Create a New Column

Difficulty: Easy | Acceptance: 90.10% | Paid: No Topics: N/A

Write a solution to create a new column named ‘bonus’ in the DataFrame employees. The values in this column should be double the values in the ‘salary’ column.

• Examples
• Constraints
• Approach 1: Direct Column Assignment
• Approach 2: Using assign Method
• Approach 3: Using apply Function

Examples

Example 1:

Input:

+---------+--------+
| name    | salary |
+---------+--------+
| Piper   | 10000  |
| Grace   | 20000  |
| Georgia | 50000  |
+---------+--------+

Output:

+---------+--------+-------+
| name    | salary | bonus |
+---------+--------+-------+
| Piper   | 10000  | 20000 |
| Grace   | 20000  | 40000 |
| Georgia | 50000  | 100000|
+---------+--------+-------+

Explanation: A new column ‘bonus’ is created by doubling the values in the ‘salary’ column.

Constraints

0 <= employees.length <= 100
1000 <= employees['salary'] <= 10^5

Approach 1: Direct Column Assignment

Intuition Pandas allows vectorized operations, meaning we can multiply an entire column (Series) by a scalar value directly without iterating through rows.

Steps

• Access the ‘salary’ column of the DataFrame.
• Multiply the Series by 2.
• Assign the result to a new column named ‘bonus’.

import pandas as pd

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

Complexity

• Time: O(n) where n is the number of rows.
• Space: O(n) to store the new column.
• Notes: This is the most idiomatic and efficient way to perform column-wise arithmetic in Pandas.

Approach 2: Using assign Method

Intuition The assign method returns a new object with all original columns in addition to new ones. It is useful for method chaining.

Steps

• Call the assign method on the DataFrame.
• Pass a keyword argument bonus set to employees['salary'] * 2.
• Return the resulting DataFrame.

import pandas as pd

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

Complexity

• Time: O(n) where n is the number of rows.
• Space: O(n) to store the new column (and potentially a copy of the DataFrame depending on implementation).
• Notes: This approach creates a new DataFrame copy, which can be safer for functional programming styles but uses slightly more memory than in-place assignment.

Approach 3: Using apply Function

Intuition The apply function allows applying a custom function along an axis of the DataFrame. While slower than vectorization, it is flexible for complex logic.

Steps

• Select the ‘salary’ column.
• Use the apply method with a lambda function that multiplies the input by 2.
• Assign the result to the ‘bonus’ column.

import pandas as pd

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

Complexity

• Time: O(n) where n is the number of rows.
• Space: O(n) to store the new column.
• Notes: In Pandas, apply is generally slower than vectorized operations because it often loops at the Python level (or Cython level with overhead). Use vectorization (Approach 1) for simple arithmetic.