Beginner Python Project · Learn by Building

Build the tool.
Learn the Python.

You will build a real spreadsheet auditing and cleaning tool — step by step, layer by layer. Every line of code explained. No experience needed.

Real output from day one.  ·  Guided comments throughout  ·  Plain-English docs

python cleaner.py sample_data/messy_customers.xlsx
──────────────────────────────────────────
SPREADSHEET QUALITY REPORT
──────────────────────────────────────────

File: messy_customers.xlsx
Rows: 847  ·  Columns: 9

✓ customer_id — complete (847/847)
✓ email — complete (847/847)
⚠ phone_number — 34 missing (813/847)
⚠ signup_date — 12 inconsistent formats
✖ region — 91 missing (756/847)
⚠ duplicate rows — 6 found

──────────────────────────────────────────
Overall quality score: 74%
Issues found: 143 cells need attention
──────────────────────────────────────────

A real tool.
Layer by layer.

Each layer builds on the last. Each one gives you a working result you can run and show someone. You never write code that doesn't do something.

01
Basic — Quality Report Read a spreadsheet. Print a plain-English audit: missing cells, row counts, column names. Your first working version.
02
Cleaning — Fix the Data Trim whitespace. Standardize date formats. Flag duplicates. Write the clean version to a new file. The tool now does work.
03
Logging — Track What Changed Every change gets logged with a timestamp. Run the tool twice — the log tells you exactly what happened each time.
04
App — Desktop Interface Wrap everything in a simple desktop window. File picker, run button, status output. A real app you built yourself.

Python Skills You'll Use

Every concept is introduced when you need it — not before.

pandas openpyxl pathlib logging tkinter datetime functions file I/O error handling data types

What You Walk Away With

A working Python project written by you, that you can explain, run, and extend. Something concrete to show on a resume or in an interview.

Data Skills You'll Practice

Missing value detection, duplicate identification, format standardization, data quality scoring — the same work real analysts do every day.

Data Quality Cleaning Audit Trails Standardization

You don't need experience.
You need a project.

The best way to learn Python is to build something real. This is that something.

📊

Excel Users Learning Python

You already know what "clean data" means. You've done it in Excel. This project takes that knowledge and translates it into code you write and own.

🎓

Students & Bootcamp Grads

You've done tutorials. You know the syntax. But building something from scratch — with a real use case and real output — is different. This bridges that gap.

💼

Analysts Who Want to Code

Your job involves cleaning spreadsheets. Imagine automating it. This project builds the tool that does exactly that — in the language employers want to see.

Start in five minutes.

Python 3.8+ and two packages. The repo does the rest.

1
Clone the repo Or download the ZIP directly from GitHub
2
Install the requirements pip install -r requirements.txt — pandas and openpyxl
3
Run it on the sample data python basic/cleaner.py sample_data/messy_customers.xlsx
4
Read the code, change something, run it again Every file has guided comments. That's how you learn.
terminal
# clone
$ git clone https://github.com/michaelnocito/spreadsheet-cleaner
$ cd spreadsheet-cleaner

# install
$ pip install -r requirements.txt

# run on sample data
$ python basic/cleaner.py \
    sample_data/messy_customers.xlsx

✔ Quality report printed. You built that.