Day 4: Data Structures - The Backbone of Data Science

Welcome to Day 4! In AI and Machine Learning, you are constantly managing massive amounts of data. Individual variables like x = 10 or name = "Alice" are not enough anymore.

We need ways to group data together, search through it, and modify it efficiently. Python provides four built-in Data Structures to do exactly this: Lists, Tuples, Dictionaries, and Sets.

1. Lists

A List is exactly what it sounds like: an ordered collection of items. Lists are mutable, meaning you can add, remove, and change items after the list is created.

# Creating different types of lists
fruits = ["apple", "banana", "cherry"]
mixed_list = [1, "apple", True]

# Accessing an element (Lists are 0-indexed!)
print(fruits[0]) # Output: "apple"

# Modifying the list
fruits.append("orange") # Adds an item to the end
fruits.insert(1, "grape") # Inserts 'grape' at index 1
fruits.remove("banana") # Removes a specific item
print("Fruits", fruits)

# Slicing a list to get a sub-list
sliced_fruits = fruits[2:4] 
print("Sliced", sliced_fruits)

apple
Fruits ['apple', 'grape', 'cherry', 'orange']
Sliced ['cherry', 'orange']

2. Tuples

Tuples are almost identical to Lists, but with one massive difference: they are immutable. Once created, you cannot change them.

Why use them? Because they are faster than lists and guarantee that your data won't be accidentally modified by another part of your code.

# Creating Tuples uses parenthesis ()
colors = ("red", "green", "blue")

# Creating a single item tuple requires a trailing comma
single_item = ("glass",)

print(colors[0]) # Output: red
# colors.append("yellow") # ERROR! Tuples cannot be modified!

red

3. Dictionaries

If Lists are arrays, Dictionaries are Key-Value mappings (similar to JSON objects). You look up data using completely custom keys instead of numerical indexes.

# Creating a dictionary uses curly braces {} with key:value pairs
student = {"name": "Alice", "age": 25, "grade": "A"}

# Modifying data
student["subject"] = "Math" # Adds a new key-value pair
student["age"] = 32 # Updates an existing value

# Removing data
del student["grade"]

print(student)

{'name': 'Alice', 'age': 32, 'subject': 'Math'}

4. Sets

Sets are unordered collections of unique elements. They are incredibly useful for removing duplicates from data or performing mathematical operations like Intersections and Unions.

set1 = {1, 2, 3}
set2 = {3, 4, 5}

# Set Difference: What is in set1 but NOT in set2?
print(set1 - set2)

{1, 2}

Hands-On Let's Code!

Let's apply these structures to real problems!

Exercise 1: Manipulate Data in a Dictionary

Here, we take a basic dictionary, add new features to it, update existing data, and clean out data we no longer need (simulating data cleaning).

# exercise1.py
person = {"name": "Alice", "age": 25, "grade": "A"}

# Adding a new Address Key
person["address"] = "123 Main St"

# Updating the Age Key
person["age"] = 32

# Safely cleaning out the Grade Key
if "grade" in person:
    del person["grade"]

print(person)

{'name': 'Alice', 'age': 32, 'address': '123 Main St'}

Exercise 2: Word Frequency Counter (NLP Basics)

This is an incredibly common task in Natural Language Processing (NLP)—counting how many times specific words appear in a sentence. We use a combination of string .split() and a Dictionary to store the counts.

# exercise2.py
sentence = input("Enter a Sentence: ")

# Split the string into a List of words
words = sentence.split()

# Initialize an empty Dictionary to hold our counts
word_count = {}

# Iterate over our List of words
for word in words:
    word = word.lower() # Normalize case so 'The' and 'the' match

    # Check if the word is already a Key in our Dictionary
    if word in word_count:
        word_count[word] += 1
    else:
        word_count[word] = 1 # Create the Key if it doesn't exist

print(word_count)

Enter a Sentence: i am a coder as well as a writer
{'i': 1, 'am': 1, 'a': 2, 'coder': 1, 'as': 2, 'well': 1, 'writer': 1}

Wrapping Up Day 4

You have now unlocked the heavy machinery of Python. Lists and Dictionaries will be used in almost every single AI script you write.

Tomorrow on Day 5: Working with Strings, we take a deep dive into manipulating text data, a crucial skill for prepping data for Large Language Models!