The Tool Desk
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What is the difference between a list, tuple, set, and dictionary in Python?
The choice comes down to what you need the data to do: preserve sequence, map keys to values, enforce uniqueness, or keep a group of values fixed. This project uses the collections in those roles instead of trying to force every kind of data into one structure.
| Collection | Order | Mutable? | Distinctness | Expense tracker role |
|---|---|---|---|---|
list |
Sequence order | Yes | Duplicates allowed | Ordered transactions |
dict |
Insertion order is guaranteed in Python 3.7 and later | Yes | Keys are unique | Named transaction fields and category totals |
set |
Unordered | Yes | Elements are unique | Unique categories and membership checks |
tuple |
Sequence order | No | Duplicates allowed | Fixed groups of values |
The Python Software Foundation’s Python 3.14.8 tutorial describes a set as “an unordered collection with no duplicate elements.” Use a set when uniqueness or membership matters, not when display order does. Tuples are useful for fixed groupings; they can be dictionary keys only if all their contents are hashable. For named, editable fields such as an expense’s category and amount, a dictionary is usually clearer.
How do I use Python lists and dictionaries in an expense tracker?
Represent the transaction history as a list of dictionaries. The list retains the order in which records are stored, and each dictionary makes a transaction’s named fields easy to read and update.
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expenses = [
{
"date": "2026-10-04",
"category": "food",
"description": "lunch",
"amount": "12.34",
}
]
new_expense = {
"date": "2026-10-04",
"category": "transport",
"description": "bus fare",
"amount": "2.50",
}
expenses.append(new_expense)
for expense in expenses:
print(expense["date"], expense["category"], expense["description"], expense["amount"])Each record has the same four fields, and the amount is intentionally stored as text. Convert it to a numeric type only when doing arithmetic. append() adds a record to the end of the list; iterating over the list displays records in their stored sequence. Python’s data-structures tutorial also documents list operations such as remove() and pop(), as well as comprehensions for creating filtered or transformed lists.
Check required fields before using them
Dictionary subscripting, as in expense["amount"], raises KeyError if that key is missing. If absence is expected, check membership or use get() deliberately. For example, expense.get("category") returns None when the field is absent; it does not make an incomplete record valid. Validate required fields before storing or calculating from a transaction.
required = {"date", "category", "description", "amount"}
missing = required - expense.keys()
if missing:
raise ValueError(f"Missing fields: {', '.join(sorted(missing))}")This validation uses a set to compare field names without depending on their order. Sorting the missing names gives a stable message for display.
How do I calculate totals by category in Python?
Use a dictionary whose keys are categories and whose values are running totals. Convert each amount from its stored decimal string to Decimal before adding it.
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from decimal import Decimal
totals = {}
for expense in expenses:
category = expense["category"]
amount = Decimal(expense["amount"])
totals[category] = totals.get(category, Decimal("0")) + amount
for category in sorted(totals):
print(category, totals[category])totals.get(category, Decimal("0")) supplies zero for a category that has not appeared yet; a direct subscript of a missing key would raise KeyError. The final loop sorts dictionary keys to produce alphabetical output. The dictionary itself preserves insertion order in Python 3.7 and later, but alphabetical order is a separate display choice.
How should I handle money in Python?
Use Decimal created from strings for exact decimal currency arithmetic. Binary floating-point cannot exactly represent many decimal values; for example, decimal values such as 1.1 and 2.2 do not have exact binary floating-point representations. Python’s Decimal documentation identifies Decimal as preferred for accounting applications that need strict equality invariants.
Do not first parse the amount as a float and then wrap that result in Decimal: the binary approximation would already have entered the calculation. Keeping input as text, as in "12.34", lets Decimal(expense["amount"]) construct the intended decimal value.
Make the rounding rule explicit when displaying fixed-place currency. For example, this rounds to two decimal places using Decimal’s configured rounding context:
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from decimal import Decimal
shown_total = totals["food"].quantize(Decimal("0.01"))
print(shown_total)The example specifies two places, but does not choose a universal business rounding policy. A real tracker should choose and document its rule where amounts are accepted or displayed, especially if its currency or accounting requirements differ.
When should I use a set or tuple in the tracker?
Use a set for unique categories or membership
A set removes duplicates and supports membership checks. Derive distinct category names from the transaction list, then sort them if the user-facing output needs a predictable alphabetical order.
categories = {expense["category"] for expense in expenses}
for category in sorted(categories):
print(category)The set is not the place to keep transaction order. Convert or sort its values for display instead of relying on an iteration order.
Use a tuple only for a fixed grouping
A tuple can represent a fixed positional grouping, such as coordinates or a pair of values used as a key when all members are hashable. For an expense record with named fields that may need validation or editing, a dictionary communicates the meaning of each value more clearly. A tuple’s immutability is useful when the grouping should not change.
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How do I save expense data to a CSV or JSON file in Python?
Choose a format according to the shape and intended use of the data. CSV fits rows and columns and is convenient for spreadsheet-compatible transaction records. JSON is suited to structured data, including nested values. Neither file format by itself provides privacy, encryption, backup, or safe simultaneous multi-user access.
Save tabular transactions as CSV
Python’s CSV module includes DictWriter for writing dictionaries as rows and DictReader for reading rows back as dictionaries. Keep the amount as text so it can later be converted directly to Decimal.
import csv
fields = ["date", "category", "description", "amount"]
with open("expenses.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.DictWriter(file, fieldnames=fields)
writer.writeheader()
writer.writerows(expenses)
with open("expenses.csv", newline="", encoding="utf-8") as file:
loaded_expenses = list(csv.DictReader(file))CSV rows are textual data when read, so the amount remains suitable for Decimal(loaded_expense["amount"]) rather than being interpreted as a float.
Save structured data as JSON
Python’s standard-library JSON module serializes and parses JSON. A list of transaction dictionaries maps naturally to JSON, and the module preserves input and output order by default when the underlying containers are ordered.
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import json
with open("expenses.json", "w", encoding="utf-8") as file:
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with open("expenses.json", encoding="utf-8") as file:
loaded_expenses = json.load(file)JSON preserves the amount strings in this example. Convert them to Decimal when calculating totals, just as with CSV input. CSV remains the more direct fit if users primarily need to inspect or edit rows in spreadsheet software; JSON is useful when the saved structure grows beyond a simple table.
When are other Python collections useful?
Keep the core tracker simple until its behavior calls for another collection. A list is fine for appending and iterating through transactions. A deque becomes relevant when a feature repeatedly adds or removes items at both ends, such as queue behavior. Python documents fast operations at both ends for collections.deque; removing or inserting at the front of a list requires moving the remaining elements and takes O(n) work.
For ordinary tracker summaries, category dictionaries, sorted output, and simple list comprehensions are enough. Introducing a specialized structure without a corresponding operation makes the data flow harder to understand rather than improving the design.
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