Skip to content

Module 07 / Project 05 — Analysis Report

README · Module Index

Learn Your Way

Read Build Watch Test Review Visualize Try
— This project — — Flashcards — —

Focus

  • Complete analysis pipeline: load, clean, analyze, visualize, export
  • Date parsing and monthly grouping with pd.to_datetime() and dt.to_period()
  • Revenue calculations and ranking
  • Customer segmentation by purchase frequency
  • Exporting a text summary report

Why this project exists

This project ties together everything you learned in Projects 01 through 04 into a single realistic workflow. In a real job, you would receive a CSV of transaction data and be asked: "What is our monthly revenue trend? Which products sell best? Who are our top customers?" This project teaches you to answer those questions end to end — from raw file to finished report and chart.

Run

cd projects/modules/07-data-analysis/05-analysis-report
python project.py

Expected output

=== Step 1: Load transaction data ===
Loaded 200 transactions from data/transactions.csv

=== Step 2: Clean and prepare ===
Parsed dates. Date range: 2024-01-03 to 2024-06-30
Added revenue column (quantity * price)

=== Step 3: Monthly revenue ===
month
2024-01    $849.38
2024-02    $812.42
2024-03    $802.44
2024-04    $786.48
2024-05    $802.44
2024-06    $816.40
Total revenue: $4,869.56

=== Step 4: Top products by revenue ===
product
Bluetooth Speaker    $2,049.59
Wireless Mouse       $1,409.53
Desk Lamp            $1,189.66
Notebook Set           $363.72
...

=== Step 5: Customer segments ===
Segment           Customers  Avg Orders
One-time buyer          25        1.0
Occasional (2-3)        20        2.3
Regular (4+)             5        5.2

=== Step 6: Save visualization ===
Saved summary chart to data/summary_chart.png

=== Step 7: Export report ===
Saved report to data/report.txt

Done. Analysis complete.

The exact numbers depend on the transaction data. Open data/report.txt for the full summary and data/summary_chart.png for the visualization.

Alter it

  1. Add a "day of week" analysis: which weekday has the most sales? (Hint: df["date"].dt.day_name().)
  2. Calculate the average order value (total revenue / number of orders) per month.
  3. Add a "growth rate" column showing the percentage change in revenue from month to month. (Hint: pct_change().)
  4. Create a pie chart showing revenue share by product.

Break it

  1. Change the date column name in the CSV. What error do you get during parsing?
  2. Remove the pd.to_datetime() call and try to group by month. What happens?
  3. Try to save the report to a directory that does not exist. Read the error.

Fix it

  1. Check that the expected columns exist before processing: if "date" in df.columns.
  2. Always convert date strings to datetime objects before doing date arithmetic.
  3. Use os.makedirs() to create the output directory if it does not exist.

Explain it

  1. Why do you need pd.to_datetime() when pandas already loaded the date column?
  2. What does dt.to_period("M") do? How is it different from dt.month?
  3. What is a customer segment? Why would a business care about segmenting customers?
  4. Why is it better to save the report as a text file than just print it to the console?

Mastery check

You can move on when you can:

  • Build a complete analysis pipeline from raw CSV to finished report.
  • Parse dates and group data by month, week, or day.
  • Calculate revenue, rank products, and segment customers.
  • Create a summary visualization and save it to a file.
  • Export a text report that a non-technical person could read and understand.

Next

You have completed Module 07. Return to the Module Index to choose your next module.