Assignment Set – 1

Q1. (a) Describe Data wrangling. Name the package used for Data wrangling in R and describe some of its features.

(b) Interpret vectors. Explain the creation of vectors with examples. Also, describe how to identify and handle missing values. 5+5   

Ans 1.

(a) Description of Data Wrangling and Package in R

Data wrangling refers to the systematic process of transforming raw, unstructured, or semi-structured data into a clean and organized format suitable for analysis. Real-world datasets often contain missing values, inconsistencies, incorrect formats, and redundant information. Data wrangling involves tasks such as filtering, selecting variables, renaming columns, reshaping data, handling outliers, and merging multiple datasets. In R, data wrangling is central to any data science workflow because the quality of analysis depends heavily on how

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Q2. (a) Explain the chaining operator with an example.

(b) Articulate the summary () method with an example in R. 5+5           

Ans 2.

(a) Explanation of the Chaining Operator

The chaining operator, often referred to as the pipe operator, is used in R to create readable and intuitive sequences of data transformations. The most widely used chaining operator is %>%, provided by the magrittr package and commonly used through the tidyverse. It passes the output of one function directly as the input to the next, reducing nested function calls and improving code clarity. Instead of writing deeply nested expressions, the programmer can express operations as a logical sequence. For example, without the chaining operator, a user

Q3. (a) Criticize with an example the syntax of various looping constructs in R – For, While and Repeat statements.

(b) Discuss continuous random variable. How can that be created using R? 5+5          

Ans 3.

(a) Syntax and Use of Looping Constructs with Example

R provides three primary looping constructs: for, while, and repeat. These control structures allow repetitive execution of statements, although many operations in R are better handled through vectorization or apply-family functions. The for loop iterates over elements of a sequence. For example:

sum_val <- 0

for (i in 1:5) {

sum_val <- sum_val + i

Assignment Set – 2

Q4. (a) Explain with examples – Set, List and Tuples. What are the similarities and differences between the same.

(b) Illustrate how strings are converted into iterables in Python? Give a suitable example. 5+5

Ans 4.

(a) Sets, Lists and Tuples – Meaning, Examples, Similarities and Differences

In Python, lists, tuples and sets are fundamental collection data types used to store multiple values in a single variable, but they differ in structure, mutability and behaviour. A list is an ordered, mutable collection that can store heterogeneous elements. For example, marks = [80, 75, 90] defines a list, and elements can be updated as marks[1] = 78. Lists preserve insertion order and support operations like appending, slicing and iteration, which makes them ideal

Q5. (a) Summarize “waffle charts”. When is it used? Explain with an example in python.

(b) Discuss how simple and complex pattern searches can be performed on lists.          5+5

Ans 5.

(a) Waffle Charts – Meaning, Usage and Example in Python

A waffle chart is a visual representation used to display parts-of-a-whole relationships in a grid of small squares. Each square represents a fixed proportion, such as one percent of the total, and coloured squares indicate the contribution of a category. Unlike a pie chart, a waffle chart uses a rectangular grid, which many viewers find easier to interpret and compare. It is commonly used to show composition, such as the share of customer segments, product categories, or survey responses.

In Python, waffle charts can be built using libraries such as pywaffle in combination with

Q6. (a) Contrast the difference between loc and iloc attributes with example.

(b) When is agg() method used. Explain with example.     5+5     

Ans 6.

 (a) Difference Between loc and iloc with Example

In the pandas library, loc and iloc are two powerful indexers used to access subsets of rows and columns in a DataFrame. The loc attribute is label-based, meaning it selects rows and columns using explicit index and column names. For instance:

import pandas as pd

df = pd.DataFrame({

“name”: [“Asha”, “Ravi”, “Meera”],

“marks”: [85, 90, 88]