Scenario

A sales dataset needs to be analyzed to determine its size characteristics and calculate key metrics. You need to load the data, report its dimensions, classify its size, and calculate total revenue.

Task

Write a Python script at /home/interview/analyze_sales.py that loads the sales dataset from /home/interview/sales_data.csv, calculates dimensions (rows and columns), determines the total number of cells, classifies the dataset size based on cell count, and calculates the total sales revenue. Save the results to /home/interview/sales_report.json.

Size Classification

• Small: < 10,000 cells
• Medium: 10,000 - 99,999 cells
• Large: >= 100,000 cells

Example

Expected output format in /home/interview/sales_report.json:

{
  "rows": 250,
  "columns": 8,
  "total_cells": 2000,
  "size_classification": "small",
  "total_revenue": 125430.50
}

Note: Calculate total revenue by multiplying quantity × price for each sale.

Step 1: Load and explore the dataset

head /home/interview/sales_data.csv

Review the CSV structure to understand the columns and data format.

Step 2: Create the Python script

nano /home/interview/analyze_sales.py

Write a script that loads the data, calculates dimensions, classifies size, and computes revenue:

import pandas as pd
import json

# Load the sales dataset
df = pd.read_csv('/home/interview/sales_data.csv')

# Calculate dimensions
rows = len(df)
columns = len(df.columns)
total_cells = rows * columns

# Classify dataset size
if total_cells < 10000:
    size_classification = "small"
elif total_cells < 100000:
    size_classification = "medium"
else:
    size_classification = "large"

# Calculate total revenue (quantity × price)
df['revenue'] = df['quantity'] * df['price']
total_revenue = round(df['revenue'].sum(), 2)

# Prepare report
report = {
    "rows": rows,
    "columns": columns,
    "total_cells": total_cells,
    "size_classification": size_classification,
    "total_revenue": total_revenue
}

# Save to JSON
with open('/home/interview/sales_report.json', 'w') as f:
    json.dump(report, f, indent=2)

Step 3: Run the script

python3 /home/interview/analyze_sales.py

Step 4: Verify the output

cat /home/interview/sales_report.json

Should show the dataset dimensions, size classification, and total revenue.