How can I identify duplicate rows in a dataset without dropping them, and display the count of duplicates for each employee ID (e.g., employee ID 1, John, is duplicated)?

Using pandas you can add a column that shows how many times each employee_id appears, without removing any rows. The key function is groupby with transform:

# Assume df is your DataFrame

df['duplicate_count'] = df.groupby('employee_id')['employee_id'].transform('count')

# Now df contains a new column duplicate_count. Rows where duplicate_count > 1 are duplicates.

# To view only duplicates:

duplicates = df[df['duplicate_count'] > 1]

print(duplicates[['employee_id', 'name', 'duplicate_count']])

This keeps all original data intact, simply annotates each row with how many times that employee_id occurs. Complexity is O(n) for the transform and O(n) for filtering, so overall linear in the number of rows.