--- title: "Collections: Lists, Tuples, Sets, and Dictionaries" date: 2026-01-03T18:07:49Z modified: 2026-01-04T20:38:40Z permalink: "https://www.micheledpierri.com/python/collections/" type: page status: publish excerpt: "" wpid: 2499 featured_image: "https://www.micheledpierri.com/wp-content/uploads/2026/01/Python_07.webp" featured_image_alt: Children dressed in lab coats are studying a python timestamp: 2026-01-04T20:38:40Z tags: [] --- ## 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 ``` age1 =54 age2 =67 age3 =61 ``` you can write: ages = [54,67,61] ``` 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] ``` 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 ``` 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") ``` 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) ``` (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"} ``` risk_factors = {"smoker","hypertension","diabetes"} ``` Key properties: - unordered - no duplicates - fast membership testing ### Set Operations "a"in risk_factors ``` "a"in risk_factors ``` set1 | set2 # union set1 & set2 # intersection ``` 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" } ``` patient = { "id":"P001", "age":54, "sex":"M" } ``` Keys are unique; values can be anything. ### Accessing Dictionary Values patient["age"] patient.get("age") ``` patient["age"] patient.get("age") ``` Difference: - `[]` raises an error if missing - `.get()` returns `None` ### 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 age in ages: print(age) ``` for key, value in patient.items(): print(key, value) ``` 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] ``` ages = [54,67,61] ``` ### Exercise 2 ages.append(45) ``` ages.append(45) ``` ### Exercise 3 ages[0] ``` ages[0] ``` ### Exercise 4 patient = ("P001",54,"M") ``` patient = ("P001",54,"M") ``` ### Exercise 5 # Tuples are immutable to protect fixed structure ``` # Tuples are immutable to protect fixed structure ``` ### Exercise 6 risk_factors = {"smoker","smoker","diabetes"} ``` risk_factors = {"smoker","smoker","diabetes"} ``` ### Exercise 7 # Only unique elements remain ``` # Only unique elements remain ``` ### Exercise 8 patient = {"id":"P001","age":54,"sex":"M"} ``` patient = {"id":"P001","age":54,"sex":"M"} ``` ### Exercise 9 patient.get("age") ``` patient.get("age") ``` ### Exercise 10 # A dataset is best represented as a list of dictionaries ``` # 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 ## This is where Python projects start to look like **real software**.