Build a Data Cleaning Pipeline

Problem Statement

Write a Python script that reads a messy CSV file `raw_employees.csv` and produces a cleaned version `clean_employees.csv`. The raw data has these problems: - Some rows have missing salary (empty string) - Email column has inconsistent casing (ALICE@gmail.com, bob@GMAIL.COM) - Some names have extra whitespace (' Alice ') - Salary column has a currency symbol ('₹90,000' instead of 90000) Your pipeline should: 1. Remove rows with missing salary 2. Lowercase all emails 3. Strip whitespace from names 4. Convert salary to integer (remove ₹ and commas) 5. Save the cleaned data to clean_employees.csv 6. Print a summary: total rows read, rows dropped, rows saved

Sample Data

raw_employees.csv:
name,email,department,salary
'  Alice  ','ALICE@gmail.com','Engineering','₹90,000'
'Bob','bob@GMAIL.COM','Marketing',''
'  Carol','carol@x.com','Engineering','₹85,000'
'Dave','dave@x.com','HR','₹70,000'

Expected Output

clean_employees.csv:
name,email,department,salary
Alice,alice@gmail.com,Engineering,90000
Carol,carol@x.com,Engineering,85000
Dave,dave@x.com,HR,70000

Summary printed:
Rows read: 4
Rows dropped: 1
Rows saved: 3