Drop Missing Data

Difficulty: Easy | Acceptance: 64.50% | Paid: No Topics: N/A

Problem Description

Write a solution to drop rows from a DataFrame that contain any missing values.

Given a DataFrame students, drop all rows where any column has a missing value (NaN). Return the resulting DataFrame.

Table of Contents

• Examples
• Constraints
• Approach 1: Using dropna()
• Approach 2: Using Boolean Indexing
• Approach 3: Using notnull()

Examples

Example 1

Input:

+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 32         | Piper   | 5   |
| 217        | None    | 19  |
| 779        | Georgia | 20  |
| 849        | Willow  | 14  |
+------------+---------+-----+

Output:

+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 32         | Piper   | 5   |
| 779        | Georgia | 20  |
| 849        | Willow  | 14  |
+------------+---------+-----+

Explanation: Student with id 217 havs empty value in the name column, so it will be removed.

Constraints

- The DataFrame will have at least 1 row
- The DataFrame will have at least 1 column
- Missing values are represented as NaN

Approach 1: Using dropna()

Intuition Pandas provides the dropna() method which is specifically designed to remove missing values from a DataFrame. This is the most straightforward and idiomatic way to handle this problem.

Steps

• Call the dropna() method on the DataFrame
• By default, dropna() removes rows containing any missing values
• Return the resulting DataFrame

import pandas as pd

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    return students.dropna()

Complexity

• Time: O(n × m) where n is the number of rows and m is the number of columns
• Space: O(n × m) for the resulting DataFrame
• Notes: This is the most efficient and readable approach for pandas DataFrames

Approach 2: Using Boolean Indexing

Intuition We can create a boolean mask that indicates which rows have no missing values, then use this mask to filter the DataFrame.

Steps

• Use isna() to create a boolean DataFrame indicating missing values
• Use any() along axis 1 to find rows with at least one missing value
• Negate the result to get rows with no missing values
• Use boolean indexing to filter the DataFrame

import pandas as pd

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    mask = ~students.isna().any(axis=1)
    return students[mask]

Complexity

• Time: O(n × m) where n is the number of rows and m is the number of columns
• Space: O(n × m) for the boolean mask and resulting DataFrame
• Notes: More verbose than dropna() but demonstrates understanding of boolean indexing

Approach 3: Using notnull()

Intuition We can use the notnull() method to check for non-missing values and combine the results across all columns.

Steps

• Use notnull() to create a boolean DataFrame indicating non-missing values
• Use all() along axis 1 to find rows where all values are non-missing
• Use boolean indexing to filter the DataFrame

import pandas as pd

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    mask = students.notnull().all(axis=1)
    return students[mask]

Complexity

• Time: O(n × m) where n is the number of rows and m is the number of columns
• Space: O(n × m) for the boolean mask and resulting DataFrame
• Notes: Similar to Approach 2 but uses the opposite logic (notnull vs isna)