How do you solve Fill Missing Data on LeetCode?

Fill Missing Data (LeetCode #2887) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Using built-in functions can improve efficiency significantly.

What is the brute force solution for Fill Missing Data?

The brute force approach for Fill Missing Data is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we manually iterate through each row of the DataFrame and check if the quantity is None. If it is, we set it to 0. This is straightforward but inefficient for large datasets. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Fill Missing Data?

Always consider built-in functions for data manipulation tasks. Explain your thought process clearly during the interview.

What are common mistakes when solving Fill Missing Data?

Not checking for None values correctly. Overcomplicating the solution with unnecessary loops.

#2887

Fill Missing Data

Easy

Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(1)

Using built-in functions in pandas allows us to efficiently fill missing values in a single operation, which is much faster than manually iterating through each row. This takes advantage of optimized internal implementations.

⚙️

Algorithm

2 steps

1Step 1: Use the pandas function fillna() to replace None values in the 'quantity' column with 0.
2Step 2: Return the modified DataFrame.

solution.py15 lines

1# Full working Python code
2import pandas as pd
3
4def fill_missing_quantity(df):
5    df['quantity'] = df['quantity'].fillna(0)
6    return df
7
8# Example usage
9input_df = pd.DataFrame({
10    'name': ['Wristwatch', 'WirelessEarbuds', 'GolfClubs', 'Printer'],
11    'quantity': [None, None, 779, 849],
12    'price': [135, 821, 9319, 3051]
13})
14output_df = fill_missing_quantity(input_df)
15print(output_df)

ℹ

Complexity note: The time complexity is O(n) because we are processing each row in the DataFrame once. The space complexity is O(1) since we are modifying the DataFrame in place without using additional data structures.

1Using built-in functions can improve efficiency significantly.
2Understanding data structures like DataFrames is crucial.

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