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Python Mini Projects for Beginners: 5 Practice Projects Before Learning pandas

P13.2 Python Project 2: Bank Statement Summary from a CSV File

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Why. Banks export statements as CSV. A quick summary of money in, money out and top spending categories is a real-world task.

What. We read a CSV, convert amounts safely, and total by type and category.

How.

Summarise a statement

  1. Read rows with csv.DictReader.
  2. Convert amount with try/except; count bad rows.
  3. Add positive amounts to income, negative to spending.
  4. Total spending by category with a dictionary.
  5. Print the summary, biggest category first.

Worked example A — Read and summarise.

Step 1 — What we do. A salaried customer's March statement.

Step 2 — Make the input file statement.csv in the same folder as your code:

date,description,category,amount
2026-03-01,Salary,income,85000
2026-03-02,Rent,housing,-25000
2026-03-05,Groceries,food,-4200
2026-03-09,Electricity,bills,-1850
2026-03-12,Restaurant,food,-1600
2026-03-20,Bad row,food,abc

About this data: each line is one bank transaction: date, description, category, and amount in ₹ (positive = money in, negative = money out). One row has a bad amount on purpose.

Step 3 — Type and run this code.

import csv
income = spend = 0
by_cat = {}
bad = 0
with open("statement.csv", newline="", encoding="utf-8") as f:
    for row in csv.DictReader(f):
        try:
            amt = float(row["amount"])
        except ValueError:
            bad += 1
            continue
        if amt > 0:
            income += amt
        else:
            spend += -amt
            by_cat[row["category"]] = by_cat.get(row["category"], 0) + -amt
print(f"Income: ₹{income:,.0f}  Spending: ₹{spend:,.0f}  Saved: ₹{income - spend:,.0f}")
for cat, amt in sorted(by_cat.items(), key=lambda kv: kv[1], reverse=True):
    print(f"  {cat:<8} ₹{amt:,.0f}")
print("Bad rows skipped:", bad)

Step 4 — Output you should see.

Income: ₹85,000  Spending: ₹32,650  Saved: ₹52,350
  housing  ₹25,000
  food     ₹5,800
  bills    ₹1,850
Bad rows skipped: 1

Step 5 — What it means. Files + errors + dictionaries + sorting in one small script. pandas will make this even shorter.

Ravindra Bagale's Tip

Always print how many rows were skipped. A summary that hides bad data is a dangerous summary.