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Week 3 Checkpoint: Functions, CSV, and JSON — Python For Professionals

Week 3 Checkpoint: Functions, CSV, and JSON — Python For Professionals | zuzu.codes

Day 22 · ~12 min●

Five days, five functions — make_report_line, summarize_channel, parse_campaign_csv, load_campaigns_from_csv, campaigns_to_json. Which one shifted something for you?

The def moment on Day 17. I'd been rewriting that same f-string — rank, channel, spend with a comma, CPL to two decimals — in lesson after lesson. The instant I wrapped it in a named function it felt like... I'd been doing this on purpose all week, and I didn't know it yet.

You had been. Each lesson was building the piece that the function would eventually own. What about the file parsing on Day 19 versus Day 20?

Day 20 was the reveal. I spent Day 19 manually splitting on commas and worrying about what happens if a campaign name has a comma in it. Then csv.DictReader just... handles it. Quoted commas, headers-as-keys, the whole thing. One line.

"One line" is the entire pitch for the standard library. And Day 21?

json.dumps(campaigns, indent=2) — my data, structured, readable, formatted for the CFO's dashboard. I actually texted that output to my manager as proof of concept. One line again.

Six questions — four from this week, two from Week 1 and Week 2 to keep those patterns sharp. Let's confirm the toolkit is solid.

Quiz

0/6

6 questions80% to passretry anytime

1.What does make_report_line("Email", {"total_spend": 800, "total_leads": 40, "avg_cpl": 20.0}, 2) return?

def make_report_line(channel: str, stats: dict, rank: int) -> str:
    """Return a ranked report line for one channel."""
    spend = stats["total_spend"]
    leads = stats["total_leads"]
    cpl = stats["avg_cpl"]
    result = f"#{rank} {channel} — Spend: ${spend:,} | Leads: {leads} | CPL: ${cpl:.2f}"
    return result

2.What does summarize_channel(campaigns, "email") return when campaigns contains one email campaign with spend=1000.0 and leads=0?

def summarize_channel(campaigns: list, channel: str = "all") -> tuple:
    filtered = campaigns if channel == "all" else [c for c in campaigns if c["channel"] == channel]
    total_spend = sum(c["spend"] for c in filtered)
    total_leads = sum(c["leads"] for c in filtered)
    avg_cpl = total_spend / total_leads if total_leads > 0 else 0.0
    return total_spend, total_leads, avg_cpl

(1000.0, 0, 0.0)(1000.0, 0, None)ZeroDivisionError is raised(0.0, 0, 0.0)

3.What does parse_campaign_csv(csv_text) return for the input below?

csv_text = "name,channel,spend,leads\nEmail_Blast,email,1250.0,50"

def parse_campaign_csv(csv_text: str) -> list:
    rows = csv_text.strip().split("\n")
    result = []
    for row in rows[1:]:
        parts = row.split(",")
        name    = clean_campaign_name(parts[0])
        channel = parts[1]
        spend   = float(parts[2])
        leads   = int(parts[3])
        result.append({"name": name, "channel": channel, "spend": spend, "leads": leads})
    return result

(Assume clean_campaign_name strips, lowercases, and replaces underscores with spaces.)

[{"name": "email blast", "channel": "email", "spend": 1250.0, "leads": 50}][{"name": "Email_Blast", "channel": "email", "spend": 1250.0, "leads": 50}][{"name": "email blast", "channel": "email", "spend": "1250.0", "leads": "50"}][{"name": "email blast", "channel": "email", "spend": 1250, "leads": 50}]

4.Which csv.DictReader usage correctly reads rows from a CSV string and collects them into a list of dicts?

import csv, io

csv_text = "name,channel,spend\nEmail,email,1250.0"

list(csv.DictReader(io.StringIO(csv_text)))list(csv.DictReader(csv_text))csv.DictReader(io.StringIO(csv_text)).read()list(csv.reader(io.StringIO(csv_text)))

5.[REVIEW] What does filter_high_spend(campaigns, 1250.0) return when campaigns contains one campaign with spend = 1250.0?

def filter_high_spend(campaigns, threshold):
    result = []
    for c in campaigns:
        if c["spend"] > threshold:
            result.append(c)
    return result

A list containing that campaignAn empty listTrue1250.0

6.What does json.dumps([{"name": "Email", "spend": 1250.0}], indent=2) produce?

import json

def campaigns_to_json(campaigns: list) -> str:
    return json.dumps(campaigns, indent=2)

A multi-line string with 2-space indentation and keys in quotesA Python dict with string keysA one-line string with no whitespaceA file named campaigns.json