How do you solve Get the Size of a DataFrame on LeetCode?

Get the Size of a DataFrame (LeetCode #2878) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(1) time complexity and O(1) space complexity. Key insight: Using built-in functions can greatly simplify code and improve performance.

What is the time complexity of Get the Size of a DataFrame?

The optimal solution for Get the Size of a DataFrame runs in O(1) time and uses O(1) space. The time complexity is O(1) because we directly access the shape of the DataFrame, which is a constant time operation.

What is the brute force solution for Get the Size of a DataFrame?

The brute force approach for Get the Size of a DataFrame is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves manually counting the rows and columns of the DataFrame. This is straightforward but inefficient for large datasets. The optimal Optimal Solution approach improves this to O(1).

What are the interview tips for Get the Size of a DataFrame?

Always consider built-in functions for common tasks. Explain your thought process clearly to the interviewer. Practice with different DataFrame structures to gain confidence.

What are common mistakes when solving Get the Size of a DataFrame?

Not using built-in functions and attempting to manually count rows and columns. Assuming the DataFrame is not empty without checking.

#2878

Get the Size of a DataFrame

Easy

ArrayDataFrame Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(1)Space O(1)

Using built-in functions in pandas allows us to efficiently retrieve the size of the DataFrame without manual counting.

⚙️

Algorithm

2 steps

1Step 1: Use the shape attribute of the DataFrame to get dimensions.
2Step 2: Return the shape as an array.

solution.py14 lines

1# Full working Python code
2import pandas as pd
3
4def get_dataframe_size(players):
5    return players.shape.tolist()
6
7# Example usage
8players = pd.DataFrame({
9    'player_id': [846, 749, 155, 583, 388, 883, 355, 247, 761, 642],
10    'name': ['Mason', 'Riley', 'Bob', 'Isabella', 'Zachary', 'Ava', 'Violet', 'Thomas', 'Jack', 'Charlie'],
11    'age': [21, 30, 28, 32, 24, 23, 18, 27, 33, 36],
12    'position': ['Forward', 'Winger', 'Striker', 'Goalkeeper', 'Midfielder', 'Defender', 'Striker', 'Striker', 'Midfielder', 'Center-back']
13})
14print(get_dataframe_size(players))

ℹ

Complexity note: The time complexity is O(1) because we directly access the shape of the DataFrame, which is a constant time operation.

1Using built-in functions can greatly simplify code and improve performance.
2Understanding DataFrame properties like shape is crucial for efficient data manipulation.

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