Understanding .add() in the CS50 Degrees Project: How Multiple IDs Are Stored for the Same Name

[Rajeev Bagra]

A beginner studying the CS50 AI Degrees project often encounters code like this:

if row["name"].lower() not in names:
    names[row["name"].lower()] = {row["id"]}
else:
    names[row["name"].lower()].add(row["id"])

At first glance, the last line can be confusing:

names[row["name"].lower()].add(row["id"])

Many learners naturally wonder:

Does this line create a new dictionary entry, or does it add a new ID to an existing name?

Let’s break it down carefully.

---

1. What Is the Purpose of the names Dictionary?

The names dictionary is designed like this:

person name → set of person IDs

For example:

names = {
    "kevin bacon": {"102"}
}

This means:

"kevin bacon" has person ID 102

---

2. When a Name Appears for the First Time

Suppose the CSV row is:

row = {
    "id": "102",
    "name": "Kevin Bacon"
}

Initially:

names = {}

The condition:

if row["name"].lower() not in names:

becomes:

if "kevin bacon" not in names:

which is True.

Therefore Python executes:

names["kevin bacon"] = {"102"}

The dictionary now becomes:

names = {
    "kevin bacon": {"102"}
}

Notice that the value is a set containing one ID.

---

3. When the Same Name Appears Again

Now suppose another CSV row contains:

row = {
    "id": "205",
    "name": "Kevin Bacon"
}

The condition:

if "kevin bacon" not in names:

is now False because the name already exists.

So Python moves to:

else:
    names[row["name"].lower()].add(row["id"])

which becomes:

names["kevin bacon"].add("205")

---

4. What Does .add() Actually Do?

The .add() method belongs to Python sets.

It adds a new element into an existing set.

Before:

{"102"}

After:

.add("205")

the set becomes:

{"102", "205"}

The dictionary now looks like:

names = {
    "kevin bacon": {"102", "205"}
}

---

5. Does .add() Create a New Dictionary Entry?

No.

This is a very important distinction.

The code:

names["kevin bacon"].add("205")

does NOT create another key called:

"kevin bacon"

Instead, it modifies the existing set already stored under that key.

Conceptually:

existing_set = names["kevin bacon"]
existing_set.add("205")

The key stays the same.

Only the contents of the set change.

---

6. Visualizing What Happens

Before .add():

names
│
└── "kevin bacon"
      │
      ▼
    {"102"}

Python executes:

names["kevin bacon"].add("205")

Afterward:

names
│
└── "kevin bacon"
      │
      ▼
   {"102", "205"}

The dictionary still has only one key:

"kevin bacon"

but the associated set now contains two IDs.

---

7. Why Use a Set Instead of a Single ID?

Suppose the code used:

names["kevin bacon"] = "102"

Later:

names["kevin bacon"] = "205"

The second assignment would overwrite the first one.

Result:

{
    "kevin bacon": "205"
}

The original ID “102” would be lost.

Using a set prevents that problem.

---

8. The Full Flow

First Kevin Bacon row encountered
        ↓
Create new set {"102"}
        ↓
Store under key "kevin bacon"
        ↓
Second Kevin Bacon row encountered
        ↓
Find existing set {"102"}
        ↓
Add "205" using .add()
        ↓
Set becomes {"102", "205"}

---

9. Why This Matters in the Degrees Project

The CS50 Degrees project must handle cases where multiple people share the same name.

For example:

ID Name

102 Kevin Bacon
205 Kevin Bacon

The names dictionary therefore stores:

{
    "kevin bacon": {"102", "205"}
}

Later, when the user searches for “Kevin Bacon”, the program can detect multiple matching IDs and ask:

Which Kevin Bacon did you mean?

This is why the line:

names[row["name"].lower()].add(row["id"])

is so important—it preserves all matching IDs instead of overwriting earlier ones.

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