LeetCode Problem Workspace
Modify Columns
Learn how to efficiently modify a DataFrame column by applying a transformation to every value in a single pass.
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Easy · Modify Columns core interview pattern
Answer-first summary
Learn how to efficiently modify a DataFrame column by applying a transformation to every value in a single pass.
Ace coding interviews with Interview AiBoxInterview AiBox guidance for Modify Columns core interview pattern
This problem requires updating a specific column in a DataFrame by applying a simple arithmetic operation to every entry. The fastest approach is a column-wise assignment that doubles each salary, avoiding row-wise loops. GhostInterview focuses on showing this pattern clearly and helping you implement it correctly in Python or similar environments.
Problem Statement
A company wants to adjust its employee salaries. Given a DataFrame named employees with columns 'name' and 'salary', write a solution to double each salary value.
Return the updated DataFrame in the same format, ensuring that the 'salary' column reflects the multiplied values. Preserve the original order of rows and other columns unchanged.
Examples
Example 1
Input: See original problem statement.
Output: See original problem statement.
DataFrame employees +-------------+--------+ | Column Name | Type | +-------------+--------+ | name | object | | salary | int | +-------------+--------+
Example 2
Input: DataFrame employees +---------+--------+ | name | salary | +---------+--------+ | Jack | 19666 | | Piper | 74754 | | Mia | 62509 | | Ulysses | 54866 | +---------+--------+
Output: +---------+--------+ | name | salary | +---------+--------+ | Jack | 39332 | | Piper | 149508 | | Mia | 125018 | | Ulysses | 109732 | +---------+--------+
Every salary has been doubled.
Constraints
Solution Approach
Direct Column Multiplication
Use a simple assignment on the 'salary' column by multiplying it by 2. This leverages vectorized operations in DataFrame libraries for optimal speed.
Avoid Row-wise Iteration
Do not loop through rows to update salaries individually, as this increases time complexity and is prone to errors in larger datasets.
Preserve DataFrame Structure
Ensure that only the 'salary' column changes. Keep all other columns intact and maintain the row order to prevent accidental data corruption.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
Time complexity is O(n) where n is the number of rows, due to column-wise operations. Space complexity is O(1) extra space if done in place, otherwise O(n) for creating a new column copy.
What Interviewers Usually Probe
- Emphasize vectorized column operations.
- Check if the candidate avoids unnecessary loops.
- Look for correct preservation of DataFrame structure.
Common Pitfalls or Variants
Common pitfalls
- Using explicit for-loops over rows, which is slower.
- Accidentally modifying other columns or changing row order.
- Forgetting to return or assign the updated DataFrame.
Follow-up variants
- Multiply by a different constant or apply another arithmetic operation.
- Modify multiple numeric columns at once using the same pattern.
- Apply a conditional update only to certain rows based on a filter.
FAQ
What is the Modify Columns core interview pattern?
It is a strategy to update a DataFrame column efficiently by applying a transformation to all its values in a vectorized, column-wise manner.
Can I use a loop to double salaries?
While possible, looping is inefficient; vectorized column operations are faster and preferred in this problem.
Do I need to change the order of rows?
No, the original row order should remain unchanged; only the target column values are updated.
How does GhostInterview suggest handling multiple columns?
Use simultaneous column-wise assignments for each target column, preserving other data, similar to the single-column pattern.
Is this problem language-specific?
No, the pattern applies to any environment that supports column-wise operations like Python pandas, R data frames, or SQL updates.
Solution
Solution 1
#### Python3
import pandas as pd
def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame:
employees['salary'] *= 2
return employees