum(i["spend"] for i in items)
        total_leads = sum(i["leads"] for i in items)
        avg_cpl = total_spend / total_leads if total_leads > 0 else 0.0
        channels.append({"channel": channel, "avg_cpl": avg_cpl, "total_leads": total_leads})
    return sorted(channels, key=lambda x: x["avg_cpl"])

[{"channel": "social", "avg_cpl": 20.0, "total_leads": 300}, {"channel": "email", "avg_cpl": 25.0, "total_leads": 80}][{"channel": "email", "avg_cpl": 25.0, "total_leads": 80}, {"channel": "social", "avg_cpl": 20.0, "total_leads": 300}][{"channel": "social", "avg_cpl": 10.0, "total_leads": 150}, {"channel": "email", "avg_cpl": 25.0, "total_leads": 80}][{"channel": "social", "avg_cpl": 20.0, "total_leads": 50}, {"channel": "email", "avg_cpl": 25.0, "total_leads": 80}]

4.What does safe_compute_cpl(campaign) return for each input?

def safe_compute_cpl(campaign: dict) -> float:
    try:
        cpl = float(campaign["spend"]) / float(campaign["leads"])
        return round(cpl, 2)
    except (KeyError, ValueError, ZeroDivisionError):
        return 0.0

Input A: {"spend": "N/A", "leads": 50}
Input B: {"spendd": 1250.0, "leads": 50}
Input C: {"spend": 800.0, "leads": 0}

A → 0.0, B → 0.0, C → 0.0A → ValueError, B → KeyError, C → ZeroDivisionErrorA → 0.0, B → 0.0, C → ZeroDivisionErrorA → None, B → 0.0, C → 0.0

5.[REVIEW] What does is_over_budget(1000.0, 1000.0) return?

def is_over_budget(spend: float, budget: float) -> bool:
    return spend > budget

FalseTrue0None

6.[REVIEW] What does group_by_channel(campaigns).get("email") return when campaigns is [{"name": "Email A", "channel": "email"}] and group_by_channel uses result.setdefault(c["channel"], []).append(c)? What happens if you call .get("social") on the same result?

def group_by_channel(campaigns: list) -> dict:
    result = {}
    for c in campaigns:
        result.setdefault(c["channel"], []).append(c)
    return result

get("email") → [{"name": "Email A", "channel": "email"}], get("social") → Noneget("email") → [{"name": "Email A", "channel": "email"}], get("social") → KeyErrorget("email") → "email", get("social") → Noneget("email") → [{"name": "Email A", "channel": "email"}], get("social") → []

Day 29 · ~12 min●

That's the pattern click. You stopped guarding the inputs and started handling the failures. And the list comprehension on Day 24?

I did. And I understood every line. Six questions — let's close it out.

Four from this week, two from earlier to keep those patterns sharp. The track is behind you now.