Python Intermediate Lesson 10: Project – A Monthly Report Pipeline

📎 This article includes 3 downloadable practice files ↓

📘 Python Intermediate Course · Lesson 10 of 10

The whole course in one script: drop next month’s CSV into monthly/, run, and the report is ready.

The shape of a good pipeline

def load(folder):      # 1. read every file, remember where each row came from
    frames = [pd.read_csv(f).assign(source=f.name) for f in sorted(folder.glob("sales-*.csv"))]
    return pd.concat(frames, ignore_index=True)

def enrich(df):        # 2. join products and customers, add amount, GST, month
    ...

df = enrich(load(HERE / "monthly"))
kpi = ...              # 3. summarise
with pd.ExcelWriter(out / "monthly-report.xlsx") as xw:   # 4. write
    kpi.to_frame("value").to_excel(xw, sheet_name="KPIs")
    by_month.to_excel(xw, sheet_name="By month")

Small functions with one job each are easy to test and easy to change: a new data source only touches load.

Make it production-ready

• Logging (lesson 3): the row count per file catches a missing or half-downloaded month.
• Paths from HERE (lesson 1) so Task Scheduler can run it.
• Options (lesson 9): --month to rebuild a past report.
• Formatting (lesson 7): open the written file with openpyxl afterwards to add styles and a chart.

Next: read straight from a database with pd.read_sql (see the SQL Intermediate Course) instead of CSV exports.

Practice

Unzip the practice data into a folder, put this lesson’s script in the same folder and run it (python pyi-NN.py). Compare with the expected output file. Then change one thing (a filter, a column, a format) and predict the new output before you run it.