New small project 'product_analysis.py' as a assignment added

Author: midrees555

product_analysis.py

@@ -0,0 +1,91 @@
+# 📘 Assignment 1:
+# Build a Script That Integrates all the topics, thet covered in '2_Python_OOP\01_Basics\Day_01_Variables_Classes\Tasks\1_data_list_processing.py'
+
+# Problem Statement: Create a Python module/script that:
+# Reads product records from a CSV file
+# Filters products based on price threshold
+# Calculates total and average price
+# Saves the results to a new file (output.txt or summary.csv)
+# Structure your code into functions, and use try/except for errors.
+
+# ✅ Save this as product_analysis.py.
+# ✅ Use version control: git init, git add ., git commit -m "Day 1 assignment"
+# ----------------------------------------------------------------------------
+
+# import essential packages
+import csv
+
+def read_product_records(file_path):
+    """Reading product records from file at 'file_path'"""
+    
+    try:
+        with open(file_path, 'r') as file:
+            # Clean up headers by stripping whitespace & trailing commas
+            reader = csv.DictReader(file)
+            return [
+                {k.strip(): v.strip()
+                 for k, v in row.items()
+                 if k is not None}
+                for row in reader
+            ]
+    except FileNotFoundError:
+        print(f"Error: file {file_path} not found!\n")
+        return []                                   # Return empty list to avoid crashes later
+    
+    
+def filter_product(items, threshold=10000):
+    """Key Action: Filter products where price (as float) ≤ threshold.
+    Edge Case: Handle missing/incorrect price values."""
+    
+    filtered = []
+    
+    for item in items:
+        try:
+            if float(item.get("price", 0)) <= threshold:
+                filtered.append(item)
+        except (ValueError, TypeError):
+            continue        # skip invalid prices
+    
+    return filtered
+
+
+def products_summary(products):
+    """Stats to Compute:
+    Total/Average price (use sum() and len()).
+    Max sold product (track quantity_sold).
+    Max profit product (calculate price * quantity_sold)"""
+    
+    if not products:
+        return None     # Handle empty data
+    
+    prices = [float(p["price"]) for p in products]
+    total, avg = sum(prices), sum(prices) / len(prices)
+    
+    max_sold = max(products, key=lambda p: int(p["quantity_sold"]))
+    max_profit = max(products, key=lambda p: float(p["price"]) * int(p["quantity_sold"]))
+    
+    return {
+        "total_price": total,
+        "avg_price": avg,
+        "max_sold": max_sold,
+        "max_profit": max_profit
+    }
+
+
+def save_summary(summary, output_file="./assets/summary.txt"):
+    """Save to output.txt (human-readable) or summary.csv (structured)."""
+    
+    with open(output_file, 'w') as file:
+        for key, value in summary.items():
+            file.write(f"{key}: {value}\n")     # Write each state as a line
+            
+    print(f"\nAnalyze Data Saved Successfully!\nFile Path: '{output_file}'")
+    print("--------------------------------\n")
+            
+            
+# Main workflow
+# Read --> Filter --> Analyze --> Save
+products = read_product_records("./assets/assignment_sales.csv")
+filtered = filter_product(products, threshold=10000)
+summary = products_summary(filtered)
+save_summary(summary, "./assets/summary.txt")