Mini project: clean a messy orders file

Time to put it together. A vendor sends you orders.csv. Clean it and write orders_clean.json. This is a tiny ETL job: extract, transform, load.

id	amount	city
`"1"`	`" 499 "`	`"pune"`
`"2"`	`"N/A"`	`"Delhi"`
`"1"`	`" 499 "`	`"pune"`
`"3"`	`"1299"`	`" Mumbai"`

Read the CSV into a list of dicts.
Trim the city and fix its case.
Convert amount to an int. Send rows that fail to rejects.
Drop rows whose id you've already seen.
Write the clean rows to JSON and print a summary.

python

import csv, json

def clean_row(row):

    return {

        "id": int(row["id"]),

        "amount": int(row["amount"].strip()),

        "city": row["city"].strip().title(),

}

clean, rejects, seen = [], [], set()

with open("orders.csv") as f:

    for row in csv.DictReader(f):

        try:

            r = clean_row(row)

        except ValueError:

            rejects.append(row)

            continue

        if r["id"] in seen:

            continue

        seen.add(r["id"])

        clean.append(r)

with open("orders_clean.json", "w") as f:

    json.dump(clean, f, indent=2)

print(f"{len(clean)} clean, {len(rejects)} rejected")

OUTPUT

2 clean, 1 rejected

on the job This shape (read, clean each row, quarantine failures, dedupe, write) is the same one you'll build later with pandas, Spark and Airflow. Only the tools change.

LABDE-142· hands-on

Orders double-counted

Fix a real ticket in a small codebase you didn't write: read the code, change only the functions you're given, pass the tests, then submit for hidden checks and a score.

Open the lab →