Add demo-03: CSV data processor with intentional code smells

- Add employee_data_processor.py: functional but ugly code with god function, globals, hardcoded values, mixed concerns
- Add employees.csv: sample data with 10 records (4 invalid for testing)
- Generates report.json and report.html output files
- Perfect candidate for refactoring demo

demo-03/employee_data_processor.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+import csv
+import json
+import datetime
+import re
+
+processed_records = []
+skipped_records = []
+total_salary = 0
+dept_count = {}
+
+def process_employee_data():
+    global processed_records, skipped_records, total_salary, dept_count
+    file_path = "employees.csv"
+    json_output_path = "report.json"
+    html_output_path = "report.html"
+
+    try:
+        with open(file_path, 'r') as f:
+            reader = csv.DictReader(f)
+            rows = list(reader)
+
+        print(f"Reading file: {file_path}")
+        print(f"Total rows in CSV: {len(rows)}")
+
+        for row in rows:
+            name = row['name']
+            email = row['email']
+            dept = row['department']
+            salary = row['salary']
+            hire_date = row['hire_date']
+
+            # Validation part 1 - email
+            if not email or '@' not in email or '.' not in email.split('@')[1]:
+                print(f"WARNING: Invalid email for {name}: {email}")
+                skipped_records.append({'name': name, 'reason': 'Invalid email'})
+                continue
+
+            # Validation part 2 - salary
+            try:
+                sal = int(salary)
+            except:
+                print(f"WARNING: Invalid salary for {name}: {salary}")
+                skipped_records.append({'name': name, 'reason': 'Invalid salary'})
+                continue
+
+            if sal < 0:
+                print(f"WARNING: Negative salary for {name}: {sal}")
+                skipped_records.append({'name': name, 'reason': 'Negative salary'})
+                continue
+
+            # Validation part 3 - department
+            valid_depts = ['ENG', 'SALES', 'MKTG', 'HR']
+            if dept not in valid_depts:
+                print(f"WARNING: Invalid department for {name}: {dept}")
+                skipped_records.append({'name': name, 'reason': 'Invalid department'})
+                continue
+
+            # Validation part 4 - hire_date
+            try:
+                dt = datetime.datetime.strptime(hire_date, '%Y-%m-%d')
+            except:
+                print(f"WARNING: Invalid hire_date for {name}: {hire_date}")
+                skipped_records.append({'name': name, 'reason': 'Invalid hire_date'})
+                continue
+
+            # Transformations
+            if dept == 'ENG':
+                full_dept = 'Engineering'
+            elif dept == 'SALES':
+                full_dept = 'Sales'
+            elif dept == 'MKTG':
+                full_dept = 'Marketing'
+            elif dept == 'HR':
+                full_dept = 'Human Resources'
+            else:
+                full_dept = dept
+
+            annual_salary = sal * 12
+            years_experience = (datetime.datetime.now() - dt).days / 365.25
+
+            processed_records.append({
+                'name': name,
+                'email': email,
+                'department': full_dept,
+                'annual_salary': annual_salary,
+                'hire_date': hire_date,
+                'years_experience': round(years_experience, 2)
+            })
+
+            total_salary += annual_salary
+
+            if full_dept not in dept_count:
+                dept_count[full_dept] = 0
+            dept_count[full_dept] += 1
+
+        print(f"\nProcessed {len(processed_records)} valid records")
+        print(f"Skipped {len(skipped_records)} invalid records")
+        print(f"Total annual payroll: ${total_salary:,}")
+
+        print("\nEmployees by department:")
+        for d in dept_count:
+            print(f"  {d}: {dept_count[d]}")
+
+        # Generate JSON output
+        json_data = {
+            'summary': {
+                'total_employees': len(processed_records),
+                'skipped_records': len(skipped_records),
+                'total_annual_payroll': total_salary,
+                'employees_by_department': dept_count
+            },
+            'employees': processed_records,
+            'skipped': skipped_records
+        }
+
+        with open(json_output_path, 'w') as jf:
+            json.dump(json_data, jf, indent=2)
+        print(f"\nJSON report written to: {json_output_path}")
+
+        # Generate HTML output
+        html_content = "<html><head><title>Employee Report</title></head><body>"
+        html_content += "<h1>Employee Data Report</h1>"
+
+        html_content += "<h2>Summary</h2>"
+        html_content += "<ul>"
+        html_content += f"<li>Total Employees: {len(processed_records)}</li>"
+        html_content += f"<li>Skipped Records: {len(skipped_records)}</li>"
+        html_content += f"<li>Total Annual Payroll: ${total_salary:,}</li>"
+        html_content += "</ul>"
+
+        html_content += "<h2>Employees by Department</h2>"
+        html_content += "<ul>"
+        for d in dept_count:
+            html_content += f"<li>{d}: {dept_count[d]}</li>"
+        html_content += "</ul>"
+
+        html_content += "<h2>Employee Details</h2>"
+        html_content += "<table border='1' cellpadding='5'>"
+        html_content += "<tr><th>Name</th><th>Email</th><th>Department</th><th>Annual Salary</th><th>Hire Date</th><th>Years Exp</th></tr>"
+        for emp in processed_records:
+            html_content += "<tr>"
+            html_content += f"<td>{emp['name']}</td>"
+            html_content += f"<td>{emp['email']}</td>"
+            html_content += f"<td>{emp['department']}</td>"
+            html_content += f"<td>${emp['annual_salary']:,}</td>"
+            html_content += f"<td>{emp['hire_date']}</td>"
+            html_content += f"<td>{emp['years_experience']}</td>"
+            html_content += "</tr>"
+        html_content += "</table>"
+
+        html_content += "<h2>Skipped Records</h2>"
+        html_content += "<ul>"
+        for skipped in skipped_records:
+            html_content += f"<li>{skipped['name']}: {skipped['reason']}</li>"
+        html_content += "</ul>"
+
+        html_content += "</body></html>"
+
+        with open(html_output_path, 'w') as hf:
+            hf.write(html_content)
+        print(f"HTML report written to: {html_output_path}")
+
+        print("\nProcessing complete!")
+
+    except FileNotFoundError:
+        print(f"ERROR: File not found: {file_path}")
+    except Exception as e:
+        print(f"ERROR: Something went wrong: {e}")
+
+if __name__ == "__main__":
+    process_employee_data()

demo-03/employees.csv

@@ -0,0 +1,11 @@
+name,email,department,salary,hire_date
+Alice Smith,alice@example.com,ENG,75000,2023-01-15
+Bob Jones,bob@example.com,SALES,-50000,2023-02-01
+Charlie,charlie@bademail,MKTG,65000,2023-03-01
+Diana Prince,diana@example.com,ENG,82000,2022-05-20
+Eve Wilson,eve@example.com,HR,58000,2023-04-10
+Frank Miller,frank@example.com,INVALID,70000,2023-01-25
+Grace Lee,grace@example.com,SALES,0,2023-06-01
+Henry Davis,henry@example.com,ENG,90000,invalid_date
+Ivy Chen,ivy@example.com,MKTG,62000,2023-07-15
+Jack Brown,jack@example.com,HR,55000,2023-08-01

demo-03/report.html

@@ -0,0 +1 @@
+<html><head><title>Employee Report</title></head><body><h1>Employee Data Report</h1><h2>Summary</h2><ul><li>Total Employees: 6</li><li>Skipped Records: 4</li><li>Total Annual Payroll: $3,984,000</li></ul><h2>Employees by Department</h2><ul><li>Engineering: 2</li><li>Human Resources: 2</li><li>Sales: 1</li><li>Marketing: 1</li></ul><h2>Employee Details</h2><table border='1' cellpadding='5'><tr><th>Name</th><th>Email</th><th>Department</th><th>Annual Salary</th><th>Hire Date</th><th>Years Exp</th></tr><tr><td>Alice Smith</td><td>alice@example.com</td><td>Engineering</td><td>$900,000</td><td>2023-01-15</td><td>3.0</td></tr><tr><td>Diana Prince</td><td>diana@example.com</td><td>Engineering</td><td>$984,000</td><td>2022-05-20</td><td>3.66</td></tr><tr><td>Eve Wilson</td><td>eve@example.com</td><td>Human Resources</td><td>$696,000</td><td>2023-04-10</td><td>2.77</td></tr><tr><td>Grace Lee</td><td>grace@example.com</td><td>Sales</td><td>$0</td><td>2023-06-01</td><td>2.63</td></tr><tr><td>Ivy Chen</td><td>ivy@example.com</td><td>Marketing</td><td>$744,000</td><td>2023-07-15</td><td>2.51</td></tr><tr><td>Jack Brown</td><td>jack@example.com</td><td>Human Resources</td><td>$660,000</td><td>2023-08-01</td><td>2.46</td></tr></table><h2>Skipped Records</h2><ul><li>Bob Jones: Negative salary</li><li>Charlie: Invalid email</li><li>Frank Miller: Invalid department</li><li>Henry Davis: Invalid hire_date</li></ul></body></html>

demo-03/report.json

@@ -0,0 +1,81 @@
+{
+  "summary": {
+    "total_employees": 6,
+    "skipped_records": 4,
+    "total_annual_payroll": 3984000,
+    "employees_by_department": {
+      "Engineering": 2,
+      "Human Resources": 2,
+      "Sales": 1,
+      "Marketing": 1
+    }
+  },
+  "employees": [
+    {
+      "name": "Alice Smith",
+      "email": "alice@example.com",
+      "department": "Engineering",
+      "annual_salary": 900000,
+      "hire_date": "2023-01-15",
+      "years_experience": 3.0
+    },
+    {
+      "name": "Diana Prince",
+      "email": "diana@example.com",
+      "department": "Engineering",
+      "annual_salary": 984000,
+      "hire_date": "2022-05-20",
+      "years_experience": 3.66
+    },
+    {
+      "name": "Eve Wilson",
+      "email": "eve@example.com",
+      "department": "Human Resources",
+      "annual_salary": 696000,
+      "hire_date": "2023-04-10",
+      "years_experience": 2.77
+    },
+    {
+      "name": "Grace Lee",
+      "email": "grace@example.com",
+      "department": "Sales",
+      "annual_salary": 0,
+      "hire_date": "2023-06-01",
+      "years_experience": 2.63
+    },
+    {
+      "name": "Ivy Chen",
+      "email": "ivy@example.com",
+      "department": "Marketing",
+      "annual_salary": 744000,
+      "hire_date": "2023-07-15",
+      "years_experience": 2.51
+    },
+    {
+      "name": "Jack Brown",
+      "email": "jack@example.com",
+      "department": "Human Resources",
+      "annual_salary": 660000,
+      "hire_date": "2023-08-01",
+      "years_experience": 2.46
+    }
+  ],
+  "skipped": [
+    {
+      "name": "Bob Jones",
+      "reason": "Negative salary"
+    },
+    {
+      "name": "Charlie",
+      "reason": "Invalid email"
+    },
+    {
+      "name": "Frank Miller",
+      "reason": "Invalid department"
+    },
+    {
+      "name": "Henry Davis",
+      "reason": "Invalid hire_date"
+    }
+  ]
+}