Lists, Tuples, Sets, and Dictionaries

Concept

Collections store multiple values. Python's four everyday collections have different strengths: lists preserve an editable order, tuples preserve an ordered fixed grouping, sets keep unique values, and dictionaries connect keys to values.

Why It Matters

Data Science code uses collections constantly: a list of feature names, a tuple containing dataset dimensions, a set of unique categories, or a dictionary of model metrics. Choosing the collection that matches the meaning makes code simpler and prevents mistakes.

Intuition

Use a list for a changeable checklist, a tuple for a fixed pair or record shape, a set for a bag of unique labels, and a dictionary for a labeled lookup table.

Lists

Lists are ordered and mutable. Indexing starts at zero; slices select a range without including the final position.

feature_names = ["age", "tenure", "monthly_charge"]
print(feature_names[0])
print(feature_names[1:])

feature_names.append("support_calls")
feature_names.remove("tenure")
print(len(feature_names))

Expected output:

age
['tenure', 'monthly_charge']
3

The final list is ['age', 'monthly_charge', 'support_calls']. Use a list when order matters and you expect to add or remove items.

Transformation Versus In-Place Mutation

Because lists are mutable, it matters whether you create a new result or change the original object.

prices = [100, 200, 300]
taxed_prices = [round(price * 1.1, 2) for price in prices]

print(prices)
print(taxed_prices)

Expected output:

[100, 200, 300]
[110.0, 220.0, 330.0]

This transformation returns a new list and leaves prices unchanged. In contrast, list methods such as append and remove, or assigning prices[index] = value, mutate the existing list. Mutation is useful when intended; the important question is whether callers expect the original object to change.

Tuples

Tuples are ordered but immutable: their contents cannot be changed after creation.

dataset_shape = (1500, 24)
row_count, column_count = dataset_shape
print(row_count, column_count)

Expected output: 1500 24. A tuple is useful for a fixed grouping such as dimensions or a coordinate. Its immutability signals that the grouping should not be edited in place.

Sets

Sets keep unique values and support fast membership checks. They do not preserve a dependable display order.

observed_categories = {"basic", "premium", "basic", "enterprise"}
print(observed_categories)
print("premium" in observed_categories)

The printed ordering may vary, but duplicates are removed and the membership check returns True. Sets are useful for unique categories and checking whether a feature has already been seen.

Dictionaries

Dictionaries store key/value pairs. Use a key to retrieve, add, or update a related value.

model_metrics = {
    "accuracy": 0.91,
    "precision": 0.84,
    "recall": 0.78,
}

print(model_metrics["precision"])
model_metrics["f1_score"] = 0.81
model_metrics["accuracy"] = 0.92

Expected output: 0.84. The keys communicate what each metric means, which is safer than relying on positions in a list.

Shared-Reference Behavior

Two names can point to the same mutable object.

metrics = {"accuracy": 0.91, "precision": 0.84}
alias = metrics
alias["precision"] = 0.50

print(metrics)

Expected output: {'accuracy': 0.91, 'precision': 0.5}. Updating alias also changes metrics because they refer to the same dictionary. The same idea applies when a list or dictionary is passed into a function.

Real-World / Data Science Example

An analysis may need each collection type for a different reason.

feature_names = ["age", "income", "tenure"]
train_shape = (10000, 3)
unique_regions = {"north", "south", "north"}
metrics = {"precision": 0.86, "recall": 0.79}

print(feature_names)
print(train_shape)
print(unique_regions)
print(metrics["precision"])

The collection choice documents the data's meaning: editable feature order, fixed shape, unique labels, and named scores.

Explanation of the Code

List methods such as append and remove change a list. Dictionary assignment changes a dictionary in place. Tuple unpacking assigns each position to a name. Set literals use braces but contain values only; dictionary literals also use braces but contain key: value pairs. Access a dictionary value with square brackets and its key.

When to Use It

Use lists for ordered, editable items; tuples for fixed grouped values; sets for uniqueness and membership; dictionaries for named attributes or summaries. Do not select a type just because it can hold the values: select it because its behavior matches the job.

Best Practices

Use descriptive dictionary keys and avoid relying on numeric positions for important metrics. Use .get("key") when a dictionary key may be absent. Convert a set to a sorted list before presenting it to a user when stable order matters. When working with a mutable collection, be clear whether you are intentionally modifying it or returning a transformed copy.

Data Science Perspective

These collections prepare you for later tools. Feature-name lists become DataFrame columns, tuples appear in array shapes, sets help inspect unique categories, and dictionaries hold configurations, metric reports, and mappings for categorical values.

Interview Perspective

Question: When would you choose a set over a list? A strong answer: when uniqueness or frequent membership checking matters and order is not the main requirement.

Worked Example: Safely Read a Metric Summary

Use .get() when a metric may not be present in every evaluation run.

metrics = {"accuracy": 0.90, "precision": 0.82}
recall = metrics.get("recall")

if recall is None:
    print("Recall was not reported")
else:
    print(recall)

Expected output: Recall was not reported. This avoids a KeyError while making the missing metric explicit.

Practice Questions

1. Create a list of three feature names and append a fourth.
2. Store (rows, columns) for a dataset and unpack it into two names.
3. Explain the difference between returning a transformed list and mutating the original list in place.
4. Create a dictionary for accuracy, precision, and recall, then update recall.

Quick Quiz

1. Which collection is immutable: list or tuple? Answer: tuple.
2. Which collection removes duplicates: set or dictionary? Answer: set.
3. Which collection types in this lesson are mutable? Answer: lists and dictionaries.
4. How do you access accuracy in metrics? Answer: metrics["accuracy"].

Next Lesson

You now have the Python foundations needed to begin working with numerical arrays and tabular data.