How do you solve Reshape Data: Concatenate on LeetCode?

Reshape Data: Concatenate (LeetCode #2888) 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 functions for DataFrame operations is usually more efficient than manual iterations.

What is the time complexity of Reshape Data: Concatenate?

The optimal solution for Reshape Data: Concatenate runs in O(n) time and uses O(n) space. The time complexity is O(n) because we are simply concatenating the two DataFrames in one pass, which is efficient.

What is the brute force solution for Reshape Data: Concatenate?

The brute force approach for Reshape Data: Concatenate is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves manually iterating through both DataFrames and appending each row to a new DataFrame. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Reshape Data: Concatenate?

Always consider built-in functions first for data manipulation tasks. Be prepared to explain your choice of approach and its time/space complexities. Practice writing code in a clear and concise manner, as readability is important in interviews.

What are common mistakes when solving Reshape Data: Concatenate?

Not resetting the index after concatenation, which can lead to confusion in row indexing. Using loops unnecessarily when built-in functions can achieve the same result more efficiently.

#2888

Reshape Data: Concatenate

Easy

DataFrame ManipulationConcatenationData Aggregation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

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Intuition

Time O(n)Space O(n)

The optimal approach leverages built-in functions in pandas to concatenate the DataFrames efficiently. This is much faster and cleaner than manually iterating through rows.

⚙️

Algorithm

2 steps

1Step 1: Use the pandas concat function to combine df1 and df2.
2Step 2: Specify the axis parameter as 0 to concatenate vertically.

solution.py7 lines

1import pandas as pd
2
3df1 = pd.DataFrame({'student_id': [1, 2, 3, 4], 'name': ['Mason', 'Ava', 'Taylor', 'Georgia'], 'age': [8, 6, 15, 17]})
4df2 = pd.DataFrame({'student_id': [5, 6], 'name': ['Leo', 'Alex'], 'age': [7, 7]})
5
6result = pd.concat([df1, df2], axis=0).reset_index(drop=True)
7print(result)

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Complexity note: The time complexity is O(n) because we are simply concatenating the two DataFrames in one pass, which is efficient.

1Using built-in functions for DataFrame operations is usually more efficient than manual iterations.
2Understanding the axis parameter in pandas is crucial for manipulating DataFrames correctly.

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