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

Paper Details

  • Subject: Programming Skills - Advanced (PKUP)
  • Subject Code: 204_02
  • Set: A
  • 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: Vehicle Data Pipeline

Max Marks: 20

Using Python, perform the following tasks for VEHICLE REGISTRATION data management: 1. Create a dictionary containing vehicle details (REG_NO, OWNER_NAME, VEHICLE_TYPE, MODEL_YEAR, CITY, REGISTRATION_DATE). Add at least 10 vehicle records. 2. Convert the dictionary into a pandas DataFrame. 3. Save the DataFrame as a CSV file named vehicle_data.csv. 4. Load the CSV file and display specific queries (City == Ahmedabad, Year > 2018, Date > 2022-01-01).

1. Dictionary & DataFrame Creation

Initialize the vehicle data and convert it into a structured DataFrame.

Hint

A dictionary with keys as column names and values as lists is the best way to load data into Pandas. Use pd.DataFrame(dict_name) to convert.

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

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

# [1] Create dictionary with vehicle details
vehicle_dict = {
    'REG_NO': ['GJ01A1', 'GJ02B2', 'GJ01C3', 'GJ05D4', 'GJ01E5', 'GJ03F6', 'GJ01G7', 'GJ04H8', 'GJ01I9', 'GJ06J0'],
    'OWNER_NAME': ['Rahul', 'Priya', 'Amit', 'Sneha', 'Vikram', 'Neha', 'Raj', 'Pooja', 'Karan', 'Divya'],
    'VEHICLE_TYPE': ['Car', 'Bike', 'Car', 'SUV', 'Bike', 'Car', 'SUV', 'Car', 'Bike', 'Car'],
    'MODEL_YEAR': [2019, 2020, 2021, 2018, 2022, 2023, 2019, 2021, 2020, 2022],
    'CITY': ['Ahmedabad', 'Surat', 'Ahmedabad', 'Vadodara', 'Ahmedabad', 'Rajkot', 'Ahmedabad', 'Surat', 'Vadodara', 'Ahmedabad'],
    'REGISTRATION_DATE': ['2019-05-15', '2020-03-20', '2021-08-10', '2018-12-25', '2022-06-30', '2023-01-15', '2019-11-05', '2021-04-18', '2020-09-22', '2022-12-01']
}

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

Step-by-Step Explanation: 1. Initialization: Import pandas as pd and define the vehicle_dict with 10 records containing registration details. 2. Logic Flow: Use pd.DataFrame() to convert the dictionary into a tabular format and store it in the variable df. 3. Completion: Display a confirmation message and the first five rows of the DataFrame using head() to verify the data.

2. File Storage (CSV)

Save the records into a persistent file for later use.

Hint

Use the to_csv() method. Set index=False to prevent Pandas from adding an extra column for row indices.

flowchart TD
df[Pandas DataFrame]
csv[CSV File Storage]
disk[Save to vehicle_data.csv]

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

Step-by-Step Explanation: 1. Initialization: Access the existing DataFrame df that contains the vehicle registration records. 2. Logic Flow: Call the to_csv() method on the DataFrame, specifying 'vehicle_data.csv' as the filename and setting index=False to omit row numbers. 3. Completion: Verify that the file has been successfully created in the local directory with a confirmation print statement.

3. Data Loading & Queries

Retrieve the data and perform advanced filtering based on criteria.

Hint

Use pd.read_csv() to load. For date-based queries, ensure the column is converted using pd.to_datetime().

flowchart TD
load[Load read_csv]
date[Convert to Datetime]
filter[Apply Filters]
show[Display Results]

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

# Query 1: City Ahmedabad
print("\n[a] Vehicles in Ahmedabad:")
print(load_df[load_df['CITY'] == 'Ahmedabad'])

# Query 2: Model Year > 2018
print("\n[b] Vehicles after 2018:")
print(load_df[load_df['MODEL_YEAR'] > 2018])

# Query 3: Date > 2022-01-01
print("\n[c] Registration after 2022:")
print(load_df[load_df['REGISTRATION_DATE'] > '2022-01-01'])

Step-by-Step Explanation: 1. Initialization: Import the data from vehicle_data.csv using pd.read_csv() and convert the registration date strings into proper datetime objects. 2. Logic Flow: Apply boolean indexing to the DataFrame to filter records based on city name, manufacturing year, and specific registration date. 3. Completion: Print the results of each query to the console to view the filtered subsets of vehicle data.

Q2: Viva Preparation

Max Marks: 5

Potential Viva Questions
  1. Q: What is a DataFrame in Pandas?
  2. A: A two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes.
  3. Q: Why use pd.to_datetime()?
  4. A: It converts strings into datetime objects, which allows for advanced filtering (e.g., finding dates after a specific year).
  5. Q: Difference between loc and iloc?
  6. A: loc is label-based (using column names), while iloc is integer-index based (using row/column numbers).
  7. Q: How can you check for missing values in a DataFrame?
  8. A: Use the df.isnull().sum() method.
  9. Q: What is the benefit of saving data to CSV?
  10. A: CSV is a lightweight, universal format that can be read by almost any data tool, including Excel.
  11. Q: How do you add a new row to a DataFrame?
  12. A: You can use df.loc[len(df)] = [new_values] or pd.concat() for larger merges.

Common Pitfalls

  • Missing Index Rule: Forgetting index=False when saving to CSV while doing data processing.
  • Date Format: Standard strings like "2022-01-01" are treated as plain text unless explicitly converted to datetime objects.

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Last Updated: April 2025