VNSGU BCA Sem 2: Programming Skills - Advanced (204_02) Practical Solutions - April 2025 Set E

Paper Details

• Subject: Programming Skills - Advanced (PKUP)
• Subject Code: 204_02
• Set: E
• Semester: 2
• Month/Year: April 2025
• Max Marks: 25
• Time Recommendation: 45 Minutes
• Paper: View Paper | Download PDF

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Questions & Solutions

All questions are compulsory

Q1A: Student Data Pipeline

Max Marks: 20

Using Python, perform the following tasks for STUDENT data management:

1. Create a dictionary (or a set of sets) containing details of students with the following fields: STUDENT_ID, NAME, COURSE, MARKS, ADMISSION_YEAR. Add at least 10 student records.
2. Convert the dictionary into a pandas DataFrame.
3. Save the DataFrame as a CSV file named student_data.csv.
4. Load the CSV file and display: a) All students enrolled in the "B.Sc. Computer Science" course b) Students who scored more than 80 marks c) Students admitted before 2020

1. Dictionary & DataFrame Creation

Initialize the student registration database and convert it into a Pandas DataFrame.

Hint

Define a dictionary where each key matches the required columns. Use pd.DataFrame(student_dict) to create the table.

View Solution & Output

import pandas as pd

# [1] Create dictionary with student details
student_dict = {
    'STUDENT_ID': [101, 102, 103, 104, 105, 106, 107, 108, 109, 110],
    'NAME': ['Amit', 'Priya', 'Rahul', 'Sia', 'Aryan', 'Neha', 'Vikram', 'Sneha', 'Karan', 'Mira'],
    'COURSE': ['B.Sc. Computer Science', 'BCA', 'B.Sc. Computer Science', 'B.Com', 'B.Sc. Computer Science', 'BBA', 'BCA', 'B.Sc. Computer Science', 'BBA', 'BCA'],
    'MARKS': [85, 78, 92, 70, 88, 65, 81, 75, 95, 82],
    'ADMISSION_YEAR': [2019, 2021, 2018, 2022, 2019, 2020, 2021, 2018, 2023, 2019]
}

# [2] Convert dictionary into pandas DataFrame
df = pd.DataFrame(student_dict)
print("DataFrame successfully initialized.")
print(df.head())

Step-by-Step Explanation:

1. Initialization: Import pandas and define the student_dict dictionary containing records for 10 students.
2. Logic Flow: Convert the dictionary into a tabular structure using pd.DataFrame() and assign it to the variable df.
3. Completion: Use df.head() to display the first few rows to verify the data was loaded correctly.

2. File Storage (CSV)

Export the student records to a CSV file.

Hint

Use df.to_csv('student_data.csv', index=False).

View Solution & Output

# [3] Save DataFrame as CSV file
df.to_csv('student_data.csv', index=False)
print("File 'student_data.csv' created successfully.")

Step-by-Step Explanation:

1. Initialization: Use the active DataFrame df containing student information.
2. Logic Flow: Save the DataFrame to a local file named 'student_data.csv' using to_csv() with index=False to skip row labels.
3. Completion: A success message confirms the creation of the CSV file on disk.

3. Data Loading & Analytics

Load the CSV and search for specific students based on course, marks, and year.

Hint

Use string matching for courses and numeric comparisons for marks and years.

View Solution & Output

# [4] Load CSV file and perform queries
load_df = pd.read_csv('student_data.csv')

# Query 1: B.Sc. Computer Science
print("\n[a] Students in B.Sc. Computer Science:")
print(load_df[load_df['COURSE'] == 'B.Sc. Computer Science'])

# Query 2: Marks > 80
print("\n[b] Top Scorers (Marks > 80):")
print(load_df[load_df['MARKS'] > 80])

# Query 3: Admission Year < 2020
print("\n[c] Admitted Before 2020:")
print(load_df[load_df['ADMISSION_YEAR'] < 2020])

Step-by-Step Explanation:

1. Initialization: Load the records from 'student_data.csv' using pd.read_csv() into a new DataFrame.
2. Logic Flow: Apply boolean indexing to filter the dataset based on course name, score thresholds, and admission years.
3. Completion: Display the query results to help analyze student enrollment and performance.

Q2: Viva Preparation

Max Marks: 5

Potential Viva Questions

1. Q: How can you find the highest marks in the DataFrame?
   • A: Use df['MARKS'].max().
2. Q: What is the purpose of the shape attribute?
   • A: It returns a tuple representing the dimensionality of the DataFrame (rows, columns).
3. Q: How do you sort students by their names?
   • A: Use df.sort_values(by='NAME').
4. Q: How do you calculate the mean marks for each course?
   • A: Use df.groupby('COURSE')['MARKS'].mean().
5. Q: What is the difference between count() and value_counts()?
   • A: count() returns the number of non-null values in each column, while value_counts() returns the frequency of unique values in a single column.
6. Q: How do you select rows where Name starts with 'A'?
   • A: Use df[df['NAME'].str.startswith('A')].

Common Pitfalls

• Exact String Matches: If the course is "B.Sc Computer Science" (missing dot), the filter == 'B.Sc. Computer Science' will return no results.
• Column Availability: Ensure the CSV is loaded correctly before filtering to avoid KeyError.

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Last Updated: April 2026