Problem Description

You are given a DataFrame called employees that contains employee information with two columns:

• name: stores employee names (object/string type)
• salary: stores employee salary values (integer type)

The task is to add a new column called bonus to this DataFrame. The bonus column should contain values that are exactly double the corresponding salary values for each employee.

For example, if an employee has a salary of 1000, their bonus value should be 2000. The solution implements this by multiplying each salary value by 2 and storing the results in the new bonus column using the operation employees['bonus'] = employees['salary'] * 2.

The modified DataFrame with the additional bonus column should be returned as the output.

Intuition

When we need to create a new column based on calculations from existing columns in a pandas DataFrame, the most straightforward approach is to use vectorized operations. Pandas is designed to handle column-wise operations efficiently.

Since we want each employee's bonus to be double their salary, we can think of this as a simple mathematical transformation: bonus = salary × 2.

In pandas, when we perform an arithmetic operation on an entire column like employees['salary'] * 2, it automatically applies this multiplication to every single value in the salary column. This is much more efficient than looping through each row individually.

The key insight is that we can directly assign this calculated result to a new column by writing employees['bonus'] = employees['salary'] * 2. This single line of code:

1. Takes all values from the salary column
2. Multiplies each value by 2
3. Creates a new column called bonus if it doesn't exist (or overwrites it if it does)
4. Stores all the doubled values in this new column

This vectorized approach leverages pandas' built-in optimization and is the most natural way to perform element-wise operations on DataFrame columns.

Solution Approach

The solution uses a direct calculation approach to create the bonus column. Here's how the implementation works:

1. Function Definition: The function createBonusColumn takes a pandas DataFrame employees as input and returns a modified DataFrame.

2. Column Creation and Calculation: The core operation happens in a single line:

   employees['bonus'] = employees['salary'] * 2

   This line performs several operations:

   • Access the salary column using employees['salary']
   • Multiply all values in the salary column by 2 using the * operator
   • Create a new column named bonus and assign the calculated values to it

3. Vectorized Operation: The multiplication employees['salary'] * 2 is a vectorized operation in pandas. Instead of iterating through each row manually, pandas applies the multiplication to all elements in the column simultaneously. This is both more efficient and more readable.

4. In-place Modification: The DataFrame is modified in-place, meaning the original employees DataFrame gets the new column added directly to it. No new DataFrame is created; we're just adding a column to the existing one.

5. Return Statement: Finally, the modified DataFrame with the new bonus column is returned using return employees.

The entire solution leverages pandas' ability to handle column-wise operations efficiently, making the code both concise and performant. The pattern used here is a common one in data manipulation tasks where new columns are derived from existing ones through mathematical transformations.

Example Walkthrough

Let's walk through a concrete example to understand how the solution works.

Initial DataFrame: Suppose we have an employees DataFrame with 3 employees:

Step 1: Access the salary column When we execute employees['salary'], we get:

50000
75000
60000

Step 2: Multiply each salary by 2 The operation employees['salary'] * 2 performs element-wise multiplication:

• Alice: 50000 × 2 = 100000
• Bob: 75000 × 2 = 150000
• Charlie: 60000 × 2 = 120000

This creates a new series:

100000
150000
120000

Step 3: Create and assign to the bonus column When we execute employees['bonus'] = employees['salary'] * 2, pandas:

1. Creates a new column called 'bonus' in the DataFrame
2. Assigns the calculated values to this column
3. Maintains the row alignment automatically

Final DataFrame: After the operation, our DataFrame now looks like:

The beauty of this approach is that it works regardless of the DataFrame size. Whether you have 3 employees or 3 million, the same single line of code employees['bonus'] = employees['salary'] * 2 will efficiently calculate and add the bonus column for all rows simultaneously.

Solution Implementation

Time and Space Complexity

The time complexity is O(n), where n is the number of rows in the employees DataFrame. This is because the operation employees['salary'] * 2 needs to iterate through each row to multiply every salary value by 2.

The space complexity is O(n), where n is the number of rows in the employees DataFrame. This is because a new column 'bonus' is created that stores n values (one for each employee).

Note regarding the reference answer: The reference answer states O(1) for both complexities, which would only be accurate if we consider the DataFrame operations as atomic/built-in operations without accounting for the underlying iteration over rows. However, from an algorithmic analysis perspective, creating a new column by performing an operation on all rows requires linear time and space proportional to the number of rows.

Common Pitfalls

1. Modifying the Original DataFrame Unintentionally

The current solution modifies the input DataFrame in-place. If the original DataFrame is needed elsewhere in your code unchanged, this modification will affect it globally.

Problem Example:

original_df = pd.DataFrame({'name': ['Alice', 'Bob'], 'salary': [1000, 2000]})
modified_df = createBonusColumn(original_df)
# original_df now also has the 'bonus' column - may not be desired!

Solution: Create a copy of the DataFrame before modification:

def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
    employees_copy = employees.copy()
    employees_copy['bonus'] = employees_copy['salary'] * 2
    return employees_copy

2. Handling Missing or Invalid Salary Values

If the salary column contains NaN, None, or non-numeric values, the multiplication will either propagate NaN values or raise errors.

Problem Example:

df = pd.DataFrame({'name': ['Alice', 'Bob'], 'salary': [1000, None]})
# Bob's bonus will be NaN

Solution: Add validation or handle missing values:

def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
    # Fill missing values with 0 or handle as needed
    employees['salary'] = employees['salary'].fillna(0)
    employees['bonus'] = employees['salary'] * 2
    return employees

3. Overwriting Existing 'bonus' Column

If the DataFrame already has a 'bonus' column, this solution will silently overwrite it without warning.

Problem Example:

df = pd.DataFrame({'name': ['Alice'], 'salary': [1000], 'bonus': [500]})
# The existing bonus value of 500 will be lost

Solution: Check for existing column or use a different approach:

def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
    if 'bonus' in employees.columns:
        print("Warning: 'bonus' column already exists and will be overwritten")
    employees['bonus'] = employees['salary'] * 2
    return employees

4. Integer Overflow for Large Salaries

For extremely large salary values, doubling them might cause integer overflow issues depending on the data type.

Solution: Ensure appropriate data type:

def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
    employees['bonus'] = employees['salary'].astype('int64') * 2
    return employees