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Computer Science

Practical Tabular Analysis with pandas

PythonpandasJupyterLab

Practical Tabular Analysis with pandas

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.

4 modules15 lessonsComputer SciencePythonpandasJupyterLabMatplotlibCSV

What you learn by building this

Load CSV files into pandas DataFrames and inspect their structure
Select rows and columns using readable pandas expressions
Detect and handle missing values, duplicate records, and incorrect data types
Create derived columns from existing tabular data
Summarize data with groupby, aggregation, sorting, and value counts
Compare segments across time, products, and customer regions

Learning Journey

From Python Data to a DataFrame

3 lessons

Use 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 lessons

Turn 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 lessons

Use 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 lessons

Add customer context, visualize the strongest findings, and turn the notebook into a polished, reproducible project someone else can run and understand.

Public lesson

Set up the notebook and project data

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:

Click Python 3 under Notebook.
Rename the new notebook to 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.csv
customers.csv

After 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.

Course Outline

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.