How do you solve Select Data on LeetCode?

Select Data (LeetCode #2880) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using built-in DataFrame methods can greatly simplify code.

What is the brute force solution for Select Data?

The brute force approach for Select Data is Brute Force, with O(n) time complexity and O(1) space complexity. In the brute force approach, we will iterate through each student in the DataFrame and check if their student_id matches 101. If it does, we will collect their name and age. This method is straightforward but inefficient for larger datasets. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Select Data?

Always consider built-in methods for data manipulation. Explain your thought process clearly while coding. Test your code with different inputs to ensure correctness.

What are common mistakes when solving Select Data?

Not using DataFrame filtering methods, leading to unnecessary loops. Forgetting to select the correct columns after filtering.

#2880

Select Data

Easy

Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

In the optimal solution, we can leverage the capabilities of pandas to filter the DataFrame directly. This approach is efficient and concise, allowing us to avoid manual iteration and condition checking.

⚙️

Algorithm

3 steps

1Step 1: Use the DataFrame's filtering capabilities to select rows where student_id is 101.
2Step 2: Select the 'name' and 'age' columns from the filtered DataFrame.
3Step 3: Return the resulting DataFrame.

solution.py4 lines

1import pandas as pd
2
3def select_student(dataframe):
4    return dataframe[dataframe['student_id'] == 101][['name', 'age']]

ℹ

Complexity note: The time complexity remains O(n) as we still need to check each student. The space complexity is O(n) because we create a new DataFrame or list to hold the filtered results.

1Using built-in DataFrame methods can greatly simplify code.
2Filtering and selecting columns in one step is efficient.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.