Day 9 of Learning Python for Data Science – Queries Related To Functions In Python

Welcome to Day 9 of Learning Python for Data Science. Today we will explore comprehensions, dictionary comprehensions, and lambda functions — powerful tools that help write cleaner and more concise code. These features enable efficient data transformation and manipulation, especially when working with large datasets. In this article, we’ll cover list and dictionary comprehensions, introduce lambda (anonymous) functions, and see how they are used in real-world data science applications.

Day 8 of Learning Python for Data Science – All About Functions In Python

List Comprehensions

Create a List of Squares from 1 to 10

lis = [i**2 for i in range(1, 11)]
print(lis)

Create a List of Numbers from 1 to 50 Divisible by 3 and 5

lis = [i for i in range(1, 51) if i % 3 == 0 and i % 5 == 0]
print(lis)

Given a Sentence, Create a List of Word Lengths

sentence = "Given a sentence, create a list of the length of each word in the sentence using list comprehension"
word_lengths = [len(word) for word in sentence.split()]
print(word_lengths)

Extract All Even Numbers from a List

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = [num for num in numbers if num % 2 == 0]
print(even_numbers)

Flatten a Nested List

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat_list = [num for sublist in matrix for num in sublist]
print(flat_list)

Dictionary Comprehensions

Create a Dictionary with Numbers as Keys and Their Squares as Values

diction = {i: i**2 for i in range(1, 6)}
print(diction)

Create a Dictionary from Two Lists

keys = ['a', 'b', 'c']
values = [1, 2, 3]
diction = {keys[i]: values[i] for i in range(len(keys))}
print(diction)

Filter a Dictionary to Keep Only Items Where the Value is Greater Than 2

original_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
filtered_dict = {k: v for k, v in original_dict.items() if v > 2}
print(filtered_dict)

Convert a Nested List into a Dictionary

nested_list = [['a', 1], ['b', 2], ['c', 3]]
d = {k: v for k, v in nested_list}
print(d)

Working with Tuples

Accessing Elements in a Tuple

my_tuple = (10, 20, 30, 40)
print(my_tuple[1])

Lambda Functions

Create a Lambda Function to Add Two Numbers

add = lambda x, y: x + y
print(add(5, 2))

Create a Lambda Function to Get the Square of a Number

square = lambda x: x**2
print(square(2))

Using map() to Apply a Lambda Function to a List

numbers = [1, 2, 3, 4, 5]
squares = list(map(lambda x: x**2, numbers))
print(squares)

Using filter() to Extract Even Numbers from a List

numbers = [1, 2, 3, 4, 5]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)

Conclusion

List comprehensions, dictionary comprehensions, and lambda functions provide powerful tools for data processing in Python. These techniques help simplify code while maintaining efficiency. Understanding these fundamental concepts is essential for anyone working with data in Python.

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