Day 33 - Dictionaries

Skills: None

Pre-reading: 9.2

Intro (10 mins)

Today we explore dictionaries in Python, a data structure that maps unique keys to values.
Dictionaries are useful for fast lookups and flexible data storage, while dataclasses provide a fixed, type-checked structure.

Example: Conference session data, where each session has a unique session ID, a title, a speaker, and a list of topics.

# Dictionary mapping session IDs to session details (as dictionaries)
sessions = {
    "CS101": {"title": "Introduction to AI", "speaker": "Dr. Martinez", "topics": ["AI", "Machine Learning"]},
    "CS102": {"title": "Deep Learning Techniques", "speaker": "Prof. Nguyen", "topics": ["Neural Networks", "Deep Learning"]},
    "CS103": {"title": "Quantum Computing Basics", "speaker": "Dr. Patel", "topics": ["Quantum", "Computing"]},
    "CS104": {"title": "Cybersecurity Trends", "speaker": "Ms. Lee", "topics": ["Security", "Networking"]}
}
print(sessions["CS101"]["speaker"])  # Dr. Martinez

You can update values, add new keys, or remove keys:

sessions["CS103"]["speaker"] = "Dr. Singh"
sessions["CS105"] = {"title": "Data Ethics", "speaker": "Dr. Kim", "topics": ["Ethics", "Data"]}
del sessions["CS104"]

If you try to access a key that doesn't exist, you'll get a KeyError. You can use .get() to avoid this:
```
print(sessions.get("CS999", "Not found"))  # Not found
```

You can iterate through a dictionary to search or filter:

# Print all session IDs for sessions covering "AI"
for session_id, details in sessions.items():
    if "AI" in details["topics"]:
        print(session_id)

# Count how many sessions have a speaker whose name starts with "Dr."
count = 0
for details in sessions.values():
    if details["speaker"].startswith("Dr."):
        count += 1
print(count)

You can collect all unique topics (using a list and checking for duplicates):

unique_topics = []
for details in sessions.values():
    for topic in details["topics"]:
        if topic not in unique_topics:
            unique_topics.append(topic)
print(unique_topics)

Dictionaries vs. dataclasses:

from dataclasses import dataclass

@dataclass
class ConferenceSession:
    session_id: str
    title: str
    speaker: str
    topics: list

session1 = ConferenceSession("CS101", "Introduction to AI", "Dr. Martinez", ["AI", "Machine Learning"])

Dictionaries are flexible and dynamic; dataclasses are fixed and type-checked.

Class Exercises (35 mins)

Create a dictionary mapping student IDs to names. Add a new student, update a name, and remove a student.
Given a dictionary of conference sessions (as above), print all session IDs for sessions covering "AI".
Count how many sessions have a speaker whose name starts with "Dr.".
Add a new topic to the list of topics for a given session.
What happens if you try to access a session that doesn't exist? How can you avoid a crash?
Write a function that takes a dictionary of sessions and returns a list of all unique topics covered.
Convert a list of ConferenceSession dataclass instances into a dictionary mapping session IDs to dataclass instances.
Filter all sessions where the number of topics is greater than 1.

Wrap-up (5 mins)

Dictionaries map unique keys to values and are flexible for dynamic data.
Dataclasses provide a fixed, type-checked structure.

Skills: None​

Pre-reading: 9.2​

Intro (10 mins)​

Class Exercises (35 mins)​

Wrap-up (5 mins)​

Skills: None

Pre-reading: 9.2

Intro (10 mins)

Class Exercises (35 mins)

Wrap-up (5 mins)