Data Analysis and Visualization — DU B. Voc – Software Development Previous Year Question Papers

Your Roll N................ Semester VII 1. (a) Given the following data of students' test scores: (5) import numpy as np scores = np. array (145, 56, 67, 89, 90, 91, 92, 94, 95, 96, 99, 100, 45, 47, 481) Write Python commands to: i) Compute Q1, Q3, and IQR. ii) Identify the lower and upper bounds for detecting outliers using the 1.5 x IQR rule. iii) Display the outlier values (if any). (b) Consider the following data of the number of students enrolled in different B. Voc courses: (5) 'Software Development] courses = ['Web Design', 'Data Analytics', 'Banking', 'Retail Management', students = [120, 90, 80, 100, 110] Write Python commands to: (11) Annotate each bar with its respective value. (i) Create a horizontal bar chart for student enrollment. (ill) Change the figure size to (8, 5). (iv) Assign the title "Student Enrollment in B.Voc Courses". (c) What is Sampling? Discuss the three types of Sampling (5) (d) Create two NumPy arrays arrl and arr2 of size 3×3 each. Perform element-wise addition, multiplication, and division. (e) What do you understand by multi-level indexing in Pandas? Explain with example (f) Consider the following DataFrame: import pandas as po data = pd. DataFrame( { 'Department: ['HR', 'HR', 'IT', 'IT, 'Sales", 'Sales'], 3) Write Python commands to: Group data by Department and calculate the average salary of each i) ii) Display the maximum salary from each department. iii) Rename the grouped column as 'Avg_Salary. 2. (a) Write Python commands to: Import a CSV file named employee.csv into a Pandas DataFrame i) called employee_df. ii) Display the first five records of the dataframe. Compute and display the median of each numeric column in the iii) (b) Consider the following code: 3) Write Python commands to: i) Group the data by Category and calculate the sum and mean of both Use the aggo function to compute min of Value and max of Discount. columns. ii) Display the final grouped DataFrame with appropriate column labels. 3. (a) Explain the difference between variance and standard deviation with an (7) example using NumPy arrays. (b) Write a Python program using Pandas and NumPy to: (8) i) Create a DataFrame of 5 rows and 4 columns containing random numbers in the range 1 to 50. Assign custom row labels as ['LI', 'L', 'L3', 'LA', 'LS'] and column names as ['COL_A', 'Col_B', 'Col_C", 'Col_D']. iii) Display the DataFrame. 4. (a) What is normalization and standardization? Why are they required in data preprocessing? (7) (b) Consider the following NumPy arrays: (8) import numpy as np al = np. array (112, 5, 7], 19, 11, 1311) Give the output of the following commands: V) a2[0, 1] = 99 — What change will occur in array a2 after executing this statement? 5. (a) Consider the following Pandas Series showing the sales (in lakhs) of different (7) shops: Write Python commands to: Display the shops having sales greater than 80. Display the index labels. iii) Assign the name 'Shop_Name' to the index.