Data Science & Machine Learnin… on Telegram: “student = { "name": "Rahul", "age": 23 } print(s…”

student = {
    "name": "Rahul",
    "age": 23
}
print(student.keys())
print(student.values())
print(student.items())

🔹 8. Real-World Data Science Example

Suppose you have student information.

students = [
    {"name": "Amit", "marks": 90},
    {"name": "Sara", "marks": 85}
]

for student in students:
    print(student["name"], student["marks"])

Output

Amit 90
Sara 85

This is very similar to how records are stored before converting them into a Pandas DataFrame.

🔹 9. Common Mistakes

❌ Trying to Modify a Tuple

colors = ("Red", "Green")
colors[0] = "Blue"

This raises a TypeError because tuples are immutable.

❌ Accessing a Missing Dictionary Key

student = {"name": "John"}
print(student["age"])

This raises a KeyError.

A safer approach:

print(student.get("age"))

🎯 Practice Questions

1. Create a list of five cities and print the third city.

2. Create a tuple containing the days of the week.

3. Remove duplicate numbers from a list using a set.

4. Create a dictionary containing your name, age, and profession.

5. Print all keys and values of a dictionary using a loop.

🎯 Key Takeaways

✅ Lists are ordered and mutable.

✅ Tuples are ordered and immutable.

✅ Sets store only unique values.

✅ Dictionaries store data as key-value pairs.

✅ Dictionaries and Lists are the most commonly used data structures in Data Science.

Mastering these four data structures will make it much easier to work with datasets, APIs, JSON files, and machine learning projects.

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