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

Paper Details

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

Max Marks: 20

Using Python, perform the following tasks for PRODUCT data management: 1. Create a dictionary (or a set of sets) containing product details with the following fields: PRODUCT_ID, PRODUCT_NAME, CATEGORY, PRICE, STOCK_QUANTITY. Add at least 10 product records. 2. Convert the dictionary into a pandas DataFrame. 3. Save the DataFrame as a CSV file named product_data.csv. 4. Load the CSV file and display: a) All products in the "Electronics" category b) Products priced below 100 c) Products with stock quantity less than 20

1. Dictionary & DataFrame Creation

Initialize the product inventory and convert it into a Pandas DataFrame.

Hint

Define a dictionary where keys are the required fields and values are lists of 10 items. Use import pandas as pd and pd.DataFrame() for the conversion.

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

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

# [1] Create dictionary with product details
product_dict = {
    'PRODUCT_ID': ['P001', 'P002', 'P003', 'P004', 'P005', 'P006', 'P007', 'P008', 'P009', 'P010'],
    'PRODUCT_NAME': ['Mouse', 'Keyboard', 'Laptop', 'Monitor', 'USB Cable', 'Webcam', 'Headphones', 'Tablet', 'Router', 'Pen Drive'],
    'CATEGORY': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Accessories', 'Electronics', 'Accessories', 'Electronics', 'Networking', 'Accessories'],
    'PRICE': [25, 45, 1200, 150, 10, 60, 80, 300, 50, 15],
    'STOCK_QUANTITY': [50, 30, 15, 10, 100, 5, 25, 12, 40, 80]
}

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

Step-by-Step Explanation: 1. Initialization: Create the product_dict dictionary with inventory data and import the pandas library. 2. Logic Flow: Convert the raw dictionary into a structured DataFrame df using pd.DataFrame(). 3. Completion: Use the head() method to output the first five rows and confirm everything is correctly formatted.

2. File Storage (CSV)

Save the product data into a CSV file.

Hint

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

flowchart TD
df[Pandas DataFrame]
save[Save to CSV]
file[product_data.csv]

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

Step-by-Step Explanation: 1. Initialization: Select the DataFrame df which contains the latest product records. 2. Logic Flow: Export the data to a file named 'product_data.csv' using to_csv() with index=False. 3. Completion: A local CSV file is created on the disk for record-keeping and storage.

3. Data Loading & Analytics

Load the CSV and perform filtering.

Hint

Use filtering syntax like df[df['CATEGORY'] == 'Electronics'].

flowchart TD
load[Load read_csv]
filter[Apply Filters]
show[Show Results]

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

# Query 1: Category Electronics
print("\n[a] Electronics Products:")
print(load_df[load_df['CATEGORY'] == 'Electronics'])

# Query 2: Price < 100
print("\n[b] Budget Products (Price < 100):")
print(load_df[load_df['PRICE'] < 100])

# Query 3: Stock Quantity < 20
print("\n[c] Low Stock Alerts (Stock < 20):")
print(load_df[load_df['STOCK_QUANTITY'] < 20])

Step-by-Step Explanation: 1. Initialization: Use pd.read_csv() to bring the product data back into a DataFrame for analysis. 2. Logic Flow: Apply boolean filtering based on category (Electronics), price threshold (100), and low stock counts (20). 3. Completion: Display the results for each specific business query to track inventory and sales.

Q2: Viva Preparation

Max Marks: 5

Potential Viva Questions
  1. Q: What is Pandas?
  2. A: Pandas is a Python library used for data manipulation and analysis, providing high-performance data structures like Series and DataFrames.
  3. Q: How do you add a new column to a DataFrame?
  4. A: By assigning a list or Series to a new column name, e.g., df['Total_Value'] = df['PRICE'] * df['STOCK_QUANTITY'].
  5. Q: What does df.describe() do?
  6. A: It generates descriptive statistics that summarize the central tendency, dispersion, and shape of a dataset’s distribution (like mean, min, max).
  7. Q: How do you access a specific cell in a DataFrame?
  8. A: Use .at[row_label, 'column_name'] or .iat[row_index, col_index] for scalar access.
  9. Q: What is the difference between a Series and a DataFrame?
  10. A: A Series is a 1D labeled array (like a single column), while a DataFrame is a 2D labeled tabular structure (like an entire table).
  11. Q: How do you handle missing numeric data in Pandas?
  12. A: Use df.fillna(0) to replace NaNs with zero or df.dropna() to remove rows with missing values.

Common Pitfalls

  • Incorrect Column Names: Python is case-sensitive. df['price'] will fail if the column is named PRICE.
  • Data Types after Loading: Sometimes numeric columns might be loaded as objects if there's non-numeric data. Use df.info() to verify.

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Last Updated: April 2025