Capstone: From Raw Rows to Insight Report

18 min 45 XP

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Lesson 5 of 5 · View course roadmap

Step 1

Learn the idea

Time to run the full pipeline every analyst runs, whatever the tool:

Parse: split each "region,amount" row into fields
Clean: convert amounts, quarantine garbage with try/except
Aggregate: total by region with the dict accumulator
Report: row count, grand total, top region

Parse → clean → aggregate → report is the same skeleton whether the data is 6 rows in a list or 6 billion in a warehouse; only the tools scale up. Ship this and you've done real analysis: the kind that answers an actual business question.

Where you'll use this

This is the nightly job at thousands of companies: parse yesterday's transactions, skip the garbage, total by region, email the summary. You've written the core of a BI pipeline in ~15 lines.

Common mistakes

Counting a row as valid before it survives conversion: increment inside the try, after int() succeeds.
row.split(",") assuming exactly two fields: a stray comma shifts everything; in real work check the field count.
Reporting totals but not the denominator: '490 revenue' means little without 'from 5 valid rows of 6'.

Pro tip

Structure real pipelines as three functions (parse(rows), aggregate(records), report(totals)) so each stage is testable alone. The csv module then replaces your split(",") for free.

Step 2

Try it yourself

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PYexample.py

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Output appears here…

Step 3

Pass the challenge +45 XP

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Analyse rows = ["north,120", "south,95", "north,80", "west,x", "east,150", "south,45"], skipping rows whose amount isn't a number. Print three lines: the number of valid rows, the grand total, and the region with the highest total.

Target output

5
490
north

PYchallenge.py

rows = ["north,120", "south,95", "north,80", "west,x", "east,150", "south,45"]
totals = {}
valid = 0
for row in rows:
    region, raw_amount = row.split(",")
    try:
        amount = int(raw_amount)
    except ValueError:
        continue
    valid += 1
    totals[region] = totals.get(region, 0) + amount
print(valid)
print(sum(totals.values()))
print(max(totals, key=totals.get))

Run your code to check it…

Step 4

Check your understanding

1. Put the analysis pipeline in order: report → parse → clean → aggregate parse → clean → aggregate → report aggregate → parse → report → clean clean → report → parse → aggregate

2. Why 'continue' on a bad row instead of letting it crash? continue is faster Real datasets always contain garbage; the report must still ship Python requires it To keep the row for later

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