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

Paper Details

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

Questions & Solutions

All questions are compulsory

Q1A: Library Book Pipeline

Max Marks: 20

Using Python, perform the following tasks for LIBRARY BOOK data management: 1. Create a dictionary containing book details (BOOK_ID, TITLE, AUTHOR, GENRE, AVAILABILITY, ADDED_DATE). Add at least 10 book records. 2. Convert the dictionary into a pandas DataFrame. 3. Save the DataFrame as a CSV file named library_books.csv. 4. Load the CSV file and display specific queries (History, Available, Date > 2020).

1. Dictionary & DataFrame Creation

Initialize the library dataset and convert it into a Pandas DataFrame.

Hint

Define a dictionary where each key is a column and the value is a list of 10 items. Use pd.DataFrame() to transform it.

flowchart TD
dict[Library Dictionary]
df[Pandas DataFrame]
conv[pd.DataFrame Conversion]

dict --> conv
conv --> df
View Solution & Output 
import pandas as pd

# [1] Create dictionary with book details
library_data = {
    'BOOK_ID': ['LIB001', 'LIB002', 'LIB003', 'LIB004', 'LIB005', 'LIB006', 'LIB007', 'LIB008', 'LIB009', 'LIB010'],
    'TITLE': ['Py Programming', 'Data Structures', 'Indian History', 'World War II', 'ML Basics', 'Ancient Civilizations', 'Modern India', 'AI', 'Medieval History', 'Deep Learning'],
    'AUTHOR': ['John Smith', 'Robert Sedgewick', 'Bipin Chandra', 'Winston Churchill', 'Andrew Ng', 'Will Durant', 'Ramachandra Guha', 'Stuart Russell', 'Barbara Tuchman', 'Ian Goodfellow'],
    'GENRE': ['Tech', 'Tech', 'History', 'History', 'Tech', 'History', 'History', 'Tech', 'History', 'Tech'],
    'AVAILABILITY': [True, True, False, True, True, True, False, True, True, False],
    'ADDED_DATE': ['2019-03-15', '2020-01-20', '2018-11-10', '2021-05-25', '2022-08-12', '2020-07-18', '2019-09-30', '2023-02-14', '2021-03-22', '2022-12-01']
}

# [2] Convert dictionary into pandas DataFrame
df = pd.DataFrame(library_data)
print("DataFrame Created Successfully!")

Step-by-Step Explanation: 1. Initialization: Import the pandas library and define a dictionary library_data with 10 book records and details. 2. Logic Flow: Use pd.DataFrame() to transform the structured dictionary into a Pandas DataFrame. 3. Completion: Display a success message confirming the initialization of the library dataset.

2. File Storage (CSV)

Save the library records into a persistent CSV file for data sharing.

Hint

Use df.to_csv() with index=False. This prevents Pandas from adding a default numeric index column to your file.

flowchart TD
df[Pandas DataFrame]
csv[Save To CSV]
file[library_books.csv]

df --> csv
csv --> file
View Solution & Output 
# [3] Save DataFrame as CSV
df.to_csv('library_books.csv', index=False)
print("File 'library_books.csv' created.")

Step-by-Step Explanation: 1. Initialization: Use the existing df DataFrame containing the book information. 2. Logic Flow: Export the data to a CSV file named 'library_books.csv' using to_csv() with index=False. 3. Completion: A persistent CSV file is created on the disk for data sharing and long-term storage.

3. Data Loading & Filtering

Reload the CSV and search for specific books based on genre and date added.

Hint

Filter using conditions like df[df['GENRE'] == 'History']. Convert the Date column using pd.to_datetime() for accurate filtering.

flowchart TD
load[Load read_csv]
date[Convert to Datetime]
filter[Apply Queries]
show[Show Results]

load --> date
date --> filter
filter --> show
View Solution & Output 
# [4] Load CSV and perform queries
load_df = pd.read_csv('library_books.csv')
load_df['ADDED_DATE'] = pd.to_datetime(load_df['ADDED_DATE'])

# Query 1: History Genre
print("\n[a] History Books:")
print(load_df[load_df['GENRE'] == 'History'])

# Query 2: Availability
print("\n[b] Available Books:")
print(load_df[load_df['AVAILABILITY'] == True])

# Query 3: Date > 2020-06-01
print("\n[c] Books added after June 2020:")
print(load_df[load_df['ADDED_DATE'] > '2020-06-01'])

Step-by-Step Explanation: 1. Initialization: Load the library data using pd.read_csv() and convert the ADDED_DATE column to datetime objects. 2. Logic Flow: Perform filtering operations to find books by genre (History), availability, and specific addition date range. 3. Completion: Display the results for each filter to the console to analyze the available book inventory.

Q2: Viva Preparation

Max Marks: 5

Potential Viva Questions
  1. Q: How do you add a new column to an existing DataFrame?
  2. A: Use df['new_column_name'] = list_of_values or df.assign().
  3. Q: What is the difference between to_csv() and read_csv()?
  4. A: to_csv() exports data from Python to a file; read_csv() imports data from a file into Python.
  5. Q: Why did we use index=False in to_csv()?
  6. A: To prevent Pandas from writing an extra, unnamed column for the row numbers into our CSV file.
  7. Q: How can you see only the first 5 rows of a large DataFrame?
  8. A: Use the df.head() method.
  9. Q: Why use index=False in to_csv()?
  10. A: To prevent Pandas from adding an extra column for row numbers in our final CSV file.
  11. Q: How can you check for duplicate records?
  12. A: Use df.duplicated().sum() to count duplicate rows.
  13. Q: What is the purpose of read_csv()?
  14. A: It loads data from a CSV file into a Pandas DataFrame for analysis.
  15. Q: How do you filter data based on multiple conditions?
  16. A: Use bitwise operators & (AND) or | (OR), e.g., df[(df['Genre'] == 'History') & (df['Availability'] == True)].
  17. Q: What does df.info() provide?
  18. A: It provides a concise summary of the DataFrame, including the number of non-null entries and data types of each column.
  19. Q: How can you find unique values in a column?
  20. A: Use the df['column_name'].unique() method.

Common Pitfalls

  • Path Issues: Forgetting to use .csv extension when saving.
  • Case Sensitivity: Searching for "history" instead of "History" in the genre filter will return zero results.
  • Boolean Values: AVAILABILITY should be True/False (unquoted) in the dict, but might become 1/0 or True/False strings in some CSV parsers. Use == True for safety.
  • Date Format: Standard strings like "2020-06-01" are treated as plain text unless explicitly converted using pd.to_datetime().

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Last Updated: April 2025