Clean a messy Google Form sheet

Goal

Read a Google Form export with messy names, five ways of writing a phone number, duplicates and missing amounts, and print a clean table with a total.

Why it matters

Every office job has a messy sheet. Cleaning one teaches csv, regular expressions, None for missing data and de-duplication with a set: the core of real data work.

What it proves

You can take real-world messy data and make it trustworthy with a few small functions.

Steps · 6 checkpoints

Each step is one function in clean_sheet.py. In the workspace, press Check next to a step: it runs your file and tells you exactly what is still wrong. Step 1 is free; the rest are part of Pro or your 7-day free trial.

1. 1Tidy a name. Write clean_name(name): remove extra spaces and capitalise each word. ' rahul SHARMA ' becomes 'Rahul Sharma'. Hint: " ".join(name.split()) squeezes the spaces, .title() fixes the capitals.
2. 2One format for phone numbers. Write clean_phone(phone): keep only the digits, then the last 10, so +91 98200 12345, 98200-12345 and 09820012345 all become 9820012345. Fewer than 10 digits is not a phone number: return None.
3. 3Amounts as numbers. Write parse_amount(text): turn the text into a whole number, removing commas and spaces. '1,200' is 1200, ' 850 ' is 850 and an empty cell is None.
4. 4Read and clean every row. Write read_rows(raw): read the text with csv.DictReader(io.StringIO(raw)), strip the stray spaces from the column names, and return a list of dicts with keys Name, Phone, City and Amount, each cleaned with your functions. There are 7 rows.
5. 5No duplicates, no blanks. Write keep_good(rows): drop rows with no phone or no amount, and drop a row if its phone already appeared (keep the first). Rahul and Priya each appear twice; Anjali has no amount; Sneha has no valid phone. 3 rows are left.
6. 6Print the clean table. In main(), print a table of the clean rows and finish with 3 clean rows, 4 removed, total ₹4,350. The check reads everything your program printed.

Requirements · rubric

Build against this exact list — it is the spec your published project is judged on.

• 01clean_name() fixes spaces and capitals
• 02clean_phone() reduces every format to the last 10 digits, or None
• 03parse_amount() handles commas, spaces and empty cells
• 04read_rows() strips the header's stray spaces and cleans every row
• 05keep_good() drops blanks and later duplicates, keeping the order

Starter code

Copy this into a new file — or open it in a workspace as clean_sheet.py. The TODO parts are yours to fill.

"""
Form sheet cleaner.

Reads a messy Google Form export, fixes names and phone numbers, drops
duplicates and incomplete rows, and prints a clean table. Fill in one
function per step, press Run, then press "Check" on that step.
"""
import csv
import io
import re

RAW = """Name, Phone ,City,Amount
  rahul SHARMA ,+91 98200 12345,mumbai,"1,200"
Priya Nair,9820011111,Mumbai ,850
rahul sharma,98200-12345,MUMBAI,"1,200"
ANJALI rao,  080 4123 9876,bengaluru,
Vikram Singh,+91-99300-55555,Delhi,2300
priya nair,09820011111,mumbai,850
Sneha Iyer,12345,Pune,500
"""


def clean_name(name: str) -> str:
    # TODO: "  rahul   SHARMA " -> "Rahul Sharma": remove extra spaces
    # (" ".join(name.split()) does it) and capitalise each word (.title()).
    raise NotImplementedError("step 1, clean_name(): tidy spaces and capitals")


def clean_phone(phone: str) -> str | None:
    # TODO: keep only the digits (re.sub(r"\D", "", phone)), then the last 10.
    # If there are fewer than 10 digits it is not a phone number: return None.
    raise NotImplementedError("step 2, clean_phone(): last 10 digits, or None")


def parse_amount(text: str) -> int | None:
    # TODO: "1,200" -> 1200, " 850 " -> 850, "" -> None.
    raise NotImplementedError("step 3, parse_amount(): text to a number, or None")


def read_rows(raw: str) -> list[dict]:
    # TODO: read RAW with csv.DictReader(io.StringIO(raw)). The header has
    # stray spaces (" Phone "), so strip every key. Return one dict per row
    # with keys Name, Phone, City, Amount, cleaned with your three functions
    # (City uses clean_name too).
    raise NotImplementedError("step 4, read_rows(): read and clean every row")


def keep_good(rows: list[dict]) -> list[dict]:
    # TODO: drop rows whose Phone or Amount is None, and any row whose phone
    # you have already seen (keep the first one). Keep the original order.
    raise NotImplementedError("step 5, keep_good(): no duplicates, no blanks")


def main() -> None:
    print(clean_name("  rahul SHARMA "), clean_phone("+91 98200 12345"), parse_amount('1,200'))
    rows = read_rows(RAW)
    good = keep_good(rows)
    # TODO: print a table of the good rows (Name, Phone, City, Amount), then
    # "3 clean rows, 4 removed, total ₹4,350".
    raise NotImplementedError("step 6, main(): print the clean table")


if __name__ == "__main__":
    try:
        main()
    except NotImplementedError as todo:
        # A fresh starter is SUPPOSED to stop here. Say which step is next
        # instead of printing a traceback that looks like a bug.
        print(f"Not built yet: {todo}")
        print("Write that function, then press Run again. Each step you finish moves this message forward.")