How Python Stores Objects in Dictionaries: A Beginner’s Guide (With Student Example)

 

🔹 Initial Context

In many Python programs, especially when learning Object-Oriented Programming (OOP), we often see code like this:

students = {}

students["Alice"] = Student(12)
students["Bob"] = Student(7)

For many learners, this raises an important question:

“Is Student(12) stored like an array inside the dictionary?”
“How does the object know it belongs to Alice?”

This tutorial explains what really happens behind the scenes.

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🔹 Step 1: The Student Class

First, consider this simple class:

class Student:
    def __init__(self, exercises):
        self.exercises = exercises

This class represents one student’s exercise record.

Each Student object stores:

• One attribute: exercises
• The number of completed exercises

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🔹 Step 2: Creating Student Objects

Now look at this line:

Student(12)

When Python runs this:

1. A new object is created in memory
2. self.exercises is set to 12
3. A reference to this object is returned

Think of it as:

A new Student object exists somewhere in memory
It contains: exercises = 12

The object is not stored in a variable yet. It only exists in memory.

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🔹 Step 3: Storing Objects in a Dictionary

Now look at this code:

students = {}

students["Alice"] = Student(12)

This means:

“Create a Student object and store its reference under the key ‘Alice’.”

Python does NOT copy the whole object into the dictionary.

Instead, it stores a reference (memory address).

Conceptually:

students = {
    "Alice" → reference to Student(exercises=12)
}

The dictionary maps the name to the object’s location.

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🔹 Step 4: Adding More Entries

When you write:

students["Bob"] = Student(7)

Another object is created:

students = {
    "Alice" → Student(exercises=12),
    "Bob"   → Student(exercises=7)
}

Each key points to a different object.

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🔹 Step 5: Accessing Stored Objects

When you access:

students["Alice"]

Python:

1. Looks up “Alice” in the dictionary
2. Finds the reference
3. Returns the object

So:

print(students["Alice"].exercises)

Outputs:

12

Because you are reading the attribute from the actual object.

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🔹 Step 6: Objects Do NOT Store the Key

Important point:

The Student object does NOT know that it belongs to “Alice”.

It only knows:

self.exercises = 12

The dictionary manages the relationship.

This is good design. It avoids duplicate data.

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🔹 Step 7: References and Shared Objects

Consider this example:

a = Student(10)
b = a

b.exercises = 50

print(a.exercises)

Output:

50

Why?

Because a and b point to the same object.

The dictionary works the same way.

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🔹 Step 8: Common Misunderstanding

Many beginners think:

“Is Student(7) like Student[7]?”

No.

Syntax	Meaning
Student(7)	Creates an object
Student[7]	Indexing (not used here)

They are completely different.

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🔹 Step 9: Visual Memory Model

Think of memory like this:

Objects in Memory

Object A → Student(exercises=12)
Object B → Student(exercises=7)

Dictionary

"Alice" → Object A
"Bob"   → Object B

The dictionary stores links, not copies.

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🔹 Step 10: Why This Design Is Used in OOP

This design allows:

✅ Clean separation of data and lookup
✅ Easy updates
✅ No duplication
✅ Scalable programs

It is used in:

• Phone books
• Student databases
• Contact apps
• CRMs
• User management systems

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🔹 Step 11: Mini Experiment for Learners

Try this code:

class Student:
    def __init__(self, exercises):
        self.exercises = exercises


students = {}

students["Alice"] = Student(12)

x = students["Alice"]
x.exercises = 99

print(students["Alice"].exercises)

Expected output:

99

This proves that both variables refer to the same object.

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✅ Final Takeaway

In Python:

Dictionaries store references to objects, not the objects themselves.

So:

students["Alice"] = Student(12)

Means:

"Alice" → reference → Student object

Not:

"Alice" → copy of object

Understanding this is a key step toward mastering object-oriented programming.

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