PipelineTwo - Basic CSV Processing with Python

Exercise: Simple Data Analysis Without Pandas

Objective: Read a small CSV file using Python's built-in CSV module, and calculate basic statistics (min, max, sum, average) for each numerical column.

Dataset:store_sales.csv (60 lines) with the following columns:

• date (string): Sales date in YYYY-MM-DD format
• store_id (string): Store identifier ("Store1", "Store2", or "Store3")
• items_sold (integer): Number of items sold that day
• revenue (float): Total revenue for that day in dollars

Tasks: 0. read the file pipeline2.py for a psuedo-code outline of what needs to be done.

1. Read the CSV file using Python's built-in csv module
2. For each store, calculate:
   • Total items sold
   • Average items sold per day
   • Minimum daily revenue
   • Maximum daily revenue
   • Total revenue
3. Print a summary of the statistics for each store
4. Determine which store had the highest average daily revenue

Sample Data Generation

Here's a Python script to generate the sample dataset for this exercise:

importcsvimportrandomfromdatetimeimportdatetime, timedelta# Set random seed for reproducibilityrandom.seed(42)
# Create date range for 20 days (resulting in 60 rows - 20 days × 3 stores)end_date=datetime.now().date()
start_date=end_date-timedelta(days=20)
dates= [(start_date+timedelta(days=i)).strftime('%Y-%m-%d') foriinrange(20)]
# Store informationstores= ["Store1", "Store2", "Store3"]
# Open the CSV file for writingwithopen('store_sales.csv', 'w', newline='') asfile:
# Create a CSV writercsv_writer=csv.writer(file)
# Write the header rowcsv_writer.writerow(['date', 'store_id', 'items_sold', 'revenue'])
# Generate 60 records (20 days × 3 stores)fordateindates:
forstoreinstores:
# Generate random data for each store on each date# Each store has a different sales patternifstore=="Store1":
items=random.randint(20, 50)
price_per_item=random.uniform(10, 20)
elifstore=="Store2":
items=random.randint(30, 70)
price_per_item=random.uniform(8, 15)
else: # Store3items=random.randint(15, 40)
price_per_item=random.uniform(15, 25)
# Calculate revenue (with some randomness)revenue=round(items*price_per_item*random.uniform(0.9, 1.1), 2)
# Write the rowcsv_writer.writerow([date, store, items, revenue])
print(f"Created store_sales.csv with 60 records")

This exercise will help you practice basic file I/O operations and data processing in Python without relying on pandas or other external libraries. It focuses on essential programming concepts like reading files, data structures, and basic calculations.

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