Where did my salary go? Track spending with Python

Your bank app lists payments one by one. What you actually want to know is where the money went: how much on food, how much on travel, and whether you stayed under budget.

This program takes a month of payments, adds them up per category, draws a bar chart in plain text and compares the total with your budget. Type your own budget below and press Run.

Where did my salary go?

Turn a month of UPI payments into a spending chart, and spot the leak.

Monthly budget (₹)

→ press Run. The first run downloads Python (~4 MB, cached after that).

The Python behind it · 28 lines

1budget = 30000  # your monthly budget in ₹
2
3# One month of UPI payments (sample data, in your own app you'd load the bank CSV)
4payments = [
5    ("Swiggy", "Food", 420), ("Zomato", "Food", 610), ("Uber", "Travel", 280),
6    ("Rent", "Home", 12000), ("Blinkit", "Groceries", 1890), ("Netflix", "Fun", 649),
7    ("Swiggy", "Food", 380), ("Metro card", "Travel", 500), ("Amazon", "Shopping", 2399),
8    ("Zomato", "Food", 545), ("Electricity", "Home", 1430), ("Movie", "Fun", 900),
9    ("Blinkit", "Groceries", 1240), ("Ola", "Travel", 340), ("Myntra", "Shopping", 1799),
10]
11
12totals = {}
13for shop, category, amount in payments:
14    totals[category] = totals.get(category, 0) + amount
15
16spent = sum(totals.values())
17for category, amount in sorted(totals.items(), key=lambda kv: -kv[1]):
18    bar = "█" * round(amount / spent * 40)
19    print(f"{category:<10} ₹{amount:>6,}  {bar}")
20
21print(f"\nSpent ₹{spent:,} of ₹{budget:,}")
22if spent > budget:
23    extras = {c: a for c, a in totals.items() if c != "Home"}
24    print(f"⚠ Over budget by ₹{spent - budget:,}. Biggest spend after rent: {max(extras, key=extras.get)}")
25else:
26    print(f"✓ You saved ₹{budget - spent:,} this month")
27food = [a for s, c, a in payments if c == "Food"]
28print(f"Food delivery: {len(food)} orders, ₹{sum(food):,} — about ₹{sum(food) * 12:,} a year")

How the code works, step by step

1.Store each payment as a tuple

Every payment is (shop, category, amount). A list of tuples is the simplest way to hold rows of data. In a real app you would read these rows from your bank's CSV statement instead of typing them.

4payments = [
5    ("Swiggy", "Food", 420), ("Zomato", "Food", 610), ("Uber", "Travel", 280),
6    ("Rent", "Home", 12000), ("Blinkit", "Groceries", 1890), ("Netflix", "Fun", 649),
7    ("Swiggy", "Food", 380), ("Metro card", "Travel", 500), ("Amazon", "Shopping", 2399),
8    ("Zomato", "Food", 545), ("Electricity", "Home", 1430), ("Movie", "Fun", 900),
9    ("Blinkit", "Groceries", 1240), ("Ola", "Travel", 340), ("Myntra", "Shopping", 1799),
10]

2.Add up each category with a dictionary
The dictionary maps a category to its running total. totals.get(category, 0) returns 0 the first time a category appears, so you never need a separate check.
```
12totals = {}
13for shop, category, amount in payments:
14    totals[category] = totals.get(category, 0) + amount
```
3.Draw the bar chart
Sorting by the negative amount puts the biggest category first. Each bar is the █ character repeated in proportion to that category's share of the total, and the f-string pads the columns so they line up.
```
16spent = sum(totals.values())
17for category, amount in sorted(totals.items(), key=lambda kv: -kv[1]):
18    bar = "█" * round(amount / spent * 40)
19    print(f"{category:<10} ₹{amount:>6,}  {bar}")
```

4.Compare with your budget

An if/else decides the message. A list comprehension pulls out just the food orders, and multiplying by 12 turns one month into a yearly figure, which is usually the number that makes people change a habit.

21print(f"\nSpent ₹{spent:,} of ₹{budget:,}")
22if spent > budget:
23    extras = {c: a for c, a in totals.items() if c != "Home"}
24    print(f"⚠ Over budget by ₹{spent - budget:,}. Biggest spend after rent: {max(extras, key=extras.get)}")
25else:
26    print(f"✓ You saved ₹{budget - spent:,} this month")
27food = [a for s, c, a in payments if c == "Food"]
28print(f"Food delivery: {len(food)} orders, ₹{sum(food):,} — about ₹{sum(food) * 12:,} a year")

Make it yours

Download your bank statement as CSV and replace the payments list with the csv module.
Add a category limit, e.g. warn when Food goes over ₹3,000.
Run it every month and keep the totals in a file to see the trend.

The lessons that teach this, in order

1. Dictionaries 2. Lists & Looping 3. Files & CSV in memory

Build it yourself, step by step Start with Dictionaries

Questions

Do I need pandas for this?: No. This version uses only built-in Python: a list, a dictionary and a loop. pandas helps once you have thousands of rows or several months, and the same idea becomes a one-line groupby.
Is the payment data real?: It is sample data typed into the program. Replace it with your own statement to get your real numbers; nothing leaves your browser when you run it here.

Where did my salary go? Track spending with Python

Your bank app lists payments one by one. What you actually want to know is where the money went: how much on food, how much on travel, and whether you stayed under budget.

This program takes a month of payments, adds them up per category, draws a bar chart in plain text and compares the total with your budget. Type your own budget below and press Run.

1budget = 30000 # your monthly budget in ₹

3# One month of UPI payments (sample data, in your own app you'd load the bank CSV)

4payments = [

5 ("Swiggy", "Food", 420), ("Zomato", "Food", 610), ("Uber", "Travel", 280),

6 ("Rent", "Home", 12000), ("Blinkit", "Groceries", 1890), ("Netflix", "Fun", 649),

7 ("Swiggy", "Food", 380), ("Metro card", "Travel", 500), ("Amazon", "Shopping", 2399),

8 ("Zomato", "Food", 545), ("Electricity", "Home", 1430), ("Movie", "Fun", 900),

9 ("Blinkit", "Groceries", 1240), ("Ola", "Travel", 340), ("Myntra", "Shopping", 1799),

10]

12totals = {}

13for shop, category, amount in payments:

14 totals[category] = totals.get(category, 0) + amount

16spent = sum(totals.values())

17for category, amount in sorted(totals.items(), key=lambda kv: -kv[1]):

18 bar = "█" * round(amount / spent * 40)

19 print(f"{category:<10} ₹{amount:>6,} {bar}")

21print(f"\nSpent ₹{spent:,} of ₹{budget:,}")

22if spent > budget:

23 extras = {c: a for c, a in totals.items() if c != "Home"}

24 print(f"⚠ Over budget by ₹{spent - budget:,}. Biggest spend after rent: {max(extras, key=extras.get)}")

25else:

26 print(f"✓ You saved ₹{budget - spent:,} this month")

27food = [a for s, c, a in payments if c == "Food"]

28print(f"Food delivery: {len(food)} orders, ₹{sum(food):,} — about ₹{sum(food) * 12:,} a year")

How the code works, step by step

1.Store each payment as a tuple

Every payment is (shop, category, amount). A list of tuples is the simplest way to hold rows of data. In a real app you would read these rows from your bank's CSV statement instead of typing them.

4payments = [
5    ("Swiggy", "Food", 420), ("Zomato", "Food", 610), ("Uber", "Travel", 280),
6    ("Rent", "Home", 12000), ("Blinkit", "Groceries", 1890), ("Netflix", "Fun", 649),
7    ("Swiggy", "Food", 380), ("Metro card", "Travel", 500), ("Amazon", "Shopping", 2399),
8    ("Zomato", "Food", 545), ("Electricity", "Home", 1430), ("Movie", "Fun", 900),
9    ("Blinkit", "Groceries", 1240), ("Ola", "Travel", 340), ("Myntra", "Shopping", 1799),
10]

2.Add up each category with a dictionary

The dictionary maps a category to its running total. totals.get(category, 0) returns 0 the first time a category appears, so you never need a separate check.

12totals = {}
13for shop, category, amount in payments:
14    totals[category] = totals.get(category, 0) + amount

3.Draw the bar chart

Sorting by the negative amount puts the biggest category first. Each bar is the █ character repeated in proportion to that category's share of the total, and the f-string pads the columns so they line up.

16spent = sum(totals.values())
17for category, amount in sorted(totals.items(), key=lambda kv: -kv[1]):
18    bar = "█" * round(amount / spent * 40)
19    print(f"{category:<10} ₹{amount:>6,}  {bar}")

4.Compare with your budget

21print(f"\nSpent ₹{spent:,} of ₹{budget:,}")
22if spent > budget:
23    extras = {c: a for c, a in totals.items() if c != "Home"}
24    print(f"⚠ Over budget by ₹{spent - budget:,}. Biggest spend after rent: {max(extras, key=extras.get)}")
25else:
26    print(f"✓ You saved ₹{budget - spent:,} this month")
27food = [a for s, c, a in payments if c == "Food"]
28print(f"Food delivery: {len(food)} orders, ₹{sum(food):,} — about ₹{sum(food) * 12:,} a year")

Make it yours

Download your bank statement as CSV and replace the payments list with the csv module.

Add a category limit, e.g. warn when Food goes over ₹3,000.

Run it every month and keep the totals in a file to see the trend.

The lessons that teach this, in order

Questions

Do I need pandas for this?

No. This version uses only built-in Python: a list, a dictionary and a loop. pandas helps once you have thousands of rows or several months, and the same idea becomes a one-line groupby.

Is the payment data real?

It is sample data typed into the program. Replace it with your own statement to get your real numbers; nothing leaves your browser when you run it here.