Lesson Objectives
By the end of this lesson, you will be able to:
- Understand what Python collections are and why they matter
- Use lists, tuples, sets, and dictionaries appropriately
- Choose the right data structure for a given problem
- Recognize how these structures map to real-world data
This lesson is fundamental:
most data analysis and machine learning workflows are built on these four structures.
1️⃣ What Is a Collection
A collection is a container that stores multiple values under a single name.
Instead of writing:
age1 =54
age2 =67
age3 =61
you can write:
ages = [54,67,61]
Collections allow you to:
- group related data
- iterate over values
- apply transformations systematically
2️⃣ Lists (list)
Lists are ordered, mutable collections.
ages = [54,67,61]
Key properties:
- ordered (positions matter)
- mutable (can be changed)
- allow duplicates
Common List Operations
ages.append(45) # add element
ages[0] # access by index
len(ages) # number of elements
Indexes start from 0.
Lists in Data Analysis
Lists are used to:
- store observations
- accumulate results
- iterate over datasets
They are often the first step before more advanced structures (e.g. DataFrames).
3️⃣ Tuples (tuple)
Tuples are ordered but immutable collections.
patient = ("P001",54,"M")
Key properties:
- ordered
- immutable (cannot be modified)
- allow duplicates
Why Use Tuples
Tuples are useful when:
- data should not change
- structure is fixed
- values belong together
Example:
(x, y) = (3,5)
This is common in mathematical and statistical code.
4️⃣ Sets (set)
Sets are unordered collections of unique elements.
risk_factors = {"smoker","hypertension","diabetes"}
Key properties:
- unordered
- no duplicates
- fast membership testing
Set Operations
"a"in risk_factors
set1 | set2 # union
set1 & set2 # intersection
Sets in Data Analysis
Sets are ideal for:
- removing duplicates
- comparing groups
- checking membership
They are conceptually very close to mathematical sets.
5️⃣ Dictionaries (dict)
Dictionaries store key–value pairs.
patient = {
"id":"P001",
"age":54,
"sex":"M"
}
Keys are unique; values can be anything.
Accessing Dictionary Values
patient["age"]
patient.get("age")
Difference:
[]raises an error if missing.get()returnsNone
Dictionaries in Data Analysis
Dictionaries are ubiquitous because they map naturally to:
- records
- rows
- JSON objects
- structured observations
Most real-world data starts its life as dictionaries.
6️⃣ Choosing the Right Collection
A practical rule of thumb:
- list → ordered sequence of values
- tuple → fixed group of values
- set → unique values, membership logic
- dict → structured records with named fields
Choosing the right structure simplifies code dramatically.
7️⃣ Iterating Over Collections
Collections become powerful when combined with loops.
for age in ages:
print(age)
for key, value in patient.items():
print(key, value)
Iteration is the backbone of data processing.
8️⃣ Collections and Real Data
In practice:
- a dataset → list of dictionaries
- a row → dictionary
- a column → list
- categories → set
This mental model will reappear later with pandas and machine learning pipelines.
FAQ — Frequently Asked Questions
Q: Why not use only lists?
A: Different problems require different guarantees (order, mutability, uniqueness).
Q: Are dictionaries ordered?
A: Yes (since Python 3.7), but conceptually they are still key–value mappings.
Q: Can dictionary values be collections?
A: Yes. Nested structures are extremely common.
Q: Should I memorize all methods?
A: No. Understand concepts first; methods come naturally.
Exercises
Exercise 1
Create a list of ages.
Exercise 2
Add a new age to the list.
Exercise 3
Access the first element of the list.
Exercise 4
Create a tuple representing a patient (id, age, sex).
Exercise 5
Explain why tuples cannot be modified.
Exercise 6
Create a set of risk factors with duplicates.
Exercise 7
Show that duplicates are removed.
Exercise 8
Create a dictionary representing a patient.
Exercise 9
Access a value safely using .get().
Exercise 10
Explain which collection you would use to represent a dataset of patients.
Solutions
Exercise 1
ages = [54,67,61]
Exercise 2
ages.append(45)
Exercise 3
ages[0]
Exercise 4
patient = ("P001",54,"M")
Exercise 5
# Tuples are immutable to protect fixed structure
Exercise 6
risk_factors = {"smoker","smoker","diabetes"}
Exercise 7
# Only unique elements remain
Exercise 8
patient = {"id":"P001","age":54,"sex":"M"}
Exercise 9
patient.get("age")
Exercise 10
# A dataset is best represented as a list of dictionaries
Next Lesson Preview
In Lesson 8, we will move from single files to structured code:
- modules
- packages
- reading and writing files