Data Science & Machine Learning

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🎯 Practice Questions 1. Create a list of numbers from 1 to 20. 2. Create a list containing the squares of numbers from 1 to 10. 3. Create a list containing only odd numbers from 1 to 20. 4. Convert a list of names to lowercase. 5. Replace all negative values in a list with zero using a list comprehension. 🎯 Key Takeaways ✅ List comprehensions provide a concise way to create lists. ✅ They combine loops and expressions into a single line. ✅ You can filter data using "if" conditions. ✅ Conditional expressions allow values to be modified during list creation. ✅ List comprehensions are widely used in data cleaning, feature engineering, and machine learning workflows. Mastering list comprehensions will help you write cleaner, more Pythonic code and prepare you for technical interviews and real-world Data Science projects. Double Tap ❤️ For Part-9 ----- 1.35 ₽ · /balance_help

🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 8: Python List Comprehensions Welcome back! 👋 In the previous lesson, you learned about Python's built-in data structures—Lists, Tuples, Sets, and Dictionaries. Now it's time to learn one of Python's most elegant and frequently used features: List Comprehensions. List comprehensions provide a concise and readable way to create, filter, and transform lists. They are widely used in Data Science, Machine Learning, data preprocessing, and coding interviews. 🔹 1. What is a List Comprehension? A list comprehension is a compact way to create a new list by applying an expression to each item in an iterable (such as a list, tuple, or range). Instead of writing multiple lines with a loop, you can accomplish the same task in a single line. General Syntax new_list = [expression for item in iterable] 🔹 2. Creating a List Using a Loop

numbers = []
for i in range(5):
    numbers.append(i)
print(numbers)

Output

[0, 1, 2, 3, 4]

🔹 3. Creating the Same List Using List Comprehension

numbers = [i for i in range(5)]
print(numbers)

Output

[0, 1, 2, 3, 4]  

Notice how the code is shorter and easier to read. 🔹 4. Performing Calculations Create a list of squares.

squares = [x ** 2 for x in range(1, 6)]
print(squares)

Output

[1, 4, 9, 16, 25]

🔹 5. Using Conditions You can filter elements while creating a list. Example: Even Numbers

even_numbers = [x for x in range(1, 11) if x % 2 == 0]
print(even_numbers)

Output

[2, 4, 6, 8, 10]

🔹 6. Converting Strings Convert all names to uppercase.

names = ["rahul", "deepak", "anita"]
upper_names = [name.upper() for name in names]
print(upper_names)

Output

['RAHUL', 'DEEPAK', 'ANITA']

🔹 7. Using Conditional Expressions Replace negative numbers with zero.

numbers = [5, -2, 8, -1, 3]
updated = [0 if x < 0 else x for x in numbers]
print(updated)

Output

[5, 0, 8, 0, 3]

🔹 8. Nested List Comprehension Create a multiplication table.

table = [[i * j for j in range(1, 6)] for i in range(1, 4)]
print(table)

Output

[[1, 2, 3, 4, 5],
 [2, 4, 6, 8, 10],
 [3, 6, 9, 12, 15]]

🔹 9. Real-World Data Science Example Suppose you have a list of sales amounts.

sales = [1200, 850, 1500, 600, 2000]
high_sales = [sale for sale in sales if sale > 1000]
print(high_sales)

Output

[1200, 1500, 2000] 

This technique is commonly used while cleaning and filtering datasets before analysis. 🔹 10. Benefits of List Comprehensions ✅ Shorter code ✅ Easier to read ✅ Faster than traditional loops in many cases ✅ Widely used in Data Science and Machine Learning 🔹 11. Common Mistakes ❌ Forgetting the Expression

numbers = [for i in range(5)]  # SyntaxError

Correct:

numbers = [i for i in range(5)]

❌ Incorrect Order of "if"

numbers = [if x % 2 == 0 x for x in range(10)]  # SyntaxError

Correct:

numbers = [x for x in range(10) if x % 2 == 0]

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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. Double Tap ❤️ For Part-8 ----- 1.28 ₽ · /balance_help

🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 7: Python Data Structures (Lists, Tuples, Sets & Dictionaries) Welcome back! 👋 So far, you've learned variables, operators, input/output, conditional statements, loops, and functions. Now it's time to learn one of the most important topics in Python— Data Structures. Data structures help us store, organize, and manage data efficiently. In Data Science, almost every dataset you work with will be stored or manipulated using these structures. Python provides four built-in data structures: • List • Tuple • Set • Dictionary Let's understand each one in detail. 🔹 1. List A List is an ordered, mutable collection that allows duplicate values. Creating a List

fruits = ["Apple", "Banana", "Mango"]
print(fruits)

Output

['Apple', 'Banana', 'Mango']

Accessing Elements

print(fruits[0])
print(fruits[2])

Output

Apple
Mango

Modifying a List

fruits[1] = "Orange"
print(fruits)

Output

['Apple', 'Orange', 'Mango']

Adding Elements

fruits.append("Grapes")
print(fruits)

Removing Elements

fruits.remove("Orange")
print(fruits)

🔹 2. Tuple A Tuple is an ordered collection that cannot be modified after creation (immutable). Creating a Tuple

colors = ("Red", "Green", "Blue")
print(colors)

Accessing Elements

print(colors[1])

Output

Green

Why Use Tuples? Use tuples when your data should never change. Examples: • Months of the year • Days of the week • Fixed coordinates 🔹 3. Set A Set is an unordered collection of unique elements. Duplicate values are automatically removed. Creating a Set

numbers = {1, 2, 2, 3, 4, 4, 5}
print(numbers)

Output

{1, 2, 3, 4, 5}

Adding Elements

numbers.add(6)

Removing Elements

numbers.remove(3)

Common Uses • Remove duplicates • Membership testing • Mathematical set operations 🔹 4. Dictionary ⭐ A Dictionary stores data as key-value pairs. It is one of the most frequently used data structures in Data Science. Creating a Dictionary

student = {
    "name": "Deepak",
    "age": 24,
    "course": "Data Science"
}
print(student)

Accessing Values

print(student["name"])

Output

Deepak

Adding a New Key

student["city"] = "Mumbai"

Updating a Value

student["age"] = 25

Removing a Key

del student["course"]

🔹 5. Comparison of Data Structures Feature | List | Tuple | Set | Dictionary Ordered | ✅ | ✅ | ❌ | ✅ Mutable | ✅ | ❌ | ✅ | ✅ Duplicates Allowed | ✅ | ✅ | ❌ | Keys ❌ Indexed | ✅ | ✅ | ❌ | By Key 🔹 6. Common List Methods

numbers = [10, 20, 30]
numbers.append(40)
numbers.insert(1, 15)
numbers.remove(20)
numbers.sort()
print(numbers)

🔹 7. Common Dictionary Methods

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