Tangy is building your next step
Shaping the lesson around what you want to make.
Tangy is building your next step
Shaping the lesson around what you want to make.
Computer Science
Turn familiar Python syntax into useful data analysis. You will build a runnable Jupyter notebook that cleans, explores, combines, and explains a small retail dataset with pandas, ending with evidence-based findings and clear charts.
From Python Data to a DataFrame
3 lessonsUse familiar Python ideas to begin a self-contained retail analysis and learn only the pandas model needed to work with tabular data.
Make the Data Trustworthy
4 lessonsTurn a raw table into an analysis-ready table by selecting useful fields, checking quality problems, and creating a reliable revenue measure.
Answer Questions with Summaries
4 lessonsUse pandas summaries to move from rows of data to evidence about products, regions, order status, and monthly performance.
Combine Tables and Explain the Evidence
4 lessonsAdd customer context, visualize the strongest findings, and turn the notebook into a polished, reproducible project someone else can run and understand.
Public lesson
You’ll create a small workspace, open it in JupyterLab, and check that Python can see both data files. No pandas yet—the first check uses Python you already know.
Tasks
Open a terminal and run:
mkdir orders-notebook
cd orders-notebook
python -m jupyter lab
mkdir orders-notebook
cd orders-notebook
python -m jupyter lab
The folder and launch command are just setup; JupyterLab should open in your browser.
Check: In the JupyterLab file browser, the current location should be the empty orders-notebook folder.
If python -m jupyter lab does not work, try:
jupyter lab
jupyter lab
Tasks
In JupyterLab:
orders_check.ipynb.You should now see a notebook with an empty code cell.
Tasks
Use the Upload Files button in the JupyterLab file browser. Select both provided files:
orders.csvcustomers.csvAfter the upload finishes, the two filenames should appear beside orders_check.ipynb.
Check: You should see all three files:
orders_check.ipynb
orders.csv
customers.csv
orders_check.ipynb
orders.csv
customers.csv
Keep the spelling and capitalization exactly as shown. Python will use these names later to find the data.
Tasks
In the first notebook cell, enter three numbers of your choice and calculate their total:
items = [____, ____, ____]
total = sum(items)
print("Items:", items)
print("Total:", total)
items = [____, ____, ____]
total = sum(items)
print("Items:", items)
print("Total:", total)
Replace each blank with a number, then run the cell with Shift + Enter.
Check: The output should show your list and the correct sum. For example, if you chose 4, 6, and 10, the total should be 20.
Create a new cell. This uses Python’s built-in pathlib module, so it does not require pandas:
Tasks
from pathlib import Path
files_to_check = [____, ____]
for filename in files_to_check:
file_path = Path(filename)
print(filename, "found:", file_path.exists())
from pathlib import Path
files_to_check = [____, ____]
for filename in files_to_check:
file_path = Path(filename)
print(filename, "found:", file_path.exists())
Fill the list with the two filenames as strings, then run the cell. The finished list should contain:
["orders.csv", "customers.csv"]
["orders.csv", "customers.csv"]
Check: Both lines should end with:
found: True
found: True
Find the bug
Something's wrong — can you spot it?
If either says False, look at the JupyterLab file browser and compare the filename carefully. A file in a different folder, or a spelling difference such as Orders.csv, will not match.
Tasks
Leave this notebook open with both checks visible.
4 modules · 15 lessons
From Python Data to a DataFrame
Make the Data Trustworthy
Answer Questions with Summaries
Combine Tables and Explain the Evidence
Learn by building your own version.
Remix this public project to open the workspace, follow the guided build, and let the AI mentor teach you through the work instead of doing it for you.