data-structures.mdx

title	Data Structures
sidebar_label	Data Structures
description	Mastering Python's built-in collections: Lists, Tuples, Dictionaries, and Sets, and their specific roles in data science pipelines.
tags	python data-structures lists dictionaries tuples sets mathematics-for-ml

While basic types hold single values, Data Structures allow us to group, organize, and manipulate large amounts of information. In ML, choosing the wrong structure can lead to code that runs $100\times$ slower than it should.

1. Lists: The Versatile Workhorse

A List is an ordered, mutable collection of items. Think of it as a flexible array that can grow or shrink.

• Syntax: my_list = [0.1, 0.2, 0.3]
• ML Use Case: Storing the history of "Loss" values during training so you can plot them later.

losses = []
for epoch in range(10):
    current_loss = train_step()
    losses.append(current_loss) # Dynamic growth

2. Tuples: The Immutable Safeguard

A Tuple is like a list, but it cannot be changed after creation (immutable).

• Syntax: shape = (224, 224, 3)
• ML Use Case: Defining image dimensions or model architectures. Since these shouldn't change accidentally during execution, a tuple is safer than a list.

graph LR
    L[List: Mutable] --> L_Edit["Can change: my_list[0] = 5"]
    T[Tuple: Immutable] --> T_Edit["Error: 'tuple' object does not support assignment"]
    style T fill:#fffde7,stroke:#fbc02d,color:#333

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3. Dictionaries: Key-Value Mapping

A Dictionary stores data in pairs: a unique Key and its associated Value. It uses a "Hash Table" internally, making lookups incredibly fast ( complexity).

• Syntax: params = {"learning_rate": 0.001, "batch_size": 32}
• ML Use Case: Managing hyperparameters or mapping integer IDs back to human-readable text labels.

graph TD
    Key["Key: 'Cat'"] --> Hash["Hash Function"]
    Hash --> Index["Memory Index: 0x42"]
    Index --> Val["Value: [0.98, 0.02, ...]"]
    style Hash fill:#e1f5fe,stroke:#01579b,color:#333

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4. Sets: Uniqueness and Logic

A Set is an unordered collection of unique items.

• Syntax: classes = {"dog", "cat", "bird"}
• ML Use Case: Finding the unique labels in a messy dataset or performing mathematical operations like Union and Intersection on feature sets.

5. Performance Comparison

Choosing the right structure is about balancing Speed and Memory.

Feature	List	Tuple	Dictionary	Set
Ordering	Ordered	Ordered	Ordered (Python 3.7+)	Unordered
Mutable	Yes	No	Yes	Yes
Duplicates	Allowed	Allowed	Keys must be unique	Must be unique
Search Speed	(Slow)	(Slow)	(Very Fast)	(Very Fast)

xychart-beta
    title "Search Speed (Lower is Better)"
    x-axis ["List", "Tuple", "Set", "Dict"]
    y-axis "Time Complexity" 0 --> 120
    bar [100, 100, 5, 5]

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6. Slicing and Indexing

In ML, we often need to "slice" our data (e.g., taking the first 80% for training and the last 20% for testing).

$$ \text{Syntax: } \text{data}[\text{start} : \text{stop} : \text{step}] $$

data = [10, 20, 30, 40, 50, 60]
train = data[:4]  # [10, 20, 30, 40]
test = data[4:]   # [50, 60]

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Now that we can organize data, we need to control the flow of our program—making decisions based on that data and repeating tasks efficiently.